1
|
Zhao Y, Lan T, Zhong G, Hagen J, Pan H, Chung WK, Shen Y. A probabilistic graphical model for estimating selection coefficients of nonsynonymous variants from human population sequence data. Nat Commun 2025; 16:4670. [PMID: 40393980 DOI: 10.1038/s41467-025-59937-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 05/06/2025] [Indexed: 05/22/2025] Open
Abstract
Accurately predicting the effect of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Commonly used computational methods predict pathogenicity, which does not capture the quantitative impact on fitness in humans. We develop a method, MisFit, to estimate missense fitness effect using a graphical model. MisFit jointly models the effect at a molecular level ( d ) and a population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . We train it by maximizing probability of observed allele counts in 236,017 individuals of European ancestry. We show that s is informative in predicting allele frequency across ancestries and consistent with the fraction of de novo mutations in sites under strong selection. Further, s outperforms previous methods in prioritizing de novo missense variants in individuals with neurodevelopmental disorders. In conclusion, MisFit accurately predicts s and yields new insights from genomic data.
Collapse
Affiliation(s)
- Yige Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Tian Lan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Guojie Zhong
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY, USA
| | - Jake Hagen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hongbing Pan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Yufeng Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- JP Sulzberger Columbia Genome Center, Columbia University, New York, NY, USA.
| |
Collapse
|
2
|
Yurtseven A, Keller S, Hirsch P, Kalinina OV, Gress A. StructMAn 2.0 Web: a web server for structural annotation of protein sequences and mutations. Nucleic Acids Res 2025:gkaf381. [PMID: 40326516 DOI: 10.1093/nar/gkaf381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2025] [Revised: 04/11/2025] [Accepted: 04/25/2025] [Indexed: 05/07/2025] Open
Abstract
StructMAn is a method for protein structural annotation. It describes each position of a protein sequence or specific variants in it in terms of their importance for the three-dimensional (3D) structure of the protein and its interactions with other molecules. StructMAn maps, aligns, and aggregates data from experimentally resolved and predicted 3D structures of proteins and their homologs for any given protein sequence and/or a combination of mutations in it. The results provide structural annotation for every amino acid position allowing a detailed structural analysis. Furthermore, StructMAn enables generation of a wide variety of position-specific high-quality structural features that can be leveraged in machine learning applications. With the new web server StructMAn 2.0 Web, we provide a user-friendly way to use StructMAn offering an easy-to-use input interface and a comprehensive visualization for the various results of StructMAn. StructMAn 2.0 Web is available at https://tools.helmholtz-hips.de/structman.
Collapse
Affiliation(s)
- Alper Yurtseven
- Research Group Drug Bioinformatics, Department Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, 66123 Saarbrücken, Saarland, Germany
- Graduate School of Computer Science, Saarland University, 66123 Saarbrücken, Saarland, Germany
| | - Sebastian Keller
- Research Group Drug Bioinformatics, Department Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, 66123 Saarbrücken, Saarland, Germany
| | - Pascal Hirsch
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Saarland, Germany
| | - Olga V Kalinina
- Research Group Drug Bioinformatics, Department Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, 66123 Saarbrücken, Saarland, Germany
- Drug Bioinformatics, Medical Faculty, Saarland University, 66421 Homburg, Saarland, Germany
- Center for Bioinformatics, Saarland University, 66123 Saarbrücken, Saarland, Germany
| | - Alexander Gress
- Research Group Drug Bioinformatics, Department Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI), Campus E8.1, 66123 Saarbrücken, Saarland, Germany
| |
Collapse
|
3
|
Arnab SP, Campelo dos Santos AL, Fumagalli M, DeGiorgio M. Efficient Detection and Characterization of Targets of Natural Selection Using Transfer Learning. Mol Biol Evol 2025; 42:msaf094. [PMID: 40341942 PMCID: PMC12062966 DOI: 10.1093/molbev/msaf094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 04/16/2025] [Accepted: 04/17/2025] [Indexed: 05/11/2025] Open
Abstract
Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pretrained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.
Collapse
Affiliation(s)
- Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | | | - Matteo Fumagalli
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| |
Collapse
|
4
|
Garcia-Calleja J, Biagini SA, de Cid R, Calafell F, Bosch E. Inferring past demography and genetic adaptation in Spain using the GCAT cohort. Sci Rep 2025; 15:14225. [PMID: 40274920 PMCID: PMC12022144 DOI: 10.1038/s41598-025-98272-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Accepted: 04/10/2025] [Indexed: 04/26/2025] Open
Abstract
Located in the southwestern corner of Europe, the Iberian Peninsula is separated from the rest of the continent by the Pyrenees Mountains and from Africa by the Strait of Gibraltar. This geographical position may have conditioned distinct selective pressures compared to the rest of Europe and influenced differential patterns of gene flow. In this work, we analyse 704 whole-genome sequences from the GCAT reference panel to quantify gene flow into Spain from various historical sources and identify the top signatures of positive (adaptive) selection. While we found no clear evidence of a 16th-century admixture event putatively related to the French diaspora during the Wars of Religion, we detected signals of North African admixture matching the Muslim period and the subsequent Christian Reconquista. Notably, besides finding that well-known candidate genes previously described in Eurasians also seem to be adaptive in Spain, we discovered novel top candidates for positive selection putatively associated with immunity and diet (UBL7, SMYD1, VAC14 and FDFT1). Finally, local ancestry deviation analysis revealed that the MHCIII genomic region underwent post-admixture selection following the post-Neolithic admixture with Steppe ancestry.
Collapse
Affiliation(s)
- Jorge Garcia-Calleja
- Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain
| | - Simone A Biagini
- Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain
- Department of Archaeology and Museology, Masaryk University, Brno, Czech Republic
- Center of Molecular Medicine, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Rafael de Cid
- Genomes for Life-GCAT lab, CORE Program, Germans Trias i Pujol Research Institute (IGTP), 08916, Badalona, Spain
- Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra), Germans Trias I Pujol Research Institute (IGTP), 08916, Badalona, Spain
| | - Francesc Calafell
- Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain.
| | - Elena Bosch
- Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, 08003, Barcelona, Spain.
| |
Collapse
|
5
|
Soni V, Jensen JD. Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data. G3 (BETHESDA, MD.) 2025; 15:jkaf019. [PMID: 39883523 PMCID: PMC12005166 DOI: 10.1093/g3journal/jkaf019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2024] [Revised: 01/14/2025] [Accepted: 01/27/2025] [Indexed: 01/31/2025]
Abstract
The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.
Collapse
Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
| | - Jeffrey D Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ 85281, USA
| |
Collapse
|
6
|
Wang YX, Fei CJ, Shen C, Ou YN, Liu WS, Yang L, Wu BS, Deng YT, Feng JF, Cheng W, Yu JT. Exome sequencing identifies protein-coding variants associated with loneliness and social isolation. J Affect Disord 2025; 375:192-204. [PMID: 39842675 DOI: 10.1016/j.jad.2025.01.096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 10/31/2024] [Accepted: 01/18/2025] [Indexed: 01/24/2025]
Abstract
BACKGROUND Loneliness and social isolation are serious yet underappreciated public health problems, with their genetic underpinnings remaining largely unknown. We aimed to explore the role of protein-coding variants in the manifestation of loneliness and social isolation. METHODS We conducted the first exome-wide association analysis on loneliness and social isolation, utilizing 336,115 participants of white-British ancestry for loneliness and 346,115 for social isolation. Sensitivity analyses were performed to validate the genetic findings. We estimated the genetic burden heritability of loneliness and social isolation and provided biological insights into them. RESULTS We identified six novel risk genes (ANKRD12, RIPOR2, PTEN, ARL8B, NF1, and PIMREG) associated with loneliness and two (EDARADD and GIGYF1) with social isolation through analysis of rare coding variants. Brain-wide association analysis uncovered 47 associations between identified genes and brain structure phenotypes, many of which are critical for social processing and interaction. Phenome-wide association analysis established significant links between these genes and phenotypes across five categories, mostly blood biomarkers and cognitive measures. LIMITATIONS The measurements of loneliness and social isolation in UK Biobank are brief for these multi-layer social factors, some relevant aspects may be missed. CONCLUSIONS Our study revealed 13 risk genes associated with loneliness and 6 with social isolation, with the majority being novel discoveries. These findings advance our understanding of the genetic basis of these two traits. The study provides a foundation for future studies aimed at exploring the functional mechanisms of these genes and their potential implications for public health interventions targeting loneliness and social isolation.
Collapse
Affiliation(s)
- Yi-Xuan Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Chen-Jie Fei
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Ya-Nan Ou
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China; Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| |
Collapse
|
7
|
Kolbe D, Dose J, Putter P, Ziemann M, Laudes M, Slagboom PE, Franke A, Deelen J, Nebel A. German longevity study reveals novel rare pro-longevity alleles clustering in mTOR signaling pathway. GeroScience 2025:10.1007/s11357-025-01640-7. [PMID: 40232348 DOI: 10.1007/s11357-025-01640-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2025] [Accepted: 03/30/2025] [Indexed: 04/16/2025] Open
Abstract
In this study, we investigated the contribution of rare coding variants to human longevity by analyzing whole exome sequencing data from 1245 German long-lived individuals (LLI) and 4105 geographically matched younger controls. We identified novel exome-wide significant associations at both the single-variant and gene level, with a significant over-representation of genes involved in mechanistic target of rapamycin (mTOR) signaling. As such, three rare single variants in the mTOR-pathway genes RPS6, FLCN, and SIK3 were enriched in LLI. Additionally, RWDD1 emerged as a strong candidate gene for longevity, with LLI exhibiting a statistically significant burden of rare missense variants in this gene. Other associations involved PRAC2, SLC16 A6, FOCAD, IHH, MESD, HOXA4, and DNAJB13. Furthermore, we observed an enrichment of protein-truncating variants in the genes ASXL1 and TET2 amongst LLI, likely as a result of clonal haematopoiesis. The study emphasizes the role of rare variants in human longevity, particularly through mTOR signaling.
Collapse
Affiliation(s)
- Daniel Kolbe
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany.
| | - Janina Dose
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Pasquale Putter
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Malte Ziemann
- Institute of Transfusion Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Matthias Laudes
- Institute for Diabetes and Clinical Metabolic Research, University Hospital Schleswig-Holstein, Kiel, Germany
| | - P Eline Slagboom
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Joris Deelen
- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
- Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Ageing-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Almut Nebel
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| |
Collapse
|
8
|
Zhao Y, Lan T, Zhong G, Hagen J, Pan H, Chung WK, Shen Y. A probabilistic graphical model for estimating selection coefficient of nonsynonymous variants from human population sequence data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2025:2023.12.11.23299809. [PMID: 38168397 PMCID: PMC10760286 DOI: 10.1101/2023.12.11.23299809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Accurately predicting the effect of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Commonly used computational methods predict pathogenicity, which does not capture the quantitative impact on fitness in humans. We developed a method, MisFit, to estimate missense fitness effect using a graphical model. MisFit jointly models the effect at a molecular level (𝑑) and a population level (selection coefficient, 𝑠), assuming that in the same gene, missense variants with similar 𝑑 have similar 𝑠. We trained it by maximizing probability of observed allele counts in 236,017 European individuals. We show that 𝑠 is informative in predicting allele frequency across ancestries and consistent with the fraction of de novo mutations in sites under strong selection. Further, 𝑠 outperforms previous methods in prioritizing de novo missense variants in individuals with neurodevelopmental disorders. In conclusion, MisFit accurately predicts 𝑠 and yields new insights from genomic data.
Collapse
Affiliation(s)
- Yige Zhao
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY 10032
| | - Tian Lan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
| | - Guojie Zhong
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- The Integrated Program in Cellular, Molecular, and Biomedical Studies, Columbia University, New York, NY 10032
| | - Jake Hagen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- . Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Hongbing Pan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032
| | - Wendy K. Chung
- . Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115
| | - Yufeng Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032
- JP Sulzberger Columbia Genome Center, Columbia University, New York, NY 10032
| |
Collapse
|
9
|
Xu IRL, Danzi MC, Raposo J, Züchner S. The continued promise of genomic technologies and software in neurogenetics. J Neuromuscul Dis 2025:22143602251325345. [PMID: 40208247 DOI: 10.1177/22143602251325345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2025]
Abstract
The continued evolution of genomic technologies over the past few decades has revolutionized the field of neurogenetics, offering profound insights into the genetic underpinnings of neurological disorders. Identification of causal genes for numerous monogenic neurological conditions has informed key aspects of disease mechanisms and facilitated research into critical proteins and molecular pathways, laying the groundwork for therapeutic interventions. However, the question remains: has this transformative trend reached its zenith? In this review, we suggest that despite significant strides in genome sequencing and advanced computational analyses, there is still ample room for methodological refinement. We anticipate further major genetic breakthroughs corresponding with the increased use of long-read genomes, variant calling software, AI tools, and data aggregation databases. Genetic progress has historically been driven by technological advancements from the commercial sector, which are developed in response to academic research needs, creating a continuous cycle of innovation and discovery. This review explores the potential of genomic technologies to address the challenges of neurogenetic disorders. By outlining both established and modern resources, we aim to emphasize the importance of genetic technologies as we enter an era poised for discoveries.
Collapse
Affiliation(s)
- Isaac R L Xu
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Matt C Danzi
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jacquelyn Raposo
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Stephan Züchner
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| |
Collapse
|
10
|
Patin E, Quintana-Murci L. Tracing the Evolution of Human Immunity Through Ancient DNA. Annu Rev Immunol 2025; 43:57-82. [PMID: 39705165 DOI: 10.1146/annurev-immunol-082323-024638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2024]
Abstract
Infections have imposed strong selection pressures throughout human evolution, making the study of natural selection's effects on immunity genes highly complementary to disease-focused research. This review discusses how ancient DNA studies, which have revolutionized evolutionary genetics, increase our understanding of the evolution of human immunity. These studies have shown that interbreeding between modern humans and Neanderthals or Denisovans has influenced present-day immune responses, particularly to viruses. Additionally, ancient genomics enables the tracking of how human immunity has evolved across cultural transitions, highlighting strong selection since the Bronze Age in Europe (<4,500 years) and potential genetic adaptations to epidemics raging during the Middle Ages and the European colonization of the Americas. Furthermore, ancient genomic studies suggest that the genetic risk for noninfectious immune disorders has gradually increased over millennia because alleles associated with increased risk for autoimmunity and inflammation once conferred resistance to infections. The challenge now is to extend these findings to diverse, non-European populations and to provide a more global understanding of the evolution of human immunity.
Collapse
Affiliation(s)
- Etienne Patin
- Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Human Evolutionary Genetics Unit, Paris, France;
| | - Lluis Quintana-Murci
- Human Genomics and Evolution, Collège de France, Paris, France
- Institut Pasteur, Université Paris Cité, CNRS UMR 2000, Human Evolutionary Genetics Unit, Paris, France;
| |
Collapse
|
11
|
Achouri-Rassas A, Fray S, Said Z, Ben Sassi S, Ben Ali N, Baraket G. Genetic association study between rs2234253 (p.T96K) variant of TREM2 and Alzheimer's disease in a Tunisian population. Neurol Res 2025; 47:290-295. [PMID: 40043316 DOI: 10.1080/01616412.2025.2472841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Accepted: 02/21/2025] [Indexed: 04/02/2025]
Abstract
BACKGROUND Alzheimer's disease (AD) is the leading cause of major neurodegenerative cognitive impairment. The risk of developing AD is influenced by a complex interaction of genetic predisposition and environmental factors. Among the genetic risk factors, the APOE ɛ4 allele is the most significant, while variants in the TREM2 (Triggering Receptor Expressed on Myeloid Cells 2) and ABCA7 (ATP-binding cassette transporter A7) genes have also been associated with an increased risk of AD. OBJECTIVE This study aimed to investigate the association of APOE ɛ4, TREM2 gene variants (rs75932628 [p.R47H] and rs2234253 [p.T96K]), and ABCA7 gene variants (rs142076058 and rs115550680) with sporadic AD in the Tunisian population. Methods: A case-control study was conducted including 222 Tunisian patients diagnosed with sporadic AD and 99 cognitively healthy controls. Genotyping was performed to assess the presence and association of the selected genetic variants with AD. Statistical analyses were conducted to determine the significance of genetic associations. RESULTS A significant association was found between the TREM2 rs2234253 (p.T96K) variant and AD, with the T allele identified as a risk factor in the Tunisian population. The APOE ɛ4 allele was also associated with an increased risk of developing AD. However, no significant association was observed for the ABCA7 gene variants or the TREM2 rs75932628 (p.R47H) variant in either the AD or control groups. CONCLUSION Our findings suggest that the TREM2 rs2234253 (p.T96K) variant is a significant genetic risk factor for late-onset AD (LOAD) in the Tunisian population. Further studies with larger cohorts are needed to validate these findings and explore potential gene-gene interactions contributing to AD risk.
Collapse
Affiliation(s)
- Afef Achouri-Rassas
- Research Laboratory LR12SP01 Temporal Lobe Pathology, Charles Nicolle Hospital Tunis, Tunisia
| | - Saloua Fray
- Research Laboratory LR12SP01 Temporal Lobe Pathology, Charles Nicolle Hospital Tunis, Tunisia
- Neurological Department, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Zakaria Said
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
- Department of Neurology, National Institute Mongi Ben Hamida of Neurology, Tunis, Tunisia
| | - Samia Ben Sassi
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
- Department of Neurology, National Institute Mongi Ben Hamida of Neurology, Tunis, Tunisia
| | - Nadia Ben Ali
- Research Laboratory LR12SP01 Temporal Lobe Pathology, Charles Nicolle Hospital Tunis, Tunisia
- Neurological Department, Charles Nicolle Hospital, Tunis, Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Ghada Baraket
- Faculty of Sciences of Tunis, Tunis El Manar University, Tunis, Tunisia
| |
Collapse
|
12
|
Vasquez C, Osgood NB, Zepeda M, Sandel D, Cowan Q, Peiris M, Donoghue D, Komor A. Precision genome editing and in-cell measurements of oxidative DNA damage repair enable functional and mechanistic characterization of cancer-associated MUTYH variants. Nucleic Acids Res 2025; 53:gkaf037. [PMID: 40156857 PMCID: PMC11952967 DOI: 10.1093/nar/gkaf037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 01/08/2025] [Accepted: 01/16/2025] [Indexed: 04/01/2025] Open
Abstract
Functional characterization of genetic variants has the potential to advance the field of precision medicine by enhancing the efficacy of current therapies and accelerating the development of new approaches to combat genetic diseases. MUTYH is a DNA repair enzyme that recognizes and repairs oxidatively damaged guanines [8-oxoguanine (8-oxoG)] mispaired with adenines (8-oxoG·A). While some mutations in the MUTYH gene are associated with colorectal cancer, most MUTYH variants identified in sequencing databases are classified as variants of uncertain significance. Convoluting clinical classification is the absence of data directly comparing homozygous versus heterozygous MUTYH mutations. In this study, we present the first effort to functionally characterize MUTYH variants using precision genome editing to generate heterozygous and homozygous isogenic cell lines. Using a MUTYH-specific lesion reporter in which we site-specifically incorporate an 8-oxoG·A lesion in a fluorescent protein gene, we measure endogenous MUTYH enzymatic activity and classify them as pathogenic or benign. Further, we modify this reporter to incorporate the MUTYH repair intermediate (8-oxoG across from an abasic site) and validate it with co-immunoprecipitation experiments to demonstrate its ability to characterize the mechanism by which MUTYH mutants are defective at DNA repair.
Collapse
Affiliation(s)
- Carlos A Vasquez
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Nicola R B Osgood
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Marcanthony U Zepeda
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Dominika K Sandel
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Quinn T Cowan
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Malalage N Peiris
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
| | - Daniel J Donoghue
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
- Moores UCSD Cancer Center, University of California San Diego, La Jolla, San Diego, CA 92093, United States
| | - Alexis C Komor
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, United States
- Moores UCSD Cancer Center, University of California San Diego, La Jolla, San Diego, CA 92093, United States
- Sanford Stem Cell Institute, University of California San Diego, La Jolla, CA 92037, United States
| |
Collapse
|
13
|
Shastry V, Berg JJ. Allele ages provide limited information about the strength of negative selection. Genetics 2025; 229:iyae211. [PMID: 39698825 PMCID: PMC11912868 DOI: 10.1093/genetics/iyae211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2024] [Accepted: 12/12/2024] [Indexed: 12/20/2024] Open
Abstract
For many problems in population genetics, it is useful to characterize the distribution of fitness effects (DFE) of de novo mutations among a certain class of sites. A DFE is typically estimated by fitting an observed site frequency spectrum (SFS) to an expected SFS given a hypothesized distribution of selection coefficients and demographic history. The development of tools to infer gene trees from haplotype alignments, along with ancient DNA resources, provides us with additional information about the frequency trajectories of segregating mutations. Here, we ask how useful this additional information is for learning about the DFE, using the joint distribution on allele frequency and age to summarize information about the trajectory. To this end, we introduce an accurate and efficient numerical method for computing the density on the age of a segregating variant found at a given sample frequency, given the strength of selection and an arbitrarily complex population size history. We then use this framework to show that the unconditional age distribution of negatively selected alleles is very closely approximated by reweighting the neutral age distribution in terms of the negatively selected SFS, suggesting that allele ages provide little information about the DFE beyond that already contained in the present day frequency. To confirm this prediction, we extended the standard Poisson random field method to incorporate the joint distribution of frequency and age in estimating selection coefficients, and test its performance using simulations. We find that when the full SFS is observed and the true allele ages are known, including ages in the estimation provides only small increases in the accuracy of estimated selection coefficients. However, if only sites with frequencies above a certain threshold are observed, then the true ages can provide substantial information about the selection coefficients, especially when the selection coefficient is large. When ages are estimated from haplotype data using state-of-the-art tools, uncertainty about the age abrogates most of the additional information in the fully observed SFS case, while the neutral prior assumed in these tools when estimating ages induces a downward bias in the case of the thresholded SFS.
Collapse
Affiliation(s)
- Vivaswat Shastry
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA
| | - Jeremy J Berg
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
| |
Collapse
|
14
|
Arnab SP, Dos Santos ALC, Fumagalli M, DeGiorgio M. Efficient detection and characterization of targets of natural selection using transfer learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.03.05.641710. [PMID: 40093065 PMCID: PMC11908262 DOI: 10.1101/2025.03.05.641710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 03/19/2025]
Abstract
Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.
Collapse
Affiliation(s)
- Sandipan Paul Arnab
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| | | | - Matteo Fumagalli
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Michael DeGiorgio
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA
| |
Collapse
|
15
|
Ragsdale AP. Archaic introgression and the distribution of shared variation under stabilizing selection. PLoS Genet 2025; 21:e1011623. [PMID: 40163477 PMCID: PMC11964463 DOI: 10.1371/journal.pgen.1011623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2024] [Revised: 04/02/2025] [Accepted: 02/14/2025] [Indexed: 04/02/2025] Open
Abstract
Many phenotypic traits are under stabilizing selection, which maintains a population's mean phenotypic value near some optimum. The dynamics of traits and trait architectures under stabilizing selection have been extensively studied for single populations at steady state. However, natural populations are seldom at steady state and are often structured in some way. Admixture and introgression events may be common, including over human evolutionary history. Because stabilizing selection results in selection against the minor allele at a trait-affecting locus, alleles from the minor parental ancestry will be selected against after admixture. We show that the site-frequency spectrum can be used to model the genetic architecture of such traits, allowing for the study of trait architecture dynamics in complex multi-population settings. We use a simple deterministic two-locus model to predict the reduction of introgressed ancestry around trait-contributing loci. From this and individual-based simulations, we show that introgressed-ancestry is depleted around such loci. When introgression between two diverged populations occurs in both directions, as has been inferred between humans and Neanderthals, the locations of such regions with depleted introgressed ancestry will tend to be shared across populations. We argue that stabilizing selection for shared phenotypic optima may explain recent observations in which regions of depleted human-introgressed ancestry in the Neanderthal genome overlap with Neanderthal-ancestry deserts in humans.
Collapse
Affiliation(s)
- Aaron P Ragsdale
- Department of Integrative Biology, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| |
Collapse
|
16
|
Martins Rodrigues F, Jasielec J, Perpich M, Kim A, Moma L, Li Y, Storrs E, Wendl MC, Jayasinghe RG, Fiala M, Stefka A, Derman B, Jakubowiak AJ, DiPersio JF, Vij R, Godley LA, Ding L. Germline predisposition in multiple myeloma. iScience 2025; 28:111620. [PMID: 39845416 PMCID: PMC11750583 DOI: 10.1016/j.isci.2024.111620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2024] [Revised: 10/04/2024] [Accepted: 11/14/2024] [Indexed: 01/24/2025] Open
Abstract
We present a study of rare germline predisposition variants in 954 unrelated individuals with multiple myeloma (MM) and 82 MM families. Using a candidate gene approach, we identified such variants across all age groups in 9.1% of sporadic and 18% of familial cases. Implicated genes included genes suggested in other MM risk studies as potential risk genes (DIS3, EP300, KDM1A, and USP45); genes involved in predisposition to other cancers (ATM, BRCA1/2, CHEK2, PMS2, POT1, PRF1, and TP53); and BRIP1, EP300, and FANCM in individuals of African ancestry. Variants were characterized using loss of heterozygosity (LOH), biallelic events, and gene expression analyses, revealing 31 variants in 3.25% of sporadic cases for which pathogenicity was supported by multiple lines of evidence. Our results suggest that the disruption of DNA damage repair pathways may play a role in MM susceptibility. These results will inform improved surveillance in high-risk groups and potential therapeutic strategies.
Collapse
Affiliation(s)
- Fernanda Martins Rodrigues
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Jagoda Jasielec
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Melody Perpich
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Aelin Kim
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Luke Moma
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Yize Li
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Erik Storrs
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Michael C. Wendl
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Reyka G. Jayasinghe
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Mark Fiala
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Andrew Stefka
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Benjamin Derman
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Andrzej J. Jakubowiak
- Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - John F. DiPersio
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Ravi Vij
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lucy A. Godley
- Division of Hematology/Oncology, Department of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Li Ding
- Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
- Division of Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
- Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| |
Collapse
|
17
|
Zou X, Zhao Z, Chen Y, Xiong K, Wang Z, Chen S, Chen H, Wei GH, Xu S, Li W, Ni T, Li L. Impact of rare non-coding variants on human diseases through alternative polyadenylation outliers. Nat Commun 2025; 16:682. [PMID: 39819850 PMCID: PMC11739498 DOI: 10.1038/s41467-024-55407-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 12/11/2024] [Indexed: 01/19/2025] Open
Abstract
Although rare non-coding variants (RVs) play crucial roles in complex traits and diseases, understanding their mechanisms and identifying disease-associated RVs continue to be major challenges. Here we constructed a comprehensive atlas of alternative polyadenylation (APA) outliers (aOutliers), including 1334 3' UTR and 200 intronic aOutliers, from 15,201 samples across 49 human tissues. These aOutliers exhibit unique characteristics from transcription or splicing outliers, with a pronounced RV enrichment. Mechanistically, aOutlier-RVs alter poly(A) signals and splicing sites, and perturbation indeed triggers APA events. Furthermore, we developed a Bayesian-based APA RV prediction model, which successfully pinpointed a specific set of 1799 RVs impacting 278 genes with significantly large disease effect sizes. Notably, we observed a convergence effect between rare and common cancer variants, exemplified by regulation in the DDX18 gene. Together, this study introduced an APA-enhanced framework for genome annotation, underscoring APA's role in uncovering functional RVs linked to complex traits and diseases.
Collapse
Affiliation(s)
- Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Zhaozhao Zhao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai, China
| | - Yu Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai, China
| | - Kewei Xiong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Zeyang Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Shuxin Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Hui Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China
| | - Gong-Hong Wei
- Fudan University Shanghai Cancer Center & MOE Key Laboratory of Metabolism and Molecular Medicine, Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai, China
- Center for Evolutionary Biology, and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, China
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, USA.
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Shanghai Engineering Research Center of Industrial Microorganisms, School of Life Sciences, Fudan University, Shanghai, China.
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
- State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, Institutes of Biomedical Sciences, College of Life Sciences, Inner Mongolia University, Hohhot, China.
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, China.
| |
Collapse
|
18
|
Meisner J, Benros ME, Rasmussen S. Leveraging haplotype information in heritability estimation and polygenic prediction. Nat Commun 2025; 16:126. [PMID: 39747034 PMCID: PMC11695728 DOI: 10.1038/s41467-024-55477-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 12/13/2024] [Indexed: 01/04/2025] Open
Abstract
Polygenic prediction has yet to make a major clinical breakthrough in precision medicine and psychiatry, where the application of polygenic risk scores is expected to improve clinical decision-making. Most widely used approaches for estimating polygenic risk scores are based on summary statistics from external large-scale genome-wide association studies, which rely on assumptions of matching data distributions. This may hinder the impact of polygenic risk scores in modern diverse populations due to small differences in genetic architectures. Reference-free estimators of polygenic scores are instead based on genomic best linear unbiased predictions and model the population of interest directly. We introduce a framework, named hapla, with a novel algorithm for clustering haplotypes in phased genotype data to estimate heritability and perform reference-free polygenic prediction in complex traits. We utilize inferred haplotype clusters to compute accurate heritability estimates and polygenic scores in a simulation study and the iPSYCH2012 case-cohort for depression disorders and schizophrenia. We demonstrate that our haplotype-based approach robustly outperforms standard genotype-based approaches, which can help pave the way for polygenic risk scores in the future of precision medicine and psychiatry.
Collapse
Affiliation(s)
- Jonas Meisner
- Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Hellerup, Denmark.
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
| | - Michael Eriksen Benros
- Copenhagen Research Center for Biological and Precision Psychiatry, Mental Health Centre Copenhagen, Copenhagen University Hospital, Hellerup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon Rasmussen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
19
|
Popova L, Carabetta VJ. The Use of Next-Generation Sequencing in Personalized Medicine. Methods Mol Biol 2025; 2866:287-315. [PMID: 39546209 DOI: 10.1007/978-1-0716-4192-7_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2024]
Abstract
The revolutionary progress in development of next-generation sequencing (NGS) technologies has made it possible to deliver accurate genomic information in a timely manner. Over the past several years, NGS has transformed biomedical and clinical research and found its application in the field of personalized medicine. Here we discuss the rise of personalized medicine and the history of NGS. We discuss current applications and uses of NGS in medicine, including infectious diseases, oncology, genomic medicine, and dermatology. We provide a brief discussion of selected studies where NGS was used to respond to wide variety of questions in biomedical research and clinical medicine. Finally, we discuss the challenges of implementing NGS into routine clinical use.
Collapse
Affiliation(s)
- Liya Popova
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Valerie J Carabetta
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden, NJ, USA.
| |
Collapse
|
20
|
Saifullah, Ma Z, Li M, Maqbool MQ, Chen F. Family physician service quality and sustainability: a roadmap for Pakistan's healthcare sector. Front Med (Lausanne) 2024; 11:1455807. [PMID: 39703521 PMCID: PMC11655198 DOI: 10.3389/fmed.2024.1455807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Accepted: 11/12/2024] [Indexed: 12/21/2024] Open
Abstract
Introduction The number of family medicine consultants has increased during and after the COVID-19 pandemic. However, research on family medicine services specific to Pakistan remains limited. Therefore, this study aimed to explore family physician services in Pakistan. Methods To meet the study goals, we collected data using snowball and purposive sampling. A questionnaire was used exclusively to collect data from family physician consultations. The data were examined using the SmartPLS structural equation model to test the study model's reliability and validity. Results The study findings showed that using resource utilization and allocation, utilization of technology, professionalism improvement, medical attention, cooperation, and caring were positively significant to employee welfare and assistance in family medicine services. These dimensions were also positively significant to community involvement and advocacy for the sustainable development of family medical services in Pakistan. Conclusion The study concluded that effective resource utilization, professionalism, medical care, cooperation, and the evaluation of quality and outcomes are key factors in promoting the growth of family medicine services. These indicators may enhance staff satisfaction, community involvement, and family physician service sustainability.
Collapse
Affiliation(s)
- Saifullah
- School of Management, Jiangsu University, Zhenjiang, China
| | - Zhiqiang Ma
- School of Management, Jiangsu University, Zhenjiang, China
| | - Mingxing Li
- School of Management, Jiangsu University, Zhenjiang, China
| | | | - Feng Chen
- School of Management, Jiangsu University, Zhenjiang, China
| |
Collapse
|
21
|
Seddon JM, De D, Grunenkovaite L, Ferrara D. Clinical and Imaging Characteristics of PRPH2 Retinopathies in a Longitudinal Cohort and Diagnostic Implications. Invest Ophthalmol Vis Sci 2024; 65:31. [PMID: 39693084 DOI: 10.1167/iovs.65.14.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2024] Open
Abstract
Purpose The purpose of this study was to define genotypic-phenotypic correlations related to PRPH2-associated retinopathies in an observational longitudinal cohort and to improve diagnostic accuracy. Methods Individuals with PRPH2 variants were identified by genetic sequencing of 263 individuals (including 59 families). Ocular examinations with multimodal imaging were evaluated. Results Two pathogenic/likely pathogenic PRPH2 variants were identified in 22 individuals with retinopathies, low genetic susceptibility to age-related macular degeneration (AMD) and younger age of onset. The mean follow-up was 14 years. One family and 4 independent cases (n = 7) were heterozygous for the variant rs121918563 L185P (p.Leu185Pro). The individuals developed retinopathy compatible with autosomal dominant pattern dystrophy (PD), including adult-onset vitelliform macular dystrophy and butterfly macular dystrophy in their fourth to fifth decades of life, evolving to retinal pigment epithelial (RPE) irregularities and central macular atrophy 20 years later. Two families and an independent case (n = 15) had the rs281865373 splice-site variant c.828+3A>T (IVS2+3A>T) presenting as retinal flecks consistent with adult-onset fundus flavimaculatus with macular dystrophy and diffuse RPE atrophy consistent with central areolar chorioretinal dystrophy (CACD) in the fifth decade of life progressing to extensive atrophy in the sixth to eighth decades. The L185P variant was associated with better visual acuity (VA) during follow-up versus c.828+3A>T variant. Some individuals were initially misdiagnosed with geographic atrophy secondary to AMD. Conclusions Individuals with the L185P variant had less severe disease with clinical manifestation typical of PD and better VA. More advanced disease with CACD and worse VA were associated with the c.828+3A>T variant. Results contribute to knowledge about genotypic-phenotypic associations of PRPH2 retinopathies and inform clinical and therapeutic end points.
Collapse
Affiliation(s)
- Johanna M Seddon
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
| | - Dikha De
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
| | - Laura Grunenkovaite
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States
| | - Daniela Ferrara
- Department of Ophthalmology, Tufts University School of Medicine, Boston, Massachusetts, United States
- Roche Personalized Healthcare, Genentech, Inc., South San Francisco, California, United States
| |
Collapse
|
22
|
Thompson MD, Reiner-Link D, Berghella A, Rana BK, Rovati GE, Capra V, Gorvin CM, Hauser AS. G protein-coupled receptor (GPCR) pharmacogenomics. Crit Rev Clin Lab Sci 2024; 61:641-684. [PMID: 39119983 DOI: 10.1080/10408363.2024.2358304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 09/03/2023] [Accepted: 05/18/2024] [Indexed: 08/10/2024]
Abstract
The field of pharmacogenetics, the investigation of the influence of one or more sequence variants on drug response phenotypes, is a special case of pharmacogenomics, a discipline that takes a genome-wide approach. Massively parallel, next generation sequencing (NGS), has allowed pharmacogenetics to be subsumed by pharmacogenomics with respect to the identification of variants associated with responders and non-responders, optimal drug response, and adverse drug reactions. A plethora of rare and common naturally-occurring GPCR variants must be considered in the context of signals from across the genome. Many fundamentals of pharmacogenetics were established for G protein-coupled receptor (GPCR) genes because they are primary targets for a large number of therapeutic drugs. Functional studies, demonstrating likely-pathogenic and pathogenic GPCR variants, have been integral to establishing models used for in silico analysis. Variants in GPCR genes include both coding and non-coding single nucleotide variants and insertion or deletions (indels) that affect cell surface expression (trafficking, dimerization, and desensitization/downregulation), ligand binding and G protein coupling, and variants that result in alternate splicing encoding isoforms/variable expression. As the breadth of data on the GPCR genome increases, we may expect an increase in the use of drug labels that note variants that significantly impact the clinical use of GPCR-targeting agents. We discuss the implications of GPCR pharmacogenomic data derived from the genomes available from individuals who have been well-phenotyped for receptor structure and function and receptor-ligand interactions, and the potential benefits to patients of optimized drug selection. Examples discussed include the renin-angiotensin system in SARS-CoV-2 (COVID-19) infection, the probable role of chemokine receptors in the cytokine storm, and potential protease activating receptor (PAR) interventions. Resources dedicated to GPCRs, including publicly available computational tools, are also discussed.
Collapse
Affiliation(s)
- Miles D Thompson
- Krembil Brain Institute, Toronto Western Hospital, Toronto, Ontario, Canada
| | - David Reiner-Link
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Alessandro Berghella
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brinda K Rana
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - G Enrico Rovati
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Valerie Capra
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Milan, Italy
| | - Caroline M Gorvin
- Institute of Metabolism and Systems Research (IMSR), University of Birmingham, Birmingham, United Kingdom
| | - Alexander S Hauser
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
23
|
Soni V, Jensen JD. Inferring demographic and selective histories from population genomic data using a two-step approach in species with coding-sparse genomes: an application to human data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.19.613979. [PMID: 39605418 PMCID: PMC11601476 DOI: 10.1101/2024.09.19.613979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.
Collapse
Affiliation(s)
- Vivak Soni
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, US
| | - Jeffrey D. Jensen
- School of Life Sciences, Center for Evolution & Medicine, Arizona State University, Tempe, AZ, US
| |
Collapse
|
24
|
Pandey D, Harris M, Garud NR, Narasimhan VM. Leveraging ancient DNA to uncover signals of natural selection in Europe lost due to admixture or drift. Nat Commun 2024; 15:9772. [PMID: 39532856 PMCID: PMC11557891 DOI: 10.1038/s41467-024-53852-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 10/23/2024] [Indexed: 11/16/2024] Open
Abstract
Large ancient DNA (aDNA) studies offer the chance to examine genomic changes over time, providing direct insights into human evolution. While recent studies have used time-stratified aDNA for selection scans, most focus on single-locus methods. We conducted a multi-locus genotype scan on 708 samples spanning 7000 years of European history. We show that the G12 statistic, originally designed for unphased diploid data, can effectively detect selection in aDNA processed to create 'pseudo-haplotypes'. In simulations and at known positive control loci (e.g., lactase persistence), G12 outperforms the allele frequency-based selection statistic, SweepFinder2, previously used on aDNA. Applying our approach, we identified 14 candidate regions of selection across four time periods, with half the signals detectable only in the earliest period. Our findings suggest that selective events in European prehistory, including from the onset of animal domestication, have been obscured by neutral processes like genetic drift and demographic shifts such as admixture.
Collapse
Affiliation(s)
- Devansh Pandey
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
| | - Mariana Harris
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
| | - Nandita R Garud
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin, Austin, TX, USA.
- Department of Statistics and Data Science, The University of Texas at Austin, Austin, TX, USA.
| |
Collapse
|
25
|
Hemstrom W, Grummer JA, Luikart G, Christie MR. Next-generation data filtering in the genomics era. Nat Rev Genet 2024; 25:750-767. [PMID: 38877133 DOI: 10.1038/s41576-024-00738-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/25/2024] [Indexed: 06/16/2024]
Abstract
Genomic data are ubiquitous across disciplines, from agriculture to biodiversity, ecology, evolution and human health. However, these datasets often contain noise or errors and are missing information that can affect the accuracy and reliability of subsequent computational analyses and conclusions. A key step in genomic data analysis is filtering - removing sequencing bases, reads, genetic variants and/or individuals from a dataset - to improve data quality for downstream analyses. Researchers are confronted with a multitude of choices when filtering genomic data; they must choose which filters to apply and select appropriate thresholds. To help usher in the next generation of genomic data filtering, we review and suggest best practices to improve the implementation, reproducibility and reporting standards for filter types and thresholds commonly applied to genomic datasets. We focus mainly on filters for minor allele frequency, missing data per individual or per locus, linkage disequilibrium and Hardy-Weinberg deviations. Using simulated and empirical datasets, we illustrate the large effects of different filtering thresholds on common population genetics statistics, such as Tajima's D value, population differentiation (FST), nucleotide diversity (π) and effective population size (Ne).
Collapse
Affiliation(s)
- William Hemstrom
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
| | - Jared A Grummer
- Flathead Lake Biological Station, Wildlife Biology Program and Division of Biological Sciences, University of Montana, Missoula, MT, USA
| | - Gordon Luikart
- Flathead Lake Biological Station, Wildlife Biology Program and Division of Biological Sciences, University of Montana, Missoula, MT, USA
| | - Mark R Christie
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN, USA.
| |
Collapse
|
26
|
Lv FH, Wang DF, Zhao SY, Lv XY, Sun W, Nielsen R, Li MH. Deep Ancestral Introgressions between Ovine Species Shape Sheep Genomes via Argali-Mediated Gene Flow. Mol Biol Evol 2024; 41:msae212. [PMID: 39404100 PMCID: PMC11542629 DOI: 10.1093/molbev/msae212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2024] [Revised: 09/12/2024] [Accepted: 10/04/2024] [Indexed: 11/08/2024] Open
Abstract
Previous studies revealed extensive genetic introgression between Ovis species, which affects genetic adaptation and morphological traits. However, the exact evolutionary scenarios underlying the hybridization between sheep and allopatric wild relatives remain unknown. To address this problem, we here integrate the reference genomes of several ovine and caprine species: domestic sheep, argali, bighorn sheep, snow sheep, and domestic goats. Additionally, we use 856 whole genomes representing 169 domestic sheep populations and their six wild relatives: Asiatic mouflon, urial, argali, snow sheep, thinhorn sheep, and bighorn sheep. We implement a comprehensive set of analyses to test introgression among these species. We infer that the argali lineage originated ∼3.08 to 3.35 Mya and hybridized with the ancestor of Pachyceriforms (e.g. bighorn sheep and snow sheep) at ∼1.56 Mya. Previous studies showed apparent introgression from North American Pachyceriforms into the Bashibai sheep, a Chinese native sheep breed, despite of their wide geographic separation. We show here that, in fact, the apparent introgression from the Pachyceriforms into Bashibai can be explained by the old introgression from Pachyceriforms into argali and subsequent recent introgression from argali into Bashibai. Our results illustrate the challenges of estimating complex introgression histories and provide an example of how indirect and direct introgression can be distinguished.
Collapse
Affiliation(s)
- Feng-Hua Lv
- Frontiers Science Center for Molecular Design Breeding (MOE), State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Dong-Feng Wang
- CAS Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences (CAS), Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Si-Yi Zhao
- Frontiers Science Center for Molecular Design Breeding (MOE), State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xiao-Yang Lv
- International Joint Research Laboratory, Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou 225001, China
| | - Wei Sun
- International Joint Research Laboratory, Universities of Jiangsu Province of China for Domestic Animal Germplasm Resources and Genetic Improvement, Yangzhou 225001, China
| | - Rasmus Nielsen
- Department of Integrative Biology, University of California at Berkeley, Berkeley, CA 94720, USA
- Department of Statistics, UC Berkeley, Berkeley, CA 94707, USA
- Globe Institute, University of Copenhagen, Copenhagen 1350, Denmark
| | - Meng-Hua Li
- Frontiers Science Center for Molecular Design Breeding (MOE), State Key Laboratory of Animal Biotech Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| |
Collapse
|
27
|
Mattoo A, Jaffe IS, Keating B, Montgomery RA, Mangiola M. Improving long-term kidney allograft survival by rethinking HLA compatibility: from molecular matching to non-HLA genes. Front Genet 2024; 15:1442018. [PMID: 39415982 PMCID: PMC11480002 DOI: 10.3389/fgene.2024.1442018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 09/19/2024] [Indexed: 10/19/2024] Open
Abstract
Optimizing immunologic compatibility in organ transplantation extends beyond the conventional approach of Human Leukocyte Antigen (HLA) antigen matching, which exhibits significant limitations. A broader comprehension of the roles of classical and non-classical HLA genes in transplantation is imperative for enhancing long-term graft survival. High-resolution molecular HLA genotyping, despite its inherent challenges, has emerged as the cornerstone for precise patient-donor compatibility assessment. Leveraging understanding of eplet biology and indirect immune activation, eplet mismatch calculators and the PIRCHE-II algorithm surpass traditional methods in predicting allograft rejection. Understanding minor histocompatibility antigens may also present an opportunity to personalize the compatibility process. While the application of molecular matching in deceased donor organ allocation presents multiple technical, logistical, and conceptual barriers, rendering it premature for mainstream use, several other areas of donor-recipient matching and post-transplant management are ready to incorporate molecular matching. Provision of molecular mismatch scores to physicians during potential organ offer evaluations could potentially amplify long-term outcomes. The implementation of molecular matching in living organ donation and kidney paired exchange programs is similarly viable. This article will explore the current understanding of immunologic matching in transplantation and the potential applications of epitope and non-epitope molecular biology and genetics in clinical transplantation.
Collapse
Affiliation(s)
- Aprajita Mattoo
- *Correspondence: Aprajita Mattoo, ; Ian S. Jaffe, ; Massimo Mangiola,
| | - Ian S. Jaffe
- *Correspondence: Aprajita Mattoo, ; Ian S. Jaffe, ; Massimo Mangiola,
| | | | | | - Massimo Mangiola
- NYU Langone Transplant Institute, New York University Langone Health, New York, NY, United States
| |
Collapse
|
28
|
Milas I, Kaštelan Ž, Petrik J, Bingulac-Popović J, Čikić B, Šribar A, Jukić I. ABO Blood Type and Urinary Bladder Cancer: Phenotype, Genotype, Allelic Association with a Clinical or Histological Stage and Recurrence Rate. Glob Med Genet 2024; 11:233-240. [PMID: 39040623 PMCID: PMC11262885 DOI: 10.1055/s-0044-1788614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024] Open
Abstract
Background Previous research on connection between the ABO blood group and bladder cancer has been based on determining the ABO phenotype. This specific research is extended to the molecular level, providing more information about particular ABO alleles. Aim To investigate the impact of the ABO blood group genotype or phenotype as a risk factor for urinary bladder cancer. Materials and Methods In the case-control study, we included 74 patients who underwent surgery for a urinary bladder tumor at the Urology Clinic, Clinical Hospital Centre Zagreb, in 2021 and 2022. The control group comprised 142 asymptomatic and healthy blood donors. ABO genotyping to five basic alleles was done using a polymerase chain reaction with sequence-specific primers. We compared ABO phenotypes, genotypes, and alleles between patients and the healthy controls and investigated their distribution according to the clinical and histological stage and recurrence rate. Results No statistically significant difference was found among the groups, nor for the observed disease stages in terms of the phenotype and genotype. At the allele level, the results show a significantly lower proportion of malignancy in O1 ( p < 0.001), A1 ( p < 0.001), and B ( p = 0.013), and a lower proportion of metastatic disease in A2 (0%, p = 0.024). We also found significantly higher proportions of high-grade tumors in patients with O1 (71.4%, p < 0.001), A1 (70.1%, p = 0.019), of nonmuscle invasive tumors in patients with O1 (55.1%, p < 0.001), O2 (100%, p = 0.045), and recurrent tumors in patients with O1 (70.2%, p < 0.001) and A1 (74.2%, p = 0.007) alleles. Conclusion We did not find an association between the ABO blood group genotype or phenotype as a genetic risk factor for urinary bladder cancer. However, an analysis at the allelic level revealed a statistically significant association between certain alleles of the ABO blood group system and urinary bladder tumors, clinical or histological stage, and recurrence rate, respectively.
Collapse
Affiliation(s)
- Ivan Milas
- Department of Urology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Željko Kaštelan
- Department of Urology, University Hospital Centre Zagreb, Zagreb, Croatia
- Department of Medical Sciences, Croatian Academy of Sciences and Art, Zagreb, Croatia
| | - Jószef Petrik
- Department of Medical Biochemistry and Hematology, Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | | | - Bojan Čikić
- Department of Urology, University Hospital Centre Zagreb, Zagreb, Croatia
| | - Andrej Šribar
- Clinical Department of Anesthesiology and Intensive Care Medicine, Dubrava University Hospital
, Zagreb, Croatia
| | - Irena Jukić
- Medical Department, Croatian Institute of Transfusion Medicine, Zagreb, Croatia
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, Coratia
| |
Collapse
|
29
|
van den Belt S, Zhao H, Alachiotis N. Scalable CNN-based classification of selective sweeps using derived allele frequencies. Bioinformatics 2024; 40:ii29-ii36. [PMID: 39230693 PMCID: PMC11373383 DOI: 10.1093/bioinformatics/btae385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
MOTIVATION Selective sweeps can successfully be distinguished from neutral genetic data using summary statistics and likelihood-based methods that analyze single nucleotide polymorphisms (SNPs). However, these methods are sensitive to confounding factors, such as severe population bottlenecks and old migration. By virtue of machine learning, and specifically convolutional neural networks (CNNs), new accurate classification models that are robust to confounding factors have been recently proposed. However, such methods are more computationally expensive than summary-statistic-based ones, yielding them impractical for processing large-scale genomic data. Moreover, SNP data are frequently preprocessed to improve classification accuracy, further exacerbating the long analysis times. RESULTS To this end, we propose a 1D CNN-based model, dubbed FAST-NN, that does not require any preprocessing while using only derived allele frequencies instead of summary statistics or raw SNP data, thereby yielding a sample-size-invariant, scalable solution. We evaluated several data fusion approaches to account for the variance of the density of genetic diversity across genomic regions (a selective sweep signature), and performed an extensive neural architecture search based on a state-of-the-art reference network architecture (SweepNet). The resulting model, FAST-NN, outperforms the reference architecture by up to 12% inference accuracy over all challenging evolutionary scenarios with confounding factors that were evaluated. Moreover, FAST-NN is between 30× and 259× faster on a single CPU core, and between 2.0× and 6.2× faster on a GPU, when processing sample sizes between 128 and 1000 samples. Our work paves the way for the practical use of CNNs in large-scale selective sweep detection. AVAILABILITY AND IMPLEMENTATION https://github.com/SjoerdvandenBelt/FAST-NN.
Collapse
Affiliation(s)
- Sjoerd van den Belt
- Department of Computer Science, Faculty of EEMCS, University of Twente, 7522NB Enschede, The Netherlands
| | - Hanqing Zhao
- Department of Computer Science, Faculty of EEMCS, University of Twente, 7522NB Enschede, The Netherlands
| | - Nikolaos Alachiotis
- Department of Computer Science, Faculty of EEMCS, University of Twente, 7522NB Enschede, The Netherlands
| |
Collapse
|
30
|
Kyriazis CC, Lohmueller KE. Constraining models of dominance for nonsynonymous mutations in the human genome. PLoS Genet 2024; 20:e1011198. [PMID: 39302992 PMCID: PMC11446423 DOI: 10.1371/journal.pgen.1011198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 10/02/2024] [Accepted: 09/04/2024] [Indexed: 09/22/2024] Open
Abstract
Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h = 0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species.
Collapse
Affiliation(s)
- Christopher C. Kyriazis
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America
| | - Kirk E. Lohmueller
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
| |
Collapse
|
31
|
DeVore SB, Schuetz M, Alvey L, Lujan H, Ochayon DE, Williams L, Chang WC, Filuta A, Ruff B, Kothari A, Hahn JM, Brandt E, Satish L, Roskin K, Herr AB, Biagini JM, Martin LJ, Cagdas D, Keles S, Milner JD, Supp DM, Khurana Hershey GK. Regulation of MYC by CARD14 in human epithelium is a determinant of epidermal homeostasis and disease. Cell Rep 2024; 43:114589. [PMID: 39110589 PMCID: PMC11469028 DOI: 10.1016/j.celrep.2024.114589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/19/2024] [Accepted: 07/19/2024] [Indexed: 09/01/2024] Open
Abstract
Caspase recruitment domain family member 14 (CARD14) and its variants are associated with both atopic dermatitis (AD) and psoriasis, but their mechanistic impact on skin barrier homeostasis is largely unknown. CARD14 is known to signal via NF-κB; however, CARD14-NF-κB signaling does not fully explain the heterogeneity of CARD14-driven disease. Here, we describe a direct interaction between CARD14 and MYC and show that CARD14 signals through MYC in keratinocytes to coordinate skin barrier homeostasis. CARD14 directly binds MYC and influences barrier formation in an MYC-dependent fashion, and this mechanism is undermined by disease-associated CARD14 variants. These studies establish a paradigm that CARD14 activation regulates skin barrier function by two distinct mechanisms, including activating NF-κB to bolster the antimicrobial (chemical) barrier and stimulating MYC to bolster the physical barrier. Finally, we show that CARD14-dependent MYC signaling occurs in other epithelia, expanding the impact of our findings beyond the skin.
Collapse
Affiliation(s)
- Stanley B DeVore
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Human Genetics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Matthew Schuetz
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Lauren Alvey
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Henry Lujan
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - David E Ochayon
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Lindsey Williams
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Wan Chi Chang
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Alyssa Filuta
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Brandy Ruff
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Arjun Kothari
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Jennifer M Hahn
- Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Eric Brandt
- Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Latha Satish
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Krishna Roskin
- Division of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Andrew B Herr
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Immunobiology, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Jocelyn M Biagini
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
| | - Lisa J Martin
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA
| | - Deniz Cagdas
- Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Medical School, Ihsan Dogramaci Children's Hospital, Institutes of Child Health, Ankara 06230, Turkey
| | - Sevgi Keles
- Division of Pediatric Immunology and Allergy, Necmettin Erbakan University, Konya 42090, Turkey
| | - Joshua D Milner
- Department of Pediatrics, Columbia University, New York, NY 10027, USA
| | - Dorothy M Supp
- Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Scientific Staff, Shriners Children's Ohio, Dayton, OH 45404, USA
| | - Gurjit K Khurana Hershey
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA; Division of Asthma Research, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA.
| |
Collapse
|
32
|
Kobren SN, Moldovan MA, Reimers R, Traviglia D, Li X, Barnum D, Veit A, Corona RI, Carvalho Neto GDV, Willett J, Berselli M, Ronchetti W, Nelson SF, Martinez-Agosto JA, Sherwood R, Krier J, Kohane IS, Sunyaev SR. Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580158. [PMID: 38405764 PMCID: PMC10888768 DOI: 10.1101/2024.02.13.580158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.
Collapse
Affiliation(s)
| | | | | | - Daniel Traviglia
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Xinyun Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT
| | | | - Alexander Veit
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Rosario I. Corona
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - George de V. Carvalho Neto
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Julian Willett
- Department of Pathology and Laboratory Medicine, NewYork-Presbyterian Weill Cornell Medical Center, New York, NY
| | - Michele Berselli
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - William Ronchetti
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | - Stanley F. Nelson
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Julian A. Martinez-Agosto
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA
| | - Richard Sherwood
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Joel Krier
- Department of Genetics, Atrius Health, Boston, MA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| | | | - Shamil R. Sunyaev
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA
| |
Collapse
|
33
|
Berardi S, Rhodes JA, Berner MC, Greenblum SI, Bitter MC, Behrman EL, Betancourt NJ, Bergland AO, Petrov DA, Rajpurohit S, Schmidt P. Drosophila melanogaster pigmentation demonstrates adaptive phenotypic parallelism but genomic unpredictability over multiple timescales. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.09.607378. [PMID: 39211235 PMCID: PMC11361081 DOI: 10.1101/2024.08.09.607378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
Populations are capable of responding to environmental change over ecological timescales via adaptive tracking. However, the translation from patterns of allele frequency change to rapid adaptation of complex traits remains unresolved. We used abdominal pigmentation in Drosophila melanogaster as a model phenotype to address the nature, genetic architecture, and repeatability of rapid adaptation in the field. We show that D. melanogaster pigmentation evolves as a highly parallel and deterministic response to shared environmental gradients across latitude and season in natural North American populations. We then experimentally evolved replicate, genetically diverse fly populations in field mesocosms to remove any confounding effects of demography and/or cryptic structure that may drive patterns in wild populations; we show that pigmentation rapidly responds, in parallel, in fewer than ten generations. Thus, pigmentation evolves concordantly in response to spatial and temporal climatic gradients. We next examined whether phenotypic differentiation was associated with allele frequency change at loci with established links to genetic variance in pigmentation in natural populations. We found that across all spatial and temporal scales, phenotypic patterns were associated with variation at pigmentation-related loci, and the sets of genes we identified in each context were largely nonoverlapping. Therefore, our findings suggest that parallel phenotypic evolution is associated with an unpredictable genomic response, with distinct components of the polygenic architecture shifting across each environmental gradient to produce redundant adaptive patterns. Significance Statement Shifts in global climate conditions have heightened our need to understand the dynamics and pace of adaptation in natural populations. In order to anticipate the population-level response to rapidly changing environmental conditions, we need to understand whether trait evolution is predictable over short timescales, and whether the genetic basis of adaptation is shared or distinct across multiple timescales. Here, we explored parallelism in the adaptive response of a complex phenotype, D. melanogaster pigmentation, to shared conditions that varied over multiple spatiotemporal scales. Our results demonstrate that while phenotypic adaptation proceeds as a predictable response to environmental gradients, even over short timescales, the genetic basis of the adaptive response is variable and nuanced across spatial and temporal contexts.
Collapse
|
34
|
Li Y, Cacciottolo TM, Yin N, He Y, Liu H, Liu H, Yang Y, Henning E, Keogh JM, Lawler K, Mendes de Oliveira E, Gardner EJ, Kentistou KA, Laouris P, Bounds R, Ong KK, Perry JRB, Barroso I, Tu L, Bean JC, Yu M, Conde KM, Wang M, Ginnard O, Fang X, Tong L, Han J, Darwich T, Williams KW, Yang Y, Wang C, Joss S, Firth HV, Xu Y, Farooqi IS. Loss of transient receptor potential channel 5 causes obesity and postpartum depression. Cell 2024; 187:4176-4192.e17. [PMID: 38959890 PMCID: PMC11961024 DOI: 10.1016/j.cell.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/24/2024] [Accepted: 05/31/2024] [Indexed: 07/05/2024]
Abstract
Hypothalamic neural circuits regulate instinctive behaviors such as food seeking, the fight/flight response, socialization, and maternal care. Here, we identified microdeletions on chromosome Xq23 disrupting the brain-expressed transient receptor potential (TRP) channel 5 (TRPC5). This family of channels detects sensory stimuli and converts them into electrical signals interpretable by the brain. Male TRPC5 deletion carriers exhibited food seeking, obesity, anxiety, and autism, which were recapitulated in knockin male mice harboring a human loss-of-function TRPC5 mutation. Women carrying TRPC5 deletions had severe postpartum depression. As mothers, female knockin mice exhibited anhedonia and depression-like behavior with impaired care of offspring. Deletion of Trpc5 from oxytocin neurons in the hypothalamic paraventricular nucleus caused obesity in both sexes and postpartum depressive behavior in females, while Trpc5 overexpression in oxytocin neurons in knock-in mice reversed these phenotypes. We demonstrate that TRPC5 plays a pivotal role in mediating innate human behaviors fundamental to survival, including food seeking and maternal care.
Collapse
Affiliation(s)
- Yongxiang Li
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Tessa M Cacciottolo
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Na Yin
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Yang He
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Department of Pediatrics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
| | - Hesong Liu
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Hailan Liu
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Yuxue Yang
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Taizhou People's Hospital, Medical School of Yangzhou University, Taizhou, Jiangsu, China
| | - Elana Henning
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Julia M Keogh
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Katherine Lawler
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Edson Mendes de Oliveira
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Eugene J Gardner
- MRC Epidemiology Unit, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Katherine A Kentistou
- MRC Epidemiology Unit, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Panayiotis Laouris
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Rebecca Bounds
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Ken K Ong
- MRC Epidemiology Unit, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - John R B Perry
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK; MRC Epidemiology Unit, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
| | - Inês Barroso
- Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Longlong Tu
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Jonathan C Bean
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Meng Yu
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Kristine M Conde
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Mengjie Wang
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Olivia Ginnard
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Xing Fang
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Lydia Tong
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Junying Han
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Tia Darwich
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Kevin W Williams
- Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9077, USA
| | - Yongjie Yang
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Chunmei Wang
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Shelagh Joss
- West of Scotland Regional Genetics Service, Queen Elizabeth University Hospital, Glasgow, UK
| | - Helen V Firth
- Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust & Wellcome Sanger Institute, Cambridge, UK
| | - Yong Xu
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK.
| |
Collapse
|
35
|
Nagelberg AL, Sihota TS, Chuang YC, Shi R, Chow JLM, English J, MacAulay C, Lam S, Lam WL, Lockwood WW. Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response. Br J Cancer 2024; 131:534-550. [PMID: 38890444 PMCID: PMC11300780 DOI: 10.1038/s41416-024-02755-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 06/03/2024] [Accepted: 06/06/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer. METHODS Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo. RESULTS We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage. CONCLUSIONS These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.
Collapse
Affiliation(s)
- Amy L Nagelberg
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Tianna S Sihota
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Yu-Chi Chuang
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada
| | - Rocky Shi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada
| | - Justine L M Chow
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - John English
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Calum MacAulay
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Lam
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Wan L Lam
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada
| | - William W Lockwood
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
- Interdisciplinary Oncology Program, University of British Columbia, Vancouver, BC, Canada.
| |
Collapse
|
36
|
Burt CH. Polygenic Indices (a.k.a. Polygenic Scores) in Social Science: A Guide for Interpretation and Evaluation. SOCIOLOGICAL METHODOLOGY 2024; 54:300-350. [PMID: 39091537 PMCID: PMC11293310 DOI: 10.1177/00811750241236482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Polygenic indices (PGI)-the new recommended label for polygenic scores (PGS) in social science-are genetic summary scales often used to represent an individual's liability for a disease, trait, or behavior based on the additive effects of measured genetic variants. Enthusiasm for linking genetic data with social outcomes and the inclusion of premade PGIs in social science datasets have facilitated increased uptake of PGIs in social science research-a trend that will likely continue. Yet, most social scientists lack the expertise to interpret and evaluate PGIs in social science research. Here, we provide a primer on PGIs for social scientists focusing on key concepts, unique statistical genetic considerations, and best practices in calculation, estimation, reporting, and interpretation. We summarize our recommended best practices as a checklist to aid social scientists in evaluating and interpreting studies with PGIs. We conclude by discussing the similarities between PGIs and standard social science scales and unique interpretative considerations.
Collapse
|
37
|
Li L, Comi TJ, Bierman RF, Akey JM. Recurrent gene flow between Neanderthals and modern humans over the past 200,000 years. Science 2024; 385:eadi1768. [PMID: 38991054 DOI: 10.1126/science.adi1768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 05/14/2024] [Indexed: 07/13/2024]
Abstract
Although it is well known that the ancestors of modern humans and Neanderthals admixed, the effects of gene flow on the Neanderthal genome are not well understood. We develop methods to estimate the amount of human-introgressed sequences in Neanderthals and apply it to whole-genome sequence data from 2000 modern humans and three Neanderthals. We estimate that Neanderthals have 2.5 to 3.7% human ancestry, and we leverage human-introgressed sequences in Neanderthals to revise estimates of Neanderthal ancestry in modern humans, show that Neanderthal population sizes were significantly smaller than previously estimated, and identify two distinct waves of modern human gene flow into Neanderthals. Our data provide insights into the genetic legacy of recurrent gene flow between modern humans and Neanderthals.
Collapse
Affiliation(s)
- Liming Li
- Department of Medical Genetics and Developmental Biology, School of Medicine, The Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, Southeast University, Nanjing 210009, China
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Troy J Comi
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Rob F Bierman
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| | - Joshua M Akey
- The Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08540, USA
| |
Collapse
|
38
|
Saifullah, Ma Z, Li M, Maqbool MQ, Chen J. Enhancing telehealth services development in Pakistani healthcare sectors through examining various medical service quality characteristics. Front Public Health 2024; 12:1376534. [PMID: 39045155 PMCID: PMC11263101 DOI: 10.3389/fpubh.2024.1376534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/03/2024] [Indexed: 07/25/2024] Open
Abstract
Introduction The telehealth service increased attention both during and after the Covid-19 outbreak. Nevertheless, there is a dearth of research in developing countries, including Pakistan. Hence, the objective of this study was to examine telehealth service quality dimensions to promote the telehealth behavior intention and sustainable growth of telehealth in Pakistan. Methods This study employed a cross-sectional descriptive design. Data were collected from doctors who were delivering telehealth services through a well-designed questionnaire. To examine the hypothesis of the study, we employed the Smart PLS structural equation modeling program, namely version 0.4. Results The study findings indicate that medical service quality, affordability, information quality, waiting time, and safety have a positive impact on the intention to engage in telehealth behavior. Furthermore, the adoption of telehealth behavior has a significant favorable effect on the actual utilization of telehealth services, which in turn has a highly good impact on sustainable development. Conclusion The study determined that telehealth services effectively decrease the amount of time and money spent on travel, while still offering convenient access to healthcare. Furthermore, telehealth has the potential to revolutionize payment methods, infrastructure, and staffing in the healthcare industry. Implementing a well-structured telehealth service model can yield beneficial results for a nation and its regulatory efforts in the modern age of technology.
Collapse
Affiliation(s)
- Saifullah
- School of Management, Jiangsu University, Zhenjiang, China
| | - Zhiqiang Ma
- School of Management, Jiangsu University, Zhenjiang, China
| | - Mingxing Li
- School of Management, Jiangsu University, Zhenjiang, China
| | | | - Jing Chen
- Affiliated Hospital of Jiangsu University, Zhenjiang, China
| |
Collapse
|
39
|
Mavillard F, Perez-Florido J, Ortuño FM, Valladares A, Álvarez-Villegas ML, Roldán G, Carmona R, Soriano M, Susarte S, Fuentes P, López-López D, Nuñez-Negrillo AM, Carvajal A, Morgado Y, Arteaga D, Ufano R, Mir P, Gamella JF, Dopazo J, Paradas C, Cabrera-Serrano M. The Iberian Roma Population Variant Server (IRPVS). J Genet Genomics 2024; 51:769-773. [PMID: 38548101 DOI: 10.1016/j.jgg.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/01/2024] [Accepted: 03/17/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Fabiola Mavillard
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain; Centro Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Sevilla, Spain
| | - Javier Perez-Florido
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain; Grupo de medicina computacional de sistemas, Instituto de Biomedicina de Sevilla (IBiS), Hospital Virgen del Rocío, Sevilla, Spain; Nodo de Genómica Funcional, (INB-ELIXIR-es), Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain; Bioinformática en Enfermedades raras (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de salud Carlos III, Sevilla, Spain.
| | - Francisco M Ortuño
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain; Departamento de Ingeniería de Computadores, Automática y Robótica, Universidad de Granada, Granada, Spain
| | - Amador Valladares
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain
| | | | - Gema Roldán
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Rosario Carmona
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain; Bioinformática en Enfermedades raras (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de salud Carlos III, Sevilla, Spain
| | - Manuel Soriano
- Centro de Servicios Sociales, Negociado de Servicios Especializados, Ayuntamiento de Sevilla, Sevilla, Spain
| | - Santiago Susarte
- Centro de Servicios Sociales, Negociado de Servicios Especializados, Ayuntamiento de Sevilla, Sevilla, Spain
| | - Pilar Fuentes
- Centro de Servicios Sociales, Negociado de Servicios Especializados, Ayuntamiento de Sevilla, Sevilla, Spain
| | - Daniel López-López
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain; Grupo de medicina computacional de sistemas, Instituto de Biomedicina de Sevilla (IBiS), Hospital Virgen del Rocío, Sevilla, Spain; Nodo de Genómica Funcional, (INB-ELIXIR-es), Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain; Bioinformática en Enfermedades raras (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de salud Carlos III, Sevilla, Spain
| | - Ana María Nuñez-Negrillo
- Departamento de Enfermería, Facultad de Ciencias de la Salud, Universidad de Granada, Granada, Spain
| | - Alejandra Carvajal
- Departamento de Neurología, Hospital Virgen de las Nieves, Granada, Spain
| | - Yolanda Morgado
- Departamento de Neurología, Hospital Virgen de Valme, Sevilla, Spain
| | | | - Rosa Ufano
- Centro de Salud Polígono Sur, Sevilla, Spain
| | - Pablo Mir
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain; Centro Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Sevilla, Spain; Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Hospital Universitario Virgen del Rocío, Sevilla, Spain; Departamento de Medicina, Facultad de Medicina, Universidad de Sevilla, Seville, Spain
| | - Juan F Gamella
- Departamento de Antropología Social, Universidad de Granada, Spain
| | - Joaquín Dopazo
- Plataforma Andaluza de Medicina Computacional, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain; Grupo de medicina computacional de sistemas, Instituto de Biomedicina de Sevilla (IBiS), Hospital Virgen del Rocío, Sevilla, Spain; Nodo de Genómica Funcional, (INB-ELIXIR-es), Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla 41013, Spain; Bioinformática en Enfermedades raras (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de salud Carlos III, Sevilla, Spain.
| | - Carmen Paradas
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain; Centro Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Sevilla, Spain; Unidad Enfermedades Neuromusculares, Servicio de Neurología y Neurofisiología Clínica, Hospital Universitario Virgen del Rocío, Sevilla, Spain.
| | - Macarena Cabrera-Serrano
- Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Sevilla, Spain; Centro Investigación Biomédica en Red Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Sevilla, Spain; Unidad Enfermedades Neuromusculares, Servicio de Neurología y Neurofisiología Clínica, Hospital Universitario Virgen del Rocío, Sevilla, Spain.
| |
Collapse
|
40
|
Parsons BL, Beal MA, Dearfield KL, Douglas GR, Gi M, Gollapudi BB, Heflich RH, Horibata K, Kenyon M, Long AS, Lovell DP, Lynch AM, Myers MB, Pfuhler S, Vespa A, Zeller A, Johnson GE, White PA. Severity of effect considerations regarding the use of mutation as a toxicological endpoint for risk assessment: A report from the 8th International Workshop on Genotoxicity Testing (IWGT). ENVIRONMENTAL AND MOLECULAR MUTAGENESIS 2024. [PMID: 38828778 DOI: 10.1002/em.22599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 03/13/2024] [Accepted: 04/15/2024] [Indexed: 06/05/2024]
Abstract
Exposure levels without appreciable human health risk may be determined by dividing a point of departure on a dose-response curve (e.g., benchmark dose) by a composite adjustment factor (AF). An "effect severity" AF (ESAF) is employed in some regulatory contexts. An ESAF of 10 may be incorporated in the derivation of a health-based guidance value (HBGV) when a "severe" toxicological endpoint, such as teratogenicity, irreversible reproductive effects, neurotoxicity, or cancer was observed in the reference study. Although mutation data have been used historically for hazard identification, this endpoint is suitable for quantitative dose-response modeling and risk assessment. As part of the 8th International Workshops on Genotoxicity Testing, a sub-group of the Quantitative Analysis Work Group (WG) explored how the concept of effect severity could be applied to mutation. To approach this question, the WG reviewed the prevailing regulatory guidance on how an ESAF is incorporated into risk assessments, evaluated current knowledge of associations between germline or somatic mutation and severe disease risk, and mined available data on the fraction of human germline mutations expected to cause severe disease. Based on this review and given that mutations are irreversible and some cause severe human disease, in regulatory settings where an ESAF is used, a majority of the WG recommends applying an ESAF value between 2 and 10 when deriving a HBGV from mutation data. This recommendation may need to be revisited in the future if direct measurement of disease-causing mutations by error-corrected next generation sequencing clarifies selection of ESAF values.
Collapse
Affiliation(s)
- Barbara L Parsons
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Marc A Beal
- Bureau of Chemical Safety, Health Products and Food Branch, Health Canada, Ottawa, Ontario, Canada
| | - Kerry L Dearfield
- U.S. Environmental Protection Agency and U.S. Department of Agriculture, Washington, DC, USA
| | - George R Douglas
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - Min Gi
- Department of Environmental Risk Assessment, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
| | | | - Robert H Heflich
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Michelle Kenyon
- Portfolio and Regulatory Strategy, Drug Safety Research and Development, Pfizer, Groton, Connecticut, USA
| | - Alexandra S Long
- Existing Substances Risk Assessment Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| | - David P Lovell
- Population Health Research Institute, St George's Medical School, University of London, London, UK
| | | | - Meagan B Myers
- Division of Genetic and Molecular Toxicology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alisa Vespa
- Pharmaceutical Drugs Directorate, Health Products and Food Branch, Health Canada, Ottawa, Ontario, Canada
| | - Andreas Zeller
- Pharmaceutical Sciences, pRED Innovation Center Basel, Hoffmann-La Roche Ltd, Basel, Switzerland
| | - George E Johnson
- Swansea University Medical School, Swansea University, Swansea, Wales, UK
| | - Paul A White
- Environmental Health Science and Research Bureau, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, Ontario, Canada
| |
Collapse
|
41
|
Yin R, Gutierrez A, Kobren SN, Avillach P. VarPPUD: Variant post prioritization developed for undiagnosed genetic disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305876. [PMID: 38699371 PMCID: PMC11065012 DOI: 10.1101/2024.04.15.24305876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Rare and ultra-rare genetic conditions are estimated to impact nearly 1 in 17 people worldwide, yet accurately pinpointing the diagnostic variants underlying each of these conditions remains a formidable challenge. Because comprehensive, in vivo functional assessment of all possible genetic variants is infeasible, clinicians instead consider in silico variant pathogenicity predictions to distinguish plausibly disease-causing from benign variants across the genome. However, in the most difficult undiagnosed cases, such as those accepted to the Undiagnosed Diseases Network (UDN), existing pathogenicity predictions cannot reliably discern true etiological variant(s) from other deleterious candidate variants that were prioritized through N-of-1 efforts. Pinpointing the disease-causing variant from a pool of plausible candidates remains a largely manual effort requiring extensive clinical workups, functional and experimental assays, and eventual identification of genotype- and phenotype-matched individuals. Here, we introduce VarPPUD, a tool trained on prioritized variants from UDN cases, that leverages gene-, amino acid-, and nucleotide-level features to discern pathogenic variants from other deleterious variants that are unlikely to be confirmed as disease relevant. VarPPUD achieves a cross-validated accuracy of 79.3% and precision of 77.5% on a held-out subset of uniquely challenging UDN cases, respectively representing an average 18.6% and 23.4% improvement over nine traditional pathogenicity prediction approaches on this task. We validate VarPPUD's ability to discriminate likely from unlikely pathogenic variants on synthetic, GAN-generated candidate variants as well. Finally, we show how VarPPUD can be probed to evaluate each input feature's importance and contribution toward prediction-an essential step toward understanding the distinct characteristics of newly-uncovered disease-causing variants.
Collapse
Affiliation(s)
- Rui Yin
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL 32610
| | - Alba Gutierrez
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| | | | | | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115
| |
Collapse
|
42
|
Murga-Moreno J, Casillas S, Barbadilla A, Uricchio L, Enard D. An efficient and robust ABC approach to infer the rate and strength of adaptation. G3 (BETHESDA, MD.) 2024; 14:jkae031. [PMID: 38365205 PMCID: PMC11090462 DOI: 10.1093/g3journal/jkae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/10/2023] [Accepted: 01/29/2024] [Indexed: 02/18/2024]
Abstract
Inferring the effects of positive selection on genomes remains a critical step in characterizing the ultimate and proximate causes of adaptation across species, and quantifying positive selection remains a challenge due to the confounding effects of many other evolutionary processes. Robust and efficient approaches for adaptation inference could help characterize the rate and strength of adaptation in nonmodel species for which demographic history, mutational processes, and recombination patterns are not currently well-described. Here, we introduce an efficient and user-friendly extension of the McDonald-Kreitman test (ABC-MK) for quantifying long-term protein adaptation in specific lineages of interest. We characterize the performance of our approach with forward simulations and find that it is robust to many demographic perturbations and positive selection configurations, demonstrating its suitability for applications to nonmodel genomes. We apply ABC-MK to the human proteome and a set of known virus interacting proteins (VIPs) to test the long-term adaptation in genes interacting with viruses. We find substantially stronger signatures of positive selection on RNA-VIPs than DNA-VIPs, suggesting that RNA viruses may be an important driver of human adaptation over deep evolutionary time scales.
Collapse
Affiliation(s)
- Jesús Murga-Moreno
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
| | - Sònia Casillas
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | - Antonio Barbadilla
- Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, Bellaterra, Barcelona 08193, Spain
| | | | - David Enard
- Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85719, USA
| |
Collapse
|
43
|
Song H, Chu J, Li W, Li X, Fang L, Han J, Zhao S, Ma Y. A Novel Approach Utilizing Domain Adversarial Neural Networks for the Detection and Classification of Selective Sweeps. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304842. [PMID: 38308186 PMCID: PMC11005742 DOI: 10.1002/advs.202304842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/10/2024] [Indexed: 02/04/2024]
Abstract
The identification and classification of selective sweeps are of great significance for improving the understanding of biological evolution and exploring opportunities for precision medicine and genetic improvement. Here, a domain adaptation sweep detection and classification (DASDC) method is presented to balance the alignment of two domains and the classification performance through a domain-adversarial neural network and its adversarial learning modules. DASDC effectively addresses the issue of mismatch between training data and real genomic data in deep learning models, leading to a significant improvement in its generalization capability, prediction robustness, and accuracy. The DASDC method demonstrates improved identification performance compared to existing methods and excels in classification performance, particularly in scenarios where there is a mismatch between application data and training data. The successful implementation of DASDC in real data of three distinct species highlights its potential as a useful tool for identifying crucial functional genes and investigating adaptive evolutionary mechanisms, particularly with the increasing availability of genomic data.
Collapse
Affiliation(s)
- Hui Song
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Jinyu Chu
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Wangjiao Li
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
| | - Xinyun Li
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
| | - Lingzhao Fang
- Center for Quantitative Genetics and GenomicsAarhus UniversityAarhus8000Denmark
| | - Jianlin Han
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- CAAS‐ILRI Joint Laboratory on Livestock and Forage Genetic ResourcesInstitute of Animal ScienceChinese Academy of Agricultural Sciences (CAAS)Beijing100193China
- Livestock Genetics ProgramInternational Livestock Research Institute (ILRI)Nairobi00100Kenya
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
- Lingnan Modern Agricultural Science and Technology Guangdong LaboratoryGuangzhou510642China
| | - Yunlong Ma
- Key Laboratory of Agricultural Animal GeneticsBreeding, and Reproduction of the Ministry of Education & Key Laboratory of Swine Genetics and Breeding of the Ministry of AgricultureHuazhong Agricultural UniversityWuhan430070China
- Hubei Hongshan LaboratoryWuhan430070China
- Lingnan Modern Agricultural Science and Technology Guangdong LaboratoryGuangzhou510642China
| |
Collapse
|
44
|
Popova L, Carabetta VJ. The use of next-generation sequencing in personalized medicine. ARXIV 2024:arXiv:2403.03688v1. [PMID: 38495572 PMCID: PMC10942477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The revolutionary progress in development of next-generation sequencing (NGS) technologies has made it possible to deliver accurate genomic information in a timely manner. Over the past several years, NGS has transformed biomedical and clinical research and found its application in the field of personalized medicine. Here we discuss the rise of personalized medicine and the history of NGS. We discuss current applications and uses of NGS in medicine, including infectious diseases, oncology, genomic medicine, and dermatology. We provide a brief discussion of selected studies where NGS was used to respond to wide variety of questions in biomedical research and clinical medicine. Finally, we discuss the challenges of implementing NGS into routine clinical use.
Collapse
Affiliation(s)
- Liya Popova
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden NJ, 08103
| | - Valerie J. Carabetta
- Department of Biomedical Sciences, Cooper Medical School of Rowan University, Camden NJ, 08103
| |
Collapse
|
45
|
Takahashi K, Yachida N, Tamura R, Adachi S, Kondo S, Abé T, Umezu H, Nyuzuki H, Okuda S, Nakaoka H, Yoshihara K. Clonal origin and genomic diversity in Lynch syndrome-associated endometrial cancer with multiple synchronous tumors: Identification of the pathogenicity of MLH1 p.L582H. Genes Chromosomes Cancer 2024; 63:e23231. [PMID: 38459936 DOI: 10.1002/gcc.23231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/06/2024] [Accepted: 02/20/2024] [Indexed: 03/11/2024] Open
Abstract
Lynch syndrome-associated endometrial cancer patients often present multiple synchronous tumors and this assessment can affect treatment strategies. We present a case of a 27-year-old woman with tumors in the uterine corpus, cervix, and ovaries who was diagnosed with endometrial cancer and exhibited cervical invasion and ovarian metastasis. Her family history suggested Lynch syndrome, and genetic testing identified a variant of uncertain significance, MLH1 p.L582H. We conducted immunohistochemical staining, microsatellite instability analysis, and Sanger sequencing for Lynch syndrome-associated cancers in three generations of the family and identified consistent MLH1 loss. Whole-exome sequencing for the corpus, cervical, and ovarian tumors of the proband identified a copy-neutral loss of heterozygosity (LOH) occurring at the MLH1 position in all tumors. This indicated that the germline variant and the copy-neutral LOH led to biallelic loss of MLH1 and was the cause of cancer initiation. All tumors shared a portion of somatic mutations with high mutant allele frequencies, suggesting a common clonal origin. There were no mutations shared only between the cervix and ovary samples. The profiles of mutant allele frequencies shared between the corpus and cervix or ovary indicated that two different subclones originating from the corpus independently metastasized to the cervix or ovary. Additionally, all tumors presented unique mutations in endometrial cancer-associated genes such as ARID1A and PIK3CA. In conclusion, we demonstrated clonal origin and genomic diversity in a Lynch syndrome-associated endometrial cancer, suggesting the importance of evaluating multiple sites in Lynch syndrome patients with synchronous tumors.
Collapse
Affiliation(s)
- Kotaro Takahashi
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Department of Cancer Genome Research, Sasaki Institute, Tokyo, Japan
| | - Nozomi Yachida
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Ryo Tamura
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Sosuke Adachi
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shuhei Kondo
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Tatsuya Abé
- Division of Oral Pathology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
- Division of Molecular and Diagnostic Pathology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hajime Umezu
- Division of Pathology, Niigata University Medical and Dental Hospital, Niigata, Japan
| | - Hiromi Nyuzuki
- Department of Pediatrics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shujiro Okuda
- Division of bioinformatics, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Hirofumi Nakaoka
- Department of Cancer Genome Research, Sasaki Institute, Tokyo, Japan
| | - Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| |
Collapse
|
46
|
Chen CL, Lee NC, Chien YH, Hwu WL, Hung MZ, Lin YL, Lin SY, Lee CN. Ethnically unique disease burden and limitations of current expanded carrier screening panels. Int J Gynaecol Obstet 2024; 164:918-924. [PMID: 37681470 DOI: 10.1002/ijgo.15072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/28/2023] [Accepted: 08/17/2023] [Indexed: 09/09/2023]
Abstract
OBJECTIVES The purpose of the study is to identify the recessive diseases currently affecting real-world pediatric patients in Taiwan, and whether current extended carrier screening panels have the coverage and detective power to identify the pathogenic variants in the carrier parents. METHODS A total of 132 trio-samples were collected from May 2017 to March 2022. The participants were parents of pediatric intensive care unit patients who were critically ill or infants with abnormal newborn screening results. A retrospective carrier screening scheme was applied to analyze only the carrier status of pathogenic or likely pathogenic recessive variants resulting in diseases in their children. The recessive disorders diagnosed in our cohort were compared with the gene content in commercial panels. RESULTS Mutations in COQ4, PEX1, OTC, and IKBKG were the most frequently identified. In the parents of 44 children with confirmed diagnoses of recessive diseases, 47 (53.40%) screened positive for being the carriers of the same recessive disorders diagnosed in their children. The commercial panels covered 35.13% to 54.05% of the disorders diagnosed in this cohort. CONCLUSION Clinicians and genetic counselors should be aware of the limitations of current extended carrier screening and interpret negative screening results with caution. Future panels should also consider genes with ethnically unique mutations such as pathogenic variants of the COQ4 gene in the East Asian population.
Collapse
Affiliation(s)
- Chih-Ling Chen
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ni-Chung Lee
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Pediatrics, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Yin-Hsiu Chien
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Pediatrics, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Wuh-Liang Hwu
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Pediatrics, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Miao-Zi Hung
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Yi-Lin Lin
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
| | - Shin-Yu Lin
- Department of Medical Genetics, National Taiwan University Hospital, Taipei, Taiwan
- Department of Obstetrics and Gynecology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Chien-Nan Lee
- Department of Obstetrics and Gynecology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| |
Collapse
|
47
|
Buffalo V, Kern AD. A quantitative genetic model of background selection in humans. PLoS Genet 2024; 20:e1011144. [PMID: 38507461 PMCID: PMC10984650 DOI: 10.1371/journal.pgen.1011144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 04/01/2024] [Accepted: 01/19/2024] [Indexed: 03/22/2024] Open
Abstract
Across the human genome, there are large-scale fluctuations in genetic diversity caused by the indirect effects of selection. This "linked selection signal" reflects the impact of selection according to the physical placement of functional regions and recombination rates along chromosomes. Previous work has shown that purifying selection acting against the steady influx of new deleterious mutations at functional portions of the genome shapes patterns of genomic variation. To date, statistical efforts to estimate purifying selection parameters from linked selection models have relied on classic Background Selection theory, which is only applicable when new mutations are so deleterious that they cannot fix in the population. Here, we develop a statistical method based on a quantitative genetics view of linked selection, that models how polygenic additive fitness variance distributed along the genome increases the rate of stochastic allele frequency change. By jointly predicting the equilibrium fitness variance and substitution rate due to both strong and weakly deleterious mutations, we estimate the distribution of fitness effects (DFE) and mutation rate across three geographically distinct human samples. While our model can accommodate weaker selection, we find evidence of strong selection operating similarly across all human samples. Although our quantitative genetic model of linked selection fits better than previous models, substitution rates of the most constrained sites disagree with observed divergence levels. We find that a model incorporating selective interference better predicts observed divergence in conserved regions, but overall our results suggest uncertainty remains about the processes generating fitness variation in humans.
Collapse
Affiliation(s)
- Vince Buffalo
- Department of Integrative Biology, University of California, Berkeley, Berkeley, California, United States of America
- Institute of Ecology and Evolution and Department of Biology, University of Oregon, Eugene, Oregon, United States of America
| | - Andrew D. Kern
- Institute of Ecology and Evolution and Department of Biology, University of Oregon, Eugene, Oregon, United States of America
| |
Collapse
|
48
|
Zhang M, Gong C, Ge F, Yu DJ. FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework. J Chem Inf Model 2024; 64:1394-1406. [PMID: 38349747 DOI: 10.1021/acs.jcim.3c02025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Nonsynonymous single-nucleotide polymorphisms (nsSNPs), implicated in over 6000 diseases, necessitate accurate prediction for expedited drug discovery and improved disease diagnosis. In this study, we propose FCMSTrans, a novel nsSNP predictor that innovatively combines the transformer framework and multiscale modules for comprehensive feature extraction. The distinctive attribute of FCMSTrans resides in a deep feature combination strategy. This strategy amalgamates evolutionary-scale modeling (ESM) and ProtTrans (PT) features, providing an understanding of protein biochemical properties, and position-specific scoring matrix, secondary structure, predicted relative solvent accessibility, and predicted disorder (PSPP) features, which are derived from four protein sequences and structure-oriented characteristics. This feature combination offers a comprehensive view of the molecular dynamics involving nsSNPs. Our model employs the transformer's self-attention mechanisms across multiple layers, extracting higher-level and abstract representations. Simultaneously, varied-level features are captured by multiscale convolutions, enriching feature abstraction at multiple echelons. Our comparative analyses with existing methodologies highlight significant improvements made possible by the integrated feature fusion approach adopted in FCMSTrans. This is further substantiated by performance assessments based on diverse data sets, such as PredictSNP, MMP, and PMD, with areas under the curve (AUCs) of 0.869, 0.819, and 0.693, respectively. Furthermore, FCMSTrans shows robustness and superiority by outperforming the current best predictor, PROVEAN, in a blind test conducted on a third-party data set, achieving an impressive AUC score of 0.7838. The Python code of FCMSTrans is available at https://github.com/gc212/FCMSTrans for academic usage.
Collapse
Affiliation(s)
- Ming Zhang
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Chao Gong
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| |
Collapse
|
49
|
Coban-Akdemir Z, Song X, Ceballos FC, Pehlivan D, Karaca E, Bayram Y, Mitani T, Gambin T, Bozkurt-Yozgatli T, Jhangiani SN, Muzny DM, Lewis RA, Liu P, Boerwinkle E, Hamosh A, Gibbs RA, Sutton VR, Sobreira N, Carvalho CM, Shaw CA, Posey JE, Valle D, Lupski JR. The impact of the Turkish population variome on the genomic architecture of rare disease traits. GENETICS IN MEDICINE OPEN 2024; 2:101830. [PMID: 39669594 PMCID: PMC11613692 DOI: 10.1016/j.gimo.2024.101830] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 12/14/2024]
Abstract
Purpose The variome of the Turkish (TK) population, a population with a considerable history of admixture and consanguinity, has not been deeply investigated for insights on the genomic architecture of disease. Methods We generated and analyzed a database of variants derived from exome sequencing data of 773 TK unrelated, clinically affected individuals with various suspected Mendelian disease traits and 643 unaffected relatives. Results Using uniform manifold approximation and projection, we showed that the TK genomes are more similar to those of Europeans and consist of 2 main subpopulations: clusters 1 and 2 (N = 235 and 1181, respectively), which differ in admixture proportion and variome (https://turkishvariomedb.shinyapps.io/tvdb/). Furthermore, the higher inbreeding coefficient values observed in the TK affected compared with unaffected individuals correlated with a larger median span of long-sized (>2.64 Mb) runs of homozygosity (ROH) regions (P value = 2.09e-18). We show that long-sized ROHs are more likely to be formed on recently configured haplotypes enriched for rare homozygous deleterious variants in the TK affected compared with TK unaffected individuals (P value = 3.35e-11). Analysis of genotype-phenotype correlations reveals that genes with rare homozygous deleterious variants in long-sized ROHs provide the most comprehensive set of molecular diagnoses for the observed disease traits with a systematic quantitative analysis of Human Phenotype Ontology terms. Conclusion Our findings support the notion that novel rare variants on newly configured haplotypes arising within the recent past generations of a family or clan contribute significantly to recessive disease traits in the TK population.
Collapse
Affiliation(s)
- Zeynep Coban-Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Xiaofei Song
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | | | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Section of Neurology, Department of Pediatrics, Baylor College of Medicine, Houston, TX
| | - Ender Karaca
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Pathology, Baylor University Medical Center, Dallas, TX
- Texas A&M School of Medicine, Texas A&M University, Dallas, TX
| | - Yavuz Bayram
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Tadahiro Mitani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Tomasz Gambin
- Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland
| | - Tugce Bozkurt-Yozgatli
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | | | - Donna M. Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Richard A. Lewis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Pediatrics, Baylor College of Medicine, Houston, TX
- Department of Ophthalmology, Cullen Eye Institute, Baylor College of Medicine, Houston, TX
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Richard A. Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - V. Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Texas Children’s Hospital, Houston, TX
| | - Nara Sobreira
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Claudia M.B. Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Pacific Northwest Research Institute, Seattle, WA
| | - Chad A. Shaw
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Baylor Genetics, Houston, TX
| | - Jennifer E. Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - David Valle
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - James R. Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
- Department of Pediatrics, Baylor College of Medicine, Houston, TX
- Texas Children’s Hospital, Houston, TX
| |
Collapse
|
50
|
Pandey P, Alexov E. Most Monogenic Disorders Are Caused by Mutations Altering Protein Folding Free Energy. Int J Mol Sci 2024; 25:1963. [PMID: 38396641 PMCID: PMC10888012 DOI: 10.3390/ijms25041963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024] Open
Abstract
Revealing the molecular effect that pathogenic missense mutations have on the corresponding protein is crucial for developing therapeutic solutions. This is especially important for monogenic diseases since, for most of them, there is no treatment available, while typically, the treatment should be provided in the early development stages. This requires fast targeted drug development at a low cost. Here, we report an updated database of monogenic disorders (MOGEDO), which includes 768 proteins and the corresponding 2559 pathogenic and 1763 benign mutations, along with the functional classification of the corresponding proteins. Using the database and various computational tools that predict folding free energy change (ΔΔG), we demonstrate that, on average, 70% of pathogenic cases result in decreased protein stability. Such a large fraction indicates that one should aim at in silico screening for small molecules stabilizing the structure of the mutant protein. We emphasize that knowledge of ΔΔG is essential because one wants to develop stabilizers that compensate for ΔΔG, but do not make protein over-stable, since over-stable protein may be dysfunctional. We demonstrate that, by using ΔΔG and predicted solvent exposure of the mutation site, one can develop a predictive method that distinguishes pathogenic from benign mutations with a success rate even better than some of the leading pathogenicity predictors. Furthermore, hydrophobic-hydrophobic mutations have stronger correlations between folding free energy change and pathogenicity compared with others. Also, mutations involving Cys, Gly, Arg, Trp, and Tyr amino acids being replaced by any other amino acid are more likely to be pathogenic. To facilitate further detection of pathogenic mutations, the wild type of amino acids in the 768 proteins mentioned above was mutated to other 19 residues (14,847,817 mutations), the ΔΔG was calculated with SAAFEC-SEQ, and 5,506,051 mutations were predicted to be pathogenic.
Collapse
Affiliation(s)
| | - Emil Alexov
- Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA;
| |
Collapse
|