|
1
|
Shang J, Xu A, Bi M, Zhang Y, Li F, Liu JX. A review: simulation tools for genome-wide interaction studies. Brief Funct Genomics 2024; 23:745-753. [PMID: 39173096 DOI: 10.1093/bfgp/elae034] [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: 05/22/2024] [Revised: 07/25/2024] [Accepted: 08/10/2024] [Indexed: 08/24/2024] Open
Abstract
Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.
Collapse
Affiliation(s)
- Junliang Shang
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Anqi Xu
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Mingyuan Bi
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Yuanyuan Zhang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Feng Li
- School of Computer Science, Qufu Normal University, Rizhao 276826, China
| | - Jin-Xing Liu
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266114, China
| |
Collapse
|
|
2
|
Zhang Q, Liu J, Liu H, Ao L, Xi Y, Chen D. Genome-wide epistasis analysis reveals gene-gene interaction network on an intermediate endophenotype P-tau/Aβ 42 ratio in ADNI cohort. Sci Rep 2024; 14:3984. [PMID: 38368488 PMCID: PMC10874417 DOI: 10.1038/s41598-024-54541-8] [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: 10/22/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the elderly worldwide. The exact etiology of AD, particularly its genetic mechanisms, remains incompletely understood. Traditional genome-wide association studies (GWAS), which primarily focus on single-nucleotide polymorphisms (SNPs) with main effects, provide limited explanations for the "missing heritability" of AD, while there is growing evidence supporting the important role of epistasis. In this study, we performed a genome-wide SNP-SNP interaction detection using a linear regression model and employed multiple GPUs for parallel computing, significantly enhancing the speed of whole-genome analysis. The cerebrospinal fluid (CSF) phosphorylated tau (P-tau)/amyloid-[Formula: see text] (A[Formula: see text]) ratio was used as a quantitative trait (QT) to enhance statistical power. Age, gender, and clinical diagnosis were included as covariates to control for potential non-genetic factors influencing AD. We identified 961 pairs of statistically significant SNP-SNP interactions, explaining a high-level variance of P-tau/A[Formula: see text] level, all of which exhibited marginal main effects. Additionally, we replicated 432 previously reported AD-related genes and found 11 gene-gene interaction pairs overlapping with the protein-protein interaction (PPI) network. Our findings may contribute to partially explain the "missing heritability" of AD. The identified subnetwork may be associated with synaptic dysfunction, Wnt signaling pathway, oligodendrocytes, inflammation, hippocampus, and neuronal cells.
Collapse
Affiliation(s)
- Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China
| | - Lang Ao
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China
| | - Dandan Chen
- School of Automation Engineering, Northeast Electric Power University, 169 Changchun Street, Jilin, 132012, China.
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, 145 Nantong Street, Harbin, China.
| |
Collapse
|
|
3
|
Li J, Chen D, Liu H, Xi Y, Luo H, Wei Y, Liu J, Liang H, Zhang Q. Identifying potential genetic epistasis implicated in Alzheimer's disease via detection of SNP-SNP interaction on quantitative trait CSF Aβ 42. Neurobiol Aging 2024; 134:84-93. [PMID: 38039940 DOI: 10.1016/j.neurobiolaging.2023.10.003] [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: 06/22/2023] [Revised: 10/06/2023] [Accepted: 10/10/2023] [Indexed: 12/03/2023]
Abstract
Although genome-wide association studies have identified multiple Alzheimer's disease (AD)-associated loci by selecting the main effects of individual single-nucleotide polymorphisms (SNPs), the interpretation of genetic variance in AD is limited. Based on the linear regression method, we performed genome-wide SNP-SNP interaction on cerebrospinal fluid Aβ42 to identify potential genetic epistasis implicated in AD, with age, gender, and diagnosis as covariates. A GPU-based method was used to address the computational challenges posed by the analysis of epistasis. We found 368 SNP pairs to be statistically significant, and highly significant SNP-SNP interactions were identified between the marginal main effects of SNP pairs, which explained a relatively high variance at the Aβ42 level. Our results replicated 100 previously reported AD-related genes and 5 gene-gene interaction pairs of the protein-protein interaction network. Our bioinformatics analyses provided preliminary evidence that the 5-overlapping gene-gene interaction pairs play critical roles in inducing synaptic loss and dysfunction, thereby leading to memory decline and cognitive impairment in AD-affected brains.
Collapse
Affiliation(s)
- Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China; School of Automation Engineering, Northeast Electric Power University, Jilin, China
| | - Hongwei Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China
| | - Junfeng Liu
- School of Computer Science, Northeast Electric Power University, Jilin, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, China.
| |
Collapse
|
|
4
|
Periyasamy S, Youssef P, John S, Thara R, Mowry BJ. Genetic interactions of schizophrenia using gene-based statistical epistasis exclusively identify nervous system-related pathways and key hub genes. Front Genet 2024; 14:1301150. [PMID: 38259618 PMCID: PMC10800577 DOI: 10.3389/fgene.2023.1301150] [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: 09/24/2023] [Accepted: 12/12/2023] [Indexed: 01/24/2024] Open
Abstract
Background: The relationship between genotype and phenotype is governed by numerous genetic interactions (GIs), and the mapping of GI networks is of interest for two main reasons: 1) By modelling biological robustness, GIs provide a powerful opportunity to infer compensatory biological mechanisms via the identification of functional relationships between genes, which is of interest for biological discovery and translational research. Biological systems have evolved to compensate for genetic (i.e., variations and mutations) and environmental (i.e., drug efficacy) perturbations by exploiting compensatory relationships between genes, pathways and biological processes; 2) GI facilitates the identification of the direction (alleviating or aggravating interactions) and magnitude of epistatic interactions that influence the phenotypic outcome. The generation of GIs for human diseases is impossible using experimental biology approaches such as systematic deletion analysis. Moreover, the generation of disease-specific GIs has never been undertaken in humans. Methods: We used our Indian schizophrenia case-control (case-816, controls-900) genetic dataset to implement the workflow. Standard GWAS sample quality control procedure was followed. We used the imputed genetic data to increase the SNP coverage to analyse epistatic interactions across the genome comprehensively. Using the odds ratio (OR), we identified the GIs that increase or decrease the risk of a disease phenotype. The SNP-based epistatic results were transformed into gene-based epistatic results. Results: We have developed a novel approach by conducting gene-based statistical epistatic analysis using an Indian schizophrenia case-control genetic dataset and transforming these results to infer GIs that increase the risk of schizophrenia. There were ∼9.5 million GIs with a p-value ≤ 1 × 10-5. Approximately 4.8 million GIs showed an increased risk (OR > 1.0), while ∼4.75 million GIs had a decreased risk (OR <1.0) for schizophrenia. Conclusion: Unlike model organisms, this approach is specifically viable in humans due to the availability of abundant disease-specific genome-wide genotype datasets. The study exclusively identified brain/nervous system-related processes, affirming the findings. This computational approach fills a critical gap by generating practically non-existent heritable disease-specific human GIs from human genetic data. These novel datasets can train innovative deep-learning models, potentially surpassing the limitations of conventional GWAS.
Collapse
Affiliation(s)
- Sathish Periyasamy
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| | - Pierre Youssef
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Sujit John
- Schizophrenia Research Foundation, Chennai, Tamil Nadu, India
| | | | - Bryan J. Mowry
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Wacol, QLD, Australia
| |
Collapse
|
|
5
|
Zuo P, Zhang C, Gao Y, Zhao L, Guo J, Yang Y, Yu Q, Li Y, Wang Z, Yang H. Genome-wide unraveling SNP pairwise epistatic effects associated with sheep body weight. Anim Biotechnol 2023; 34:3416-3427. [PMID: 36495095 DOI: 10.1080/10495398.2022.2152349] [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: 12/14/2022]
Abstract
Epistatic effects are an important part of the genetic effect of complex traits in livestock. In this study, we used 218 synthetic ewes from the Xinjiang Academy of Agricultural Reclamation in China to identify interacting paired with genome-wide single nucleotide polymorphisms (SNPs) associated with birth weight, weaning weight, and one-yearling weight. We detected 2 and 66 SNP-SNP interactions of sheep birth weight and weaning weight, respectively. No significant epistatic interaction of one-year-old body weight was detected. The genetic interaction of sheep body weight is dynamic and time-dependent. Most significant interactions of weaning body weight contributed 1% or higher. In the weaning weight trait, 66 significant SNP pairs consisted of 98 single SNPs covering 23 chromosomes, 5 of which were nonsynonymous SNPs (nsSNPs), resulting in single amino acid substitution. We found that genes that interact with transcription factors (TFs) are target genes for the corresponding TFs. Four epitron networks affecting weaning weight, including subnetworks of HIVEP3 and BACH2 transcription factors, constructed using significant SNP pairs, were also analyzed and annotated. These results suggest that transcription factors may play an important role in explaining epistatic effects. It provides a new idea to study the genetic mechanism of weight developing.
Collapse
Affiliation(s)
- Peng Zuo
- College of Science, Northeast Agricultural University, Harbin
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
| | - Chaoxin Zhang
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Yupeng Gao
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Engineering, Northeast Agricultural University, Harbin, China
| | - Lijunyi Zhao
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Information and Electrical Engineering, Northeast Agricultural University, Harbin, China
| | - Jiaxu Guo
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Life Sciences, Northeast Agricultural University, Harbin, China
| | - Yonglin Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation, Shihezi, Hebei, China
| | - Qian Yu
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation, Shihezi, Hebei, China
| | - Yunna Li
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
| | - Zhipeng Wang
- Bioinformatics Center, Northeast Agricultural University, Harbin, China
- College of Animal Science and Technology, Northeast Agricultural University, Harbin, China
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation, Shihezi, Hebei, China
| | - Hua Yang
- State Key Laboratory of Sheep Genetic Improvement and Healthy Production, Xinjiang Academy of Agricultural Reclamation, Shihezi, Hebei, China
| |
Collapse
|
|
6
|
Chen D, Li J, Liu H, Liu X, Zhang C, Luo H, Wei Y, Xi Y, Liang H, Zhang Q. Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort. Genes (Basel) 2023; 14:1322. [PMID: 37510227 PMCID: PMC10379656 DOI: 10.3390/genes14071322] [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: 06/01/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.
Collapse
Affiliation(s)
- Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Hongwei Liu
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Xiaolong Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Chenghao Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| |
Collapse
|
|
7
|
Adak A, Murray SC, Calderón CI, Infante V, Wilker J, Varela JI, Subramanian N, Isakeit T, Ané JM, Wallace J, de Leon N, Stull MA, Brun M, Hill J, Johnson CD. Genetic mapping and prediction for novel lesion mimic in maize demonstrates quantitative effects from genetic background, environment and epistasis. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:155. [PMID: 37329482 DOI: 10.1007/s00122-023-04394-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 05/29/2023] [Indexed: 06/19/2023]
Abstract
KEY MESSAGE A novel locus was discovered on chromosome 7 associated with a lesion mimic in maize; this lesion mimic had a quantitative and heritable phenotype and was predicted better via subset genomic markers than whole genome markers across diverse environments. Lesion mimics are a phenotype of leaf micro-spotting in maize (Zea mays L.), which can be early signs of biotic or abiotic stresses. Dissecting its inheritance is helpful to understand how these loci behave across different genetic backgrounds. Here, 538 maize recombinant inbred lines (RILs) segregating for a novel lesion mimic were quantitatively phenotyped in Georgia, Texas, and Wisconsin. These RILs were derived from three bi-parental crosses using a tropical pollinator (Tx773) as the common parent crossed with three inbreds (LH195, LH82, and PB80). While this lesion mimic was heritable across three environments based on phenotypic ([Formula: see text] = 0.68) and genomic ([Formula: see text] = 0.91) data, transgressive segregation was observed. A genome-wide association study identified a single novel locus on chromosome 7 (at 70.6 Mb) also covered by a quantitative trait locus interval (69.3-71.0 Mb), explaining 11-15% of the variation, depending on the environment. One candidate gene identified in this region, Zm00001eb308070, is related to the abscisic acid pathway involving in cell death. Genomic predictions were applied to genome-wide markers (39,611 markers) contrasted with a marker subset (51 markers). Population structure explained more variation than environment in genomic prediction, but other substantial genetic background effects were additionally detected. Subset markers explained substantially less genetic variation (24.9%) for the lesion mimic than whole genome markers (55.4%) in the model, yet predicted the lesion mimic better (0.56-0.66 vs. 0.26-0.29). These results indicate this lesion mimic phenotype was less affected by environment than by epistasis and genetic background effects, which explain its transgressive segregation.
Collapse
Affiliation(s)
- Alper Adak
- Department of Soil and Crop Sciences, Texas A&M University, Agronomy Field Lab 110/111, College Station, TX, 77843, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, Agronomy Field Lab 110/111, College Station, TX, 77843, USA.
| | - Claudia Irene Calderón
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Escuela de Biología, Universidad de San Carlos de Guatemala, Guatemala City, Guatemala
| | - Valentina Infante
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Jennifer Wilker
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - José I Varela
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Nithya Subramanian
- Department of Soil and Crop Sciences, Texas A&M University, Agronomy Field Lab 110/111, College Station, TX, 77843, USA
| | - Thomas Isakeit
- Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA
| | - Jean-Michel Ané
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Jason Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens, GA, 30602, USA
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Matthew A Stull
- Genomics and Bioinformatics Services, Texas A&M AgriLife Research, College Station, TX, 77843, USA
| | - Marcel Brun
- Genomics and Bioinformatics Services, Texas A&M AgriLife Research, College Station, TX, 77843, USA
| | - Joshua Hill
- Genomics and Bioinformatics Services, Texas A&M AgriLife Research, College Station, TX, 77843, USA
| | - Charles D Johnson
- Genomics and Bioinformatics Services, Texas A&M AgriLife Research, College Station, TX, 77843, USA
| |
Collapse
|
|
8
|
Yang CH, Hou MF, Chuang LY, Yang CS, Lin YD. Dimensionality reduction approach for many-objective epistasis analysis. Brief Bioinform 2023; 24:6858949. [PMID: 36458451 DOI: 10.1093/bib/bbac512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/07/2022] [Accepted: 10/26/2022] [Indexed: 12/04/2022] Open
Abstract
In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.
Collapse
Affiliation(s)
- Cheng-Hong Yang
- Department of Information Management at the Tainan University of Technology, and at the Department of Electronic Engineering at National Kaohsiung of Science and Technology, Taiwan.,Biomedical Engineering, Kaohsiung Medical University, Taiwan
| | - Ming-Feng Hou
- Kaohsiung Medical University Hospital, and Professor at the Department of Surgery, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering & Institute of Biotechnology and Chemical Engineering at I-Shou University, Taiwan
| | - Cheng-San Yang
- Department of Plastic Surgery, and serves as the Medical Matters Secretary of Chia-Yi Christian Hospital, Taiwan
| | - Yu-Da Lin
- Department of Computer Science and Information Engineering, and at the National Penghu University of Science and Technology, Taiwan
| |
Collapse
|
|
9
|
Lazarenko V, Churilin M, Azarova I, Klyosova E, Bykanova M, Ob’edkova N, Churnosov M, Bushueva O, Mal G, Povetkin S, Kononov S, Luneva Y, Zhabin S, Polonikova A, Gavrilenko A, Saraev I, Solodilova M, Polonikov A. Comprehensive Statistical and Bioinformatics Analysis in the Deciphering of Putative Mechanisms by Which Lipid-Associated GWAS Loci Contribute to Coronary Artery Disease. Biomedicines 2022; 10:259. [PMID: 35203469 PMCID: PMC8868589 DOI: 10.3390/biomedicines10020259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/22/2022] [Accepted: 01/23/2022] [Indexed: 11/17/2022] Open
Abstract
The study was designed to evaluate putative mechanisms by which lipid-associated loci identified by genome-wide association studies (GWAS) are involved in the molecular pathogenesis of coronary artery disease (CAD) using a comprehensive statistical and bioinformatics analysis. A total of 1700 unrelated individuals of Slavic origin from the Central Russia, including 991 CAD patients and 709 healthy controls were examined. Sixteen lipid-associated GWAS loci were selected from European studies and genotyped using the MassArray-4 system. The polymorphisms were associated with plasma lipids such as total cholesterol (rs12328675, rs4846914, rs55730499, and rs838880), LDL-cholesterol (rs3764261, rs55730499, rs1689800, and rs838880), HDL-cholesterol (rs3764261) as well as carotid intima-media thickness/CIMT (rs12328675, rs11220463, and rs1689800). Polymorphisms such as rs4420638 of APOC1 (p = 0.009), rs55730499 of LPA (p = 0.0007), rs3136441 of F2 (p < 0.0001), and rs6065906 of PLTP (p = 0.002) showed significant associations with the risk of CAD, regardless of sex, age, and body mass index. A majority of the observed associations were successfully replicated in large independent cohorts. Bioinformatics analysis allowed establishing (1) phenotype-specific and shared epistatic gene-gene and gene-smoking interactions contributing to all studied cardiovascular phenotypes; (2) lipid-associated GWAS loci might be allele-specific binding sites for transcription factors from gene regulatory networks controlling multifaceted molecular mechanisms of atherosclerosis.
Collapse
Affiliation(s)
- Victor Lazarenko
- Department of Surgical Diseases, Institute of Continuing Education, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Mikhail Churilin
- Department of Infectious Diseases and Epidemiology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Iuliia Azarova
- Department of Biological Chemistry, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia;
| | - Elena Klyosova
- Laboratory of Biochemical Genetics and Metabolomics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia;
| | - Marina Bykanova
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia; (M.B.); (O.B.)
| | - Natalia Ob’edkova
- Department of Polyclinical Therapy and General Medical Practice, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State University, 85 Pobedy Street, 308015 Belgorod, Russia;
| | - Olga Bushueva
- Laboratory of Genomic Research, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia; (M.B.); (O.B.)
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (A.P.); (M.S.); (A.P.)
| | - Galina Mal
- Department of Pharmacology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Sergey Povetkin
- Department of Clinical Pharmacology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (S.P.); (Y.L.)
| | - Stanislav Kononov
- Department of Internal Medicine No 2, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (S.K.); (I.S.)
| | - Yulia Luneva
- Department of Clinical Pharmacology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (S.P.); (Y.L.)
| | - Sergey Zhabin
- Department of Surgical Diseases No 1, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Anna Polonikova
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (A.P.); (M.S.); (A.P.)
| | - Alina Gavrilenko
- Department of Infectious Diseases and Epidemiology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia;
| | - Igor Saraev
- Department of Internal Medicine No 2, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (S.K.); (I.S.)
| | - Maria Solodilova
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (A.P.); (M.S.); (A.P.)
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, 3 Karl Marx Street, 305041 Kursk, Russia; (A.P.); (M.S.); (A.P.)
- Laboratory of Statistical Genetics and Bioinformatics, Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, 18 Yamskaya Street, 305041 Kursk, Russia
| |
Collapse
|
|
10
|
Liu D, Ban HJ, El Sergani AM, Lee MK, Hecht JT, Wehby GL, Moreno LM, Feingold E, Marazita ML, Cha S, Szabo-Rogers HL, Weinberg SM, Shaffer JR. PRICKLE1 × FOCAD Interaction Revealed by Genome-Wide vQTL Analysis of Human Facial Traits. Front Genet 2021; 12:674642. [PMID: 34434215 PMCID: PMC8381734 DOI: 10.3389/fgene.2021.674642] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 06/03/2021] [Indexed: 12/14/2022] Open
Abstract
The human face is a highly complex and variable structure resulting from the intricate coordination of numerous genetic and non-genetic factors. Hundreds of genomic loci impacting quantitative facial features have been identified. While these associations have been shown to influence morphology by altering the mean size and shape of facial measures, their effect on trait variance remains unclear. We conducted a genome-wide association analysis for the variance of 20 quantitative facial measurements in 2,447 European individuals and identified several suggestive variance quantitative trait loci (vQTLs). These vQTLs guided us to conduct an efficient search for gene-by-gene (G × G) interactions, which uncovered an interaction between PRICKLE1 and FOCAD affecting cranial base width. We replicated this G × G interaction signal at the locus level in an additional 5,128 Korean individuals. We used the hypomorphic Prickle1 Beetlejuice (Prickle1 Bj ) mouse line to directly test the function of Prickle1 on the cranial base and observed wider cranial bases in Prickle1 Bj/Bj . Importantly, we observed that the Prickle1 and Focadhesin proteins co-localize in murine cranial base chondrocytes, and this co-localization is abnormal in the Prickle1 Bj/Bj mutants. Taken together, our findings uncovered a novel G × G interaction effect in humans with strong support from both epidemiological and molecular studies. These results highlight the potential of studying measures of phenotypic variability in gene mapping studies of facial morphology.
Collapse
Affiliation(s)
- Dongjing Liu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Hyo-Jeong Ban
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Ahmed M. El Sergani
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Myoung Keun Lee
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jacqueline T. Hecht
- Department of Pediatrics, McGovern Medical Center, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - George L. Wehby
- Department of Health Management and Policy, The University of Iowa, Iowa City, IA, United States
| | - Lina M. Moreno
- Department of Orthodontics, The University of Iowa, Iowa City, IA, United States
| | - Eleanor Feingold
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mary L. Marazita
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Psychiatry, Clinical and Translational Science Institute, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seongwon Cha
- Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Heather L. Szabo-Rogers
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Developmental Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Regenerative Medicine at the McGowan Institute, University of Pittsburgh, Pittsburgh, PA, United States
- Center for Craniofacial Regeneration, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Seth M. Weinberg
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - John R. Shaffer
- Center for Craniofacial and Dental Genetics, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Oral and Craniofacial Sciences, School of Dental Medicine, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
|
11
|
Villa TG, Abril AG, Sánchez-Pérez A. Mastering the control of the Rho transcription factor for biotechnological applications. Appl Microbiol Biotechnol 2021; 105:4053-4071. [PMID: 33963893 DOI: 10.1007/s00253-021-11326-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/22/2021] [Accepted: 04/27/2021] [Indexed: 12/25/2022]
Abstract
The present review represents an update on the fundamental role played by the Rho factor, which facilitates the process of Rho-dependent transcription termination in the prokaryotic world; it also provides a summary of relevant mutations in the Rho factor and the insights they provide into the functions carried out by this protein. Furthermore, a section is dedicated to the putative future use of Rho (the 'taming' of Rho) to facilitate biotechnological processes and adapt them to different technological contexts. Novel bacterial strains can be designed, containing mutations in the rho gene, that are better suited for different biotechnological applications. This process can obtain novel microbial strains that are adapted to lower temperatures of fermentation, shorter production times, exhibit better nutrient utilization, or display other traits that are beneficial in productive Biotechnology. Additional important issues reviewed here include epistasis, the design of TATA boxes, the role of small RNAs, and the manipulation of clathrin-mediated endocytosis, by some pathogenic bacteria, to invade eukaryotic cells. KEY POINTS: • It is postulated that controlling the action of the prokaryotic Rho factor could generate major biotechnological improvements, such as an increase in bacterial productivity or a reduction of the microbial-specific growth rate. • The review also evaluates the putative impact of epistatic mechanisms on Biotechnology, both as possible responsible for unexpected failures in gene cloning and more important for the genesis of new strains for biotechnological applications • The use of clathrin-coated vesicles by intracellular bacterial microorganisms is included too and proposed as a putative delivery mechanism, for drugs and vaccines.
Collapse
Affiliation(s)
- Tomás G Villa
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, La Coruña, 15706, Santiago de Compostela, Spain.
| | - Ana G Abril
- Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Santiago de Compostela, La Coruña, 15706, Santiago de Compostela, Spain.
| | - Angeles Sánchez-Pérez
- Sydney School of Veterinary Science, Faculty of Science, University of Sydney, Sydney, NSW, 2006, Australia.
| |
Collapse
|
|
12
|
Epistasis Analysis: Classification Through Machine Learning Methods. Methods Mol Biol 2021. [PMID: 33733366 DOI: 10.1007/978-1-0716-0947-7_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for complex diseases have produced a large amount of data, it is extremely difficult to analyze the data due to the high feature dimension and the combination in the epistasis analysis. In this work, we introduce machine learning methods to effectively reduce the gene dimensionality, retain the key epistatic effects, and effectively characterize the relationship between epistatic effects and complex diseases.
Collapse
|
|
13
|
Contreras MG, Keys K, Magaña J, Goddard PC, Risse-Adams O, Zeiger AM, Mak AC, Samedy-Bates LA, Neophytou AM, Lee E, Thakur N, Elhawary JR, Hu D, Huntsman S, Eng C, Hu T, Burchard EG, White MJ. Native American Ancestry and Air Pollution Interact to Impact Bronchodilator Response in Puerto Rican Children with Asthma. Ethn Dis 2021; 31:77-88. [PMID: 33519158 PMCID: PMC7843041 DOI: 10.18865/ed.31.1.77] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Objective Asthma is the most common chronic disease in children. Short-acting bronchodilator medications are the most commonly prescribed asthma treatment worldwide, regardless of disease severity. Puerto Rican children display the highest asthma morbidity and mortality of any US population. Alarmingly, Puerto Rican children with asthma display poor bronchodilator drug response (BDR). Reduced BDR may explain, in part, the increased asthma morbidity and mortality observed in Puerto Rican children with asthma. Gene-environment interactions may explain a portion of the heritability of BDR. We aimed to identify gene-environment interactions associated with BDR in Puerto Rican children with asthma. Setting Genetic, environmental, and psycho-social data from the Genes-environments and Admixture in Latino Americans (GALA II) case-control study. Participants Our discovery dataset consisted of 658 Puerto Rican children with asthma; our replication dataset consisted of 514 Mexican American children with asthma. Main Outcome Measures We assessed the association of pairwise interaction models with BDR using ViSEN (Visualization of Statistical Epistasis Networks). Results We identified a non-linear interaction between Native American genetic ancestry and air pollution significantly associated with BDR in Puerto Rican children with asthma. This interaction was robust to adjustment for age and sex but was not significantly associated with BDR in our replication population. Conclusions Decreased Native American ancestry coupled with increased air pollution exposure was associated with increased BDR in Puerto Rican children with asthma. Our study acknowledges BDR's phenotypic complexity, and emphasizes the importance of integrating social, environmental, and biological data to further our understanding of complex disease.
Collapse
Affiliation(s)
- María G. Contreras
- Department of Medicine, University of California, San Francisco, CA
- SF BUILD, San Francisco State University, San Francisco, CA
- MARC, San Francisco State University, San Francisco, CA
| | - Kevin Keys
- Department of Medicine, University of California, San Francisco, CA
| | - Joaquin Magaña
- Department of Medicine, University of California, San Francisco, CA
| | - Pagé C. Goddard
- Department of Medicine, University of California, San Francisco, CA
| | - Oona Risse-Adams
- Department of Medicine, University of California, San Francisco, CA
- Lowell Science Research Program, Lowell High School, San Francisco, CA
| | - Andrew M. Zeiger
- Department of Medicine, University of California, San Francisco, CA
- Department of Biology, University of Washington, Seattle, WA
| | - Angel C.Y. Mak
- Department of Medicine, University of California, San Francisco, CA
| | - Lesly-Anne Samedy-Bates
- Department of Medicine, University of California, San Francisco, CA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA
| | - Andreas M. Neophytou
- Environmental Health Sciences Division, Berkeley School of Public Health, Berkeley, CA
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO
| | - Eunice Lee
- National Institute of Environmental Health Sciences, Cary NC
| | - Neeta Thakur
- Department of Medicine, University of California, San Francisco, CA
| | | | - Donglei Hu
- Department of Medicine, University of California, San Francisco, CA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, CA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, CA
| | - Ting Hu
- School of Computing, Queen’s University, Kingston, ON, Canada
| | - Esteban G. Burchard
- Department of Medicine, University of California, San Francisco, CA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA
| | | |
Collapse
|
|
14
|
Pagano L, Toto A, Malagrinò F, Visconti L, Jemth P, Gianni S. Double Mutant Cycles as a Tool to Address Folding, Binding, and Allostery. Int J Mol Sci 2021; 22:E828. [PMID: 33467625 PMCID: PMC7830974 DOI: 10.3390/ijms22020828] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/13/2021] [Accepted: 01/13/2021] [Indexed: 11/16/2022] Open
Abstract
Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted 'energetic coupling' describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein-ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.
Collapse
Affiliation(s)
- Livia Pagano
- Istituto Pasteur—Fondazione Cenci Bolognetti, Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Istituto di Biologia e Patologia Molecolari del CNR, Sapienza Università di Roma, 00185 Rome, Italy; (L.P.); (A.T.); (F.M.); (L.V.)
| | - Angelo Toto
- Istituto Pasteur—Fondazione Cenci Bolognetti, Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Istituto di Biologia e Patologia Molecolari del CNR, Sapienza Università di Roma, 00185 Rome, Italy; (L.P.); (A.T.); (F.M.); (L.V.)
| | - Francesca Malagrinò
- Istituto Pasteur—Fondazione Cenci Bolognetti, Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Istituto di Biologia e Patologia Molecolari del CNR, Sapienza Università di Roma, 00185 Rome, Italy; (L.P.); (A.T.); (F.M.); (L.V.)
| | - Lorenzo Visconti
- Istituto Pasteur—Fondazione Cenci Bolognetti, Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Istituto di Biologia e Patologia Molecolari del CNR, Sapienza Università di Roma, 00185 Rome, Italy; (L.P.); (A.T.); (F.M.); (L.V.)
| | - Per Jemth
- Department of Medical Biochemistry and Microbiology, Uppsala University, SE-75123 Uppsala, Sweden
| | - Stefano Gianni
- Istituto Pasteur—Fondazione Cenci Bolognetti, Dipartimento di Scienze Biochimiche ‘A. Rossi Fanelli’ and Istituto di Biologia e Patologia Molecolari del CNR, Sapienza Università di Roma, 00185 Rome, Italy; (L.P.); (A.T.); (F.M.); (L.V.)
| |
Collapse
|
|
15
|
Zhou F, Ren J, Lu X, Ma S, Wu C. Gene-Environment Interaction: A Variable Selection Perspective. Methods Mol Biol 2021; 2212:191-223. [PMID: 33733358 DOI: 10.1007/978-1-0716-0947-7_13] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Gene-environment interactions have important implications for elucidating the genetic basis of complex diseases beyond the joint function of multiple genetic factors and their interactions (or epistasis). In the past, G × E interactions have been mainly conducted within the framework of genetic association studies. The high dimensionality of G × E interactions, due to the complicated form of environmental effects and the presence of a large number of genetic factors including gene expressions and SNPs, has motivated the recent development of penalized variable selection methods for dissecting G × E interactions, which has been ignored in the majority of published reviews on genetic interaction studies. In this article, we first survey existing studies on both gene-environment and gene-gene interactions. Then, after a brief introduction to the variable selection methods, we review penalization and relevant variable selection methods in marginal and joint paradigms, respectively, under a variety of conceptual models. Discussions on strengths and limitations, as well as computational aspects of the variable selection methods tailored for G × E studies, have also been provided.
Collapse
Affiliation(s)
- Fei Zhou
- Department of Statistics, Kansas State University, Manhattan, KS, USA
| | - Jie Ren
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xi Lu
- Department of Statistics, Kansas State University, Manhattan, KS, USA
| | - Shuangge Ma
- Department of Biostatistics, School of Public Health, Yale University, New Haven, CT, USA
| | - Cen Wu
- Department of Statistics, Kansas State University, Manhattan, KS, USA.
| |
Collapse
|
|
16
|
Zhou X, Chan KCC, Huang Z, Wang J. Determining dependency and redundancy for identifying gene-gene interaction associated with complex disease. J Bioinform Comput Biol 2020; 18:2050035. [PMID: 33064052 DOI: 10.1142/s0219720020500353] [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: 11/18/2022]
Abstract
As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene-gene interactions can be better understood. Towards this goal, we have attempted to uncover patterns in gene-gene interactions and the patterns reveal an interesting property that can be reflected in an inequality that describes the relationship between two genotype variables and a disease-status variable. We show, in this paper, that this inequality can be generalized to [Formula: see text] genotype variables. Based on this inequality, we establish a conditional independence and redundancy (CIR)-based definition of gene-gene interaction and the concept of an interaction group. From these new definitions, a novel measure of gene-gene interaction is then derived. We discuss the properties of these concepts and explain how they can be used in a novel algorithm to detect high-order gene-gene interactions. Experimental results using both simulated and real datasets show that the proposed method can be very promising.
Collapse
Affiliation(s)
- Xiangdong Zhou
- College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China
| | - Keith C C Chan
- Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong, P. R. China
| | - Zhihua Huang
- College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China
| | - Jingbin Wang
- College of Mathematics and Computer Science, Fuzhou University Fuzhou, Fujian 350108, P. R. China
| |
Collapse
|
|
17
|
Magaña J, Contreras MG, Keys KL, Risse-Adams O, Goddard PC, Zeiger AM, Mak ACY, Elhawary JR, Samedy-Bates LA, Lee E, Thakur N, Hu D, Eng C, Salazar S, Huntsman S, Hu T, Burchard EG, White MJ. An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma. BioData Min 2020; 13:7. [PMID: 32636926 PMCID: PMC7333373 DOI: 10.1186/s13040-020-00218-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Accepted: 06/23/2020] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Asthma is one of the leading chronic illnesses among children in the United States. Asthma prevalence is higher among African Americans (11.2%) compared to European Americans (7.7%). Bronchodilator medications are part of the first-line therapy, and the rescue medication, for acute asthma symptoms. Bronchodilator drug response (BDR) varies substantially among different racial/ethnic groups. Asthma prevalence in African Americans is only 3.5% higher than that of European Americans, however, asthma mortality among African Americans is four times that of European Americans; variation in BDR may play an important role in explaining this health disparity. To improve our understanding of disparate health outcomes in complex phenotypes such as BDR, it is important to consider interactions between environmental and biological variables. RESULTS We evaluated the impact of pairwise and three-variable interactions between environmental, social, and biological variables on BDR in 233 African American youth with asthma using Visualization of Statistical Epistasis Networks (ViSEN). ViSEN is a non-parametric entropy-based approach able to quantify interaction effects using an information-theory metric known as Information Gain (IG). We performed analyses in the full dataset and in sex-stratified subsets. Our analyses identified several interaction models significantly, and suggestively, associated with BDR. The strongest interaction significantly associated with BDR was a pairwise interaction between pre-natal smoke exposure and socioeconomic status (full dataset IG: 2.78%, p = 0.001; female IG: 7.27%, p = 0.004)). Sex-stratified analyses yielded divergent results for females and males, indicating the presence of sex-specific effects. CONCLUSIONS Our study identified novel interaction effects significantly, and suggestively, associated with BDR in African American children with asthma. Notably, we found that all of the interactions identified by ViSEN were "pure" interaction effects, in that they were not the result of strong main effects on BDR, highlighting the complexity of the network of biological and environmental factors impacting this phenotype. Several associations uncovered by ViSEN would not have been detected using regression-based methods, thus emphasizing the importance of employing statistical methods optimized to detect both additive and non-additive interaction effects when studying complex phenotypes such as BDR. The information gained in this study increases our understanding and appreciation of the complex nature of the interactions between environmental and health-related factors that influence BDR and will be invaluable to biomedical researchers designing future studies.
Collapse
Affiliation(s)
- J. Magaña
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - M. G. Contreras
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Biology, San Francisco State University, San Francisco, CA USA
| | - K. L. Keys
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Berkeley Institute for Data Science, University of California, Berkeley, CA USA
| | - O. Risse-Adams
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Lowell Science Research Program, Lowell High School, San Francisco, CA USA
- Department of Biology, University of California, Santa Cruz, CA USA
| | - P. C. Goddard
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Genetics, Stanford University, Stanford, CA USA
| | - A. M. Zeiger
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA USA
| | - A. C. Y. Mak
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - J. R. Elhawary
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - L. A. Samedy-Bates
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - E. Lee
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - N. Thakur
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - D. Hu
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - C. Eng
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - S. Salazar
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - S. Huntsman
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| | - T. Hu
- School of Computing, Queen’s University, Kingston, ON Canada
| | - E. G. Burchard
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA USA
| | - M. J. White
- Department of Medicine, University of California, 1550 4th Street, UCSF Rock Hall, Box 2911, San Francisco, CA 94158 USA
| |
Collapse
|
|
18
|
Mak ACY, Sajuthi S, Joo J, Xiao S, Sleiman PM, White MJ, Lee EY, Saef B, Hu D, Gui H, Keys KL, Lurmann F, Jain D, Abecasis G, Kang HM, Nickerson DA, Germer S, Zody MC, Winterkorn L, Reeves C, Huntsman S, Eng C, Salazar S, Oh SS, Gilliland FD, Chen Z, Kumar R, Martínez FD, Wu AC, Ziv E, Hakonarson H, Himes BE, Williams LK, Seibold MA, Burchard EG. Lung Function in African American Children with Asthma Is Associated with Novel Regulatory Variants of the KIT Ligand KITLG/SCF and Gene-By-Air-Pollution Interaction. Genetics 2020; 215:869-886. [PMID: 32327564 PMCID: PMC7337089 DOI: 10.1534/genetics.120.303231] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 04/22/2020] [Indexed: 01/12/2023] Open
Abstract
Baseline lung function, quantified as forced expiratory volume in the first second of exhalation (FEV1), is a standard diagnostic criterion used by clinicians to identify and classify lung diseases. Using whole-genome sequencing data from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine project, we identified a novel genetic association with FEV1 on chromosome 12 in 867 African American children with asthma (P = 1.26 × 10-8, β = 0.302). Conditional analysis within 1 Mb of the tag signal (rs73429450) yielded one major and two other weaker independent signals within this peak. We explored statistical and functional evidence for all variants in linkage disequilibrium with the three independent signals and yielded nine variants as the most likely candidates responsible for the association with FEV1 Hi-C data and expression QTL analysis demonstrated that these variants physically interacted with KITLG (KIT ligand, also known as SCF), and their minor alleles were associated with increased expression of the KITLG gene in nasal epithelial cells. Gene-by-air-pollution interaction analysis found that the candidate variant rs58475486 interacted with past-year ambient sulfur dioxide exposure (P = 0.003, β = 0.32). This study identified a novel protective genetic association with FEV1, possibly mediated through KITLG, in African American children with asthma. This is the first study that has identified a genetic association between lung function and KITLG, which has established a role in orchestrating allergic inflammation in asthma.
Collapse
Affiliation(s)
- Angel C Y Mak
- Department of Medicine, University of California, San Francisco, California 94143
| | - Satria Sajuthi
- Center for Genes, Environment, and Health, National Jewish Health, Denver, Colorado 80206
| | - Jaehyun Joo
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Pennsylvania, 19104
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Marquitta J White
- Department of Medicine, University of California, San Francisco, California 94143
| | - Eunice Y Lee
- Department of Medicine, University of California, San Francisco, California 94143
| | - Benjamin Saef
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, California 94143
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, California 94143
- Berkeley Institute for Data Science, University of California, Berkeley, California 94720
| | | | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, Washington 98195
| | - Gonçalo Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109
| | - Hyun Min Kang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195
- Northwest Genomics Center, Seattle, Washington, 98195
- Brotman Baty Institute for Precision Medicine, Seattle, Washington, 98195
| | | | | | | | | | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, California 94143
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, California 94143
| | - Sandra Salazar
- Department of Medicine, University of California, San Francisco, California 94143
| | - Sam S Oh
- Department of Medicine, University of California, San Francisco, California 94143
| | - Frank D Gilliland
- Department of Preventive Medicine, Division of Environmental Health, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Zhanghua Chen
- Department of Preventive Medicine, Division of Environmental Health, Keck School of Medicine, University of Southern California, Los Angeles, California 90033
| | - Rajesh Kumar
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois 60611
| | - Fernando D Martínez
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona 85721
| | - Ann Chen Wu
- Precision Medicine Translational Research (PRoMoTeR) Center, Department of Population Medicine, Harvard Medical School and Pilgrim Health Care Institute, Boston, Massachusetts 02215
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, California 94143
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Pennsylvania, 19104
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Blanca E Himes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Health System, Detroit, Michigan 48202
| | - Max A Seibold
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Esteban G Burchard
- Department of Medicine, University of California, San Francisco, California 94143
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California 94143
| |
Collapse
|
|
19
|
Sierksma A, Lu A, Mancuso R, Fattorelli N, Thrupp N, Salta E, Zoco J, Blum D, Buée L, De Strooper B, Fiers M. Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology. EMBO Mol Med 2020; 12:e10606. [PMID: 31951107 PMCID: PMC7059012 DOI: 10.15252/emmm.201910606] [Citation(s) in RCA: 189] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 12/20/2019] [Accepted: 12/20/2019] [Indexed: 12/20/2022] Open
Abstract
Polygenic risk scores have identified that genetic variants without genome-wide significance still add to the genetic risk of developing Alzheimer's disease (AD). Whether and how subthreshold risk loci translate into relevant disease pathways is unknown. We investigate here the involvement of AD risk variants in the transcriptional responses of two mouse models: APPswe/PS1L166P and Thy-TAU22. A unique gene expression module, highly enriched for AD risk genes, is specifically responsive to Aβ but not TAU pathology. We identify in this module 7 established AD risk genes (APOE, CLU, INPP5D, CD33, PLCG2, SPI1, and FCER1G) and 11 AD GWAS genes below the genome-wide significance threshold (GPC2, TREML2, SYK, GRN, SLC2A5, SAMSN1, PYDC1, HEXB, RRBP1, LYN, and BLNK), that become significantly upregulated when exposed to Aβ. Single microglia sequencing confirms that Aβ, not TAU, pathology induces marked transcriptional changes in microglia, including increased proportions of activated microglia. We conclude that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.
Collapse
Affiliation(s)
- Annerieke Sierksma
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Ashley Lu
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Renzo Mancuso
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Nicola Fattorelli
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Nicola Thrupp
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Evgenia Salta
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - Jesus Zoco
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| | - David Blum
- INSERM, CHU Lille, LabEx DISTALZ, UMR‐S 1172, Alzheimer & TauopathiesUniversité LilleLilleFrance
| | - Luc Buée
- INSERM, CHU Lille, LabEx DISTALZ, UMR‐S 1172, Alzheimer & TauopathiesUniversité LilleLilleFrance
| | - Bart De Strooper
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
- UK Dementia Research InstituteUniversity College LondonLondonUK
| | - Mark Fiers
- VIB Center for Brain & Disease ResearchLeuvenBelgium
- Laboratory for the Research of Neurodegenerative DiseasesDepartment of NeurosciencesLeuven Brain Institute (LBI)KU Leuven (University of Leuven)LeuvenBelgium
| |
Collapse
|
|
20
|
Yang CH, Lin YD, Chuang LY. Class Balanced Multifactor Dimensionality Reduction to Detect Gene-Gene Interactions. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:71-81. [PMID: 30040653 DOI: 10.1109/tcbb.2018.2858776] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.
Collapse
|
|
21
|
Joiret M, Mahachie John JM, Gusareva ES, Van Steen K. Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies. BioData Min 2019; 12:11. [PMID: 31198442 PMCID: PMC6558841 DOI: 10.1186/s13040-019-0199-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 05/09/2019] [Indexed: 01/07/2023] Open
Abstract
Background In Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs. Results Using real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block. Conclusions Our results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.
Collapse
Affiliation(s)
- Marc Joiret
- BIO3, GIGA-R Medical Genomics, Avenue de l'Hôpital 1-B34-CHU, Liège, 4000 Belgium.,Biomechanics Research Unit, GIGA-R in-silico medicine, Liège, Avenue de l'Hôpital 1-B34-CHU, Liège, 4000 Belgium
| | | | - Elena S Gusareva
- BIO3, GIGA-R Medical Genomics, Avenue de l'Hôpital 1-B34-CHU, Liège, 4000 Belgium
| | - Kristel Van Steen
- BIO3, GIGA-R Medical Genomics, Avenue de l'Hôpital 1-B34-CHU, Liège, 4000 Belgium.,WELBIO researcher, Avenue de l'Hôpital 1-B34-CHU, Liège, 4000 Belgium
| |
Collapse
|
|
22
|
Abstract
Identifying gene-gene and gene-environment interactions may help us to better describe the genetic architecture for complex traits. While advances have been made in identifying genetic variants associated with complex traits through more dense panels of genetic variants and larger sample sizes, genome-wide interaction analyses are still limited in power to detect interactions with small effect sizes, rare frequencies, and higher order interactions. This chapter outlines methods for detecting both gene-gene and gene-environment interactions both through explicit tests for interactions (i.e., ones in which the interaction is tested directly) and non-explicit tests (i.e., ones in which an interaction is allowed for in the test, but does not test for the interaction directly) as well as approaches for increasing power by reducing the search space. Issues relating to multiple test correction, replication, and the reporting of interaction results in publications.
Collapse
Affiliation(s)
- Andrew T DeWan
- Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
| |
Collapse
|
|
23
|
Bourg S, Jacob L, Menu F, Rajon E. Hormonal pleiotropy and the evolution of allocation trade-offs. Evolution 2019; 73:661-674. [PMID: 30734273 DOI: 10.1111/evo.13693] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 01/09/2019] [Indexed: 12/15/2022]
Abstract
Recent empirical evidence suggests that trade-off relationships can evolve, challenging the classical image of their high entrenchment. For energy reliant traits, this relationship should depend on the endocrine system that regulates resource allocation. Here, we model changes in this system by mutating the expression and conformation of its constitutive hormones and receptors. We show that the shape of trade-offs can indeed evolve in this model through the combined action of genetic drift and selection, such that their evolutionarily expected curvature and length depend on context. In particular, the shape of a trade-off should depend on the cost associated with resource storage, itself depending on the traded resource and on the ecological context. Despite this convergence at the phenotypic level, we show that a variety of physiological mechanisms may evolve in similar simulations, suggesting redundancy at the genetic level. This model should provide a useful framework to interpret and unify the overly complex observations of evolutionary endocrinology and evolutionary ecology.
Collapse
Affiliation(s)
- Salomé Bourg
- Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France
| | - Laurent Jacob
- Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France
| | - Frédéric Menu
- Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France
| | - Etienne Rajon
- Univ Lyon, Université Lyon 1, CNRS, Laboratoire de Biométrie et Biologie Évolutive UMR5558, F-69622 Villeurbanne, France
| |
Collapse
|
|
24
|
Yang J, Zhao X, Ma J, Qiao Z, Yang X, Zhao E, Ban B, Zhu X, Cao D, Yang Y, Qiu X. The Interaction of TPH2 and 5-HT2A Polymorphisms on Major Depressive Disorder Susceptibility in a Chinese Han Population: A Case-Control Study. Front Psychiatry 2019; 10:172. [PMID: 31019472 PMCID: PMC6458236 DOI: 10.3389/fpsyt.2019.00172] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 03/08/2019] [Indexed: 12/31/2022] Open
Abstract
Purpose: TPH2 and 5-HT2A appear to play vital roles in the homeostatic regulation of serotonin levels in the brain, their genetic variations may lead to impaired homeostatic regulation of serotonin resulting in abnormal levels of serotonin in the brain, thus predisposing individuals to MDD. However, research studies have yet to confirm which gene-gene interaction effect between TPH2 and 5-HT2A polymorphisms results in increased susceptibility to MDD. Methods: A total of 565 participants, consisting of 278 MDD patients and 287 healthy controls from the Chinese Han population, were recruited for the present study. Six single nucleotide polymorphisms (SNPs) of TPH2/5-HT2A were selected to assess their interaction by use of a generalized multifactor dimensionality reduction method. Results: A-allele carriers of rs11178997 and rs120074175 were more likely to suffer from MDD than T-allele carriers of rs11178997, or G-allele carriers of rs120074175. The interaction between TPH2 (rs120074175, rs11178997) and 5-HT2A (rs7997012) was considered as the best multi-locus model upon the MDD susceptibility. Conclusions: Our data identified an important effect of TPH2 genetic variants (rs11178997 and rs120074175) upon the risk of MDD, and suggested that the interaction of TPH2/5-HT2A polymorphism variants confer a greater susceptibility to MDD in Chinese Han population.
Collapse
Affiliation(s)
- Jiarun Yang
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Xueyan Zhao
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Jingsong Ma
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Zhengxue Qiao
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Xiuxian Yang
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Erying Zhao
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Bo Ban
- Department of Endocrinology, Affiliated Hosptial of Jining Medical University, Jining, China
| | - Xiongzhao Zhu
- Medical Psychological, Institute of the Second Xiangya Hospital of Central South University, Changsha, China
| | - Depin Cao
- Department of Medical Education Management, Harbin Medical University, Harbin, China
| | - Yanjie Yang
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| | - Xiaohui Qiu
- Department of Medical Psychology, Institute of Public Health, Harbin Medical University, Harbin, China
| |
Collapse
|
|
25
|
Male-specific epistasis between WWC1 and TLN2 genes is associated with Alzheimer's disease. Neurobiol Aging 2018; 72:188.e3-188.e12. [PMID: 30201328 PMCID: PMC6769421 DOI: 10.1016/j.neurobiolaging.2018.08.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 07/05/2018] [Accepted: 08/01/2018] [Indexed: 12/19/2022]
Abstract
Systematic epistasis analyses in multifactorial disorders are an important step to better characterize complex genetic risk structures. We conducted a hypothesis-free sex-stratified genome-wide screening for epistasis contributing to Alzheimer's disease (AD) susceptibility. We identified a statistical epistasis signal between the single nucleotide polymorphisms rs3733980 and rs7175766 that was associated with AD in males (genome-wide significant pBonferroni-corrected=0.0165). This signal pointed toward the genes WW and C2 domain containing 1, aka KIBRA; 5q34 and TLN2 (talin 2; 15q22.2). Gene-based meta-analysis in 3 independent consortium data sets confirmed the identified interaction: the most significant (pmeta-Bonferroni-corrected=9.02*10-3) was for the single nucleotide polymorphism pair rs1477307 and rs4077746. In functional studies, WW and C2 domain containing 1, aka KIBRA and TLN2 coexpressed in the temporal cortex brain tissue of AD subjects (β=0.17, 95% CI 0.04 to 0.30, p=0.01); modulated Tau toxicity in Drosophila eye experiments; colocalized in brain tissue cells, N2a neuroblastoma, and HeLa cell lines; and coimmunoprecipitated both in brain tissue and HEK293 cells. Our finding points toward new AD-related pathways and provides clues toward novel medical targets for the cure of AD.
Collapse
|
|
26
|
Shaker OG, Senousy MA. Association of SNP-SNP Interactions Between RANKL, OPG, CHI3L1, and VDR Genes With Breast Cancer Risk in Egyptian Women. Clin Breast Cancer 2018; 19:e220-e238. [PMID: 30309792 DOI: 10.1016/j.clbc.2018.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 09/10/2018] [Accepted: 09/13/2018] [Indexed: 12/16/2022]
Abstract
BACKGROUND Genetic susceptibility for breast cancer (BC) is still poorly understood. A combination of multiple low-penetrant alleles of cancer-related genes and gene-gene interactions (epistasis) contributes to BC risk. Genetic variants in receptor activator of nuclear factor κB ligand (RANKL), osteoprotegerin (OPG), chitinase-3-like protein 1 (CHI3L1), and vitamin D receptor (VDR) genes are implicated in breast carcinogenesis; however, the influence of their epistatic effects on BC susceptibility has not yet been studied. We investigated the association of single nucleotide polymorphism (SNP)-SNP interactions and haplotypes of 6 SNPs in these 4 genes with the genetic predisposition of BC in Egyptian women. PATIENTS AND METHODS Data of 115 BC patients and 120 cancer-free controls were studied. Association tests were conducted using logistic regression models. RESULTS Individual SNPs showed weak statistical significance with BC susceptibility. The interactions between RANKL-rs9533156 and OPG-rs2073618; OPG-rs2073618 with CHI3L1-rs4950928, VDR-rs2228570 and VDR-rs1544410; OPG-rs2073617 and VDR-rs1544410; VDR-rs2228570 and VDR-rs1544410 were strongly associated with increased BC risk after adjustment for multiple comparisons. No SNPs were in strong linkage disequilibrium. The TCTCTG-rs9533156-rs2073618-rs2073617-rs4950928-rs2228570-rs1544410 haplotype was significantly associated with increased BC risk (adjusted odds ratio = 8.33; 95% confidence interval, 1.32-52.46; P = .025) compared with controls. TCCCTG haplotype stratified BC patients according to estrogen receptor/progesterone receptor status. TCTCTA was positively associated, and TCTCTG and TGTCTG haplotypes inversely correlated with bone metastasis. Bioinformatic analysis revealed 13 proteins commonly interacting with our 4 genes; the most significant was signal transducer and activator of transcription 5B. CONCLUSION Our results suggested that a stronger combined effect of SNPs in RANKL, OPG, CHI3L1, and VDR genes via gene-gene interaction may help predict BC risk and prognosis.
Collapse
Affiliation(s)
- Olfat G Shaker
- Medical Biochemistry and Molecular Biology Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt
| | - Mahmoud A Senousy
- Biochemistry Department, Faculty of Pharmacy, Cairo University, Cairo, Egypt.
| |
Collapse
|
|
27
|
Ameratunga R, Woon ST, Bryant VL, Steele R, Slade C, Leung EY, Lehnert K. Clinical Implications of Digenic Inheritance and Epistasis in Primary Immunodeficiency Disorders. Front Immunol 2018; 8:1965. [PMID: 29434582 PMCID: PMC5790765 DOI: 10.3389/fimmu.2017.01965] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 12/19/2017] [Indexed: 12/16/2022] Open
Abstract
The existence of epistasis in humans was first predicted by Bateson in 1909. Epistasis describes the non-linear, synergistic interaction of two or more genetic loci, which can substantially modify disease severity or result in entirely new phenotypes. The concept has remained controversial in human genetics because of the lack of well-characterized examples. In humans, it is only possible to demonstrate epistasis if two or more genes are mutated. In most cases of epistasis, the mutated gene products are likely to be constituents of the same physiological pathway leading to severe disruption of a cellular function such as antibody production. We have recently described a digenic family, who carry mutations of TNFRSF13B/TACI as well as TCF3 genes. Both genes lie in tandem along the immunoglobulin isotype switching and secretion pathway. We have shown they interact in an epistatic way causing severe immunodeficiency and autoimmunity in the digenic proband. With the advent of next generation sequencing, it is likely other families with digenic inheritance will be identified. Since digenic inheritance does not always cause epistasis, we propose an epistasis index which may help quantify the effects of the two mutations. We also discuss the clinical implications of digenic inheritance and epistasis in humans with primary immunodeficiency disorders.
Collapse
Affiliation(s)
- Rohan Ameratunga
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand.,Department of Clinical Immunology, Auckland City Hospital, Auckland, New Zealand
| | - See-Tarn Woon
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand
| | - Vanessa L Bryant
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Richard Steele
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand
| | - Charlotte Slade
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Allergy and Clinical Immunology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Euphemia Yee Leung
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Klaus Lehnert
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
| |
Collapse
|
|
28
|
Verma SS, Ritchie MD. Another Round of "Clue" to Uncover the Mystery of Complex Traits. Genes (Basel) 2018; 9:E61. [PMID: 29370075 PMCID: PMC5852557 DOI: 10.3390/genes9020061] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 12/19/2017] [Accepted: 01/15/2018] [Indexed: 12/13/2022] Open
Abstract
A plethora of genetic association analyses have identified several genetic risk loci. Technological and statistical advancements have now led to the identification of not only common genetic variants, but also low-frequency variants, structural variants, and environmental factors, as well as multi-omics variations that affect the phenotypic variance of complex traits in a population, thus referred to as complex trait architecture. The concept of heritability, or the proportion of phenotypic variance due to genetic inheritance, has been studied for several decades, but its application is mainly in addressing the narrow sense heritability (or additive genetic component) from Genome-Wide Association Studies (GWAS). In this commentary, we reflect on our perspective on the complexity of understanding heritability for human traits in comparison to model organisms, highlighting another round of clues beyond GWAS and an alternative approach, investigating these clues comprehensively to help in elucidating the genetic architecture of complex traits.
Collapse
Affiliation(s)
- Shefali Setia Verma
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Marylyn D Ritchie
- The Huck Institute of Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| |
Collapse
|
|
29
|
Zhu G, Su H, Lu L, Guo H, Chen Z, Sun Z, Song R, Wang X, Li H, Wang Z. Association of nineteen polymorphisms from seven DNA repair genes and the risk for bladder cancer in Gansu province of China. Oncotarget 2017; 7:31372-83. [PMID: 27153553 PMCID: PMC5058763 DOI: 10.18632/oncotarget.9146] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 03/31/2016] [Indexed: 11/25/2022] Open
Abstract
Background Balance of DNA damage and proper repair plays an important role in progression of bladder cancer. Here we aimed to assess the associations of nineteen polymorphisms from seven DNA repair–associated genes (PRAP1, OGG1, APEX1, MUTYH, XRCC1, XRCC2 and XRCC3) with bladder cancer and their interactions in the disease in a Han Chinese population. Methodology/Principal Findings A chip-based TaqMan genotyping for the candidate genes was performed on 227 bladder cancer patients and 260 healthy controls. APEX1 rs3136817, MUTYH rs3219493, three SNPs (rs3213356, rs25487 and rs1799782) in XRCC1, and three SNPs (rs1799794, rs861531 and rs861530) in XRCC3 showed significant associations with the risk of bladder cancer. In haplotype analysis, elevated risks of bladder cancer were observed in those with either haplotype GT (OR = 1.56, P = 0.003) of APEX1, or GGGTC (OR = 2.05, P = 0.002) of XRCC1, whereas decreased risks were in individuals with either GCGCC (OR = 0.40, P = 0.001), or GCGTT (OR = 0.60, = 0.005) of XRCC1, or CCC (OR = 0.65, P = 0.004) of MUTYH, or TTTAT (OR = 0.36, P = 0.009) of XRCC3. Interaction analysis showed that the two-loci model (rs1799794 and rs861530) was the best with the maximal testing accuracy of 0.701, and the maximal 100% cross-validation consistency (P = 0.001). Conclusions Polymorphisms and haplotypes of DNA repair genes are associated with the risk of bladder cancer, and of which the SNPs (rs1799794 and rs861530) in XRCC3 gene might be two major loci in relation to the susceptibility to bladder cancer in a northwest Chinese population.
Collapse
Affiliation(s)
- Gongjian Zhu
- School of Life Sciences, Lanzhou University, Lanzhou, Gansu 730000, China.,Gansu Provincial Academy of Medical Sciences, Gansu Provincial Cancer Hospital, Lanzhou, Gansu 730050, China
| | - Haixiang Su
- Gansu Provincial Academy of Medical Sciences, Gansu Provincial Cancer Hospital, Lanzhou, Gansu 730050, China
| | - Lingeng Lu
- Department of Chronic Disease Epidemiology, Yale School of Public Health, School of Medicine, Yale Cancer Center, Yale University, New Haven, CT 06520-8034, USA
| | - Hongyun Guo
- Gansu Provincial Academy of Medical Sciences, Gansu Provincial Cancer Hospital, Lanzhou, Gansu 730050, China
| | - Zhaohui Chen
- Institute of Urology, the Second Hospital of Lanzhou University, Lanzhou, Gansu 730000, China
| | - Zhen Sun
- School of Life Sciences, Lanzhou University, Lanzhou, Gansu 730000, China
| | - Ruixia Song
- Institute of Urology, the Second Hospital of Lanzhou University, Lanzhou, Gansu 730000, China
| | - Xiaomin Wang
- Department of Internal Medicine, Xigu District of Lanzhou City People's Hospital, Lanzhou, Gansu 730050, China
| | - Haining Li
- Gansu Provincial Academy of Medical Sciences, Gansu Provincial Cancer Hospital, Lanzhou, Gansu 730050, China
| | - Zhiping Wang
- Institute of Urology, the Second Hospital of Lanzhou University, Lanzhou, Gansu 730000, China
| |
Collapse
|
|
30
|
Hall MA, Moore JH, Ritchie MD. Embracing Complex Associations in Common Traits: Critical Considerations for Precision Medicine. Trends Genet 2017; 32:470-484. [PMID: 27392675 DOI: 10.1016/j.tig.2016.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Revised: 06/01/2016] [Accepted: 06/02/2016] [Indexed: 10/21/2022]
Abstract
Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships between genetic loci and across traits. As we move toward making precision medicine a reality, whereby we make predictions about disease risk based on genomic profiles, we need to identify improved predictive models of the relationship between genome and phenome. Methods that embrace pleiotropy (the effect of one locus on more than one trait), and gene-environment (G×E) and gene-gene (G×G) interactions, will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on the genotype and environment of an individual, the whole premise of precision medicine.
Collapse
Affiliation(s)
- Molly A Hall
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Departments of Genetics and Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 3535 Market Street, Philadelphia, PA 19104, USA
| | - Marylyn D Ritchie
- Biomedical and Translational Informatics, Geisinger Health System, Danville, PA, USA; Department of Biochemistry and Molecular Biology, Center for Systems Genomics, Eberly College of Science, The Pennsylvania State University, University Park, PA, USA.
| |
Collapse
|
|
31
|
Ameratunga R, Koopmans W, Woon ST, Leung E, Lehnert K, Slade CA, Tempany JC, Enders A, Steele R, Browett P, Hodgkin PD, Bryant VL. Epistatic interactions between mutations of TACI ( TNFRSF13B) and TCF3 result in a severe primary immunodeficiency disorder and systemic lupus erythematosus. Clin Transl Immunology 2017; 6:e159. [PMID: 29114388 PMCID: PMC5671988 DOI: 10.1038/cti.2017.41] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2017] [Revised: 07/21/2017] [Accepted: 07/21/2017] [Indexed: 12/22/2022] Open
Abstract
Common variable immunodeficiency disorders (CVID) are a group of primary immunodeficiencies where monogenetic causes account for only a fraction of cases. On this evidence, CVID is potentially polygenic and epistatic although there are, as yet, no examples to support this hypothesis. We have identified a non-consanguineous family, who carry the C104R (c.310T>C) mutation of the Transmembrane Activator Calcium-modulator and cyclophilin ligand Interactor (TACI, TNFRSF13B) gene. Variants in TNFRSF13B/TACI are identified in up to 10% of CVID patients, and are associated with, but not solely causative of CVID. The proband is heterozygous for the TNFRSF13B/TACI C104R mutation and meets the Ameratunga et al. diagnostic criteria for CVID and the American College of Rheumatology criteria for systemic lupus erythematosus (SLE). Her son has type 1 diabetes, arthritis, reduced IgG levels and IgA deficiency, but has not inherited the TNFRSF13B/TACI mutation. Her brother, homozygous for the TNFRSF13B/TACI mutation, is in good health despite profound hypogammaglobulinemia and mild cytopenias. We hypothesised that a second unidentified mutation contributed to the symptomatic phenotype of the proband and her son. Whole-exome sequencing of the family revealed a de novo nonsense mutation (T168fsX191) in the Transcription Factor 3 (TCF3) gene encoding the E2A transcription factors, present only in the proband and her son. We demonstrate mutations of TNFRSF13B/TACI impair immunoglobulin isotype switching and antibody production predominantly via T-cell-independent signalling, while mutations of TCF3 impair both T-cell-dependent and -independent pathways of B-cell activation and differentiation. We conclude that epistatic interactions between mutations of the TNFRSF13B/TACI and TCF3 signalling networks lead to the severe CVID-like disorder and SLE in the proband.
Collapse
Affiliation(s)
- Rohan Ameratunga
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand.,Department of Clinical Immunology, Auckland City Hospital, Auckland, New Zealand
| | - Wikke Koopmans
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand
| | - See-Tarn Woon
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand
| | - Euphemia Leung
- Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Klaus Lehnert
- School of Biological Sciences, University of Auckland, Auckland, New Zealand
| | - Charlotte A Slade
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.,Department of Allergy and Clinical Immunology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Jessica C Tempany
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Anselm Enders
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research and Centre for Personalised Immunology, Australian National University, Canberra, ACT, Australia
| | - Richard Steele
- Department of Virology and Immunology, Auckland City Hospital, Auckland, New Zealand
| | - Peter Browett
- Department of Hematology, LabPlus, Auckland City Hospital, Auckland, New Zealand.,Department of Molecular Medicine, and Pathology University of Auckland, Auckland, New Zealand
| | - Philip D Hodgkin
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia
| | - Vanessa L Bryant
- Department of Immunology, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.,Department of Medical Biology, University of Melbourne, Parkville, VIC, Australia.,Department of Allergy and Clinical Immunology, Royal Melbourne Hospital, Parkville, VIC, Australia
| |
Collapse
|
|
32
|
Yang CH, Chuang LY, Lin YD. Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene-gene interactions. Sci Rep 2017; 7:12869. [PMID: 28993686 PMCID: PMC5634479 DOI: 10.1038/s41598-017-12773-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 09/15/2017] [Indexed: 12/11/2022] Open
Abstract
Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.
Collapse
Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, 84004, Taiwan.
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan.
| |
Collapse
|
|
33
|
Zhou D, Du Q, Chen J, Wang Q, Zhang D. Identification and allelic dissection uncover roles of lncRNAs in secondary growth of Populus tomentosa. DNA Res 2017; 24:473-486. [PMID: 28453813 PMCID: PMC5737087 DOI: 10.1093/dnares/dsx018] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 04/04/2017] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) function in various biological processes. However, their roles in secondary growth of plants remain poorly understood. Here, 15,691 lncRNAs were identified from vascular cambium, developing xylem, and mature xylem of Populus tomentosa with high and low biomass using RNA-seq, including 1,994 lncRNAs that were differentially expressed (DE) among the six libraries. 3,569 cis-regulated and 3,297 trans-regulated protein-coding genes were predicted as potential target genes (PTGs) of the DE lncRNAs to participate in biological regulation. Then, 476 and 28 lncRNAs were identified as putative targets and endogenous target mimics (eTMs) of Populus known microRNAs (miRNAs), respectively. Genome re-sequencing of 435 individuals from a natural population of P. tomentosa found 34,015 single nucleotide polymorphisms (SNPs) within 178 lncRNA loci and 522 PTGs. Single-SNP associations analysis detected 2,993 associations with 10 growth and wood-property traits under additive and dominance model. Epistasis analysis identified 17,656 epistatic SNP pairs, providing evidence for potential regulatory interactions between lncRNAs and their PTGs. Furthermore, a reconstructed epistatic network, representing interactions of 8 lncRNAs and 15 PTGs, might enrich regulation roles of genes in the phenylpropanoid pathway. These findings may enhance our understanding of non-coding genes in plants.
Collapse
MESH Headings
- Cambium/genetics
- Cambium/growth & development
- Cambium/metabolism
- Epistasis, Genetic
- Gene Expression Regulation, Plant
- Genetic Association Studies
- Polymorphism, Single Nucleotide
- Populus/genetics
- Populus/growth & development
- Populus/metabolism
- Quantitative Trait, Heritable
- RNA, Long Noncoding/genetics
- RNA, Long Noncoding/physiology
- RNA, Plant/genetics
- RNA, Plant/physiology
- Sequence Analysis, DNA
- Sequence Analysis, RNA
- Transcriptome
- Xylem/genetics
- Xylem/growth & development
- Xylem/metabolism
Collapse
Affiliation(s)
- Daling Zhou
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Qingzhang Du
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Jinhui Chen
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Qingshi Wang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| | - Deqiang Zhang
- Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P.R. China
- Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, P. R. China
| |
Collapse
|
|
34
|
Mating of natural Saccharomyces cerevisiae strains for improved glucose fermentation and lignocellulosic inhibitor tolerance. Folia Microbiol (Praha) 2017; 63:155-168. [PMID: 28887734 DOI: 10.1007/s12223-017-0546-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 09/01/2017] [Indexed: 10/18/2022]
Abstract
Natural Saccharomyces cerevisiae isolates from vineyards in the Western Cape, South Africa were evaluated for ethanol production in industrial conditions associated with the production of second-generation biofuels. The strains displayed high phenotypic diversity including the ability to grow at 45 °C and in the presence of 20% (v/v) ethanol, strain YI13. Strains HR4 and YI30 were inhibitor-tolerant under aerobic and oxygen-limited conditions, respectively. Spore-to-spore hybridization generated progeny that displayed heterosis, including increased ethanol productivity and improved growth in the presence of a synthetic inhibitor cocktail. Hybrid strains HR4/YI30#6 and V3/YI30#6 were able to grow at a high salt concentration (2 mol/L NaCl) with V3/YI30#6 also able to grow at a high temperature (45 °C). Strains HR4/YI30#1 and #3 were inhibitor-tolerant, with strain HR4/YI30#3 having similar productivity (0.36 ± 0.0036 g/L per h) as the superior parental strain, YI30 (0.35 ± 0.0058 g/L per h). This study indicates that natural S. cerevisiae strains display phenotypic variation and heterosis can be achieved through spore-to-spore hybridization. Several of the phenotypes (temperature-, osmo-, and inhibitor tolerance) displayed by both the natural strains and the generated progeny were at the maximum conditions reported for S. cerevisiae strains.
Collapse
|
|
35
|
Moore JH, Andrews PC, Olson RS, Carlson SE, Larock CR, Bulhoes MJ, O'Connor JP, Greytak EM, Armentrout SL. Grid-based stochastic search for hierarchical gene-gene interactions in population-based genetic studies of common human diseases. BioData Min 2017; 10:19. [PMID: 28572842 PMCID: PMC5450417 DOI: 10.1186/s13040-017-0139-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 05/18/2017] [Indexed: 11/18/2022] Open
Abstract
Background Large-scale genetic studies of common human diseases have focused almost exclusively on the independent main effects of single-nucleotide polymorphisms (SNPs) on disease susceptibility. These studies have had some success, but much of the genetic architecture of common disease remains unexplained. Attention is now turning to detecting SNPs that impact disease susceptibility in the context of other genetic factors and environmental exposures. These context-dependent genetic effects can manifest themselves as non-additive interactions, which are more challenging to model using parametric statistical approaches. The dimensionality that results from a multitude of genotype combinations, which results from considering many SNPs simultaneously, renders these approaches underpowered. We previously developed the multifactor dimensionality reduction (MDR) approach as a nonparametric and genetic model-free machine learning alternative. Approaches such as MDR can improve the power to detect gene-gene interactions but are limited in their ability to exhaustively consider SNP combinations in genome-wide association studies (GWAS), due to the combinatorial explosion of the search space. We introduce here a stochastic search algorithm called Crush for the application of MDR to modeling high-order gene-gene interactions in genome-wide data. The Crush-MDR approach uses expert knowledge to guide probabilistic searches within a framework that capitalizes on the use of biological knowledge to filter gene sets prior to analysis. Here we evaluated the ability of Crush-MDR to detect hierarchical sets of interacting SNPs using a biology-based simulation strategy that assumes non-additive interactions within genes and additivity in genetic effects between sets of genes within a biochemical pathway. Results We show that Crush-MDR is able to identify genetic effects at the gene or pathway level significantly better than a baseline random search with the same number of model evaluations. We then applied the same methodology to a GWAS for Alzheimer’s disease and showed base level validation that Crush-MDR was able to identify a set of interacting genes with biological ties to Alzheimer’s disease. Conclusions We discuss the role of stochastic search and cloud computing for detecting complex genetic effects in genome-wide data.
Collapse
Affiliation(s)
- Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Peter C Andrews
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | - Randal S Olson
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, 19104 PA USA
| | | | | | | | | | | | | |
Collapse
|
|
36
|
Yang CH, Chuang LY, Lin YD. CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies. Bioinformatics 2017; 33:2354-2362. [DOI: 10.1093/bioinformatics/btx163] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 03/21/2017] [Indexed: 12/31/2022] Open
Affiliation(s)
- Cheng-Hong Yang
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
- Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Li-Yeh Chuang
- Department of Chemical Engineering and Institute of Biotechnology and Chemical Engineering, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Da Lin
- Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan
| |
Collapse
|
|
37
|
Özdaş T, Özdaş S, Babademez MA, Muz SE, M Atilla H, Baştimur S, Izbirak A, Kurt K, Öz I. Significant association between SCGB1D4 gene polymorphisms and susceptibility to adenoid hypertrophy in a pediatric population. Turk J Med Sci 2017; 47:201-210. [PMID: 28263490 DOI: 10.3906/sag-1512-93] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 06/05/2016] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND/AIM Adenoid hypertrophy (AH) is chronic enlargement of the adenoid tissue. The pathophysiology of the disease is unclear. We analyzed SCGB1D4 gene polymorphisms in order to determine the effect of the variants or their genetic combinations on AH. MATERIALS AND METHODS We genotyped the SCGB1D4 (IIS) gene in 167 participants (95 children with AH and 72 controls) by performing DNA sequencing in blood samples. RESULTS We genotyped three single nucleotide polymorphisms (SNPs). In the analysis, we found that in the presence of those SNPs and the minor alleles of individual SNPs four haplotypes were associated with an increased risk of AH. In addition, those SNPs were significantly associated with asthma, allergy, sleep-disordered breathing, AH grade +4, and a high level of IgE. As indicated on multifactor dimensionality reduction analysis, single-locus (rs35328961), two-locus (rs35328961_rs56196602), and three-locus models (rs200327820_rs35328961_rs56196602) had the highest synergistic interaction effect on AH. The three-factor model was also significantly associated with some genotypes of rs35328961 and allergic-asthmatic AH. CONCLUSION SNPs of SCGB1D4 and their combinations are associated with an increased risk for developing AH. We highlighted the importance of genetic factors on AH and AH-related clinical phenotypes.
Collapse
Affiliation(s)
- Talih Özdaş
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Sibel Özdaş
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Adana Science and Technology University, Adana, Turkey
| | - Mehmet Ali Babademez
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Sami Engin Muz
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Huntürk M Atilla
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Sibel Baştimur
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Afife Izbirak
- Department of Biology, Faculty of Science, Hacettepe University, Ankara, Turkey
| | - Kenan Kurt
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| | - Işı Öz
- Yenimahalle Education and Research Hospital, Otolaryngology Clinic, Ankara, Turkey
| |
Collapse
|
|
38
|
Fu G, Dai X, Symanzik J, Bushman S. Quantitative gene-gene and gene-environment mapping for leaf shape variation using tree-based models. THE NEW PHYTOLOGIST 2017; 213:455-469. [PMID: 27650962 DOI: 10.1111/nph.14131] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 07/01/2016] [Indexed: 05/28/2023]
Abstract
Leaf shape traits have long been a focus of many disciplines, but the complex genetic and environmental interactive mechanisms regulating leaf shape variation have not yet been investigated in detail. The question of the respective roles of genes and environment and how they interact to modulate leaf shape is a thorny evolutionary problem, and sophisticated methodology is needed to address it. In this study, we investigated a framework-level approach that inputs shape image photographs and genetic and environmental data, and then outputs the relative importance ranks of all variables after integrating shape feature extraction, dimension reduction, and tree-based statistical models. The power of the proposed framework was confirmed by simulation and a Populus szechuanica var. tibetica data set. This new methodology resulted in the detection of novel shape characteristics, and also confirmed some previous findings. The quantitative modeling of a combination of polygenetic, plastic, epistatic, and gene-environment interactive effects, as investigated in this study, will improve the discernment of quantitative leaf shape characteristics, and the methods are ready to be applied to other leaf morphology data sets. Unlike the majority of approaches in the quantitative leaf shape literature, this framework-level approach is data-driven, without assuming any pre-known shape attributes, landmarks, or model structures.
Collapse
Affiliation(s)
- Guifang Fu
- Department of Mathematics and Statistics, Utah State University, Logan, UT, 84321, USA
| | - Xiaotian Dai
- Department of Mathematics and Statistics, Utah State University, Logan, UT, 84321, USA
| | - Jürgen Symanzik
- Department of Mathematics and Statistics, Utah State University, Logan, UT, 84321, USA
| | - Shaun Bushman
- Forage and Range Research Lab, USDA-ARS, Logan, UT, 84322, USA
| |
Collapse
|
|
39
|
Zhang H, Deng Y, Zhang Y, Ping Y, Zhao H, Pang L, Zhang X, Wang L, Xu C, Xiao Y, Li X. Cooperative genomic alteration network reveals molecular classification across 12 major cancer types. Nucleic Acids Res 2016; 45:567-582. [PMID: 27899621 PMCID: PMC5314758 DOI: 10.1093/nar/gkw1087] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 10/18/2016] [Accepted: 10/27/2016] [Indexed: 11/22/2022] Open
Abstract
The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.
Collapse
Affiliation(s)
- Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Hongying Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Lin Pang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Li Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Chaohan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| |
Collapse
|
|
40
|
Solini A, Simeon V, Derosa L, Orlandi P, Rossi C, Fontana A, Galli L, Di Desidero T, Fioravanti A, Lucchesi S, Coltelli L, Ginocchi L, Allegrini G, Danesi R, Falcone A, Bocci G. Genetic interaction of P2X7 receptor and VEGFR-2 polymorphisms identifies a favorable prognostic profile in prostate cancer patients. Oncotarget 2016; 6:28743-54. [PMID: 26337470 PMCID: PMC4745689 DOI: 10.18632/oncotarget.4926] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 08/10/2015] [Indexed: 12/12/2022] Open
Abstract
VEGFR-2 and P2X7 receptor (P2X7R) have been described to stimulate the angiogenesis and inflammatory processes of prostate cancer. The present study has been performed to investigate the genetic interactions among VEGFR-2 and P2X7R SNPs and their correlation with overall survival (OS) in a population of metastatic prostate cancer patients. Analyses were performed on germline DNA obtained from blood samples and SNPs were investigated by real-time PCR technique. The survival dimensionality reduction (SDR) methodology was applied to investigate the genetic interaction between SNPs. One hundred patients were enrolled. The SDR software provided two genetic interaction profiles consisting of the combination between specific VEGFR-2 (rs2071559, rs11133360) and P2X7R (rs3751143, rs208294) genotypes. The median OS was 126 months (95% CI, 115.94–152.96) and 65.65 months (95% CI, 52.95–76.53) for the favorable and the unfavorable genetic profile, respectively (p < 0.0001). The genetic statistical interaction between VEGFR-2 (rs2071559, rs11133360) and P2X7R (rs3751143, rs208294) genotypes may identify a population of prostate cancer patients with a better prognosis.
Collapse
Affiliation(s)
- Anna Solini
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Vittorio Simeon
- Laboratory of Pre-Clinical and Translational Research, IRCCS - CROB Referral Cancer Center of Basilicata, Rionero in Vulture, Potenza, Italy
| | - Lisa Derosa
- Oncology Unit 2, University Hospital of Pisa, Pisa, Italy
| | - Paola Orlandi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Chiara Rossi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Andrea Fontana
- Oncology Unit 2, University Hospital of Pisa, Pisa, Italy
| | - Luca Galli
- Oncology Unit 2, University Hospital of Pisa, Pisa, Italy
| | - Teresa Di Desidero
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Anna Fioravanti
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Sara Lucchesi
- Division of Medical Oncology, Pontedera Hospital, Azienda USL of Pisa, Pontedera, Italy
| | - Luigi Coltelli
- Division of Medical Oncology, Pontedera Hospital, Azienda USL of Pisa, Pontedera, Italy
| | - Laura Ginocchi
- Division of Medical Oncology, Pontedera Hospital, Azienda USL of Pisa, Pontedera, Italy
| | - Giacomo Allegrini
- Division of Medical Oncology, Pontedera Hospital, Azienda USL of Pisa, Pontedera, Italy
| | - Romano Danesi
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | | | - Guido Bocci
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| |
Collapse
|
|
41
|
Jurecka-Lubieniecka B, Bednarczuk T, Ploski R, Krajewska J, Kula D, Kowalska M, Tukiendorf A, Kolosza Z, Jarzab B. Differences in Gene-Gene Interactions in Graves' Disease Patients Stratified by Age of Onset. PLoS One 2016; 11:e0150307. [PMID: 26943356 PMCID: PMC4778933 DOI: 10.1371/journal.pone.0150307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2015] [Accepted: 02/11/2016] [Indexed: 12/25/2022] Open
Abstract
Background Graves’ disease (GD) is a complex disease in which genetic predisposition is modified by environmental factors. Each gene exerts limited effects on the development of autoimmune disease (OR = 1.2–1.5). An epidemiological study revealed that nearly 70% of the risk of developing inherited autoimmunological thyroid diseases (AITD) is the result of gene interactions. In the present study, we analyzed the effects of the interactions of multiple loci on the genetic predisposition to GD. The aim of our analyses was to identify pairs of genes that exhibit a multiplicative interaction effect. Material and Methods A total of 709 patients with GD were included in the study. The patients were stratified into more homogeneous groups depending on the age at time of GD onset: younger patients less than 30 years of age and older patients greater than 30 years of age. Association analyses were performed for genes that influence the development of GD: HLADRB1, PTPN22, CTLA4 and TSHR. The interactions among polymorphisms were analyzed using the multiple logistic regression and multifactor dimensionality reduction (MDR) methods. Results GD patients stratified by the age of onset differed in the allele frequencies of the HLADRB1*03 and 1858T polymorphisms of the PTPN22 gene (OR = 1.7, p = 0.003; OR = 1.49, p = 0.01, respectively). We evaluated the genetic interactions of four SNPs in a pairwise fashion with regard to disease risk. The coexistence of HLADRB1 with CTLA4 or HLADRB1 with PTPN22 exhibited interactions on more than additive levels (OR = 3.64, p = 0.002; OR = 4.20, p < 0.001, respectively). These results suggest that interactions between these pairs of genes contribute to the development of GD. MDR analysis confirmed these interactions. Conclusion In contrast to a single gene effect, we observed that interactions between the HLADRB1/PTPN22 and HLADRB1/CTLA4 genes more closely predicted the risk of GD onset in young patients.
Collapse
Affiliation(s)
- Beata Jurecka-Lubieniecka
- Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
- * E-mail:
| | - Tomasz Bednarczuk
- Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland
| | - Rafal Ploski
- Department of Medical Genetics, Forensic Medicine, Medical University of Warsaw, Warsaw, Poland
| | - Jolanta Krajewska
- Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Dorota Kula
- Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Malgorzata Kowalska
- Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Andrzej Tukiendorf
- Department of Epidemiology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Zofia Kolosza
- Department of Epidemiology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Barbara Jarzab
- Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Gliwice Branch, Gliwice, Poland
| |
Collapse
|
|
42
|
A forest-based feature screening approach for large-scale genome data with complex structures. BMC Genet 2015; 16:148. [PMID: 26698561 PMCID: PMC4690313 DOI: 10.1186/s12863-015-0294-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2015] [Accepted: 11/13/2015] [Indexed: 01/06/2023] Open
Abstract
Background Genome-wide association studies (GWAS) interrogate large-scale whole genome to characterize the complex genetic architecture for biomedical traits. When the number of SNPs dramatically increases to half million but the sample size is still limited to thousands, the traditional p-value based statistical approaches suffer from unprecedented limitations. Feature screening has proved to be an effective and powerful approach to handle ultrahigh dimensional data statistically, yet it has not received much attention in GWAS. Feature screening reduces the feature space from millions to hundreds by removing non-informative noise. However, the univariate measures used to rank features are mainly based on individual effect without considering the mutual interactions with other features. In this article, we explore the performance of a random forest (RF) based feature screening procedure to emphasize the SNPs that have complex effects for a continuous phenotype. Results Both simulation and real data analysis are conducted to examine the power of the forest-based feature screening. We compare it with five other popular feature screening approaches via simulation and conclude that RF can serve as a decent feature screening tool to accommodate complex genetic effects such as nonlinear, interactive, correlative, and joint effects. Unlike the traditional p-value based Manhattan plot, we use the Permutation Variable Importance Measure (PVIM) to display the relative significance and believe that it will provide as much useful information as the traditional plot. Conclusion Most complex traits are found to be regulated by epistatic and polygenic variants. The forest-based feature screening is proven to be an efficient, easily implemented, and accurate approach to cope whole genome data with complex structures. Our explorations should add to a growing body of enlargement of feature screening better serving the demands of contemporary genome data.
Collapse
|
|
43
|
Hu T, Andrew AS, Karagas MR, Moore JH. Functional dyadicity and heterophilicity of gene-gene interactions in statistical epistasis networks. BioData Min 2015; 8:43. [PMID: 26697115 PMCID: PMC4687149 DOI: 10.1186/s13040-015-0062-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 07/03/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The interaction effect among multiple genetic factors, i.e. epistasis, plays an important role in explaining susceptibility on common human diseases and phenotypic traits. The uncertainty over the number of genetic attributes involved in interactions poses great challenges in genetic association studies and calls for advanced bioinformatics methodologies. Network science has gained popularity in modeling genetic interactions thanks to its structural characterization of large numbers of entities and their complex relationships. However, little has been done on functionally interpreting statistically inferred epistatic interactions using networks. RESULTS In this study, we propose to characterize gene functional properties in the context of interaction network structure. We used Gene Ontology (GO) to functionally annotate genes as vertices in a statistical epistasis network, and quantitatively characterize the correlation between the distribution of gene functional properties and the network structure by measuring dyadicity and heterophilicity of each functional category in the network. These two parameters quantify whether genetic interactions tend to occur more frequently for genes from the same functional category, i.e. dyadic effect, or more frequently for genes from across different functional categories, i.e. heterophilic effect. CONCLUSIONS By applying this framework to a population-based bladder cancer dataset, we were able to identify several GO categories that have significant dyadicity or heterophilicity associated with bladder cancer susceptibility. Thus, our informatics framework suggests a new methodology for embedding functional analysis in network modeling of statistical epistasis in genetic association studies.
Collapse
Affiliation(s)
- Ting Hu
- Department of Computer Science, Memorial University, St. John's, NL, Canada
| | - Angeline S Andrew
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH USA
| | - Margaret R Karagas
- Department of Community and Family Medicine, Geisel School of Medicine, Dartmouth College, Hanover, NH USA
| | - Jason H Moore
- Department of Biostatistics and Epidemiology, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
| |
Collapse
|
|
44
|
Epistatic interaction between common AGT G(-6)A (rs5051) and AGTR1 A1166C (rs5186) variants contributes to variation in kidney size at birth. Gene 2015; 572:72-78. [PMID: 26142106 DOI: 10.1016/j.gene.2015.06.071] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2015] [Revised: 06/02/2015] [Accepted: 06/29/2015] [Indexed: 11/22/2022]
Abstract
Low nephron number has been recognised as an important cardiovascular risk factor and recently a strong correlation between renal mass and nephron number has been demonstrated in newborns. The aim of this study was to investigate individual, as well as combined, effects of common variants of genes which encode for major components of the renin-angiotensin system (REN G10601A, AGT G(-6)A, ACE I/D, AGTR1 A1166C) on kidney size in healthy, full-term newborns. A significant additive main effect of the ACE I/D polymorphism, as well as an additive-by-additive interaction between AGT G(-6)A and AGTR1 A1166C variants, were found. The variance attributed to the epistatic effect was 27.9 ml(2)/m(4), which accounted for 73.8% of the interaction variance (37.8 ml(2)/m(4)), 66.4% of the genetic variance (42.0 ml(2)/m(4)) and 4.4% to the total phenotypic variance (628 ml(2)/m(4)). No other statistically significant main or epistatic effects were detected. Our results highlight the importance of considering gene-gene interactions as part of the genetic architecture of congenital nephron number, even when the loci do not show significant single locus effects. Unravelling the genetic determinants of low nephron number, along with early molecular screening, may well help to identify children at risk for cardiovascular disease.
Collapse
|
|
45
|
Nido GS, Ryan MM, Benuskova L, Williams JM. Dynamical properties of gene regulatory networks involved in long-term potentiation. Front Mol Neurosci 2015; 8:42. [PMID: 26300724 PMCID: PMC4528166 DOI: 10.3389/fnmol.2015.00042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 07/22/2015] [Indexed: 11/13/2022] Open
Abstract
The long-lasting enhancement of synaptic effectiveness known as long-term potentiation (LTP) is considered to be the cellular basis of long-term memory. LTP elicits changes at the cellular and molecular level, including temporally specific alterations in gene networks. LTP can be seen as a biological process in which a transient signal sets a new homeostatic state that is “remembered” by cellular regulatory systems. Previously, we have shown that early growth response (Egr) transcription factors are of fundamental importance to gene networks recruited early after LTP induction. From a systems perspective, we hypothesized that these networks will show less stable architecture, while networks recruited later will exhibit increased stability, being more directly related to LTP consolidation. Using random Boolean network (RBN) simulations we found that the network derived at 24 h was markedly more stable than those derived at 20 min or 5 h post-LTP. This temporal effect on the vulnerability of the networks is mirrored by what is known about the vulnerability of LTP and memory itself. Differential gene co-expression analysis further highlighted the importance of the Egr family and found a rapid enrichment in connectivity at 20 min, followed by a systematic decrease, providing a potential explanation for the down-regulation of gene expression at 24 h documented in our preceding studies. We also found that the architecture exhibited by a control and the 24 h LTP co-expression networks fit well to a scale-free distribution, known to be robust against perturbations. By contrast the 20 min and 5 h networks showed more truncated distributions. These results suggest that a new homeostatic state is achieved 24 h post-LTP. Together, these data present an integrated view of the genomic response following LTP induction by which the stability of the networks regulated at different times parallel the properties observed at the synapse.
Collapse
Affiliation(s)
- Gonzalo S Nido
- Department of Computer Science, University of Otago Dunedin, New Zealand ; Brain Health Research Centre, University of Otago Dunedin, New Zealand
| | - Margaret M Ryan
- Brain Health Research Centre, University of Otago Dunedin, New Zealand ; Department of Anatomy, Otago School of Medical Sciences, University of Otago Dunedin, New Zealand
| | - Lubica Benuskova
- Department of Computer Science, University of Otago Dunedin, New Zealand ; Brain Health Research Centre, University of Otago Dunedin, New Zealand
| | - Joanna M Williams
- Brain Health Research Centre, University of Otago Dunedin, New Zealand ; Department of Anatomy, Otago School of Medical Sciences, University of Otago Dunedin, New Zealand
| |
Collapse
|
|
46
|
Abstract
Here we introduce the ReliefF machine learning algorithm and some of its extensions for detecting and characterizing epistasis in genetic association studies. We provide a general overview of the method and then highlight some of the modifications that have greatly improved its power for genetic analysis. We end with a few examples of published studies of complex human diseases that have used ReliefF.
Collapse
|
|
47
|
Association of Interleukin-10 gene promoter polymorphisms with obstructive sleep apnea. Sleep Breath 2015; 20:855-66. [PMID: 26139223 DOI: 10.1007/s11325-015-1216-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 05/02/2015] [Accepted: 06/02/2015] [Indexed: 10/23/2022]
Abstract
BACKGROUND Interleukin-10 (IL) is an anti-inflammatory cytokine that regulates normal sleep patterns, and recent studies have reported that it is a potential useful biomarker to identify presence and severity of sleep apnea syndrome (OSAS). Promoter polymorphisms of IL-10 gene have been associated with altered expression levels, which contributes to OSAS. OBJECTIVE The aim of this study was to determine the prevalence of -1082 G/A, -819 C/T, and -592 C/A promoter polymorphisms of IL-10 gene in individuals with OSAS and controls. SUBJECTS AND METHODS An open-label study was performed in the Otorhinolaryngology and Sleep Disorders Outpatient Clinics. One hundred four cases with OSAS were included as the study group, and 78 individuals without OSAS were included as the controls. DNAs were extracted from peripheral blood leukocytes, and the sites that encompassed those polymorphisms were identified by DNA sequencing analyses. Data were analyzed with SNPStats and multifactor dimensionality reduction (MDR) software. RESULTS The prevalence of OSAS was higher in males in the study group when compared to controls (P = 0.0003). The IL-10-1082 G/A, -819 C/T, and -592 C/A SNPs, and their minor alleles were associated with a significantly increased risk for OSAS compared to the controls (P ˂ 0.05 for all). Furthermore, ATA haplotype frequency was significantly higher in the study group compared to the control group, but the GCC haplotype frequency was lower (P = 0.0001 and P = 0.0001). As indicated in MDR analysis, combinations of IL-10 gene were associated with OSAS in single-, double-, and triple-locus analyses. CONCLUSION The prevalences of the IL-10 gene promoter polymorphisms were different in OSAS patients and the controls in Turkish population. IL-10 gene polymorphisms may lead to altered inflammatory cascade, which might contribute to OSAS. Further studies on larger cohorts are needed to validate our findings.
Collapse
|
|
48
|
Nie Q, Yue X, Liu B. Development of Vibrio spp. infection resistance related SNP markers using multiplex SNaPshot genotyping method in the clam Meretrix meretrix. FISH & SHELLFISH IMMUNOLOGY 2015; 43:469-476. [PMID: 25655323 DOI: 10.1016/j.fsi.2015.01.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2014] [Revised: 01/22/2015] [Accepted: 01/26/2015] [Indexed: 06/04/2023]
Abstract
The clam Meretrix meretrix is a commercially important mollusc species in the coastal areas of South and Southeast Asia. In the present study, large-scale SNPs were genotyped by the Multiplex SNaPshot genotyping method among the stocks of M. meretrix with different Vibrio spp. infection resistance profile. Firstly, the AUTOSNP software was applied to mine SNPs from M. meretrix transcriptome, and 323 SNP loci (including 120 indels) located on 64 contigs were selected based on Uniprot-GO associations. Then, 38 polymorphic SNP loci located on 15 contigs were genotyped successfully in the clam stocks with different resistance to Vibrio parahaemolyticus infection (11-R and 11-S groups). Pearson's Chi-square test was applied to compare the allele and genotype frequency distributions of the SNPs between the different stocks, and seven SNP markers located on three contigs were found to be associated with V. parahaemolyticus infection resistance trait. Haplotype-association analysis showed that six haplotypes had significantly different frequency distributions in 11-S and 11-R (P < 0.05). With selective genotyping between 09-R and 09-C populations, which had different resistance to Vibrio harveyi infection, four out of the seven selected SNPs had significantly different distributions (P < 0.05) and therefore they were considered to be associated with Vibrio spp. infection resistance. Sequence alignments and annotations indicated that the contigs containing the associated SNPs had high similarity to the immune related genes. All these results would be useful for the future marker-assisted selection of M. meretrix strains with high Vibrio spp. infection resistance.
Collapse
Affiliation(s)
- Qing Nie
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China; University of Chinese Academy of Sciences, Beijing 100039, China
| | - Xin Yue
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
| | - Baozhong Liu
- Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China.
| |
Collapse
|
|
49
|
Fitzpatrick DJ, Ryan CJ, Shah N, Greene D, Molony C, Shields DC. Genome-wide epistatic expression quantitative trait loci discovery in four human tissues reveals the importance of local chromosomal interactions governing gene expression. BMC Genomics 2015; 16:109. [PMID: 25765234 PMCID: PMC4345003 DOI: 10.1186/s12864-015-1300-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2014] [Accepted: 01/29/2015] [Indexed: 12/15/2022] Open
Abstract
Background Epistasis (synergistic interaction) among SNPs governing gene expression is likely to arise within transcriptional networks. However, the power to detect it is limited by the large number of combinations to be tested and the modest sample sizes of most datasets. By limiting the interaction search space firstly to cis-trans and then cis-cis SNP pairs where both SNPs had an independent effect on the expression of the most variable transcripts in the liver and brain, we greatly reduced the size of the search space. Results Within the cis-trans search space we discovered three transcripts with significant epistasis. Surprisingly, all interacting SNP pairs were located nearby each other on the chromosome (within 290 kb-2.16 Mb). Despite their proximity, the interacting SNPs were outside the range of linkage disequilibrium (LD), which was absent between the pairs (r2 < 0.01). Accordingly, we redefined the search space to detect cis-cis interactions, where a cis-SNP was located within 10 Mb of the target transcript. The results of this show evidence for the epistatic regulation of 50 transcripts across the tissues studied. Three transcripts, namely, HLA-G, PSORS1C1 and HLA-DRB5 share common regulatory SNPs in the pre-frontal cortex and their expression is significantly correlated. This pattern of epistasis is consistent with mediation via long-range chromatin structures rather than the binding of transcription factors in trans. Accordingly, some of the interactions map to regions of the genome known to physically interact in lymphoblastoid cell lines while others map to known promoter and enhancer elements. SNPs involved in interactions appear to be enriched for promoter markers. Conclusions In the context of gene expression and its regulation, our analysis indicates that the study of cis-cis or local epistatic interactions may have a more important role than interchromosomal interactions. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1300-3) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Darren J Fitzpatrick
- School of Medicine and Medical Sciences, University College Dublin, Belfield, Dublin, 4, Ireland.
| | - Colm J Ryan
- School of Medicine and Medical Sciences, University College Dublin, Belfield, Dublin, 4, Ireland.
| | - Naisha Shah
- School of Medicine and Medical Sciences, University College Dublin, Belfield, Dublin, 4, Ireland.
| | - Derek Greene
- School of Computer Science and Informatics, University College Dublin, Belfield, Dublin, 4, Ireland.
| | - Cliona Molony
- Merck Research Laboratories, Merck & Co. Inc. 33 Avenue Louis Pasteur, Boston, MA, 02115, USA.
| | - Denis C Shields
- School of Medicine and Medical Sciences, University College Dublin, Belfield, Dublin, 4, Ireland.
| |
Collapse
|
|
50
|
Beam AL, Motsinger-Reif AA, Doyle J. An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics. BioData Min 2015; 8:6. [PMID: 25691918 PMCID: PMC4330980 DOI: 10.1186/s13040-015-0039-3] [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: 07/07/2014] [Accepted: 01/18/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Best practice for statistical methodology in cell-based dose-response studies has yet to be established. We examine the ability of MANOVA to detect trait-associated genetic loci in the presence of gene-gene interactions. We present a novel Bayesian nonparametric method designed to detect such interactions. RESULTS MANOVA and the Bayesian nonparametric approach show good ability to detect trait-associated genetic variants under various possible genetic models. It is shown through several sets of analyses that this may be due to marginal effects being present, even if the underlying genetic model does not explicitly contain them. CONCLUSIONS Understanding how genetic interactions affect drug response continues to be a critical goal. MANOVA and the novel Bayesian framework present a trade-off between computational complexity and model flexibility.
Collapse
Affiliation(s)
- Andrew L Beam
- Center for Biomedical Informatics, Boston, Massachusetts
| | - Alison A Motsinger-Reif
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina ; Department of Statistics, North Carolina State University, Raleigh, North Carolina
| | - Jon Doyle
- Department of Computer Science, North Carolina State University, Raleigh, North Carolina
| |
Collapse
|