Epilepsy refers to the occurrence of two or more than two reiterative seizures. The occurrence of seizure is governed by the excessive electrical discharges in the cortex of the brain. Bioinformatics is crucial in diagnosing, prognosticating, and treating neurological disorders. It uses methodologies, computational tools, software, and databases to probe disease molecular underpinnings and identify biomarkers. It aids clinicians in addressing patient parameters and translational research. Artificial neural networks (ANNs) are computer models that attempt to mimic the neurons present in the human brain. This computerized neuronal model is used for analyzing and comprehending large and complex data sets. In the present study, three GEO datasets (GSE190451, GSE140393, and GSE134697) were retrieved from NCBI for the identification of differentially expressed genes using the DESeq2 package. The study identified 7 up-regulated genes (PRRC2A, FCGR3B, HLA-DRB, ENSG00000280614, ENSG00000281181, SLN, C4A) in patients with epilepsy. Furthermore, WEKA software was used for feature selection and classification of DEGs using feature selection algorithms namely Correlation Feature Selection, ReliefF, and Information Gain and classification methods such as Logistic regression, Classification via regression, Random forest, Random subspace, and Logistic model trees. After the analysis, out of the 7 genes, the C4A gene was removed as it yielded the lowest feature selection statistics. Lastly, R Studio was used for constructing the Artificial Neural Network of the 6 identified DEGs. The model's performance was evaluated using the "pROC" R package, and an AUC of 0.720 was obtained, indicating that the model had excellent classification accuracy. The NeuralNet package of R revealed that PRRC2A had the highest generalized weight value indicating the increased expression of these genes when all other parameters are constant. Therefore, PRRC2A can be used as a potential biomarker for the diagnosis of epilepsy.

Global epidemiological reports indicate a steady increase in the tendency to develop ovarian cancer. The symptoms of ovarian cancer are non-specific, and there is no effective screening tool. Most often, surgery, chemotherapy, and radiotherapy, alone or in combination, are used to treat ovarian cancer. We have a better understanding of the biology of ovarian cancer, the genetic basis of hereditary ovarian cancer, the stage of the disease, and the role of cytoreductive surgery and more effective chemotherapy, which translates into an increase in the percentage of patients who survive 5 years after diagnosis. A growing body of evidence points to the role of genetic factors in the development of cancer. It is known that mutations in the BRCA1 gene are responsible for an increased risk of developing ovarian cancer. The role of other genetic disorders, such as polymorphic variants, in increasing the risk of developing cancer is still being investigated. Ovarian cancer is a hormone-dependent cancer and its steroid hormones are estrogens. Estrogens affect cells through the estrogen receptors ERα and ERβ. An imbalance between ERα and ERβ receptor expression may, therefore, be a key step in estrogen-dependent carcinogenesis. In 60% of cancer cases, significantly elevated levels of ERα receptors are detected. The ERα receptor is encoded by the ESR1 gene, so its polymorphisms can be considered molecular markers of ovarian cancer. This article discusses the epidemiology, pathogenesis, risk factors, genetic testing, treatment, and diagnosis of ovarian cancer, as well as providing an overview of standard treatment approaches and new, targeted biologic therapies.

COVID-19 is a disease caused by SARS-CoV-2. It became a health problem affecting the lives of millions of people. Toll-like receptors are responsible for recognizing viral particles and activating the innate immune system. The genetic factors associated with COVID-19 remain unclear. Thus, this study aims to assess the association between the polymorphism in Toll-like receptors and susceptibility to COVID-19. We searched the electronic databases (Science Direct, PUBMED, Web of Science, and Scopus) for studies assessing the association between Toll-like receptor polymorphisms and susceptibility to COVID-19. The quality of the studies was assessed using the Q-Genie tool. Thirteen studies were included in this systematic review. The studies analyzed polymorphisms in TLR2, TLR3, TLR4, TLR7, TLR8 and TLR9. We used SNP2TFBS bioinformatic analysis to identify the variants influencing transcription factor binding sites. The Ensembl Genome Browser was used to assess the allele and genotype frequencies in different populations. The bioinformatic analysis revealed that the variant rs5743836 of TLR9 affects the transcription factor binding sites NFKB1 and RELA. The genotype frequency of the variants rs3775291, rs3853839, rs3764880 were higher in East Asian population compared to the other populations. The frequency of the rs3775290 variant was higher in East and South Asian populations. The rs179008 variant was higher in the European population, and the rs5743836 was higher in the African population. Toll-like receptors play an important role in COVID-19 susceptibility. Further studies in different populations are necessary to elucidate the role of Toll-like receptors polymorphisms in SARS-CoV-2 infection.

The IDH1 gene encodes isocitrate dehydrogenase 1 enzyme (IDH1), crucial in the citric acid cycle that converts isocitrate to alpha-ketoglutarate. Mutations in IDH1 at R132 lead to the production of the oncometabolite 2-hydroxyglutarate, which impacts cellular metabolism, differentiation, and epigenetic regulation, and is associated with GBM. This study utilized in silico methods (SIFT, PROVEAN, PolyPhen2, Predict SNP, MutPred2, InterPro, MusiteDeep, I-Mutant 3.0, MUpro, and INSP-MD) to identify high-risk missense SNPs in IDH1, located in highly conserved regions and overlapping with protein-ligand and PTM sites and assessed their impact on the structure and function of IDH1 protein. A total of 12 high-risk missense SNPs were found at T77, N96, S94, and K260 leading to the gain or loss of catalytic and allosteric sites, alteration in the metal binding site, as well as the gain or loss of PTMs such as acetylation, methylation, N-linked glycosylation, and ubiquitylation at different residues within the active sites of mutated IDH1 enzymes. These changes may significantly impact the structure and function of the IDH1 protein. Furthermore, GEPIA and survival analysis were performed to evaluate IDH1 expression in GBM and LGG and survival outcomes. GEPIA analysis showed significant (p < 0.05) upregulation of IDH1 expression in GBM and LGG. Survival analysis indicated that GBM patients with low IDH1 expression group had better survival outcomes, while LGG patients with low IDH1 expression group showed poorer survival rates. Overall, this study highlights the diagnostic and prognostic potential biomarker of IDH1 in glioblastoma. However, additional in vitro and in vivo studies will be valuable in confirming the role of IDH1 proteins in GBM.

Therapeutic target identification is challenging in drug discovery, particularly for rare and orphan diseases. Here, we propose a disease signature, TRESOR, which characterizes the functional mechanisms of each disease through genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) data, and develop machine learning methods for predicting inhibitory and activatory therapeutic targets for various diseases from target perturbation signatures (i.e., gene knockdown and overexpression). TRESOR enables highly accurate identification of target candidate proteins that counteract disease-specific transcriptome patterns, and the Bayesian optimization with omics-based disease similarities achieves the performance enhancement for diseases with few or no known targets. We make comprehensive predictions for 284 diseases with 4345 inhibitory target candidates and 151 diseases with 4040 activatory target candidates, and elaborate the promising targets using several independent cohorts. The methods are expected to be useful for understanding disease-disease relationships and identifying therapeutic targets for rare and orphan diseases.

Straightforward genotyping can provide timely diagnostic information for diseases prevention and treatment. Taking advantages of speediness and convenience, although numerous genotyping strategies combined loop-mediated isothermal amplification (LAMP) and lateral flow have been reported to satisfy the demand of point-of-care test, the false positive result caused by aerosol and primer dimer as an innate conflict seriously limits their practical application. In this study, both aerosol and primer dimer as extrinsic and intrinsic inducements respectively are first broken through at one stroke based on an integrated immunochromatographic biosensor. By introducing digoxigenin labeled dUTP into LAMP, not only the amplicon can be analyzed through naked eye, but also the aerosol contamination can be eliminated thoroughly by uracil DNA glycosylase ignoring the open vessel. Primer dimer, the significant drawback in lateral flow-based strategies, has been overcome due to the bio-labeled deoxyribonucleotide and oligonucleotide cannot couple for signal generation even under the high primer concentration. Instead of colloidal gold, the gold magnetic nanoparticle is synthesized and assembled into this biosensor as a nanoprobe, which enables the result to be quantified by the magnetic signal for subjective bias elimination. The polymorphism of C677T in methylenetetrahydrofolate reductase, a crucial genetic code related to folate metabolism, is genotyped using saliva as noninvasive specimen dispense with DNA purification. Only 1 ng genomic DNA can provide accurate result within 25 min by a simple heater, which proves the potential of this biosensor to facilitate precision medicine.

Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).

This study presents a novel machine learning approach using multimodal vector-quantized variational autoencoders (VQ-VAEs) for predicting therapeutic target molecules across diseases. To address the lack of known therapeutic target-disease associations, we incorporate the information on uncharacterized diseases without known targets or uncharacterized proteins without known indications (applicable diseases) in the semi-supervised learning (SSL) framework. The method integrates disease-specific and protein perturbation profiles with genetic perturbations (e.g. gene knockdowns and gene overexpressions) at the transcriptome level. Cross-cell representation learning, facilitated by VQ-VAEs, was performed to extract informative features from protein perturbation profiles across diverse human cell types. Concurrently, cross-disease representation learning was performed, leveraging VQ-VAE, to extract informative features reflecting disease states from disease-specific profiles. The model's applicability to uncharacterized diseases or proteins is enhanced by considering the consistency between disease-specific and patient-specific signatures. The efficacy of the method is demonstrated across three practical scenarios for 79 diseases: target repositioning for target-disease pairs, new target prediction for uncharacterized diseases, and new indication prediction for uncharacterized proteins. This method is expected to be valuable for identifying therapeutic targets across various diseases.

Code: github.com/YamanishiLab/SSL-VQ and Data: 10.5281/zenodo.14644837.

(1) Background: Prostate cancer treatment efficacy is significantly influenced by androgen receptor (AR) signaling pathways. SLC22A3, a membrane transporter, has been linked to SNP rs9364554 risk loci for drug efficacy in prostate cancer. (2) Methods: We examined the location of SNP rs9364554 in the genome and utilized TCGA and other publicly available datasets to analyze the association of this SNP with SLC22A3 transcription levels. We verified onco-mining findings in prostate cancer cell lines using quantitative PCR and Western blots. Additionally, we employed electrophoretic mobility shift assay (EMSA) to detect the binding affinity of transcription factors to this SNP. The ChIP-Seq was used to analyze the enrichment of H3K27ac on the SLC22A3 promoter. (3) Results: In this study, we revealed that SNP rs9364554 resides in the SLC22A3 gene and affects its transcription. The downregulation of SLC22A3 is associated with drug resistance. More importantly, we found that this SNP has different binding affinities with transcription factors, specifically FOXA1 and AR, which significantly affects their regulation of SLC22A3 transcription. (4) Conclusions: Our findings highlight the potential of using this SNP as a biomarker for predicting chemotherapeutic outcomes and uncover possible mechanisms underlying drug resistance in advanced prostate cancers. More importantly, it provides a clinical foundation for targeting FOXA1 to enhance drug efficacy in prostate cancer patients.

SNP based association studies have revolutionized the field of biomedicines. Enteric fever is a systemic disease with etiologic agent Salmonella enterica serovar typhi and paratyphi. It is a serious health issue worldwide and presents wide variations in incidence, rates, and severity. Previous investigations revealed that genetic variations may lead to susceptibility to typhoid fever. The current study was performed to investigate the potential association of PARK2_e01(-697) polymorphism with the susceptibility to typhoid in the Punjabi population.

For this case-control study, blood samples obtained from typhoid patients with positive Typhidot or blood culture test (n=72) and healthy controls (n=73) were processed for DNA extraction. The polymorphism PARK2_e01(-697) analysis was carried out by using PCR and RFLP.

No allelic association was found between PARK2_e01(-697) and susceptibility to typhoid fever in the understudy population.

This case control study is the demonstration of the non-association of PARK2_e01(-697) with typhoid in the Pakistani population. Future research, using larger population size, will help to elucidate the role of PARK2_e01(-697) polymorphism in typhoid pathogenesis.

Osteoporosis is characterized by increased resorption and decreased bone formation; it is predominantly influenced by genetic factors. G-protein coupled receptors (GPCRs) play a vital role in bone homeostasis, and mutations in these genes are associated with osteoporosis. This study aimed to investigate the impact of single nucleotide polymorphism (SNP) rs1042713 in the ADRB2 gene, encoding the beta-2-adrenergic receptor, on osteoblastogenesis. Herein, using quantitative polymerase chain reaction, western immunoblotting, immunofluorescence assays, and flow cytometry, we examined the expression of ADRB2 and markers of bone matrix synthesis in mesenchymal stem cells (MSCs) derived from osteoporosis patient (OP-MSCs) carrying ADRB2 SNP in comparison with MSCs from healthy donor (HD-MSCs). The results showed significantly reduced ADRB2 expression in OP-MSCs at both the mRNA and protein levels, alongside decreased type 1 collagen expression, a key bone matrix component. Notably, OP-MSCs exhibited increased ERK kinase expression during differentiation, indicating sustained cell cycle progression, unlike that going to HD-MSC. These results provide novel insights into the association of ADRB2 gene polymorphisms with osteogenic differentiation. The preserved proliferative activity of OP-MSCs with rs1042713 in ADRB2 contributes to their inability to undergo effective osteogenic differentiation. This research suggests that targeting genetic factors may offer new therapeutic strategies to mitigate osteoporosis progression.

Genomic epidemiology (GE) is an integration of genomics and epidemiology. The field has evolved significantly in the past decade, enhancing our understanding of genetic susceptibility, drug resistance, disease transmission patterns, outbreak surveillance, and vaccine development. It employs an arsenal of advanced tools such as whole-genome sequencing and single-nucleotide polymorphisms for analysis, tracing pathogen evolution, mapping genetic variations, and tracking drug resistance. The role of GE in infectious disease management extends beyond outbreak control to routine public health practices, precision medicine, and determining treatment policies. The expansion of GE can significantly bolster global health defenses by effectively enabling the detection and response to emerging health threats. However, challenges such as sampling bias, data quality, integration, standardization of computational pipelines, and need for trained personnel remain. To tackle these challenges, we must invest in building capacity, improving infrastructure, providing training, and fostering collaborations between scientists and public health officials. Concerted efforts must focus on overcoming existing hurdles and promoting seamless integration of basic research into public health frameworks to fully realize its potential. It is timely for India to rapidly expand its base in GE to gain valuable insights into genetic variations and disease susceptibilities. This will provide a fillip towards eliminating the three dominant vector-borne diseases in India: filariasis, malaria, and visceral leishmaniasis.

A systematic evaluation of how model architectures and training strategies impact genomics model performance is needed. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. All top-performing models used neural networks but diverged in architectures and training strategies. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide models into modular building blocks. We tested all possible combinations for the top three models, further improving their performance. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets, demonstrating the progress that can be driven by gold-standard genomics datasets.

Hair loss is a common phenomenon associated with various environmental and genetic factors. Mitochondrial dysfunction-induced oxidative stress has been recognized as a crucial determinant of hair follicle (HF) biology. Aldehyde dehydrogenase 2 (ALDH2) mitigates oxidative stress by detoxifying acetaldehyde. This study investigated the potential role of ALDH2 modulation in HF function and hair growth promotion.

To evaluate the effects of ALDH2 activation on oxidative stress in HFs and hair growth promotion.

The modulatory role of ALDH2 on HFs was investigated using an ALDH2 activator. ALDH2 expression in human HFs was evaluated through in vitro immunofluorescence staining. Ex vivo HF organ culture was employed to assess hair shaft elongation, while the fluorescence probe 2',7'- dichlorodihydrofluorescein diacetate was utilized to detect reactive oxygen species (ROS). An in vivo mouse model was used to determine whether ALDH2 activation induces anagen.

During the anagen phase, ALDH2 showed significantly higher intensity than that in the telogen phase, and its expression was primarily localized along the outer layer of HFs. ALDH2 activation promoted anagen phase induction by reducing ROS levels and enhancing reactive aldehyde clearance, which indicated that ALDH2 functions as a ROS scavenger within HFs. Moreover, ALDH2 activation upregulated Akt/GSK 3β/β-catenin signaling in HFs.

Our findings highlight the hair growth promotion effects of ALDH2 activation in HFs and its potential as a promising therapeutic approach for promoting anagen induction.

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Trace Amine Associated Receptor 1 (TAAR1) is a novel pharmaceutical target under investigation for the treatment of several neuropsychiatric conditions. TAAR1 single nucleotide variants (SNV) have been found in patients with schizophrenia and metabolic disorders. However, the frequency of variants in geographically diverse populations and the functional effects of such variants are unknown. In this study, we aimed to characterise the distribution of TAAR1 SNVs in five different WHO regions using the Database of Genotypes and Phenotypes (dbGaP) and conducted a critical computational analysis using available TAAR1 structural data to identify SNVs affecting ligand binding and/or functional regions. Our analysis shows 19 orthosteric, 9 signalling and 16 micro-switch SNVs hypothesised to critically influence the agonist induced TAAR1 activation. These SNVs may non-proportionally influence populations from discrete regions and differentially influence the activity of TAAR1-targeting therapeutics in genetically and geographically diverse populations. Notably, our dataset presented with orthosteric SNVs D1033.32N (found only in the South-East Asian Region and Western Pacific Region) and T1945.42A (found only in South-East Asian Region), and 2 signalling SNVs (V1253.54A/T2526.36A, found in African Region and commonly, respectively), all of which have previously demonstrated to influence ligand induced functions of TAAR1. Furthermore, bioinformatics analysis using SIFT4G, MutationTaster 2, PROVEAN and MutationAssessor predicted all 16 micro-switch SNVs are damaging and may further influence the agonist activation of TAAR1, thereby possibly impacting upon clinical outcomes. Understanding the genetic basis of TAAR1 function and the impact of common mutations within clinical populations is important for the safe and effective utilisation of novel and existing pharmacotherapies.

Single Nucleotide Polymorphisms (SNPs) in candidate autophagy gene BECN1 could influence its functions thereby autophagy process. BECN1 noncoding SNPs were found to be significantly associated with neurodegenerative disease and type 2 diabetes mellitus. This study aimed to develop a simultaneous genotyping technique for two BECN1 SNPs (rs10512488 and rs11552192).

A mutagenic primer-based approach was used to introduce a NdeI restriction site to genotype rs10512488 by Artificial-Restriction Fragment Length Polymorphism (A-RFLP) along with rs11552192 by Polymerase Chain Reaction (PCR)-RFLP. Multiplexing PCR and restriction digestion reactions were set up for simultaneous genotyping of both SNPs in 100 healthy individuals. Genotypic and allele frequencies were manually calculated, and the Hardy-Weinberg Equilibrium was assessed using the chi-square test.

We successfully developed PCR and RFLP conditions for the amplification and restriction digestion of both SNPs within the same tube for genotyping. The results of genotyping by newly developed multiplexing PCR-RFLP technique were concordant with the genotypes obtained by Sanger sequencing of samples. Allelic frequencies of rs10512488 obtained were 0.15 (A) and 0.85 (G), whereas allelic frequencies of rs11552192 were 0.16 (T) and 0.84 (A).

The newly developed technique is rapid, cost-effective and time-saving for large-scale applications compared to sequencing methods and would play an important role in low-income settings. For the first time, allelic frequencies of rs10512488 and rs11552192 were reported among the North Indian population.

Interleukin (IL)-17 A plays a crucial role in protecting hosts from invading bacterial pathogens. In this study, we investigated if single nucleotide polymorphisms (SNPs) in IL-17A are associated with susceptibility and outcome of bacterial meningitis (BM) in Angolan children. The study sample comprised 241 confirmed BM patients and 265 controls, which were matched for age and ethnicity. Three IL-17A SNPs - rs2275913 (-197G > A), rs8193036 (-737C > T) and rs4711998 (-877 A > G) - were determined by high-resolution melting analysis (HRMA). The frequency of variant genotype rs4711998 was significantly higher in patients with BM caused by Haemophilus influenzae (odds ratio [OR] 3.5; 95% confidence interval [CI] 1.49-8.23; P = 0.0025) than in controls. Also, patients with BM caused by Gram-negative bacteria and who carried the variant genotype rs2275913 had a lower glucose level (P = 0.0051) in cerebrospinal fluid (CSF). Patients with BM caused by Streptococcus pneumoniae who carried the variant type rs8193036 had a reduced risk for severe neurological sequelae (OR: 0.14; 95% CI: 0.029-0.68; P = 0.0079), blindness (OR: 0.012; 95% CI: 0.012-0.87; P = 0.017) and ataxia (OR: 0.28; 95% CI: 0.091-0.83; P = 0.023). This study suggests an association of IL-17A genetic variations with susceptibility and outcome of bacterial meningitis in Angolan children.

Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, others differed substantially. All top-performing models used neural networks, but diverged in architectures and novel training strategies, tailored to genomics sequence data. To dissect how architectural and training choices impact performance, we developed the Prix Fixe framework to divide any given model into logically equivalent building blocks. We tested all possible combinations for the top three models and observed performance improvements for each. The DREAM Challenge models not only achieved state-of-the-art results on our comprehensive yeast dataset but also consistently surpassed existing benchmarks on Drosophila and human genomic datasets. Overall, we demonstrate that high-quality gold-standard genomics datasets can drive significant progress in model development.

Cytochrome P450 oxidoreductase (POR) protein is essential for steroidogenesis, and POR gene mutations are frequently associated with P450 Oxidoreductase Deficiency (PORD), a disorder of hormone production. To our knowledge, no previous attempt has been made to identify and analyze the deleterious/pathogenic non-synonymous single nucleotide polymorphisms (nsSNPs) in the human POR gene through an extensive computational approach. Computational algorithms and tools were employed to identify, characterize, and validate the pathogenic SNPs associated with certain diseases. To begin with, all the high-confidence SNPs were collected, and their structural and functional impacts on the protein structures were explored. The results of various in silico analyses affirm that the A287P and R457H variants of POR could destabilize the interactions between the amino acids and the hydrogen bond networks, resulting in functional deviations of POR. The literature study further confirms that the pathogenic mutations (A287P and R457H) are associated with the onset of PORD. Molecular dynamics simulations (MDS) and essential dynamics (ED) studies characterized the structural consequences of prioritized deleterious mutations, representing the structural destabilization that might disrupt POR biological function. The identified deleterious mutations at the cofactor's binding domains might interfere with the essential interactions between the protein and cofactors, thus inhibiting POR catalytic activity. The consolidated insights from the computational analyses can be used to predict potential deleterious mutants and understand the disease's pathological basis and the molecular mechanism of drug metabolism for the application of personalized medication. HIGHLIGHTSNADPH cytochrome P450 oxidoreductase (POR) mutations are associated with a broad spectrum of human diseasesIdentified and analyzed the most deleterious nsSNPs of POR through the sequence and structure-based prediction toolsInvestigated the structural and functional impacts of the most significant mutations (A287P and R457H) associated with PORDMolecular dynamics and PCA-based FEL analysis were utilized to probe the mutation-induced structural alterations in PORCommunicated by Ramaswamy H. Sarma.

The natural circadian rhythm in an individual governs the sleep-wake cycle over 24 h. Disruptions in this internal cycle can lead to major health hazards and sleep disorders. Reports suggest that at least 50 % of people worldwide suffer from sleep-related disorders. An increase in screen time, especially in the wake of the COVID-19 pandemic, is one of the external causative factors for this condition. While many factors govern the circadian clock and its aberrance, the PER2 gene has been strongly linked to chronotypes by many researchers. The current paper provides an extensive examination of key Single Nucleotide Polymorphisms within the PER2 gene and their potential connection to four major types of sleep disorders. This study investigates whether these SNPs play a causative role in sleep disorders or if they are solely associated with these conditions. Additionally, we explore whether these genetic variations exert a lifelong influence on these sleep patterns or if external triggers contribute to the development of sleep disorders. This gene is a crucial regulator of the circadian cycle responsible for the transcription of other clock genes. It regulates a variety of physiological systems such as metabolism, sleep, body temperature, blood pressure, endocrine, immunological, cardiovascular, and renal function. We aim to establish some clarity to the multifaceted nature of this gene, which is often overlooked, and seek to establish the mechanistic role of PER2 gene mutations in sleep disorders. This will improve further understanding, assessment, and treatment of these conditions in future.

Eimeria tenella is a major cause of caecal coccidiosis in commercial poultry chickens worldwide. Here, we report chromosomal scale assembly of Eimeria tenella strain APU2, a strain isolated from commercial broiler chickens in the U.S. We obtained 100× sequencing Oxford Nanopore Technology (ONT) and more than 800× Coverage of Illumina Next-Seq. We created the assembly using the hybrid approach implemented in MaSuRCA, achieving a contiguous 51.34 Mb chromosomal-scale scaffolding enabling identification of structural variations. The AUGUSTUS pipeline predicted 8060 genes, and BUSCO deemed the genomes 99% complete; 6278 (78%) genes were annotated with Pfam domains, and 1395 genes were assigned GO-terms. Comparing E. tenella strains (APU2, US isolate and Houghton, UK isolate) derived Houghton strain of E. tenella revealed 62,905 high stringency differences, of which 45,322 are single nucleotide polymorphisms (SNPs) (0.088%). The rate of transitions/transversions among the SNPs are 1.63 ts/tv. The strains possess conserved gene order but have profound sequence heterogeneity in a several chromosomal segments (chr 2, 11 and 15). Genic and intergenic variation in defined gene families was evaluated between the two strains to possibly identify sequences under selection. The average genic nucleotide diversity of 2.8 with average 2 kb gene length (0.145%) at genic level. We examined population structure using available E. tenella sequences in NCBI, revealing that the two E. tenella isolates from the U.S. (E. tenella APU2 and Wisconsin, "ERR296879") share a common maternal inheritance with the E. tenella Houghton. Our chromosomal level assembly promotes insight into Eimeria biology and evolution, hastening drug discovery and vaccine development.

Preterm birth (PTB) refers to delivery before 37 weeks of gestation. Premature neonates exhibit higher neonatal morbidity and mortality rates than term neonates; therefore, it is crucial to predict and prevent PTB. Advancements enable the prediction and prevention of PTB using genetic approaches, especially by investigating its correlation with single nucleotide polymorphisms (SNPs). We aimed to identify impactive and relevant SNPs for the prediction of PTB via whole-genome sequencing analyses of the blood of 31 pregnant women with PTB (n = 13) and term birth (n = 18) who visited the Ewha Womans University Mokdong Hospital from November 1, 2018 to February 29, 2020. A genome-wide association study was performed using PLINK 1.9 software and 256 SNPs were selected and traced through protein-protein interactions. Moreover, a validation study by genotyping was performed on 60 other participants (preterm birth, n = 30; term birth, n = 30) for 25 SNPs related to ion channel binding and receptor complex pathways. Odds ratios were calculated using additive, dominant, and recessive genetic models. The risk of PTB in women with the AG allele of rs2485579 (gene name: RYR2) was significantly 4.82-fold increase, and the risk of PTB in women with the AG allele of rs7903957 (gene name: TBX5) was significantly 0.25-fold reduce. Our results suggest that rs2485579 (in RYR2) can be a genetic marker of PTB, which is considered through the association with abnormal cytoplasmic Ca2+ concentration and dysfunctional uterine contraction due to differences of RYR2 in the sarcoplasmic reticulum.

DNA damage and repair have been widely studied in relation to cancer and therapeutics. Y-family DNA polymerases can bypass DNA lesions, which may result from external or internal DNA damaging agents, including some chemotherapy agents. Overexpression of the Y-family polymerase human pol kappa can result in tumorigenesis and drug resistance in cancer. This report describes the use of computational tools to predict the effects of single nucleotide polymorphism variants on pol kappa activity. Partial Order Optimum Likelihood (POOL), a machine learning method that uses input features from Theoretical Microscopic Titration Curve Shapes (THEMATICS), was used to identify amino acid residues most likely involved in catalytic activity. The μ4 value, a metric obtained from POOL and THEMATICS that serves as a measure of the degree of coupling between one ionizable amino acid and its neighbors, was then used to identify which protein mutations are likely to impact the biochemical activity. Bioinformatic tools SIFT, PolyPhen-2, and FATHMM predicted most of these variants to be deleterious to function. Along with computational and bioinformatic predictions, we characterized the catalytic activity and stability of 17 cancer-associated DNA pol kappa variants. We identified pol kappa variants R48I, H105Y, G147D, G154E, V177L, R298C, E362V, and R470C as having lower activity relative to wild-type pol kappa; the pol kappa variants T102A, H142Y, R175Q, E210K, Y221C, N330D, N338S, K353T, and L383F were identified as being similar in catalytic efficiency to WT pol kappa. We observed that POOL predictions can be used to predict which variants have decreased activity. Predictions from bioinformatic tools like SIFT, PolyPhen-2, and FATHMM are based on sequence comparisons and therefore are complementary to POOL but are less capable of predicting biochemical activity. These bioinformatic and computational tools can be used to identify SNP variants with deleterious effects and altered biochemical activity from a large data set.

Long non-coding RNAs (lncRNA) are emerging as a new class of regulatory RNAs with remarkable potential to be utilized as therapeutic targets against many human diseases. Several genome-wide association studies (GWAS) have catalogued Single Nucleotide Polymorphisms (SNPs) present in the noncoding regions of the genome from where lncRNAs originate. In this study, we have selected 67 lncRNAs with GWAS-tagged SNPs and have also investigated their role in affecting the local secondary structures. Majority of the SNPs lead to changes in the secondary structure of lncRNAs to a different extent by altering the base pairing patterns. These structural changes in lncRNA are also manifested in form of alteration in the binding site for RNA binding proteins (RBPs) along with affecting their binding efficacies. Ultimately, these structural modifications may influence the transcriptional and post-transcriptional pathways of these RNAs, leading to the causation of diseases. Hence, it is important to understand the possible underlying mechanism of RBPs in association with GWAS-tagged SNPs in human diseases.

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretations of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within the domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism for variant dysfunction.

This study investigates the functional significance of assorted variants of uncertain significance (VUS) in euchromatic histone lysine methyltransferase 1 (EHMT1), which is critical for early development and normal physiology. EHMT1 mutations cause Kleefstra syndrome and are linked to various human cancers. However, accurate functional interpretation of these variants are yet to be made, limiting diagnoses and future research. To overcome this, we integrate conventional tools for variant calling with computational biophysics and biochemistry to conduct multi-layered mechanistic analyses of the SET catalytic domain of EHMT1, which is critical for this protein function. We use molecular mechanics and molecular dynamics (MD)-based metrics to analyze the SET domain structure and functional motions resulting from 97 Kleefstra syndrome missense variants within this domain. Our approach allows us to classify the variants in a mechanistic manner into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Our findings reveal that the damaging variants are mostly mapped around the active site, substrate binding site, and pre-SET regions. Overall, we report an improvement for this method over conventional tools for variant interpretation and simultaneously provide a molecular mechanism of variant dysfunction.

Acute ischemic stroke is a major cause of morbidity and mortality worldwide, and genetic factors play a role in the risk of stroke. Single nucleotide polymorphisms (SNPs) in the VKORC1, CYP4F2, and GGCX genes have been linked to clinical outcomes, such as bleeding and cardiovascular diseases. This study aimed to investigate the association between specific polymorphisms in these genes and the risk of developing the first episode of acute ischemic stroke in patients without a known embolic source. This retrospective, cross-sectional, observational, analytical, case-control study included adult patients diagnosed with acute ischemic stroke. The SNPs in VKORC1 rs9923231, CYP4F2 rs2108622, GGCX rs11676382 genes were genotyped and analyzed together with the demographic and clinical factors of the 2 groups of patients. The presence of SNPs in VKORC1 or CYP4F2 genes significantly increased the risk of ischemic stroke in the context of smoking, arterial hypertension, and carotid plaque burden. The multivariate logistic model revealed that smoking (odds ratio [OR] = 3.920; P < .001), the presence of carotid plaques (OR = 2.661; P < .001) and low-density lipoprotein cholesterol values >77 mg/dL (OR = 2.574; P < .001) were independently associated with stroke. Polymorphisms in the VKORC1 and CYP4F2 genes may increase the risk of ischemic stroke in patients without a determined embolic source. Smoking, the presence of carotid plaques, and high low-density lipoprotein cholesterol levels were reconfirmed as important factors associated with ischemic stroke.

Drugs pharmacology is defined by pharmacokinetics and pharmacodynamics and both of them are affected by genetic variability. Genetic variability varies from population to population, and sometimes even within the population, it exists. Single nucleotide polymorphisms (SNPs) are one of the major genetic variability factors which are found to be associated with the pharmacokinetics and pharmacodynamics process of a drug and are responsible for variable drug response and clinical phenotypes. Studies of SNPs can help to perform genome-wide association studies for their association with pharmacological and clinical events, at the same time; their information can direct genome-wide association studies for their use as biomarkers. With the aim to mine and characterize Indian populated SNPs of pharmacological and clinical importance. Two hundred six candidate SNPs belonging to 43 genes were retrieved from Indian Genome Variation Database. The distribution pattern of considered SNPs was observed against all five world super-populations (AFR, AMR, EAS, EUR, and SAS). Further, their annotation was done through SNP-nexus by considering Human genome reference builds - hg38, pharmacological and clinical information was supplemented by PharmGKB and ClinVar database. At last, to find out the association between SNPs linkage disequilibrium was observed in terms of r2. Overall, the study reported 53 pharmaco-clinical active SNPs and found 24 SNP-pairs as potential markers, and recommended their clinical and experimental validation.

The online version contains supplementary material available at 10.1007/s40203-023-00154-4.

COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability.

We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2).

Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups.

Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.

PRKCG encodes PKC γ, which is categorized under the classical protein kinase C family. No studies have specifically established the relationship between PRKCG nsSNPs with structural and functional variations in PKC γ in the context of hepatocellular carcinoma (HCC). The present study aims to uncover this link through in-silico and experimental studies.

The 3D structure of PKC γ was predicted. Molecular Dynamic (MD) Simulations were run and estimates were made for interactions, stability, conservation and post-translational alterations between wild and mutant structures. The association of PRKCG levels with HCC survival rate was determined. Genotyping analyses were conducted to investigate the deleterious PRKCG nsSNP association with HCC. mRNA expression of PKC γ, HIF-1 alpha, AKT, SOCS3 and VEGF in the blood of controls and HCC patients was analyzed and a genetic cascade was constructed depicting these interactions.

The expression level of studied oncogenes was compared to tumour suppressor genes. Through Alphafold, the 3D structure of PKC γ was explored. Fifteen SNPs were narrowed down for in-silico analyses that were identified in exons 5, 10 and 18 and the regulatory and kinase domain of PKC γ. Root mean square deviation and fluctuation along with the radius of gyration unveiled potential changes between the wild and mutated variant structures. Mutant genotype AA (homozygous) corresponding to nsSNP, rs386134171 had more frequency in patients with OR (2.446), RR (1.564) and P-values (< 0.0029) that highlights its significant association with HCC compared to controls in which the wild genotype GG was found more prevalent.

nsSNP rs386134171 can be a genetic marker for HCC diagnosis and therapeutic studies. This study has laid down a road map for future studies to be conducted on HCC.

GRIN2A is a gene that encodes NMDA receptors found in the central nervous system and plays a pivotal role in excitatory synaptic transmission, plasticity and excitotoxicity in the mammalian central nervous system. Changes in this gene have been associated with a spectrum of neurodevelopmental disorders such as epilepsy. Previous studies on GRIN2A suggest that non-synonymous single nucleotide polymorphisms (nsSNPs) can alter the protein's structure and function. To gain a better understanding of the impact of potentially deleterious variants of GRIN2A, a range of bioinformatics tools were employed in this study. Out of 1320 nsSNPs retrieved from the NCBI database, initially 16 were predicted as deleterious by 9 tools. Further assessment of their domain association, conservation profile, homology models, interatomic interaction, and Molecular Dynamic Simulation revealed that the variant I463S is likely to be the most deleterious for the structure and function of the protein. Despite the limitations of computational algorithms, our analyses have provided insights that can be a valuable resource for further in vitro and in vivo research on GRIN2A-associated diseases.

Research into the human microbiome has implicated its constituents in a variety of non-communicable diseases, with certain microbes found to promote health and others leading to dysbiosis and pathogenesis.Microbes communicate and coordinate their behaviour through the secretion of small molecules, such as cyclic dipeptides (CDPs), into their surrounding environment. CDPs are ubiquitous signalling molecules thatexhibit a wide range of biological activities, with particular relevance to human health due to their potential to act as microbiome modulators.

Targeted insertion of large DNA fragments holds promise for genome engineering and gene therapy. Prime editing (PE) effectively inserts short (<50 bp) sequences. Employing paired prime editing guide RNAs (pegRNAs) has enabled PE to better mediate relatively large insertions in vitro, but the efficiency of larger insertions (>400 bp) remains low and in vivo application has not been demonstrated. Inspired by the efficient genomic insertion mechanism of retrotransposons, we develop a template-jumping (TJ) PE approach for the insertion of large DNA fragments using a single pegRNA. TJ-pegRNA harbors the insertion sequence as well as two primer binding sites (PBSs), with one PBS matching a nicking sgRNA site. TJ-PE precisely inserts 200 bp and 500 bp fragments with up to 50.5 and 11.4% efficiency, respectively, and enables GFP (~800 bp) insertion and expression in cells. We transcribe split circular TJ-petRNA in vitro via a permuted group I catalytic intron for non-viral delivery in cells. Finally, we demonstrate that TJ-PE can rewrite an exon in the liver of tyrosinemia I mice to reverse the disease phenotype. TJ-PE has the potential to insert large DNA fragments without double-stranded DNA breaks and facilitate mutation hotspot exon rewriting in vivo.

The study focuses on examining the relationship between a single nucleotide polymorphism (SNP) in KLF14 rs4731702 and risk of type 2 diabetes mellitus (T2DM) and dyslipidemia in different ethnic populations. The purpose of this study was to evaluate the association between KLF14 rs4731702 and serum lipid profile and to determine the frequency distribution of KLF14 rs4731702 among T2DM and cardiometabolic patients.

A total of 300 volunteers were recruited, consisting of three groups: 100 healthy individuals, 100 individuals diagnosed with T2DM, and 100 individuals diagnosed with cardiometabolic disorders. Biochemical analysis of blood samples was conducted to assess various biomarkers related to glycemic control and lipid profile. This involved measuring levels of glucose, triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and ApoA1. Genotyping analysis was performed to investigate KLF14 rs4731702 polymorphism. The Tetra ARMS-PCR method was employed for genotyping analysis.

The results of biochemical profiling revealed a significant association between altered glycemic biomarkers and lipid profile in diseased patients compared to healthy participants. The frequencies of KLF14 rs4731702 alleles and genotypes were compared between the control group and T2DM group. A statistically significant difference was observed, indicating a potential association between KLF14 rs4731702 and T2DM. In the dominant inheritance model of KLF14 rs4731702 SNP, a statistically significant difference [odds ratio (95% confidence interval)] of 0.56 (0.34 -0.96) was found between the control and T2DM subjects. This suggests that the presence of certain genotypes influences the risk of T2DM. In T2DM patients, individuals carrying the C allele exhibited compromised insulin sensitivity, decreased HDL-C and ApoA1 levels, and increased serum glucose, TG, and LDL-C concentrations. Conversely, TT genotype carriers demonstrated increased levels of HDL-C and ApoA1, lower insulin resistance, serum glucose, LDL-C, and TG levels.

The study's findings indicate that dyslipidemia in T2DM patients is associated with reduced KLF14 functionality due to CC and CT genotypes, leading to insulin resistance and an increased risk of cardiovascular diseases. Additionally, risk of KLF14 rs4731702 polymorphism was found to increase with age and was more prevalent in female than in male individuals. These insights contribute to understanding genetic factors influencing the development and progression of T2DM and dyslipidemia in different ethnic populations.

Hypnotizability is a stable trait that moderates the benefit of hypnosis for treating pain, but limited availability of hypnotizability testing deters widespread use of hypnosis. Inexpensive genotyping of four single-nucleotide polymorphisms in the catechol-o-methyltransferase (COMT) gene was performed using giant magnetoresistive biosensors to determine if hypnotizable individuals can be identified for targeted hypnosis referrals. For individuals with the proposed optimal COMT diplotypes, 89.5% score highly on the Hypnotic Induction Profile (odds ratio, 6.12; 95% CI, 1.26-28.75), which identified 40.5% of the treatable population. Mean hypnotizability scores of the optimal group were significantly higher than the total population (P = 0.015; effect size = 0.60), an effect that was present in women (P = 0.0015; effect size = 0.83), but not in men (P = 0.28). In an exploratory cohort, optimal individuals also reported significantly higher postoperative pain scores (P = 0.00030; effect size = 1.93), indicating a greater need for treatment.

Recently, several studies have highlighted a skewed prevalence of infectious diseases within the African continent. Furthermore, a growing number of studies have demonstrated unique genetic variants found within the African genome are one of the contributing factors to the disease severity of infectious diseases within Africa. Understanding the host genetic mechanisms that offer protection against infectious diseases provides an opportunity to develop unique therapeutic interventions. Over the past two decades, several studies have linked the 2'-5'-oligoadenylate synthetase (OAS) family with a range of infectious diseases. More recently, the OAS-1 gene has also been associated with disease severity caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to a global pandemic. The OAS family serves as an antiviral factor through the interaction with Ribonuclease-Latent (RNase-L). This review explores the genetic variants observed within the OAS genes and the associations with various viral infections and how previously reported ethnic-specific polymorphisms drive clinical significance. This review provides an overview of OAS genetic association studies with a particular focus on viral diseases affecting individuals of African descent.

The initial introduction of utilizing double helix structural oligonucleotides known as SNP typing with excellent specificity (STexS) in a standard PCR greatly improved the detection of single nucleotide polymorphisms (SNP) by enhancing amplification rates of primer-matching strands and interrupting mismatched strands by constant instability of kinetics regarding alignment attaching and detaching. The model was beneficial overall in detecting SNP variants consisting of large amounts of wildtype strands such as EGFR mutation genotyping for early detection of non-small cell lung cancer. While the STexS PCR is advantageous in detecting SNPs and biomarkers, limitations were yet observed. Despite the ability to detect variants 10 times more effective than a typical amplification-refractory mutation system PCR, it could only perform optimally in DNA concentrations around 101 ~ 105. To further enhance STexS specificity to perform detecting viral-RNA variants such as the infamous SARS-CoV-2, a novel improvement of the regular TaqMan Probe using Com-probes to inhibit high copy wild targets and amplify low copy mutant targets. By introducing the novel STexS II, omicron variants of SARS-CoV-2 were able to be successfully detected in high concentrations of normal genes.

Genetic association studies, testing the relationship between genetic variants and disease status, are useful tools for identifying genes that grant susceptibility to complex disorders. In such studies, an inadequate sample size may provide unreliable results: a small sample is unable to accurately describe the population, whereas a large sample makes the study expensive and complex to run. However, in genetic association studies, the sample size calculation is often overlooked or inadequately assessed for the small number of parameters included. In light of this, herein we list and discuss the role of the statistical and genetic parameters to be considered in the sample size calculation, show examples reporting incorrect estimation and, by using a genetic software program, we provide a practical approach for the assessment of the adequate sample size in a hypothetical study aimed at analyzing a gene-disease association.

Oxford Nanopore sequencing has great potential and advantages in population-scale studies. Due to the cost of sequencing, the depth of whole-genome sequencing for per individual sample must be small. However, the existing single nucleotide polymorphism (SNP) callers are aimed at high-coverage Nanopore sequencing reads. Detecting the SNP variants on low-coverage Nanopore sequencing data is still a challenging problem.

We developed a novel deep learning-based SNP calling method, NanoSNP, to identify the SNP sites (excluding short indels) based on low-coverage Nanopore sequencing reads. In this method, we design a multi-step, multi-scale and haplotype-aware SNP detection pipeline. First, the pileup model in NanoSNP utilizes the naive pileup feature to predict a subset of SNP sites with a Bi-long short-term memory (LSTM) network. These SNP sites are phased and used to divide the low-coverage Nanopore reads into different haplotypes. Finally, the long-range haplotype feature and short-range pileup feature are extracted from each haplotype. The haplotype model combines two features and predicts the genotype for the candidate site using a Bi-LSTM network. To evaluate the performance of NanoSNP, we compared NanoSNP with Clair, Clair3, Pepper-DeepVariant and NanoCaller on the low-coverage (∼16×) Nanopore sequencing reads. We also performed cross-genome testing on six human genomes HG002-HG007, respectively. Comprehensive experiments demonstrate that NanoSNP outperforms Clair, Pepper-DeepVariant and NanoCaller in identifying SNPs on low-coverage Nanopore sequencing data, including the difficult-to-map regions and major histocompatibility complex regions in the human genome. NanoSNP is comparable to Clair3 when the coverage exceeds 16×.

https://github.com/huangnengCSU/NanoSNP.git.

Supplementary data are available at Bioinformatics online.

Triple-negative breast cancer (TNBC) is a rare variant of breast cancer (BC) known to be aggressive and refractory. TNBC lacks effective early diagnostic and therapeutic options leading to poorer outcomes. The genomic landscape and alterations leading to BC and TNBC are vast and unclear. Single nucleotide polymorphisms (SNPs) are a widespread form of genetic alterations with a multi-faceted impact on multiple diseases, including BC and TNBC. In this study, we attempted to construct a framework that could identify genes associated with TNBC and screen the SNPs reported in these genes using a set of computational predictors. This framework helped identify BRCA1, BRCA2, EGFR, PIK3CA, PTEN, and TP53 as recurrent genes associated with TNBC. We found 2%-29% of reported SNPs across genes to be typed pathogenic by all the predictors in the framework. We demonstrate that our framework prediction on BC samples identifies 99% of alterations as pathogenic by at least one predictor and 32% as pathogenic by all the predictors. Our framework could be an initial step in developing an early diagnosis of TNBC and potentially help improve the understanding of therapeutic resistance and sensitivity.

The diversity of mutations in human chromosomes is nowadays very well documented. The mutations characterize populations in the world as well as genetic causes of diseases. In the approach that we follow, we study the patterns of gaps between mutations by means of the rescaled range analysis and the fractal dimension estimates. The results for chromosomes 1 to 22 and X indicate the existence of the so-called Hurst phenomenon in all of them. The interpretation of this outcome entails the presence of long-range correlations and we propose an explanation based on the genomic feature dubbed linkage disequilibrium, a nonrandom association of alleles at different loci. An unexpected outcome is the noteworthy uniform reduction in the Hurst phenomenon when considering the centimorgan metric instead of base position units. By contrast, such uniform reduction is not observed with the fractal dimension values.

Rheumatoid arthritis (RA) is an inflammatory disease that causes inflammation of the synovium, cartilage, and deformity of the bones. Single nucleotide polymorphism (SNPs) at 5'UTR rs1738074 (A/G) and a novel candidate non-synonymous single nucleotide polymorphism (nsSNP) rs759674898 (G/A) of TAGAP gene were studied for its association with RA in the Indian population. Real-time PCR coupled with High Resolution Melting analysis technique was employed to detect SNPs. The resulting outcomes were confirmed using the "traditional" Sanger's sequencing method. From this study, we identified that rs1738074 SNP was associated with RA. The odds ratio (OR) obtained for the AG genotype was 3.3379 (Confidence Interval (C.I) 1.7881 to 6.2350); for AA genotype 0.5510 (C.I 0.3043 to 0.9979) and GG genotype 0.5609 (CI 0.3062 to 1.0275). The study also confirmed that AG heterozygous condition had more significant association with RA than AA and GG genotypes. The obtained relative risk (RR) for the AA genotype was 0.676; for AG genotype (RR = 2.253) and GG genotype (RR = 0.6741). The novel candidate nsSNP rs759674898 had only the G allele, and the A allele was not detected in the population studied. In conclusion, this study emphasizes that the rs1738074 SNP in the TAGAP gene's 5'UTR is substantially linked to RA in the Indian population.

This study aimed to establish which worldwide population cohorts have a genetic make-up closest to that of a large sample of the Maltese population with regard to corneal dystrophy (CD) genes.

Single nucleotide polymorphisms (SNPs) in the Maltese cohort were compared with worldwide cohorts. Fixation index (F_ST) values were calculated to evaluate population differentiation. The genetic prevalence of CD subtypes in worldwide and Maltese cohorts were calculated, and single nucleotide missense mutations present in the Maltese cohort were evaluated for potential pathogenicity.

F_ST values showed that CD-related genes differ substantially among the studied cohorts. F_ST values for each SNP showed greatest differentiation between the Maltese and African cohorts and least differentiation with the Puerto Rican, Mexican, and Colombian cohorts. One TGFBI casual CD mutation, 502V, which causes a Bowman's layer CD/atypical Thiel-Behnke CD was identified in the Maltese cohort. The KRT3 NC_000012.11:g.53186088G>C mutation was potentially deleterious.

Identifying populations with least genetic differentiation can facilitate and help guide future diagnostic and treatment strategies for Maltese individuals with CDs in the absence of comparable Maltese data. Analysing the previously unknown CD genetic pool present in a large Maltese cohort adds to the global genetic bank that researchers rely on for medical progress.