Genome-wide Association Studies Catalog Research Articles

Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer's disease.

Translating human genetic findings (genome-wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge for Alzheimer's disease (AD). We present a network topology-based deep learning framework to identify disease-associated genes (NETTAG). We leverage non-coding GWAS loci effects on quantitative trait loci, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions under the protein-protein interactome. Via NETTAG, we identified 156 AD-risk genes enriched in druggable targets. Combining network-based prediction and retrospective case-control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is associated with reduced likelihood of AD incidence. Gemfibrozil (an approved lipid regulator) is significantly associated with 43% reduced risk of AD compared with simvastatin using an active-comparator design (95% confidence interval 0.51-0.63, p<0.0001). In summary, NETTAG offers a deep learning methodology that utilizes GWAS and multi-genomic findings to identify pathobiology and drug repurposing in AD.

Genetic Factors for Coronary Heart Disease and Their Mechanisms: A Meta-Analysis and Comprehensive Review of Common Variants from Genome-Wide Association Studies.

Genome-wide association studies (GWAS) have discovered 163 loci related to coronary heart disease (CHD). Most GWAS have emphasized pathways related to single-nucleotide polymorphisms (SNPs) that reached genome-wide significance in their reports, while identification of CHD pathways based on the combination of all published GWAS involving various ethnicities has yet to be performed. We conducted a systematic search for articles with comprehensive GWAS data in the GWAS Catalog and PubMed, followed by a meta-analysis of the top recurring SNPs from ≥2 different articles using random or fixed-effect models according to Cochran Q and I2 statistics, and pathway enrichment analysis. Meta-analyses showed significance for 265 of 309 recurring SNPs. Enrichment analysis returned 107 significant pathways, including lipoprotein and lipid metabolisms (rs7412, rs6511720, rs11591147, rs1412444, rs11172113, rs11057830, rs4299376), atherogenesis (rs7500448, rs6504218, rs3918226, rs7623687), shared cardiovascular pathways (rs72689147, rs1800449, rs7568458), diabetes-related pathways (rs200787930, rs12146487, rs6129767), hepatitis C virus infection/hepatocellular carcinoma (rs73045269/rs8108632, rs56062135, rs188378669, rs4845625, rs11838776), and miR-29b-3p pathways (rs116843064, rs11617955, rs146092501, rs11838776, rs73045269/rs8108632). In this meta-analysis, the identification of various genetic factors and their associated pathways associated with CHD denotes the complexity of the disease. This provides an opportunity for the future development of novel CHD genetic risk scores relevant to personalized and precision medicine.

General intelligence and executive functioning are overlapping but separable at genetic and molecular pathway levels: An analytical review of existing GWAS findings.

Understanding the genomic architecture and molecular mechanisms of cognitive functioning in healthy individuals is critical for developing tailored interventions to enhance cognitive functioning, as well as for identifying targets for treating impaired cognition. There has been substantial progress in uncovering the genetic composition of the general cognitive ability (g). However, there is an ongoing debate whether executive functioning (EF)–another key predictor of cognitive health and performance, is separable from general g. To provide an analytical review on existing findings on genetic influences on the relationship between g and EF, we re-analysed a subset of genome-wide association studies (GWAS) from the GWAS catalogue that used measures of g and EF as outcomes in non-clinical populations. We identified two sets of single nucleotide polymorphisms (SNPs) associated with g (1,372 SNPs across 12 studies), and EF (300 SNPs across 5 studies) at p<5x10-6. A comparative analysis of GWAS-identified g and EF SNPs in high linkage disequilibrium (LD), followed by pathway enrichment analyses suggest that g and EF are overlapping but separable at genetic variant and molecular pathway levels, however more evidence is required to characterize the genetic overlap/distinction between the two constructs. While not without limitations, these findings may have implications for navigating further research towards translatable genetic findings for cognitive remediation, enhancement, and augmentation.

Accurate modeling of replication rates in genome-wide association studies by accounting for Winner's Curse and study-specific heterogeneity.

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex human traits, but only a fraction of variants identified in discovery studies achieve significance in replication studies. Replication in genome-wide association studies has been well-studied in the context of Winner's Curse, which is the inflation of effect size estimates for significant variants due to statistical chance. However, Winner's Curse is often not sufficient to explain lack of replication. Another reason why studies fail to replicate is that there are fundamental differences between the discovery and replication studies. A confounding factor can create the appearance of a significant finding while actually being an artifact that will not replicate in future studies. We propose a statistical framework that utilizes genome-wide association studies and replication studies to jointly model Winner's Curse and study-specific heterogeneity due to confounding factors. We apply this framework to 100 genome-wide association studies from the Human Genome-Wide Association Studies Catalog and observe that there is a large range in the level of estimated confounding. We demonstrate how this framework can be used to distinguish when studies fail to replicate due to statistical noise and when they fail due to confounding.

Ethnic, gender and other sociodemographic biases in genome-wide association studies for the most burdensome non-communicable diseases: 2005-2022.

Since 2005, disease-related human genetic diversity has been intensively characterized using genome-wide association studies (GWAS). Understanding how and by whom this work was performed may yield valuable insights into the generalizability of GWAS discoveries to global populations and how high-impact genetics research can be equitably sustained in the future. We mined the NHGRI-EBI GWAS Catalog (2005-2022) for the most burdensome non-communicable causes of death worldwide. We then compared (i) the geographic, ethnic and socioeconomic characteristics of study populations; (ii) the geographic and socioeconomic characteristics of the regions within which researchers were located and (iii) the extent to which male and female investigators undertook and led the research. The research institutions leading the work are often US-based (37%), while the origin of samples is more diverse, with the Nordic countries having contributed as much data to GWAS as the United States (~17% of data). The majority of first (60%), senior (75%) and all (66%) authors are male; although proportions vary by disease and leadership level, male co-authors are the ubiquitous majority. The vast majority (91%) of complex trait GWAS has been performed in European ancestry populations, with cohorts and scientists predominantly located in medium-to-high socioeconomically ranked countries; apart from East Asians (~5%), other ethnicities rarely feature in published GWAS. See: https://hugofitipaldi.shinyapps.io/gwas_results/ to browse all results. Most GWAS cohorts are of European ancestry residing outside the United States, with a smaller yet meaningful proportion of East Asian ancestry. Papers describing GWAS research are predominantly authored by male scientists based in medium-to-high income countries.

Dehydroepiandrosterone Sulfate and Colorectal Cancer Risk: A Mendelian Randomization Analysis.

Colorectal cancer is the third most common and second most deadly type of cancer worldwide, with approximately 1.9 million cases and 0.9 million deaths worldwide in 2020. Previous studies have shown that estrogen and testosterone hormones are associated with colorectal cancer risk and mortality. However, the potential effect of their precursor, dehydroepiandrosterone sulfate (DHEAS), on colorectal cancer risk has not been investigated. Therefore, evaluating DHEAS's effect on colorectal cancer will expand our understanding of the hormonal contribution to colorectal cancer risk. In this study, we conducted a two-sample Mendelian randomization (MR) analysis to investigate the causal effect of DHEAS on colorectal cancer. We obtained DHEAS and colorectal cancer genomewide association study (GWAS) summary statistics from the Leipzig Health Atlas and the GWAS catalog and conducted MR analyses using the TwoSampleMR R package. Our results suggest that higher DHEAS levels are causally associated with decreased colorectal cancer risk (odds ratio per unit increase in DHEAS levels z score = 0.70; 95% confidence interval [0.51, 0.96]), which is in line with previous observations in a case-control study of colon cancer. The outcome of this study will be beneficial in developing plasma DHEAS-based biomarkers in colorectal cancer. Further studies should be conducted to interpret the DHEAS-colorectal cancer association among different ancestries and populations.

Allelic expression imbalance in articular cartilage and subchondral bone refined genome-wide association signals in osteoarthritis.

To present an unbiased approach to identify positional transcript single nucleotide polymorphisms (SNPs) of osteoarthritis (OA) risk loci by allelic expression imbalance (AEI) analyses using RNA sequencing of articular cartilage and subchondral bone from OA patients. RNA sequencing from 65 articular cartilage and 24 subchondral bone from OA patients was used for AEI analysis. AEI was determined for all genes present in the 100 regions reported by the genome-wide association studies (GWAS) catalog that were also expressed in cartilage or bone. The count fraction of the alternative allele (φ) was calculated for each heterozygous individual with the risk SNP or with the SNP in linkage disequilibrium (LD) with it (r2 > 0.6). Furthermore, a meta-analysis was performed to generate a meta-φ (null hypothesis median φ = 0.49) and P-value for each SNP. We identified 30 transcript SNPs (28 in cartilage and two in subchondral bone) subject to AEI in 29 genes. Notably, 10 transcript SNPs were located in genes not previously reported in the GWAS catalog, including two long intergenic non-coding RNAs (lincRNAs), MALAT1 (meta-φ = 0.54, FDR = 1.7×10-4) and ILF3-DT (meta-φ = 0.6, FDR = 1.75×10-5). Moreover, 12 drugs were interacting with seven genes displaying AEI, of which seven drugs have been already approved. By prioritizing proxy transcript SNPs that mark AEI in cartilage and/or subchondral bone at loci harbouring GWAS signals, we present an unbiased approach to identify the most likely functional OA risk-SNP and gene. We identified 10 new potential OA risk genes ready for further translation towards underlying biological mechanisms.

Integrated Approaches to Identify miRNA Biomarkers Associated with Cognitive Dysfunction in Multiple Sclerosis Using Text Mining, Gene Expression, Pathways, and GWAS.

Multiple sclerosis (MS), a chronic autoimmune disorder, affects the central nervous system of many young adults. More than half of MS patients develop cognition problems. Although several genomic and transcriptomic studies are currently reported in MS cognitive impairment, a comprehensive repository dealing with all the experimental data is still underdeveloped. In this study, we combined text mining, gene regulation, pathway analysis, and genome-wide association studies (GWAS) to identify miRNA biomarkers to explore the cognitive dysfunction in MS, and to understand the genomic etiology of the disease. We first identified the dysregulated miRNAs associated with MS and cognitive dysfunction using PubTator (text mining), HMDD (experimental associations), miR2Disease, and PhenomiR database (differentially expressed miRNAs). Our results suggest that miRNAs such as hsa-mir-148b-3p, hsa-mir-7b-5p, and hsa-mir-7a-5p are commonly associated with MS and cognitive dysfunction. Next, we retrieved GWAS signals from GWAS Catalog, and analyzed the enrichment analysis of association signals in genes/miRNAs and their association networks. Then, we identified susceptible genetic loci, rs17119 (chromosome 6; p = 1 × 10−10), rs1843938 (chromosome 7; p = 1 × 10−10), and rs11637611 (chromosome 15; p = 1.00 × 10−15), associated with significant genetic risk. Lastly, we conducted a pathway analysis for the susceptible genetic variants and identified novel risk pathways. The ECM receptor signaling pathway (p = 3.98 × 10−8) and PI3K/Akt signaling pathway (p = 5.98 × 10−5) were found to be associated with differentially expressed miRNA biomarkers.

A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics.

Autoimmune diseases share a general mechanism of auto-antigens harming tissues. Still. they are phenotypically diverse, with genetic as well as environmental factors contributing to their etiology at varying degrees. Associated genomic loci and variants have been identified in numerous genome-wide association studies (GWAS), whose results are increasingly used for polygenic scores (PGS) that are used to predict disease risk. At the same time, a technological shift from genotyping arrays to next generation sequencing (NGS) is ongoing. NGS allows the identification of virtually all - including rare - genetic variants, which in combination with methodological developments promises to improve the prediction of disease risk and elucidate molecular mechanisms underlying disease. Here we review current, publicly available autoimmune disease GWAS and PGS data based on information from the GWAS and PGS catalog, respectively. We summarize autoimmune diseases investigated, respective studies conducted and their results. Further, we review genetic data and autoimmune disease patients in the UK Biobank (UKB), the largest resource for genetic and phenotypic data available for academic research. We find that only comparably prevalent autoimmune diseases are covered by the UKB and at the same time assessed by both GWAS and PGS catalogs. These are systemic (systemic lupus erythematosus) as well as organ-specific, affecting the gastrointestinal tract (inflammatory bowel disease as well as specifically Crohn’s disease and ulcerative colitis), joints (juvenile ideopathic arthritis, psoriatic arthritis, rheumatoid arthritis, ankylosing spondylitis), glands (Sjögren syndrome), the nervous system (multiple sclerosis), and the skin (vitiligo).

Genetic Variants Associated with Chronic Obstructive Pulmonary Disease Risk: Cumulative Epidemiological Evidence from Meta-Analyses and Genome-Wide Association Studies.

Background Last two decades, many association studies on genetic variants and chronic obstructive pulmonary disease (COPD) risk have been published. But results from different studies are inconsistent. Therefore, we performed this article to systematically evaluate results from previous meta-analyses and genome-wide association studies (GWASs). Material and Methods. Firstly, we retrieved meta-analyses in PubMed, Embase, and China National Knowledge Infrastructure and GWASs in PubMed and GWAS catalog on or before April 7th, 2022. Then, data were extracted and screened. Finally, two main methods—Venice criteria and false-positive report probability test—were used to evaluate significant associations. Results As a result, eighty-eight meta-analyses and 5 GWASs were deemed eligible for inclusion. Fifty variants in 26 genes obtained from meta-analyses were significantly associated with COPD risk. Cumulative epidemiological evidence of an association was graded as strong for 10 variants in 8 genes (GSTM1, CHRNA, ADAM33, SP-D, TNF-α, VDBP, HMOX1, and HHIP), moderate for 6 variants in 5 genes (PI, GSTM1, ADAM33, TNF-α, and VDBP), and weak for 40 variants in 23 genes. Five variants in 4 genes showed convincing evidence of no association with COPD risk in meta-analyses. Additionally, 29 SNPs identified in GWASs were proved to be noteworthy based on the FPRP test. Conclusion In summary, more than half (52.38%) of genetic variants reported in previous meta-analyses showed no association with COPD risk. However, 13 variants in 9 genes had moderate to strong evidence for an association. This article can serve as a useful reference for further studies.

Multiancestral polygenic risk score for pediatric asthma

Asthma is the most common chronic condition in children and the third leading cause of hospitalization in pediatrics. The genome-wide association study catalog reports 140 studies with genome-wide significance. Apolygenic risk score (PRS) with predictive value across ancestries has not been evaluated for this important trait. This study aimed to train and validate a PRS relying on genetic determinants for asthma to provide predictions for disease occurrence in pediatric cohorts of diverse ancestries. This study applied a Bayesian regression framework method using the Trans-National Asthma Genetic Consortium genome-wide association study summary statistics to derive a multiancestral PRS score, used one Electronic Medical Records and Genomics (eMERGE) cohort as a training set, used a second independent eMERGE cohort to validate the score, and used the UK Biobank data to replicate the findings. Aphenome-wide association study was performed using the PRS to identify shared genetic etiology with other phenotypes. The multiancestral asthma PRS was associated with asthma in the 2 pediatric validation datasets. Overall, the multiancestral asthma PRS has an area under the curve (AUC) of 0.70 (95% CI, 0.69-0.72) in the pediatric validation 1 and AUC of 0.66 (0.65-0.66) in the pediatric validation 2 datasets. We found significant discrimination across pediatric subcohorts of European (AUC, 95% CI, 0.60 and 0.66), African (AUC, 95% CI, 0.61 and 0.66), admixed American (AUC, 0.64 and 0.70), Southeast Asian (AUC, 0.65), and East Asian (AUC, 0.73) ancestry. Pediatric participants with the top 5% PRS had 2.80 to 5.82 increased odds of asthma compared to the bottom 5% across the training, validation 1, and validation 2 cohorts when adjusted for ancestry. Phenome-wide association study analysis confirmed the strong association of the identified PRS with asthma (odds ratio, 2.71, PFDR= 3.71× 10-65) and related phenotypes. A multiancestral PRS for asthma based on Bayesian posterior genomic effect sizes identifies increased odds of pediatric asthma.

A systematic review and functional bioinformatics analysis of genes associated with Crohn\u2019s disease identify more than 120 related genes

BackgroundCrohn’s disease is one of the two categories of inflammatory bowel diseases that affect the gastrointestinal tract. The heritability estimate has been reported to be 0.75. Several genes linked to Crohn’s disease risk have been identified using a plethora of strategies such as linkage-based studies, candidate gene association studies, and lately through genome-wide association studies (GWAS). Nevertheless, to our knowledge, a compendium of all the genes that have been associated with CD is lacking.MethodsWe conducted functional analyses of a gene set generated from a systematic review where genes potentially related to CD found in the literature were analyzed and classified depending on the genetic evidence reported and putative biological function. For this, we retrieved and analyzed 2496 abstracts comprising 1067 human genes plus 22 publications regarding 133 genes from GWAS Catalog. Then, each gene was curated and categorized according to the type of evidence associated with Crohn’s disease.ResultsWe identified 126 genes associated with Crohn’s disease risk by specific experiments. Additionally, 71 genes were recognized associated through GWAS alone, 18 to treatment response, 41 to disease complications, and 81 to related diseases. Bioinformatic analysis of the 126 genes supports their importance in Crohn’s disease and highlights genes associated with specific aspects such as symptoms, drugs, and comorbidities. Importantly, most genes were not included in commercial genetic panels suggesting that Crohn’s disease is genetically underdiagnosed.ConclusionsWe identified a total of 126 genes from PubMed and 71 from GWAS that showed evidence of association to diagnosis, 18 to treatment response, and 41 to disease complications in Crohn’s disease. This prioritized gene catalog can be explored at http://victortrevino.bioinformatics.mx/CrohnDisease.

The landscape of GWAS validation; systematic review identifying 309 validated non-coding variants across 130 human diseases

BackgroundThe remarkable growth of genome-wide association studies (GWAS) has created a critical need to experimentally validate the disease-associated variants, 90% of which involve non-coding variants.MethodsTo determine how the field is addressing this urgent need, we performed a comprehensive literature review identifying 36,676 articles. These were reduced to 1454 articles through a set of filters using natural language processing and ontology-based text-mining. This was followed by manual curation and cross-referencing against the GWAS catalog, yielding a final set of 286 articles.ResultsWe identified 309 experimentally validated non-coding GWAS variants, regulating 252 genes across 130 human disease traits. These variants covered a variety of regulatory mechanisms. Interestingly, 70% (215/309) acted through cis-regulatory elements, with the remaining through promoters (22%, 70/309) or non-coding RNAs (8%, 24/309). Several validation approaches were utilized in these studies, including gene expression (n = 272), transcription factor binding (n = 175), reporter assays (n = 171), in vivo models (n = 104), genome editing (n = 96) and chromatin interaction (n = 33).ConclusionsThis review of the literature is the first to systematically evaluate the status and the landscape of experimentation being used to validate non-coding GWAS-identified variants. Our results clearly underscore the multifaceted approach needed for experimental validation, have practical implications on variant prioritization and considerations of target gene nomination. While the field has a long way to go to validate the thousands of GWAS associations, we show that progress is being made and provide exemplars of validation studies covering a wide variety of mechanisms, target genes, and disease areas.

Genome-wide association study meta-analysis identifies three novel loci for circulating anti-Müllerian hormone levels in women.

Genome-wide association study meta-analysis identifies three novel loci for circulating anti-Müllerian hormone levels in women.

The Shared Mechanism and Candidate Drugs of Multiple Sclerosis and Sjögren's Syndrome Analyzed by Bioinformatics Based on GWAS and Transcriptome Data.

ObjectiveThis study aimed to explore the shared mechanism and candidate drugs of multiple sclerosis (MS) and Sjögren’s syndrome (SS).MethodsMS- and SS-related susceptibility genes and differentially expressed genes (DEGs) were identified by bioinformatics analysis based on genome-wide association studies (GWAS) and transcriptome data from GWAS catalog and Gene Expression Omnibus (GEO) database. Pathway enrichment, Gene Ontology (GO) analysis, and protein–protein interaction analysis for susceptibility genes and DEGs were performed. The drugs targeting common pathways/genes were obtained through Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug–Gene Interaction (DGI) Database. The target genes of approved/investigational drugs for MS and SS were obtained through DrugBank and compared with the common susceptibility genes.ResultsBased on GWAS data, we found 14 hub common susceptibility genes (HLA-DRB1, HLA-DRA, STAT3, JAK1, HLA-B, HLA-DQA1, HLA-DQA2, HLA-DQB1, HLA-DRB5, HLA-DPA1, HLA-DPB1, TYK2, IL2RA, and MAPK1), with 8 drugs targeting two or more than two genes, and 28 common susceptibility pathways, with 15 drugs targeting three or more than three pathways. Based on transcriptome data, we found 3 hub common DEGs (STAT1, GATA3, PIK3CA) with 3 drugs and 10 common risk pathways with 435 drugs. “JAK-STAT signaling pathway” was included in common susceptibility pathways and common risk pathways at the same time. There were 133 overlaps including JAK-STAT inhibitors between agents from GWAS and transcriptome data. Besides, we found that IL2RA and HLA-DRB1, identified as hub common susceptibility genes, were the targets of daclizumab and glatiramer that were used for MS, indicating that daclizumab and glatiramer may be therapeutic for SS.ConclusionWe observed the shared mechanism of MS and SS, in which JAK-STAT signaling pathway played a vital role, which may be the genetic and molecular bases of comorbidity of MS with SS. Moreover, JAK-STAT inhibitors were potential therapies for MS and SS, especially for their comorbidity.

Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans

BackgroundImputation from genotyping array to whole-genome sequence variants using resequencing of representative reference populations enhances our ability to map genetic factors affecting complex phenotypes in livestock species. The accumulation of knowledge about gene function in human and laboratory animals can provide substantial advantage for genomic research in livestock species.ResultsIn this study, 201,388 pigs from three commercial Danish breeds genotyped with low to medium (8.5k to 70k) SNP arrays were imputed to whole genome sequence variants using a two-step approach. Both imputation steps achieved high accuracies, and in total this yielded 26,447,434 markers on 18 autosomes. The average estimated imputation accuracy of markers with minor allele frequency ≥ 0.05 was 0.94. To overcome the memory consumption of running genome-wide association study (GWAS) for each breed, we performed within-breed subpopulation GWAS then within-breed meta-analysis for average daily weight gain (ADG), followed by a multi-breed meta-analysis of GWAS summary statistics. We identified 15 quantitative trait loci (QTL). Our post-GWAS analysis strategy to prioritize of candidate genes including information like gene ontology, mammalian phenotype database, differential expression gene analysis of high and low feed efficiency pig and human GWAS catalog for height, obesity, and body mass index, we proposed MRAP2, LEPROT, PMAIP1, ENSSSCG00000036234, BMP2, ELFN1, LIG4 and FAM155A as the candidate genes with biological support for ADG in pigs.ConclusionOur post-GWAS analysis strategy helped to identify candidate genes not just by distance to the lead SNP but also by multiple sources of biological evidence. Besides, the identified QTL overlap with genes which are known for their association with human growth-related traits. The GWAS with this large data set showed the power to map the genetic factors associated with ADG in pigs and have added to our understanding of the genetics of growth across mammalian species.

Surveying the Genomic Landscape Supporting the Development of Precision Military Aerospace Medicine.

INTRODUCTION: Precision medicine is an approach to healthcare that is modifying clinical management by leveraging technological advances in genomics that assess a patient's genetic information to identify unique predispositions. While the civilian sector is integrating genomics widely to personalize diagnosis and treatment, the military medical environment has reacted more slowly. The operational requirements of military service encourage a tailored approach for focusing military precision medicine on occupation-specific conditions. Here, we present a survey of the genomic landscape related to military aerospace medicine.METHODS: We collated observations from genome-wide association studies (GWAS) relating genetic markers to conditions that may negatively influence flight operations and for which the U.S. Air Force School of Aerospace Medicine's Aeromedical Consult Service (ACS) provides aeromedical waiver guidance. Our sources for identifying relevant literature were the GWAS Catalog, the Atlas of GWAS Summary Statistics, and PubMed/Google Scholar searches.RESULTS: Using the ACS guidance as a starting point, we found 1572 papers describing 84 clinical conditions with genetic associations. The earliest aeromedical GWAS publication was in 2006, increasing to 225 publications in 2019. We identified 42,020 polymorphisms from more than 84 million participants across the studies.CONCLUSION: Our study revealed areas where deeper investigations into how genetic markers manifest in clinical diagnosis, prevention, or risk management could lead to increased medical readiness. Additionally, our results show those clinical areas for which guidance could include genetic risk considerations.Chapleau RR, Regn DD, de Castro MJ. Surveying the genomic landscape supporting the development of precision military aerospace medicine. Aerosp Med Hum Perform. 2022; 93(2):89-93.

Validation and genetic heritability estimation of known type 2 diabetes related variants in the Korean population.

Genome-wide association studies (GWASs) facilitated the discovery of countless disease-associated variants. However, GWASs have mostly been conducted in European ancestry samples. Recent studies have reported that these European-based association results may reduce disease prediction accuracy when applied in non-Europeans. Therefore, previously reported variants should be validated in non-European populations to establish reliable scientific evidence for precision medicine. In this study, we validated known associations with type 2 diabetes (T2D) and related metabolic traits in 125,850 samples from a Korean population genotyped by the Korea Biobank Array (KBA). At the end of December 2020, there were 8,823 variants associated with glycemic traits, lipids, liver enzymes, and T2D in the GWAS catalog. Considering the availability of imputed datasets in the KBA genome data, publicly available East Asian T2D summary statistics, and the linkage disequilibrium among the variants (r2 < 0.2), 2,900 independent variants were selected for further analysis. Among these, 1,837 variants (63.3%) were statistically significant (p ≤ 0.05). Most of the non-replicated variants (n = 1,063) showed insufficient statistical power and decreased minor allele frequencies compared with the replicated variants. Moreover, most of known variants showed <10% genetic heritability. These results could provide valuable scientific evidence for future study designs, the current power of GWASs, and future applications in precision medicine in the Korean population.

Genome-wide Studies of knee Osteoarthritis: Review

Background. Knee osteoarthritis (OA) is a multifactorial disease resulting from the interaction of many environmental, epigenetic and genetic risk factors, and the latter account for 40% to 65%. Genetic bases of the knee OA based on genome-wide association study (GWAS) are being actively studied by many scientific teams around the world. At the same time, the results obtained are often contradictory and ambiguous, as for the conducted replicative studies of knee OA. This dictates the need for additional replicative studies in various populations, including populations of Russia. The aim of the study was to analyze genome-wide studies of knee OA and to establish GWAS-significant polymorphic loci associated with OA. Materials and Methods. The search for publications was carried out in the electronic databases PubMed, PubMedCentral, eLIBRARY, in the GWAS catalog for the period from 2008 to the present by the keywords: knee osteoarthritis, GWAS studies, candidate genes. Results. Firstly, for the period from 2008 to 2021, 15 genome-wide studies of knee OA were performed (8 GWAS, 6 meta-analyzes of GWAS data, 1 study a combination of GWAS and meta-analysis of GWAS data), as a result of which 78 polymorphic loci were found associated with the risk of developing osteoarthritis of the knee joint at p510-08. Second ly, the vast majority of these loci (62 out of 78 SNPs, 79%) showed GWAS-significant associations with OA in meta-analyzes of GWAS data and only 16 loci (21%) in GWAS studies. Third ly, almost 95% of GWAS-significant loci for knee OA (74 SNPs) were found in samples of Caucasian origin. Fourthly, 21 out of 78 GWAS-significant SNPs are associated with isolated knee OA, and 57 SNPs are associated with knee, hip and hand OA (mixed sample). Fifth, all genome-wide studies of knee OA and meta-analyzes of GWAS data were carried out abroad on samples from various foreign populations, and samples from the Russian Federation were not included in these studies. Sixth, only two GWAS-significant polymorphic loci for OA (rs143384 of the GDF5 gene for knee OA isolated localization and rs3771501 of the TGFA gene for OA of any localization) were replicated at the whole genome level of significance (p510-08) in two different studies. Conclusion. The main genome-wide studies of knee OA were reviewed and GWAS-significant polymorphisms associated with OA were identified. The obtained materials on GWAS-significant loci can be used both in the selection of polymorphisms in replicative studies of OA in various populations of Russia, and for expanding the understanding of the molecular genetic mechanisms of the disease development.

Multi-ethnic GWAS and fine-mapping of glycaemic traits identify novel loci in the PAGE Study.

Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. Four novel associations were identified (p < 5 × 10-9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog ( https://www.ebi.ac.uk/gwas/downloads/summary-statistics ).