Genome-wide Association Studies Catalog Research Articles

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 ).

Large-scale integration of the plasma proteome with genetics and disease.

The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.

Is cerebral vascular pathology a bystander of Alzheimer’s disease? Evidence from a genetic perspective

Alzheimer's disease (AD) is a multifactorial caused neurodegenerative disorder. The latest AD research framework proposed by the National Institute on aging-Alzheimer's Association described a biomarker-based pathological definition of AD, focusing on amyloid-β (Aβ), tau and neurodegeneration. This proposal separated the role of vascular involvement in the pathophysiology of AD, indicating vascular event is a bystander in AD. To testify this concept, we explored the common genetic evidences for the shared common initiative molecular pathways in AD and ischemic stroke.The common Single Nucleotide Polymorphisms (SNPs) susceptible to AD and ischemic stroke and their mapped genes were identified using GWAS Catalog database. Signaling pathways associated with these genes were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Genecards database. The Gene Radar online tool in Gene-cloud of biotechnology information (GCBI) was used to identify the transcription factors involved in these genes.Up to December 15, 2020, 54 AD and 14 ischemic stroke Genome-wide association study(GWAS) datasets were chosen. In AD, 1009 susceptibility SNPs and its 1019 mapped genes were identified; while 138 susceptibility SNPs and its 162 mapped genes were identified in ischemic stroke. No any common AD and ischemic stroke susceptibility SNP was found, but ten common risk genes, accounting for 0.98% AD susceptibility genes and 6.17% ischemic stroke susceptibility genes, were identified. They are AC008125.1, AC011029.1, AGBL1, F13A1, FARP1, HDAC9, HNF4G, MMP12, MMP3 and PTPRG. The common signaling pathways with at least two of these gene products involved are G protein-coupled receptors (GPCR) (HDAC9, MMP12, MMP3), immune response (F13A1 and MMP3) and extracellular matrix (F13A1, MMP12 and MMP3). C-Ets-1 is the common transcription factor for F13A1 and MMP3. For the most AD risk gene apolipoprotein E(ApoE), there is no evidence of its direct association with ischemic stroke, but its putative homologs SERPRINA 1 and FGB associate with ischemic stroke in GWAS studies.There are some evidences of common molecular pathways involved in AD and ischemic stroke, which require further investigations to evaluate cerebral vascular impacts on the pathogenesis of AD.

A Genome-Wide Association Study of a Korean Population Identifies Genetic Susceptibility to Hypertension Based on Sex-Specific Differences.

Genome-wide association studies have expanded our understanding of the genetic variation of hypertension. Hypertension and blood pressure are influenced by sex-specific differences; therefore, genetic variants may have sex-specific effects on phenotype. To identify the genetic factors influencing the sex-specific differences concerning hypertension, we conducted a heterogeneity analysis of a genome-wide association study (GWAS) on 13,926 samples from a Korean population. Using the Illumina exome chip data of the population, we performed GWASs of the male and female population independently and applied a statistical test that identified heterogeneous effects of the variants between the two groups. To gain information about the biological implication of the genetic heterogeneity, we used gene set enrichment analysis with GWAS catalog and pathway gene sets. The heterogeneity analysis revealed that the rs11066015 of ACAD10 was a significant locus that had sex-specific genetic effects on the development of hypertension. The rs2074356 of HECTD4 also showed significant genetic heterogeneity in systolic blood pressure. The enrichment analysis showed significant results that are consistent with the pathophysiology of hypertension. These results indicate a sex-specific genetic susceptibility to hypertension that should be considered in future genetic studies of hypertension.

Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome.

STUDY QUESTIONCan we identify novel variants associated with polycystic ovary syndrome (PCOS) by leveraging the unique population history of Northern Europe?SUMMARY ANSWERWe identified three novel genome-wide significant associations with PCOS, with two putative independent causal variants in the checkpoint kinase 2 (CHEK2) gene and a third in myosin X (MYO10).WHAT IS KNOWN ALREADYPCOS is a common, complex disorder with unknown aetiology. While previous genome-wide association studies (GWAS) have mapped several loci associated with PCOS, the analysis of populations with unique population history and genetic makeup has the potential to uncover new low-frequency variants with larger effects.STUDY DESIGN, SIZE, DURATIONA population-based case–control GWAS was carried out.PARTICIPANTS/MATERIALS, SETTING, METHODSWe identified PCOS cases from national registers by ICD codes (ICD-10 E28.2, ICD-9 256.4, or ICD-8 256.90), and all remaining women were considered controls. We then conducted a three-stage case–control GWAS: in the discovery phase, we had a total of 797 cases and 140 558 controls from the FinnGen study. For validation, we used an independent dataset from the Estonian Biobank, including 2812 cases and 89 230 controls. Finally, we performed a joint meta-analysis of 3609 cases and 229 788 controls from both cohorts. Additionally, we reran the association analyses including BMI as a covariate, with 2169 cases and 160 321 controls from both cohorts.MAIN RESULTS AND THE ROLE OF CHANCETwo out of the three novel genome-wide significant variants associating with PCOS, rs145598156 (P = 3.6×10−8, odds ratio (OR) = 3.01 [2.02–4.50] minor allele frequency (MAF) = 0.005) and rs182075939 (P = 1.9×10−16, OR = 1.69 [1.49–1.91], MAF = 0.04), were found to be enriched in the Finnish and Estonian populations and are tightly linked to a deletion c.1100delC (r2 = 0.95) and a missense I157T (r2 = 0.83) in CHEK2. The third novel association is a common variant near MYO10 (rs9312937, P = 1.7 × 10−8, OR = 1.16 [1.10–1.23], MAF = 0.44). We also replicated four previous reported associations near the genes Erb-B2 Receptor Tyrosine Kinase 4 (ERBB4), DENN Domain Containing 1A (DENND1A), FSH Subunit Beta (FSHB) and Zinc Finger And BTB Domain Containing 16 (ZBTB16). When adding BMI as a covariate only one of the novel variants remained genome-wide significant in the meta-analysis (the EstBB lead signal in CHEK2 rs182075939, P = 1.9×10−16, OR = 1.74 [1.5–2.01]) possibly owing to reduced sample size.LARGE SCALE DATAThe age- and BMI-adjusted GWAS meta-analysis summary statistics are available for download from the GWAS Catalog with accession numbers GCST90044902 and GCST90044903.LIMITATIONS, REASONS FOR CAUTIONThe main limitation was the low prevalence of PCOS in registers; however, the ones with the diagnosis most likely represent the most severe cases. Also, BMI data were not available for all (63% for FinnGen, 76% for EstBB), and the biobank setting limited the accessibility of PCOS phenotypes and laboratory values.WIDER IMPLICATIONS OF THE FINDINGSThis study encourages the use of isolated populations to perform genetic association studies for the identification of rare variants contributing to the genetic landscape of complex diseases such as PCOS.STUDY FUNDING/COMPETING INTEREST(S)This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the MATER Marie Skłodowska-Curie grant agreement No. 813707 (N.P.-G., T.L., T.P.), the Estonian Research Council grant (PRG687, T.L.), the Academy of Finland grants 315921 (T.P.), 321763 (T.P.), 297338 (J.K.), 307247 (J.K.), 344695 (H.L.), Novo Nordisk Foundation grant NNF17OC0026062 (J.K.), the Sigrid Juselius Foundation project grants (T.L., J.K., T.P.), Finska Läkaresällskapet (H.L.) and Jane and Aatos Erkko Foundation (H.L.). The funders had no role in study design, data collection and analysis, publishing or preparation of the manuscript. The authors declare no conflicts of interest.

Shift work tolerance.

Shift work is widespread due to 24-h work in many occupations. Understanding differences in individual shift work tolerance (SWT) can help develop coping strategies for shift workers. This in-depth qualitative review elucidates the architecture of SWT, providing an overview of the research advances in the last decade (2011-2021). We searched Google Scholar, PubMed and Medline for different word combinations concerning SWT. Genome-wide association studies (GWAS) for the potential genetic basis of SWT were additionally searched in GWAS Central and GWAS Catalogue. Eleven new studies were published since 2011, with the proportion of longitudinal studies on SWT having more than doubled in the past decade. They consolidate prior findings (e.g. hardiness most consistently associated with SWT) and discovered additional aspects of SWT like resistance to change and job stress. The 15 large-scale GWAS identified, most of which using UK Biobank (UKB) and 23andMe data, involved mapped genes showing overlap especially within analysis of the same phenotype (e.g. PER2/3 for morningness, PAX8 for sleep duration and LINGO1 for neuroticism). Individual GWAS for additional traits such as resilience have also been published though assessments of gene overlap are not yet possible. Progress regarding longitudinal studies on SWT has been made though a more consistent definition of SWT remains crucial for future research. Non-genetic studies on SWT suggest several important traits and factors; many of which have now also been explored using GWAS. Such evidence could serve as basis for individualized risk prediction and disease prevention approaches for night-shift workers.

An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci.

Genome-wide association studies (GWAS) have identified many variants associated with complex traits, but identifying the causal gene(s) is a major challenge. Here we present an open resource that provides systematic fine-mapping and gene prioritization across 133,441 published human GWAS loci. We integrate genetics (GWAS Catalog and UK Biobank) with transcriptomic, proteomic and epigenomic data, including systematic disease-disease and disease-molecular trait colocalization results across 92 cell types and tissues. We identify 729 loci fine-mapped to a single coding causal variant and colocalized with a single gene. We trained a machine learning model using the fine-mapped genetics and functional genomics data using 445 gold-standard curated GWAS loci to distinguish causal genes from neighboring, outperforming a naive distance-based model. Our prioritized genes were enriched for known approved drug targets (OR = 8.1, 95% CI: (5.7, 11.5)). These results are publicly available through a web portal (http://genetics.opentargets.org), enabling users to easily prioritize genes at disease-associated loci and assess their potential as drug targets.

The role of the X chromosome in infectious diseases.

Many infectious diseases in humans present with a sex bias. This bias arises from a combination of environmental factors, hormones and genetics. In this study, we review the contribution of the X chromosome to the genetic factor associated with infectious diseases. First, we give an overview of the X-linked genes that have been described in the context of infectious diseases and group them in four main pathways that seem to be dysregulated in infectious diseases: nuclear factor kappa-B, interleukin 2 and interferon γ cascade, toll-like receptors and programmed death ligand 1. Then, we review the infectious disease associations in existing genome-wide association studies (GWAS) from the GWAS Catalog and the Pan-UK Biobank, describing the main associations and their possible implications for the disease. Finally, we highlight the importance of including the X chromosome in GWAS analysis and the importance of sex-specific analysis.

Gwasfilter: an R script to filter genome-wide association study

Objective: To develop an R script that can efficiently and accurately filter genome-wide association studies (GWASs) from the GWAS Catalog Website. Methods: The selection principles of GWASs were established based on previous studies. The process of manual filtering in the GWAS Catalog was abstracted as standard algorithms. The R script (gwasfilter.R) was written by two programmers and tested many times. Results: It takes six steps for gwasfilter.R to filter GWASs. There are five main self-defined functions among this R script. GWASs can be filtered based on "whether the GWAS has been replicated" "sample size" "ethnicity of the study population" and other conditions. It takes no more than 1 second for this script to filter GWASs of a single trait. Conclusions: This R script (gwasfilter.R) is user-friendly and provides an efficient and standard process to filter GWASs flexibly. The source code is available at github (https://github.com/lab319/gwas_filter).

The Need to Shift Pharmacogenetic Research from Candidate Gene to Genome-Wide Association Studies

The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB®] and the NHGRI-EBI GWAS catalog)and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.

Sequencing-based genome-wide association studies reporting standards

SUMMARYGenome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

Risk Allele Frequency Analysis of Single-Nucleotide Polymorphisms for Vitamin D Concentrations in Different Ethnic Group.

The prevalence of vitamin D deficiency varies from 20.8% to 61.6% among populations of different ethnicities, suggesting the existence of a genetic component. The purpose of this study was to provide insights into the genetic causes of vitamin D concentration differences among individuals of diverse ancestry. We collected 320 single-nucleotide polymorphisms (SNPs) associated with vitamin D concentrations from a genome-wide association studies catalog. Their population-level allele frequencies were derived based on the 1000 Genomes Project and Korean Reference Genome Database. We used Fisher’s exact tests to assess the significance of the enrichment or depletion of the effect allele at a given SNP in the database. In addition, we calculated the SNP-based genetic risk score (GRS) and performed correlation analysis with vitamin D concentration that included latitude. European, American, and South Asian populations showed similar heatmap patterns, whereas African, East Asian, and Korean populations had distinct ones. The GRS calculated from allele frequencies of vitamin D concentration was highest among Europeans, followed by East Asians and Africans. In addition, the difference in vitamin D concentration was highly correlated with genetic factors rather than latitude effects.

A comprehensive integrated post-GWAS analysis of Type 1 diabetes reveals enhancer-based immune dysregulation.

Type 1 diabetes (T1D) is an organ-specific autoimmune disease, whereby immune cell-mediated killing leads to loss of the insulin-producing β cells in the pancreas. Genome-wide association studies (GWAS) have identified over 200 genetic variants associated with risk for T1D. The majority of the GWAS risk variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes substantially contribute to T1D. However, identification of causal regulatory variants associated with T1D risk and their affected genes is challenging due to incomplete knowledge of non-coding regulatory elements and the cellular states and processes in which they function. Here, we performed a comprehensive integrated post-GWAS analysis of T1D to identify functional regulatory variants in enhancers and their cognate target genes. Starting with 1,817 candidate T1D SNPs defined from the GWAS catalog and LDlink databases, we conducted functional annotation analysis using genomic data from various public databases. These include 1) Roadmap Epigenomics, ENCODE, and RegulomeDB for epigenome data; 2) GTEx for tissue-specific gene expression and expression quantitative trait loci data; and 3) lncRNASNP2 for long non-coding RNA data. Our results indicated a prevalent enhancer-based immune dysregulation in T1D pathogenesis. We identified 26 high-probability causal enhancer SNPs associated with T1D, and 64 predicted target genes. The majority of the target genes play major roles in antigen presentation and immune response and are regulated through complex transcriptional regulatory circuits, including those in HLA (6p21) and non-HLA (16p11.2) loci. These candidate causal enhancer SNPs are supported by strong evidence and warrant functional follow-up studies.

Intersecting Genetics of Frailty and Cardiovascular Disease.

To determine the genetic correlates of physical frailty and sarcopenia, focusing on single nucleotide polymorphisms (SNPs) in genome-wide association studies (GWAS), and to explore the genetic overlap of frailty with cardiovascular disease (CVD) and its risk factors. PubMed was systematically searched for GWAS studies investigating the association between SNPs and objective measures of physical frailty or sarcopenia. SNPs were retained if they were associated with one of the phenotypes of interest by a p-value of 5.0x10-8 or less. Ten studies were included, with a total of 237 SNPs in 181 genes being associated with physical frailty or sarcopenia; as measured by handgrip strength or lean (muscle) mass. These genes were cross-referenced in the GWAS Catalog, and many of them were found to be associated with CVD or metabolic syndrome. Evidence from GWAS has shown that frailty is associated with common genetic polymorphisms. Many of these polymorphisms have been implicated in CVD, supporting the hypothesis of a shared pathophysiology between these entities. Future studies are eagerly anticipated to map out the mechanistic links and discover therapeutic targets and novel biomarkers for frailty.

Identifying Breast Cancer-Related Genes Based on a Novel Computational Framework Involving KEGG Pathways and PPI Network Modularity

Complex diseases, such as breast cancer, are often caused by mutations of multiple functional genes. Identifying disease-related genes is a critical and challenging task for unveiling the biological mechanisms behind these diseases. In this study, we develop a novel computational framework to analyze the network properties of the known breast cancer–associated genes, based on which we develop a random-walk-with-restart (RCRWR) algorithm to predict novel disease genes. Specifically, we first curated a set of breast cancer–associated genes from the Genome-Wide Association Studies catalog and Online Mendelian Inheritance in Man database and then studied the distribution of these genes on an integrated protein–protein interaction (PPI) network. We found that the breast cancer–associated genes are significantly closer to each other than random, which confirms the modularity property of disease genes in a PPI network as revealed by previous studies. We then retrieved PPI subnetworks spanning top breast cancer–associated KEGG pathways and found that the distribution of these genes on the subnetworks are non-random, suggesting that these KEGG pathways are activated non-uniformly. Taking advantage of the non-random distribution of breast cancer–associated genes, we developed an improved RCRWR algorithm to predict novel cancer genes, which integrates network reconstruction based on local random walk dynamics and subnetworks spanning KEGG pathways. Compared with the disease gene prediction without using the information from the KEGG pathways, this method has a better prediction performance on inferring breast cancer–associated genes, and the top predicted genes are better enriched on known breast cancer–associated gene ontologies. Finally, we performed a literature search on top predicted novel genes and found that most of them are supported by at least wet-lab experiments on cell lines. In summary, we propose a robust computational framework to prioritize novel breast cancer–associated genes, which could be used for further in vitro and in vivo experimental validation.