Genome-wide Association Studies Risk Research Articles

GWAS and polygenic risk score of severe COVID-19 in Eastern Europe.

COVID-19 disease has infected more than 772 million people, leading to 7 million deaths. Although the severe course of COVID-19 can be prevented using appropriate treatments, effective interventions require a thorough research of the genetic factors involved in its pathogenesis. We conducted a genome-wide association study (GWAS) on 7,124 individuals (comprising 6,400 controls who had mild to moderate COVID-19 and 724 cases with severe COVID-19). The inclusion criteria were acute respiratory distress syndrome (ARDS), acute respiratory failure (ARF) requiring respiratory support, or CT scans indicative of severe COVID-19 infection without any competing diseases. We also developed a polygenic risk score (PRS) model to identify individuals at high risk. We identified two genome-wide significant loci (P-value <5 × 10-8) and one locus with approximately genome-wide significance (P-value = 5.92 × 10-8-6.15 × 10-8). The most genome-wide significant variants were located in the leucine zipper transcription factor like 1 (LZTFL1) gene, which has been highlighted in several previous GWAS studies. Our PRS model results indicated that individuals in the top 10% group of the PRS had twice the risk of severe course of the disease compared to those at median risk [odds ratio = 2.18 (1.66, 2.86), P-value = 8.9 × 10-9]. We conducted one of the largest studies to date on the genetics of severe COVID-19 in an Eastern European cohort. Our results are consistent with previous research and will guide further epidemiologic studies on host genetics, as well as for the development of targeted treatments.

Ethnic and region-specific genetic risk variants of stroke and its comorbid conditions can define the variations in the burden of stroke and its phenotypic traits.

Burden of stroke differs by region, which could be attributed to differences in comorbid conditions and ethnicity. Genomewide variation acts as a proxy marker for ethnicity, and comorbid conditions. We present an integrated approach to understand this variation by considering prevalence and mortality rates of stroke and its comorbid risk for 204 countries from 2009 to 2019, and Genome-wide association studies (GWAS) risk variant for all these conditions. Global and regional trend analysis of rates using linear regression, correlation, and proportion analysis, signifies ethnogeographic differences. Interestingly, the comorbid conditions that act as risk drivers for stroke differed by regions, with more of metabolic risk in America and Europe, in contrast to high systolic blood pressure in Asian and African regions. GWAS risk loci of stroke and its comorbid conditions indicate distinct population stratification for each of these conditions, signifying for population-specific risk. Unique and shared genetic risk variants for stroke, and its comorbid and followed up with ethnic-specific variation can help in determining regional risk drivers for stroke. Unique ethnic-specific risk variants and their distinct patterns of linkage disequilibrium further uncover the drivers for phenotypic variation. Therefore, identifying population- and comorbidity-specific risk variants might help in defining the threshold for risk, and aid in developing population-specific prevention strategies for stroke.

Polygenic scores and their applications in kidney disease.

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

Abstract 151: Single Cell Transcriptional And Epigenetic Map Of Human Coronary Artery Disease In A Multi-ethnic Population

Identifying target genes that are causally linked to coronary artery disease (CAD) remains an elusive problem despite the plethora of CAD genome-wide association study (GWAS) risk loci. Here, we performed single nucleus multiomic (RNA + ATAC) sequencing in 44 human coronary arteries and bulk Hi-C in 16 coronaries from multi-ethnic participants. From sequencing 126,804 nuclei, we identified cell type specific transcriptional signatures, chromatin peaks, and gene regulatory networks through peak to gene linkage analysis. Single-cell quantitative trait loci (QTL) analysis identified cell-specific caQTL’s including cell-type specific caQTLs within CAD GWAS loci. The overlap of CAD GWAS loci with ATAC peaks demonstrated that vascular smooth muscle and endothelial cells harbor the greatest genetic risk. To identify proximal and distal putative target genes for CAD loci in a cell type specific manner, we built a enhancer-gene map by leveraging coronary artery ChiP-seq, cell specific RNA/ATAC-seq, and Hi-C based chromatin contact loops. Using this map, we identified distal target genes linked to CAD GWAS loci. Finally, given the effect of ancestry on CAD GWAS risk interpretation, we leveraged Hi-C data to compare the chromatin architecture in coronary tissue from 8 European Americans vs 8 African Americans. A meta-map, sample level, and local ancestry analysis identified ancestry-specific differences in chromatin looping and topologically associated domains. Collectively, our results provide a cell-type specific transcriptomic and epigenetic map which along with bulk chromatin contacts can be used to interpret and prioritize CAD GWAS candidates and nominate cell specific causal target genes.

Multi-tissue transcriptome-wide association study identifies novel candidate susceptibility genes for cataract.

Cataract is the leading cause of blindness among the elderly worldwide. Twin and family studies support an important role for genetic factors in cataract susceptibility with heritability estimates up to 58%. To date, 55 loci for cataract have been identified by genome-wide association studies (GWAS), however, much work remains to identify the causal genes. Here, we conducted a transcriptome-wide association study (TWAS) of cataract to prioritize causal genes and identify novel ones, and examine the impact of their expression. We performed tissue-specific and multi-tissue TWAS analyses to assess associations between imputed gene expression from 54 tissues (including 49 from the Genotype Tissue Expression (GTEx) Project v8) with cataract using FUSION software. Meta-analyzed GWAS summary statistics from 59,944 cataract cases and 478,571 controls, all of European ancestry and from two cohorts (GERA and UK Biobank) were used. We then examined the expression of the novel genes in the lens tissue using the iSyTE database. Across tissue-specific and multi-tissue analyses, we identified 99 genes for which genetically predicted gene expression was associated with cataract after correcting for multiple testing. Of these 99 genes, 20 (AC007773.1, ANKH, ASIP, ATP13A2, CAPZB, CEP95, COQ6, CREB1, CROCC, DDX5, EFEMP1, EIF2S2, ESRRB, GOSR2, HERC4, INSRR, NIPSNAP2, PICALM, SENP3, and SH3YL1) did not overlap with previously reported cataract-associated loci. Tissue-specific analysis identified 202 significant gene-tissue associations for cataract, of which 166 (82.2%), representing 9 unique genes, were attributed to the previously reported 11q13.3 locus. Tissue-enrichment analysis revealed that gastrointestinal tissues represented one of the highest proportions of the Bonferroni-significant gene-tissue associations (21.3%). Moreover, this gastrointestinal tissue type was the only anatomical category significantly enriched in our results, after correcting for the number of tissue donors and imputable genes for each reference panel. Finally, most of the novel cataract genes (e.g., Capzb) were robustly expressed in iSyTE lens data. Our results provide evidence of the utility of imputation-based TWAS approaches to characterize known GWAS risk loci and identify novel candidate genes that may increase our understanding of cataract etiology. Our findings also highlight the fact that expression of genes associated with cataract susceptibility is not necessarily restricted to lens tissue.

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches.

To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches. A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma. A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index. Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.

Interpretation of Neurodegenerative GWAS Risk Alleles in Microglia and their Interplay with Other Cell Types.

Microglia have been implicated in numerous neurodegenerative and neuroinflammatory disorders; however, the causal contribution of this immune cell type is frequently debated. Genetic studies offer a unique vantage point in that they infer causality over a secondary consequence. Genome-wide association studies (GWASs) have identified hundreds of loci in the genome that are associated with susceptibility to neurodegenerative disorders. GWAS studies implicate microglia in the pathogenesis of Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and to a lesser degree suggest a role for microglia in vascular dementia (VaD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), and other neurodegenerative and neuropsychiatric disorders. The contribution and function of GWAS risk loci on disease progression is an ongoing field of study, in which large genomic datasets, and an extensive framework of computational tools, have proven to be crucial. Several GWAS risk loci are shared between disorders, pointing towards common pleiotropic mechanisms. In this chapter, we introduce key concepts in GWAS and post-GWAS interpretation of neurodegenerative disorders, with a focus on GWAS risk genes implicated in microglia, their interplay with other cell types and shared convergence of GWAS risk loci on microglia.

Rome III Criteria Capture Higher Irritable Bowel Syndrome SNP-Heritability and Highlight a Novel Genetic Link With Cardiovascular Traits

Irritable bowel syndrome (IBS) shows genetic predisposition, and large-scale genome-wide association studies (GWAS) are emerging, based on heterogeneous disease definitions. We investigated the genetic architecture of IBS defined according to gold standard Rome Criteria. We conducted GWAS meta-analyses of Rome III IBS and its subtypes in 24,735 IBS cases and 77,149 asymptomatic control subjects from 2 independent European cohorts (UK Biobank and Lifelines). Single-nucleotide polymorphism (SNP)-based heritability (h2SNP) and genetic correlations (rg) with other traits were calculated. IBS risk loci were functionally annotated to identify candidate genes. Sensitivity and conditional analyses were conducted to assess impact of confounders. Polygenic risk scores were computed and tested in independent datasets. Rome III IBS showed significant SNP-heritability (up to 13%) and similar genetic architecture across subtypes, including those with manifestations at the opposite ends of the symptom spectrum (rg= 0.48 between IBS-D and IBS-C). Genetic correlations with other traits highlighted commonalities with family history of heart disease and hypertension, coronary artery disease, and angina pectoris (rg= 0.20-0.45), among others. Four independent GWAS signals (P < 5×10-8) were detected, including 2 novel loci for IBS (rs2035380) and IBS-mixed (rs2048419) that had been previously associated with hypertension and coronary artery disease. Functional annotation of GWAS risk loci revealed genes implicated in circadian rhythm (BMAL1), intestinal barrier (CLDN23), immunomodulation (MFHAS1), and the cyclic adenosine monophosphate pathway (ADCY2). Polygenic risk scores allowed the identification of individuals at increased risk of IBS (odds ratio, 1.34; P= 1.1×10-3). Rome III Criteria capture higher SNP-heritability than previously estimated for IBS. The identified link between IBS and cardiovascular traits may contribute to the delineation of alternative therapeutic strategies, warranting further investigation.

Abstract 18244: A Multi-Ancestry GWAS of Calcific Aortic Stenosis Among 2.7 Million Individuals

Introduction: Calcific aortic stenosis (CAS) is a common, progressive fibrocalcific pathology of the aortic valve without medical therapy. The genetics of CAS remain only partially understood. Methods: We performed a genome wide association study (GWAS) of CAS among 2,799,598 individuals from the International Aortic Valve Genetics Consortium (IAVGC), comprising 28 cohorts. CAS was identified using a common ICD/CPT based phenotype. GWAS were meta-analyzed using inverse variance weighting with adjustment by linkage-disequilibrium score regression (LDSR) intercept. Unique genome-wide significant (GWS) loci and causal genes were annotated by nearest gene and eQTL colocalization. Genetic correlations were performed against atherosclerotic, adiposity, and lipid traits using LDSR with publicly available GWAS (CARDIoGRAMplusC4D for coronary artery disease [CAD], Million Veteran Program for peripheral artery disease [PAD], MEGASTROKE for ischemic stroke [IS], GIANT for body mass index [BMI], and GLGC for lipids). Results: There were 85,329 individuals with CAS (79,397 White, 3,126 Black, 1,403 Hispanic, and 1,403 East Asian) among 2,799,598 individuals. Meta-analysis of GWAS resulted in 224 unique GWS genomic regions, of which 205 were novel. The majority of the GWS genomic regions (134) did not overlap with prior risk loci for CAD, PAD, IS, BMI, or lipids. Genetic correlation demonstrated modest but significant correlations between CAS and CAD ( r =0.26, p=3.3x10 -18 ), PAD ( r =0.41, p=1.4x10 -30 ), IS ( r =0.18,p=2.8x10 -7 ), BMI ( r =0.22,p=8.6x10 -30 ), and lipids (LDL-C r =0.17,p=3.6x10 -10 ;triglycerides r =0.10,p=1.9x10 -5 ; HDL-C r =-0.07,p=8.0x10 -4 ). Conclusions: This largest to-date multi-ancestry GWAS of CAS identified 205 novel genomic regions. We demonstrate that CAS is genetically distinct from cardiometabolic traits, with only modest genetic correlations and with a majority of CAS risk loci having no overlap with cardiometabolic GWAS risk loci.

FRI455 Polycystic Ovary Syndrome-Associated DENND1A Non-Coding Risk Loci Alter Gene Expression

Abstract Disclosure: L. Sankaranarayanan: None. G. Johnson: None. A. Barrera: None. A.E. Dunaif: None. T. Reddy: None. Polycystic ovary syndrome (PCOS) is a leading cause of infertility and type 2 diabetes affecting 10-15% of menstruating people worldwide. PCOS is characterized by increased circulating testosterone levels, ovulatory dysfunction and insulin resistance. Genome wide association studies (GWAS) have identified ∼20 replicated common PCOS susceptibility loci in European and Han Chinese cohorts. The associated variants are predominantly non-coding and implicate neuroendocrine, reproductive, and metabolic causal pathways. Rare non-coding variants in DENND1A, a GWAS candidate gene that regulates androgen biosynthesis, were present in ∼50% of PCOS families by whole genome sequencing. These associations suggest non-coding genetic contributions to the development of PCOS. We tested the hypothesis that PCOS GWAS risk loci contribute to PCOS pathogenesis by altering gene regulation. We first identified ∼1500 functioning regulatory elements across the GWAS associated regions using a high throughput reporter assay, STARR-seq, in two different cell lines. We used H295R, an adrenal cell line model for testosterone production and COV434, an ovarian granulosa cell line. We identified cell-line specific and overlapping regulatory regions suggesting that there is context specific gene regulation. Sixty percent of the identified regions are in previously documented open chromatin regions from DNase-seq and ATAC-seq studies supporting our hypothesis that these regulatory regions are functional. We additionally experimentally tested the regulatory effect of five of the regulatory regions identified in the DENND1A locus in H295R cells using CRISPR-based epigenetic editing tools. This endogenous perturbation increases DENND1A gene levels (measured by qPCR) and also increased testosterone produced by the H295R cells (measured by ELISA), suggesting that these regions are biologically relevant to PCOS pathogenesis.We investigated allele-specific regulatory activity in a multi-ethnic pool of five genomes from healthy individuals. We identified 21 common genetic variants (minor allele frequency in the population &amp;gt;0.05) that alter regulatory activity in the DENND1A locus, including two that are ancestry-specific. Further, rs12237685 and rs28441318 are also eQTLS for DENND1A from the GTEX project and reside within open chromatin regions in ovarian cells. The functional consequences of non-coding genetic variants associated with complex traits has been exceptionally difficult to elucidate. Our findings suggest that both the common genetic variants and rare PCOS patient-associated DENND1A variants and altered gene expression. Further, some of those variants may contribute to allele-specific genetic risk for PCOS. Presentation Date: Friday, June 16, 2023

Multiple genes in a single GWAS risk locus synergistically mediate aberrant synaptic development and function in human neurons

The mechanistic tie between genome-wide association study (GWAS)-implicated risk variants and disease-relevant cellular phenotypes remains largely unknown. Here, using human induced pluripotent stem cell (hiPSC)-derived neurons as a neurodevelopmental model, we identify multiple schizophrenia (SZ) risk variants that display allele-specific open chromatin (ASoC) and are likely to be functional. Editing the strongest ASoC SNP, rs2027349, near vacuolar protein sorting 45 homolog (VPS45) alters the expression of VPS45, lncRNA AC244033.2, and a distal gene, C1orf54. Notably, the transcriptomic changes in neurons are associated with SZ and other neuropsychiatric disorders. Neurons carrying the risk allele exhibit increased dendritic complexity and hyperactivity. Interestingly, individual/combinatorial gene knockdown shows that these genes alter cellular phenotypes in a non-additive synergistic manner. Our study reveals that multiple genes at a single GWAS risk locus mediate a compound effect on neural function, providing a mechanistic link between a non-coding risk variant and disease-related cellular phenotypes.

Machine learning reveals genetic modifiers of the immune microenvironment of cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed yet remains largely uncharacterized. Here, we developed a machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWASs) for breast cancer (BrCa) incidence. A random forest model was trained on a positive set of immune-oncology (I-O) targets, and then used to assign I-O target probability scores to 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Cluster analysis of the most probable candidates revealed two subfamilies of genes related to effector functions and adaptive immune responses, suggesting that iTME modifiers impact multiple aspects of anticancer immunity. Two of the top ranking BrCa candidates, LSP1 and TLR1, were orthogonally validated as iTME modifiers using BrCa patient biopsies and comparative mapping studies, respectively. Collectively, these data demonstrate a robust and flexible framework for functionally fine-mapping GWAS risk loci to identify translatable therapeutic targets.

Genetic insights into immune mechanisms of Alzheimer's and Parkinson's disease.

Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer's disease (AD) and Parkinson's disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders.

A transcriptome association study of AD risk genes in the pathogenesis of Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease (AD) affects millions of people with no effective treatments making it vital to identify potential therapeutic targets. Genome‐wide association studies (GWAS) have consistently identified AD susceptibility loci or ‘hits’. Few of these are differentially expressed in brain transcriptomic studies, meaning that their effects may be manifest via interacting or downstream transcripts. We have recently used machine learning on human brain transcriptomic data to identify and validate an association between lactoferrin and amyloid (1). Here we apply the same method to identify the transcripts associated with AD susceptibility ‘loci’.MethodAnalyses were performed on the disease‐linked genes from our machine learning analyses (LTF, ADAMTS2, PPDPF, PRTN3) and the combined 58 GWAS risk loci from two recent studies (2, 3). Boruta was used to find transcripts associated with each GWAS ‘hit’ and our genes of interest in two pathologically confirmed AD cohorts (Religious Orders Study and Memory and Aging project, Mount Sinai Brain Bank). The GWAS hits and associated transcripts were correlated with disease markers (cognition, plaque number, Braak score, CERAD score) to find direct and indirect effects.ResultsTREM2 was the GWAS ‘hit’ most correlated with disease markers in both cohorts but most GWAS ‘hits’ were either not correlated or negatively correlated with disease markers. Among the GWAS ‘hit’‐associated transcripts, SWAP70, EMP3, and WWTR1 were positively and VGF negatively correlated with disease markers in both cohorts. For our genes of interest, ADAMTS2 and PPDPF shared overlaps between their associated transcripts and that of GWAS ‘hit’ ABCA7. LTF was linked with TREM2 and associated with plaques in both cohorts.ConclusionThis study has teased apart nascent associations between known susceptibility genes and gene expression data from the AD brain, but it also hints that consideration of pathological sub‐phenotypes are required to make greater progress in understanding AD pathogenesis.1. Tsatsanis et al. The acute phase protein lactoferrin is a key feature of AD …. Mol Psychiatry. 2021;26(10):5516‐31.2. Yang et al. A human brain vascular atlas reveals diverse cell mediators of AD risk. BioRxiv [Preprint]. 2021.3. Wightman et al. A GWAS with 1,126,563 individuals identifies new risk loci for Alzheimer’s disease. Nat Genet. 2021;53(9):1276‐82.

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci

Migraine—a painful, throbbing headache disorder—is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models—MASHR, elastic net, and SMultiXcan—to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

Splicing annotation of endometrial cancer GWAS risk loci reveals potentially causal variants and supports a role for NF1 and SKAP1 as susceptibility genes.

Alternative splicing contributes to cancer development. Indeed, splicing analysis of cancer genome-wide association study (GWAS) risk variants has revealed likely causal variants. To systematically assess GWAS variants for splicing effects, we developed a prioritization workflow using a combination of splicing prediction tools, alternative transcript isoforms, and splicing quantitative trait locus (sQTL) annotations. Application of this workflow to candidate causal variants from 16 endometrial cancer GWAS risk loci highlighted single-nucleotide polymorphisms (SNPs) that were predicted to upregulate alternative transcripts. For two variants, sQTL data supported the predicted impact on splicing. At the 17q11.2 locus, the protective allele for rs7502834 was associated with increased splicing of an exon in a NF1 alternative transcript encoding a truncated protein in adipose tissue and is consistent with an endometrial cancer transcriptome-wide association study (TWAS) finding in adipose tissue. Notably, NF1 haploinsufficiency is protective for obesity, a well-established risk factor for endometrial cancer. At the 17q21.32 locus, the rs2278868 risk allele was predicted to upregulate a SKAP1 transcript that is subject to nonsense-mediated decay, concordant with a corresponding sQTL in lymphocytes. This is consistent with a TWAS finding that indicates decreased SKAP1 expression in blood increases endometrial cancer risk. As SKAP1 is involved in Tcell immune responses, decreased SKAP1 expression may impact endometrial tumor immunosurveillance. In summary, our analysis has identified potentially causal endometrial cancer GWAS risk variants with plausible biological mechanisms and provides a splicing annotation workflow to aid interpretation of other GWAS datasets.

New "drugs and targets" in the GWAS era of bipolar disorder.

Due to the phenotypic heterogeneity and etiological complexity of bipolar disorder (BD), many patients do not respond well to the current medications, and developing novel effective treatment is necessary. Whether any BD genome-wide association study (GWAS) risk genes were targets of existing drugs or novel drugs that can be repurposed in the clinical treatment of BD is a hot topic in the GWAS era of BD. A list of 425 protein-coding BD risk genes was distilled through the BD GWAS, and 4479 protein-coding druggable targets were retrieved from the druggable genome. The overlapped genes/targets were subjected to further analyses in DrugBank, Pharos, and DGIdb datasets in terms of their FDA status, mechanism of action and primary indication, to identify their potential for repurposing. We identified 58 BD GWAS risk genes grouped as the druggable targets, and several genes were given higher priority. These BD risk genes were targets of antipsychotics, antidepressants, antiepileptics, calcium channel antagonists, as well as anxiolytics and analgesics, either existing clinically-approved drugs for BD or the drugs than can be repurposed for treatment of BD in the future. Those genes were also likely relevant to BD pathophysiology, as many of them encode ion channel, ion transporter or neurotransmitter receptor, or the mice manipulating those genes are likely to mimic the phenotypes manifest in BD patients. This study identifies several targets that may facilitate the discovery of novel treatments in BD, and implies the value of conducting GWAS into clinical translation.

Skin Phototype and Disease: A Comprehensive Genetic Approach to Pigmentary Traits Pleiotropy Using PRS in the GCAT Cohort.

Human pigmentation has largely been associated with different disease prevalence among populations, but most of these studies are observational and inconclusive. Known to be genetically determined, pigmentary traits have largely been studied by Genome-Wide Association Study (GWAS), mostly in Caucasian ancestry cohorts from North Europe, identifying robustly, several loci involved in many of the pigmentary traits. Here, we conduct a detailed analysis by GWAS and Polygenic Risk Score (PRS) of 13 pigmentary-related traits in a South European cohort of Caucasian ancestry (n = 20,000). We observed fair phototype strongly associated with non-melanoma skin cancer and other dermatoses and confirmed by PRS-approach the shared genetic basis with skin and eye diseases, such as melanoma (OR = 0.95), non-melanoma skin cancer (OR = 0.93), basal cell carcinoma (OR = 0.97) and darker phototype with vitiligo (OR = 1.02), cataracts (OR = 1.04). Detailed genetic analyses revealed 37 risk loci associated with 10 out of 13 analyzed traits, and 16 genes significantly associated with at least two pigmentary traits. Some of them have been widely reported, such as MC1R, HERC2, OCA2, TYR, TYRP1, SLC45A2, and some novel candidate genes C1QTNF3, LINC02876, and C1QTNF3-AMACR have not been reported in the GWAS Catalog, with regulatory potential. These results highlight the importance of the assess phototype as a genetic proxy of skin functionality and disease when evaluating open mixed populations.

Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.

The vast majority of disease-associated single nucleotide polymorphisms (SNP) identified from genome-wide association studies (GWAS) are localized in non-coding regions. A significant fraction of these variants impact transcription factors binding to enhancer elements and alter gene expression. To functionally interrogate the activity of such variants we developed snpSTARRseq, a high-throughput experimental method that can interrogate the functional impact of hundreds to thousands of non-coding variants on enhancer activity. snpSTARRseq dramatically improves signal-to-noise by utilizing a novel sequencing and bioinformatic approach that increases both insert size and the number of variants tested per loci. Using this strategy, we interrogated known prostate cancer (PCa) risk-associated loci and demonstrated that 35% of them harbor SNPs that significantly altered enhancer activity. Combining these results with chromosomal looping data we could identify interacting genes and provide a mechanism of action for 20 PCa GWAS risk regions. When benchmarked to orthogonal methods, snpSTARRseq showed a strong correlation with in vivo experimental allelic-imbalance studies whereas there was no correlation with predictive in silico approaches. Overall, snpSTARRseq provides an integrated experimental and computational framework to functionally test non-coding genetic variants.

DeepPerVar: a multi-modal deep learning framework for functional interpretation of genetic variants in personal genome.

Understanding the functional consequence of genetic variants, especially the non-coding ones, is important but particularly challenging. Genome-wide association studies (GWAS) or quantitative trait locus analyses may be subject to limited statistical power and linkage disequilibrium, and thus are less optimal to pinpoint the causal variants. Moreover, most existing machine-learning approaches, which exploit the functional annotations to interpret and prioritize putative causal variants, cannot accommodate the heterogeneity of personal genetic variations and traits in a population study, targeting a specific disease. By leveraging paired whole-genome sequencing data and epigenetic functional assays in a population study, we propose a multi-modal deep learning framework to predict genome-wide quantitative epigenetic signals by considering both personal genetic variations and traits. The proposed approach can further evaluate the functional consequence of non-coding variants on an individual level by quantifying the allelic difference of predicted epigenetic signals. By applying the approach to the ROSMAP cohort studying Alzheimer's disease (AD), we demonstrate that the proposed approach can accurately predict quantitative genome-wide epigenetic signals and in key genomic regions of AD causal genes, learn canonical motifs reported to regulate gene expression of AD causal genes, improve the partitioning heritability analysis and prioritize putative causal variants in a GWAS risk locus. Finally, we release the proposed deep learning model as a stand-alone Python toolkit and a web server. https://github.com/lichen-lab/DeepPerVar. Supplementary data are available at Bioinformatics online.