Genome-wide Association Studies Risk Loci Research Articles

Functional genomics implicates natural killer cells in the pathogenesis of ankylosing spondylitis

Multiple lines of evidence indicate that ankylosing spondylitis (AS) is a lymphocyte-driven disease. However, which lymphocyte populations are critical in AS pathogenesis is not known. In this study, we aimed to identify the key cell types mediating the genetic risk in AS using an unbiased functional genomics approach. We integrated genome-wide association study (GWAS) data with epigenomic and transcriptomic datasets of human immune cells. To quantify enrichment of cell type-specific open chromatin or gene expression in AS risk loci, we used three published methods - LDSC-SEG, SNPsea, scDRS - that have successfully identified relevant cell types in other diseases. Natural killer (NK) cell-specific open chromatin regions are significantly enriched in heritability for AS, compared to other immune cell types such as T cells, B cells, and monocytes. This finding was consistent between two AS GWAS. Using RNA-seq data, we validated that genes in AS risk loci are enriched in NK cell-specific gene expression. Using the human Space-Time Gut Cell Atlas, we also found significant upregulation of AS-associated genes predominantly in NK cells. We performed co-localization analyses between GWAS risk loci and genetic variants associated with gene expression (eQTL) to find putative target genes. This revealed four AS risk loci affecting regulation of candidate target genes in NK cells: two known loci, ERAP1 and TNFRSF1A, and two under-studied loci, ENTR1 (aka SDCCAG3) and B3GNT2. Our findings suggest that NK cells may play a crucial role in AS development and highlight four putative target genes for functional follow-up in NK cells.

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.

Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia

BackgroundThe prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder. MethodsGene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons. ResultsCommon risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate. ConclusionsWe identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.

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

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.

Prioritization of non-coding elements involved in non-syndromic cleft lip with/without cleft palate through genome-wide analysis of de novo mutations.

Non-syndromic cleft lip with/without cleft palate (nsCL/P) is a highly heritable facial disorder. To date, systematic investigations of the contribution of rare variants in non-coding regions to nsCL/P etiology are sparse. Here, we re-analyzed available whole-genome sequence (WGS) data from 211 European case-parent trios with nsCL/P and identified 13,522 de novo mutations (DNMs) in nsCL/P cases, 13,055 of which mapped to non-coding regions. We integrated these data with DNMs from a reference cohort, with results of previous genome-wide association studies (GWASs), and functional and epigenetic datasets of relevance to embryonic facial development. A significant enrichment of nsCL/P DNMs was observed at two GWAS risk loci (4q28.1 (p= 8×10-4) and 2p21 (p= 0.02)), suggesting a convergence of both common and rare variants at these loci. We also mapped the DNMs to 810 position weight matrices indicative of transcription factor (TF) binding, and quantified the effect of the allelic changes in silico. This revealed a nominally significant overrepresentation of DNMs (p= 0.037), and a stronger effect on binding strength, for DNMs located in the sequence of the core binding region of the TF Musculin (MSC). Notably, MSC is involved in facial muscle development, together with a set of nsCL/P genes located at GWAS loci. Supported by additional results from single-cell transcriptomic data and molecular binding assays, this suggests that variation in MSC binding sites contributes to nsCL/P etiology. Our study describes a set of approaches that can be applied to increase the added value of WGS data.

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders

There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challenging to identify causal genetic variations that drive DNAm levels by population-based genetic association studies. This limits the utility of mQTLs for fine-mapping risk loci underlying psychiatric disorders identified by genome-wide association studies (GWAS). Here we present INTERACT, a deep learning model that integrates convolutional neural networks with transformer, to predict effects of genetic variations on DNAm levels at CpG sites in the human brain. We show that INTERACT-derived DNAm regulatory variants are not confounded by LD, are concentrated in regulatory genomic regions in the human brain, and are convergent with mQTL evidence from genetic association analysis. We further demonstrate that predicted DNAm regulatory variants are enriched for heritability of brain-related traits and improve polygenic risk prediction for schizophrenia across diverse ancestry samples. Finally, we applied predicted DNAm regulatory variants for fine-mapping schizophrenia GWAS risk loci to identify potential novel risk genes. Our study shows the power of a deep learning approach to identify functional regulatory variants that may elucidate the genetic basis of complex traits.

Heterogeneous Nature of Trained Innate Immune Cells in Health and Disease.

Heterogeneous Nature of Trained Innate Immune Cells in Health and Disease.

The HDAC9-associated risk locus promotes coronary artery disease by governing TWIST1.

Genome wide association studies (GWAS) have identified thousands of single nucleotide polymorphisms (SNPs) associated with the risk of common disorders. However, since the large majority of these risk SNPs reside outside gene-coding regions, GWAS generally provide no information about causal mechanisms regarding the specific gene(s) that are affected or the tissue(s) in which these candidate gene(s) exert their effect. The 'gold standard' method for understanding causal genes and their mechanisms of action are laborious basic science studies often involving sophisticated knockin or knockout mouse lines, however, these types of studies are impractical as a high-throughput means to understand the many risk variants that cause complex diseases like coronary artery disease (CAD). As a solution, we developed a streamlined, data-driven informatics pipeline to gain mechanistic insights on complex genetic loci. The pipeline begins by understanding the SNPs in a given locus in terms of their relative location and linkage disequilibrium relationships, and then identifies nearby expression quantitative trait loci (eQTLs) to determine their relative independence and the likely tissues that mediate their disease-causal effects. The pipeline then seeks to understand associations with other disease-relevant genes, disease sub-phenotypes, potential causality (Mendelian randomization), and the regulatory and functional involvement of these genes in gene regulatory co-expression networks (GRNs). Here, we applied this pipeline to understand a cluster of SNPs associated with CAD within and immediately adjacent to the gene encoding HDAC9. Our pipeline demonstrated, and validated, that this locus is causal for CAD by modulation of TWIST1 expression levels in the arterial wall, and by also governing a GRN related to metabolic function in skeletal muscle. Our results reconciled numerous prior studies, and also provided clear evidence that this locus does not govern HDAC9 expression, structure or function. This pipeline should be considered as a powerful and efficient way to understand GWAS risk loci in a manner that better reflects the highly complex nature of genetic risk associated with common disorders.

Phenotypes, mechanisms and therapeutics: insights from bipolar disorder GWAS findings

Genome-wide association studies (GWAS) have reported substantial genomic loci significantly associated with clinical risk of bipolar disorder (BD), and studies combining techniques of genetics, neuroscience, neuroimaging, and pharmacology are believed to help tackle clinical problems (e.g., identifying novel therapeutic targets). However, translating findings of psychiatric genetics into biological mechanisms underlying BD pathogenesis remains less successful. Biological impacts of majority of BD GWAS risk loci are obscure, and the involvement of many GWAS risk genes in this illness is yet to be investigated. It is thus necessary to review the progress of applying BD GWAS risk genes in the research and intervention of the disorder. A comprehensive literature search found that a number of such risk genes had been investigated in cellular or animal models, even before they were highlighted in BD GWAS. Intriguingly, manipulation of many BD risk genes (e.g., ANK3, CACNA1C, CACNA1B, HOMER1, KCNB1, MCHR1, NCAN, SHANK2 etc.) resulted in altered murine behaviors largely restoring BD clinical manifestations, including mania-like symptoms such as hyperactivity, anxiolytic-like behavior, as well as antidepressant-like behavior, and these abnormalities could be attenuated by mood stabilizers. In addition to recapitulating phenotypic characteristics of BD, some GWAS risk genes further provided clues for the neurobiology of this illness, such as aberrant activation and functional connectivity of brain areas in the limbic system, and modulated dendritic spine morphogenesis as well as synaptic plasticity and transmission. Therefore, BD GWAS risk genes are undoubtedly pivotal resources for modeling this illness, and might be translational therapeutic targets in the future clinical management of BD. We discuss both promising prospects and cautions in utilizing the bulk of useful resources generated by GWAS studies. Systematic integrations of findings from genetic and neuroscience studies are called for to promote our understanding and intervention of BD.

A pan-tissue DNA methylation atlas enables in silico decomposition of human tissue methylomes at cell-type resolution

Bulk-tissue DNA methylomes represent an average over many different cell types, hampering our understanding of cell-type-specific contributions to disease development. As single-cell methylomics is not scalable to large cohorts of individuals, cost-effective computational solutions are needed, yet current methods are limited to tissues such as blood. Here we leverage the high-resolution nature of tissue-specific single-cell RNA-sequencing datasets to construct a DNA methylation atlas defined for 13 solid tissue types and 40 cell types. We comprehensively validate this atlas in independent bulk and single-nucleus DNA methylation datasets. We demonstrate that it correctly predicts the cell of origin of diverse cancer types and discovers new prognostic associations in olfactory neuroblastoma and stage 2 melanoma. In brain, the atlas predicts a neuronal origin for schizophrenia, with neuron-specific differential DNA methylation enriched for corresponding genome-wide association study risk loci. In summary, the DNA methylation atlas enables the decomposition of 13 different human tissue types at a high cellular resolution, paving the way for an improved interpretation of epigenetic data.