Functionally informed fine-mapping and polygenic localization of complex trait heritability

An evolving understanding of multiple causal variants underlying genetic association signals

Understanding how genetic variation contributes to phenotypic variation is a fundamental question in genetics. Genome-wide association studies (GWASs) have discovered numerous genetic associations with various human phenotypes, most of which contain co-inherited variants in strong linkage disequilibrium (LD) with indistinguishable statistical significance. The experimental and analytical difficulty in identifying the "causal variant" among the co-inherited variants has traditionally led mechanistic studies to focus on relatively simple loci, where a single functional variant is presumed to explain most of the association signal and affect a target gene. The notion that a single causal variant is responsible for an association signal, while other variants in LD are merely correlated, has often been assumed in functional studies. However, emerging evidence powered by high-throughput experimental tools and context-specific functional databases argues that even a single independent signal may involve multiple functional variants in strong LD, each contributing to the observed genetic association. In this perspective, we articulate this evolving understanding of causal variants through examples from both traditional locus-by-locus approaches and more recent high-throughput functional studies. We then discuss the implications and prospects of this notion in understanding the genetic architecture of complex traits and interpreting the variant-level causality in GWAS follow-up studies.

Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants

Kidney dysfunction is a major cause of mortality, but its genetic architecture remains elusive. In this study, we conducted a multiancestry genome-wide association study in 2.2 million individuals and identified 1026 (97 previously unknown) independent loci. Ancestry-specific analysis indicated an attenuation of newly identified signals on common variants in European ancestry populations and the power of population diversity for further discoveries. We defined genotype effects on allele-specific gene expression and regulatory circuitries in more than 700 human kidneys and 237,000 cells. We found 1363 coding variants disrupting 782 genes, with 601 genes also targeted by regulatory variants and convergence in 161 genes. Integrating 32 types of genetic information, we present the "Kidney Disease Genetic Scorecard" for prioritizing potentially causal genes, cell types, and druggable targets for kidney disease.

Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies

In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.

BTS: scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across omics data

Motivation

statistics from genome-wide association studies (GWAS) are widely used in fine-mapping and colocalization analyses to identify causal variants and their enrichment in functional contexts, such as affected cell types and genomic features. With the expansion of functional genomic (FG) datasets, which now include hundreds of thousands of tracks across various cell and tissue types, it is critical to establish scalable algorithms integrating thousands of diverse FG annotations with GWAS results.

Results

We propose BTS (Bayesian Tissue Score), a novel, highly efficient algorithm uniquely designed for 1) identifying affected cell types and functional elements (context-mapping) and 2) fine-mapping potentially causal variants in a context-specific manner using large collections of cell type-specific FG annotation tracks. BTS leverages GWAS summary statistics and annotation-specific Bayesian models to analyze genome-wide annotation tracks, including enhancers, open chromatin, and histone marks. We evaluated BTS on GWAS summary statistics for immune and cardiovascular traits, such as Inflammatory Bowel Disease (IBD), Rheumatoid Arthritis (RA), Systemic Lupus Erythematosus (SLE), and Coronary Artery Disease (CAD). Our results demonstrate that BTS is over 100x more efficient in estimating functional annotation effects and context-specific variant fine-mapping compared to existing methods. Importantly, this large-scale Bayesian approach prioritizes both known and novel annotations, cell types, genomic regions, and variants and provides valuable biological insights into the functional contexts of these diseases.

Availability and implementation

Docker image is available at https://hub.docker.com/r/wanglab/bts with pre-installed BTS R package ( https://bitbucket.org/wanglab-upenn/BTS-R ) and BTS GWAS summary statistics analysis pipeline ( https://bitbucket.org/wanglab-upenn/bts-pipeline ).

Leveraging pleiotropy for the improved treatment of psychiatric disorders

Over 90% of drug candidates fail in clinical trials, while it takes 10-15 years and one billion US dollars to develop a single successful drug. Drug development is more challenging for psychiatric disorders, where disease comorbidity and complex symptom profiles obscure the identification of causal mechanisms for therapeutic intervention. One promising approach for determining more suitable drug candidates in clinical trials is integrating human genetic data into the selection process. Genome-wide association studies have identified thousands of replicable risk loci for psychiatric disorders, and sophisticated statistical tools are increasingly effective at using these data to pinpoint likely causal genes. These studies have also uncovered shared or pleiotropic genetic risk factors underlying comorbid psychiatric disorders. In this article, we argue that leveraging pleiotropic effects will provide opportunities to discover novel drug targets and identify more effective treatments for psychiatric disorders by targeting a common mechanism rather than treating each disease separately.

Using omics data and genome editing methods to decipher GWAS loci associated with coronary artery disease

Coronary artery disease (CAD) is due to atherosclerosis, a pathophysiological process that involves several cell-types and results in the accumulation of lipid-rich plaque that disrupt the normal blood flow through the coronary arteries to the heart. Genome-wide association studies have identified 1000s of genetic variants robustly associated with CAD or its traditional risk factors (e.g. blood pressure, blood lipids, type 2 diabetes, smoking). However, gaining biological insights from these genetic discoveries remain challenging because of linkage disequilibrium and the difficulty to interpret the functions of non-coding regulatory elements in the human genome. In this review, we present different statistical methods (e.g. Mendelian randomization) and molecular datasets (e.g. expression or protein quantitative trait loci) that have helped connect CAD-associated variants with genes, biological pathways, and cell-types or tissues. We emphasize that these various strategies make predictions, which need to be validated in orthologous systems. We discuss specific examples where the integration of omics data with GWAS results has prioritized causal CAD variants and genes. Finally, we review how targeted and genome-wide genome editing experiments using the CRISPR/Cas9 toolbox have been used to characterize new CAD genes in human cells. Researchers now have the statistical and bioinformatic methods, the molecular datasets, and the experimental tools to dissect comprehensively the loci that contribute to CAD risk in humans.

Simplifying causal gene identification in GWAS loci

Genome-wide association studies (GWAS) help to identify disease-linked genetic variants, but pinpointing the most likely causal genes in GWAS loci remains challenging. Existing GWAS gene prioritization tools are powerful but often use complex black box models trained on datasets containing unaddressed biases. Here, we use a data-driven approach to construct a truth set of causal genes in 406 GWAS loci. We train a gene prioritization tool, CALDERA, that uses a simple logistic regression model with L1 regularization and corrects for potential confounders. Using three independent benchmarking datasets of resolved GWAS loci, we compare the performance of CALDERA with three other methods (FLAMES, L2G, and cS2G). CALDERA outperforms all these methods in two out of three datasets and ranks second in the remaining dataset. We demonstrate that CALDERA prioritizes genes with expected properties, such as mutation intolerance (OR = 1.751 for pLI > 90%, P = 8.45x10 -3 ). Overall, CALDERA provides a powerful solution for prioritizing potentially causal genes in GWAS loci and may help identify novel genetics-driven drug targets.

Genomics yields biological and phenotypic insights into bipolar disorder

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Large-scale audiometric phenotyping identifies distinct genes and pathways involved in hearing loss subtypes

Age-related hearing loss affects one-third of the population over 65 years. However, the diverse pathologies underlying these heterogenous phenotypes complicate genetic studies. To overcome challenges associated with accurate phenotyping for older adults with hearing loss, we applied computational phenotyping approaches based on audiometrically measured hearing loss. This novel phenotyping strategy uncovered distinct genetic variants associated with sensory and metabolic hearing loss. Sex-stratified analyses of these sexually dimorphic hearing loss phenotypes revealed a novel locus of relevance to sensory hearing loss in males, but not females. Enrichment analyses revealed that genes involved in frontotemporal dementia were implicated in metabolic hearing loss, while genes relating to sensory processing of sound by hair cells were implicated in sensory hearing loss. Our study has enhanced our understanding of these two distinct hearing loss phenotypes, representing the first step in the development of more precise treatments for these pathologically distinct hearing loss phenotypes.

Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation

Sequence-based machine-learning models trained on genomics data improve genetic variant interpretation by providing functional predictions describing their impact on the cis-regulatory code. However, current tools do not predict RNA-seq expression profiles because of modeling challenges. Here, we introduce Borzoi, a model that learns to predict cell-type-specific and tissue-specific RNA-seq coverage from DNA sequence. Using statistics derived from Borzoi's predicted coverage, we isolate and accurately score DNA variant effects across multiple layers of regulation, including transcription, splicing and polyadenylation. Evaluated on quantitative trait loci, Borzoi is competitive with and often outperforms state-of-the-art models trained on individual regulatory functions. By applying attribution methods to the derived statistics, we extract cis-regulatory motifs driving RNA expression and post-transcriptional regulation in normal tissues. The wide availability of RNA-seq data across species, conditions and assays profiling specific aspects of regulation emphasizes the potential of this approach to decipher the mapping from DNA sequence to regulatory function.

CAUSALdb2: an updated database for causal variants of complex traits

Unraveling the causal variants from genome wide association studies (GWASs) is pivotal for understanding genetic underpinnings of complex traits and diseases. Despite continuous efforts, tools to refine and prioritize GWAS signals need enhancement to address the direct causal implications of genetic variations. To overcome challenges related to statistical fine-mapping in identifying causal variants, CAUSALdb has been updated with novel features and comprehensive datasets, morphing into CAUSALdb2. This expanded repository integrates 15 057 updated GWAS summary statistics across 10 839 unique traits and implements both LD-based and LD-free fine-mapping approaches, including innovative applications of approximate Bayes Factor and SuSiE. Additionally, by incorporating larger LD reference panels such as TOPMED and UK Biobank, and integrating functional annotations via PolyFun, CAUSALdb2 enhances the accuracy and context of fine-mapping results. The database now supports interrogation of additional causal signals and offers sophisticated visualizations to aid researchers in deciphering complex genetic architectures. By facilitating a deeper and more precise characterisation of causal variants, CAUSALdb2 serves as a crucial tool for advancing the genetic analysis of complex diseases. Available freely, CAUSALdb2 continues to set benchmarks in the post-GWAS era, fostering the development of targeted diagnostics and therapeutics derived from responsible genetic research. Explore these advancements at http://mulinlab.org/causaldb.

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants

Protein language models have demonstrated remarkable performance in predicting the effects of missense variants but DNA language models have not yet shown a competitive edge for complex genomes such as that of humans. This limitation is particularly evident when dealing with the vast complexity of noncoding regions that comprise approximately 98% of the human genome. To tackle this challenge, we introduce GPN-MSA (genomic pretrained network with multiple-sequence alignment), a framework that leverages whole-genome alignments across multiple species while taking only a few hours to train. Across several benchmarks on clinical databases (ClinVar, COSMIC and OMIM), experimental functional assays (deep mutational scanning and DepMap) and population genomic data (gnomAD), our model for the human genome achieves outstanding performance on deleteriousness prediction for both coding and noncoding variants. We provide precomputed scores for all ~9 billion possible single-nucleotide variants in the human genome. We anticipate that our advances in genome-wide variant effect prediction will enable more accurate rare disease diagnosis and improve rare variant burden testing.

Fine-mapping causal tissues and genes at disease-associated loci

Complex diseases often have distinct mechanisms spanning multiple tissues. We propose tissue-gene fine-mapping (TGFM), which infers the posterior inclusion probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing summary statistics and expression quantitative trait loci (eQTL) data; TGFM also assigns PIPs to non-mediated variants. TGFM accounts for co-regulation across genes and tissues and models uncertainty in cis-predicted expression models, enabling correct calibration. We applied TGFM to 45 UK Biobank diseases or traits using eQTL data from 38 Genotype-Tissue Expression (GTEx) tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease or trait, of which 11% were gene-tissue pairs. Causal gene-tissue pairs identified by TGFM reflected both known biology (for example, TPO-thyroid for hypothyroidism) and biologically plausible findings (for example, SLC20A2-artery aorta for diastolic blood pressure). Application of TGFM to single-cell eQTL data from nine cell types in peripheral blood mononuclear cells (PBMCs), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs.

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.

Heritable polygenic editing: the next frontier in genomic medicine?

Polygenic genome editing in human embryos and germ cells is predicted to become feasible in the next three decades. Several recent books and academic papers have outlined the ethical concerns raised by germline genome editing and the opportunities that it may present1-3. To date, no attempts have been made to predict the consequences of altering specific variants associated with polygenic diseases. In this Analysis, we show that polygenic genome editing could theoretically yield extreme reductions in disease susceptibility. For example, editing a relatively small number of genomic variants could make a substantial difference to an individual's risk of developing coronary artery disease, Alzheimer's disease, major depressive disorder, diabetes and schizophrenia. Similarly, large changes in risk factors, such as low-density lipoprotein cholesterol and blood pressure, could, in theory, be achieved by polygenic editing. Although heritable polygenic editing (HPE) is still speculative, we completed calculations to discuss the underlying ethical issues. Our modelling demonstrates how the putatively positive consequences of gene editing at an individual level may deepen health inequalities. Further, as single or multiple gene variants can increase the risk of some diseases while decreasing that of others, HPE raises ethical challenges related to pleiotropy and genetic diversity. We conclude by arguing for a collectivist perspective on the ethical issues raised by HPE, which accounts for its effects on individuals, their families, communities and society4.

Funmap: integrating high-dimensional functional annotations to improve fine-mapping

MOTIVATION

Fine-mapping aims to prioritize causal variants underlying complex traits by accounting for the linkage disequilibrium of genome-wide association study risk locus. The expanding resources of functional annotations serve as auxiliary evidence to improve the power of fine-mapping. However, existing fine-mapping methods tend to generate many false positive results when integrating a large number of annotations.

RESULTS

In this study, we propose a unified method to integrate high-dimensional functional annotations with fine-mapping (Funmap). Funmap can effectively improve the power of fine-mapping by borrowing information from hundreds of functional annotations. Meanwhile, it relates the annotation to the causal probability with a random effects model that avoids the over-fitting issue, thereby producing a well-controlled false positive rate. Paired with a fast algorithm, Funmap enables scalable integration of a large number of annotations to facilitate prioritizing multiple causal single nucleotide polymorphisms. Our comprehensive simulations across a wide range of annotation relevance settings demonstrate that Funmap is the only method that produces well-calibrated false discovery rate under the setting of high-dimensional annotations while achieving better or comparable power gains as compared to existing methods. By integrating genome-wide association studies of 4 lipid traits with 187 functional annotations, Funmap consistently identified more variants that can be replicated in an independent cohort, achieving 15.5%-26.2% improvement over the runner-up in terms of replication rate.

AVAILABILITY AND IMPLEMENTATION

The Funmap software and all analysis code are available at https://github.com/LeeHITsz/Funmap.

Prioritizing Parkinson's disease risk genes in genome-wide association loci

Recent advancements in Parkinson's disease (PD) drug development have been significantly driven by genetic research. Importantly, drugs supported by genetic evidence are more likely to be approved. While genome-wide association studies (GWAS) are a powerful tool to nominate genomic regions associated with certain traits or diseases, pinpointing the causal biologically relevant gene is often challenging. Our aim was to prioritize genes underlying PD GWAS signals. The polygenic priority score (PoPS) is a similarity-based gene prioritization method that integrates genome-wide information from MAGMA gene-level association tests and more than 57,000 gene-level features, including gene expression, biological pathways, and protein-protein interactions. We applied PoPS to data from the largest published PD GWAS in East Asian- and European-ancestries. We identified 120 independent associations with P < 5×10-8 and prioritized 46 PD genes across these loci based on their PoPS scores, distance to the GWAS signal, and presence of non-synonymous variants in the credible set. Alongside well-established PD genes (e.g., TMEM175 and VPS13C), some of which are targeted in ongoing clinical trials (i.e., SNCA, LRRK2, and GBA1), we prioritized genes with a plausible mechanistic link to PD pathogenesis (e.g., RIT2, BAG3, and SCARB2). Many of these genes hold potential for drug repurposing or novel therapeutic developments for PD (i.e., FYN, DYRK1A, NOD2, CTSB, SV2C, and ITPKB). Additionally, we prioritized potentially druggable genes that are relatively unexplored in PD (XPO1, PIK3CA, EP300, MAP4K4, CAMK2D, NCOR1, and WDR43). We prioritized a high-confidence list of genes with strong links to PD pathogenesis that may represent our next-best candidates for disease-modifying therapeutics. We hope our findings stimulate further investigations and preclinical work to facilitate PD drug development programs.

Leveraging functional annotations to map rare variants associated with Alzheimer's disease with gruyere

The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-coding, to complex disease. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most are restricted to per-gene models and do not fully leverage the availability of variant-level functional annotations. We propose Genome-wide Rare Variant EnRichment Evaluation (gruyere), a Bayesian probabilistic model that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply gruyere to WGS data from the Alzheimer's Disease (AD) Sequencing Project, consisting of 7,966 cases and 13,412 controls, to identify AD-associated genes and annotations. Growing evidence suggests that disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not had sufficient power to examine rare non-coding effects that incorporate such cell-type specific information. To address this gap, we 1) use predicted enhancer and promoter regions in microglia and other potentially relevant cell types (oligodendrocytes, astrocytes, and neurons) to define per-gene non-coding RV test sets and 2) include cell-type specific variant effect predictions (VEPs) as functional annotations. gruyere identifies 15 significant genetic associations not detected by other RV methods and finds deep learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a novel and robust framework incorporating functional annotations, coding RVs, and cell-type associated non-coding RVs, to perform genome-wide association tests, uncovering AD-relevant genes and annotations.

The PRIMED Consortium: Reducing disparities in polygenic risk assessment

By improving disease risk prediction, polygenic risk scores (PRSs) could have a significant impact on health promotion and disease prevention. Due to the historical oversampling of populations with European ancestry for genome-wide association studies, PRSs perform less well in other, understudied populations, leading to concerns that clinical use in their current forms could widen health care disparities. The PRIMED Consortium was established to develop methods to improve the performance of PRSs in global populations and individuals of diverse genetic ancestry. To this end, PRIMED is aggregating and harmonizing multiple phenotype and genotype datasets on AnVIL, an interoperable secure cloud-based platform, to perform individual- and summary-level analyses using population and statistical genetics approaches. Study sites, the coordinating center, and representatives from the NIH work alongside other NHGRI and global consortia to achieve these goals. PRIMED is also evaluating ethical and social implications of PRS implementation and investigating the joint modeling of social determinants of health and PRS in computing disease risk. The phenotypes of interest are primarily cardiometabolic diseases and cancer, the leading causes of death and disability worldwide. Early deliverables of the consortium include methods for data sharing on AnVIL, development of a common data model to harmonize phenotype and genotype data from cohort studies as well as electronic health records, adaptation of recent guidelines for population descriptors to global cohorts, and sharing of PRS methods/tools. As a multisite collaboration, PRIMED aims to foster equity in the development and use of polygenic risk assessment.

Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph

Genome-wide association studies (GWASs) have identified tens of thousands of disease associated variants and provided critical insights into developing effective treatments. However, limited sample sizes have hindered the discovery of variants for uncommon and rare diseases. Here, we introduce KGWAS, a novel geometric deep learning method that leverages a massive functional knowledge graph across variants and genes to improve detection power in small-cohort GWASs significantly. KGWAS assesses the strength of a variant's association to disease based on the aggregate GWAS evidence across molecular elements interacting with the variant within the knowledge graph. Comprehensive simulations and replication experiments showed that, for small sample sizes ( N =1-10K), KGWAS identified up to 100% more statistically significant associations than state-of-the-art GWAS methods and achieved the same statistical power with up to 2.67× fewer samples. We applied KGWAS to 554 uncommon UK Biobank diseases ( N case <5K) and identified 183 more associations (46.9% improvement) than the original GWAS, where the gain further increases to 79.8% for 141 rare diseases (N case <300). The KGWAS-only discoveries are supported by abundant functional evidence, such as rs2155219 (on 11q13) associated with ulcerative colitis potentially via regulating LRRC32 expression in CD4+ regulatory T cells, and rs7312765 (on 12q12) associated with the rare disease myasthenia gravis potentially via regulating PPHLN1 expression in neuron-related cell types. Furthermore, KGWAS consistently improves downstream analyses such as identifying disease-specific network links for interpreting GWAS variants, identifying disease-associated genes, and identifying disease-relevant cell populations. Overall, KGWAS is a flexible and powerful AI model that integrates growing functional genomics data to discover novel variants, genes, cells, and networks, especially valuable for small cohort diseases.

Genome-wide association analysis provides insights into the molecular etiology of dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and cardiac transplantation. We report a genome-wide association study and multi-trait analysis of DCM (14,256 cases) and three left ventricular traits (36,203 UK Biobank participants). We identified 80 genomic risk loci and prioritized 62 putative effector genes, including several with rare variant DCM associations (MAP3K7, NEDD4L and SSPN). Using single-nucleus transcriptomics, we identify cellular states, biological pathways, and intracellular communications that drive pathogenesis. We demonstrate that polygenic scores predict DCM in the general population and modify penetrance in carriers of rare DCM variants. Our findings may inform the design of genetic testing strategies that incorporate polygenic background. They also provide insights into the molecular etiology of DCM that may facilitate the development of targeted therapeutics.

Partitioned polygenic risk scores identify distinct types of metabolic dysfunction-associated steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by an excess of lipids, mainly triglycerides, in the liver and components of the metabolic syndrome, which can lead to cirrhosis and liver cancer. While there is solid epidemiological evidence that MASLD clusters with cardiometabolic disease, several leading genetic risk factors for MASLD do not increase the risk of cardiovascular disease, suggesting no causal relationship between MASLD and cardiometabolic derangement. In this work, we leveraged measurements of visceral adiposity identifying 27 previously unknown genetic loci associated with MASLD (n = 36,394), six replicated in four independent cohorts (n = 3,903). Next, we generated two partitioned polygenic risk scores based on the presence of lipoprotein retention in the liver. The two polygenic risk scores suggest the presence of at least two distinct types of MASLD, one confined to the liver resulting in a more aggressive liver disease and one that is systemic and results in a higher risk of cardiometabolic disease. These findings shed light on the heterogeneity of MASLD and have the potential to improve the prediction of clinical trajectories and inform precision medicine approaches.

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

Bidirectional Mendelian Randomization Highlights Causal Relationships Between Circulating INHBC and Multiple Cardiometabolic Diseases and Traits

Human genetic and transgenic mouse studies have highlighted a potential liver-adipose tissue endocrine axis, involving activin C (Act-C) and/or Act-E and ALK7, influencing fat distribution and systemic metabolism. We investigated the bidirectional effects between circulating INHBC, which homodimerizes into Act-C, and adiposity traits, insulin resistance, inflammation, and cardiometabolic disease risk. Additionally, we examined whether Act-C is an ALK7 ligand in human adipocytes. We used Mendelian randomization and in vitro studies in immortalized human abdominal and gluteal adipocytes. Circulating INHBC was causally linked to reduced lower-body fat, dyslipidemia, and increased risks of coronary artery disease (CAD) and nonalcoholic fatty liver disease (NAFLD). Conversely, upper-body fat distribution, obesity, hypertriglyceridemia, subclinical inflammation, and type 2 diabetes positively impacted plasma INHBC levels. Mechanistically, an atherogenic lipid profile may partly explain the INHBC-CAD link, while inflammation and hypertriglyceridemia may partly explain how adiposity traits affect circulating INHBC. Phenome-wide Mendelian randomization showed weak causal relationships between higher plasma INHBC and impaired kidney function and higher gout risk. In human adipocytes, recombinant Act-C activated SMAD2/3 signaling via ALK7 and suppressed lipolysis. In summary, INHBC influences systemic metabolism by activating ALK7 in adipose tissue and may serve as a drug target for atherogenic dyslipidemia, CAD, and NAFLD.

ARTICLE HIGHLIGHTS

Genome-wide association studies and multi-omics integrative analysis reveal novel loci and their molecular mechanisms for circulating polyunsaturated, monounsaturated, and saturated fatty acids

Previous genome-wide association studies (GWAS) have identified genetic loci associated with the circulating levels of FAs, but the biological mechanisms of these genetic associations remain largely unexplored. Here, we conducted GWAS to identify additional genetic loci for 19 circulating fatty acid (FA) traits in UK Biobank participants of European ancestry (N = 239,268) and five other ancestries (N = 508 - 4,663). We leveraged the GWAS findings to characterize genetic correlations and colocalized regions among FAs, explore sex differences, examine FA loci influenced by lipoprotein metabolism, and apply statistical fine-mapping to pinpoint putative causal variants. We integrated GWAS signals with multi-omics quantitative trait loci (QTL) to reveal intermediate molecular phenotypes mediating the associations between the genetic loci and FA levels. Altogether, we identified 215 significant loci for polyunsaturated fatty acids (PUFAs)-related traits in European participants, 163 loci for monounsaturated fatty acids (MUFAs)-related traits, and 119 loci for saturated fatty acids (SFAs)-related traits, including 70, 61, and 54 novel loci, respectively. A novel locus for total FAs, the percentage of omega-6 PUFAs in total FAs, and total MUFAs (around genes GSTT1/2/2B) overlapped with QTL signals for all six molecular phenotypes examined, including gene expression, protein abundance, DNA methylation, splicing, histone modification, and chromatin accessibility. Across 19 FA traits, 65% of GWAS loci overlapped with QTL signals for at least one molecular phenotype. Our study identifies novel genetic loci for circulating FA levels and systematically uncovers their underlying molecular mechanisms.

Mapping chromatin interactions at melanoma susceptibility loci and cell-type specific dataset integration uncovers distant gene targets of cis-regulation

Genome-wide association studies (GWAS) of melanoma risk have identified 68 independent signals at 54 loci. For most loci, specific functional variants and their respective target genes remain to be established. Capture-HiC is an assay that links fine-mapped risk variants to candidate target genes by comprehensively mapping cell-type specific chromatin interactions. We performed a melanoma GWAS region-focused capture-HiC assay in human primary melanocytes to identify physical interactions between fine-mapped risk variants and potential causal melanoma susceptibility genes. Overall, chromatin interaction data alone nominated potential causal genes for 61 of the 68 melanoma risk signals, identifying many candidates beyond those reported by previous studies. We further integrated these data with cell-type specific epigenomic (chromatin state, accessibility), gene expression (eQTL/TWAS), DNA methylation (meQTL/MWAS), and massively parallel reporter assay (MPRA) data to prioritize potentially cis-regulatory variants and their respective candidate gene targets. From the set of fine-mapped variants across these loci, we identified 140 prioritized candidate causal variants linked to 195 candidate genes at 42 risk signals. In addition, we developed an integrative scoring system to facilitate candidate gene prioritization, integrating melanocyte and melanoma datasets. Notably, at several GWAS risk signals we observed long-range chromatin connections (500 kb to >1 Mb) with distant candidate target genes. We validated several such cis-regulatory interactions using CRISPR inhibition, providing evidence for known cancer driver genes MDM4 and CBL, as well as the SRY-box transcription factor SOX4, as likely melanoma risk genes.

A genome-wide investigation into the underlying genetic architecture of personality traits and overlap with psychopathology

Personality is influenced by both genetic and environmental factors and is associated with other psychiatric traits such as anxiety and depression. The 'big five' personality traits, which include neuroticism, extraversion, agreeableness, conscientiousness and openness, are a widely accepted and influential framework for understanding and describing human personality. Of the big five personality traits, neuroticism has most often been the focus of genetic studies and is linked to various mental illnesses, including depression, anxiety and schizophrenia. Our knowledge of the genetic architecture of the other four personality traits is more limited. Here, utilizing the Million Veteran Program cohort, we conducted a genome-wide association study in individuals of European and African ancestry. Adding other published data, we performed genome-wide association study meta-analysis for each of the five personality traits with sample sizes ranging from 237,390 to 682,688. We identified 208, 14, 3, 2 and 7 independent genome-wide significant loci associated with neuroticism, extraversion, agreeableness, conscientiousness and openness, respectively. These findings represent 62 novel loci for neuroticism, as well as the first genome-wide significant loci discovered for agreeableness. Gene-based association testing revealed 254 genes showing significant association with at least one of the five personality traits. Transcriptome-wide and proteome-wide analysis identified altered expression of genes and proteins such as CRHR1, SLC12A5, MAPT and STX4. Pathway enrichment and drug perturbation analyses identified complex biology underlying human personality traits. We also studied the inter-relationship of personality traits with 1,437 other traits in a phenome-wide genetic correlation analysis, identifying new associations. Mendelian randomization showed positive bidirectional effects between neuroticism and depression and anxiety, while a negative bidirectional effect was observed for agreeableness and these psychiatric traits. This study improves our comprehensive understanding of the genetic architecture underlying personality traits and their relationship to other complex human traits.

Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro

Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe .

Fine mapping of candidate effector genes for heart rate

An elevated resting heart rate (RHR) is associated with increased cardiovascular mortality. Genome-wide association studies (GWAS) have identified > 350 loci. Uniquely, in this study we applied genetic fine-mapping leveraging tissue specific chromatin segmentation and colocalization analyses to identify causal variants and candidate effector genes for RHR. We used RHR GWAS summary statistics from 388,237 individuals of European ancestry from UK Biobank and performed fine mapping using publicly available genomic annotation datasets. High-confidence causal variants (accounting for > 75% posterior probability) were identified, and we collated candidate effector genes using a multi-omics approach that combined evidence from colocalisation with molecular quantitative trait loci (QTLs), and long-range chromatin interaction analyses. Finally, we performed druggability analyses to investigate drug repurposing opportunities. The fine mapping pipeline indicated 442 distinct RHR signals. For 90 signals, a single variant was identified as a high-confidence causal variant, of which 22 were annotated as missense. In trait-relevant tissues, 39 signals colocalised with cis-expression QTLs (eQTLs), 3 with cis-protein QTLs (pQTLs), and 75 had promoter interactions via Hi-C. In total, 262 candidate genes were highlighted (79% had promoter interactions, 15% had a colocalised eQTL, 8% had a missense variant and 1% had a colocalised pQTL), and, for the first time, enrichment in nervous system pathways. Druggability analyses highlighted ACHE, CALCRL, MYT1 and TDP1 as potential targets. Our genetic fine-mapping pipeline prioritised 262 candidate genes for RHR that warrant further investigation in functional studies, and we provide potential therapeutic targets to reduce RHR and cardiovascular mortality.

Population-specific putative causal variants shape quantitative traits

Human genetic variants are associated with many traits through largely unknown mechanisms. Here, combining approximately 260,000 Japanese study participants, a Japanese-specific genotype reference panel and statistical fine-mapping, we identified 4,423 significant loci across 63 quantitative traits, among which 601 were new, and 9,406 putatively causal variants. New associations included Japanese-specific coding, splicing and noncoding variants, exemplified by a damaging missense variant rs730881101 in TNNT2 associated with lower heart function and increased risk for heart failure (P = 1.4 × 10-15 and odds ratio = 4.5, 95% confidence interval = 3.1-6.5). Putative causal noncoding variants were supported by state-of-art in silico functional assays and had comparable effect sizes to coding variants. A plausible example of new mechanisms of causal variants is an enrichment of causal variants in 3' untranslated regions (UTRs), including the Japanese-specific rs13306436 in IL6 associated with pro-inflammatory traits and protection against tuberculosis. We experimentally showed that transcripts with rs13306436 are resistant to mRNA degradation by regnase-1, an RNA-binding protein. Our study provides a list of fine-mapped causal variants to be tested for functionality and underscores the importance of sequencing, genotyping and association efforts in diverse populations.

Variants in tubule epithelial regulatory elements mediate most heritable differences in human kidney function

Kidney failure, the decrease of kidney function below a threshold necessary to support life, is a major cause of morbidity and mortality. We performed a genome-wide association study (GWAS) of 406,504 individuals in the UK Biobank, identifying 430 loci affecting kidney function in middle-aged adults. To investigate the cell types affected by these loci, we integrated the GWAS with human kidney candidate cis-regulatory elements (cCREs) identified using single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq). Overall, 56% of kidney function heritability localized to kidney tubule epithelial cCREs and an additional 7% to kidney podocyte cCREs. Thus, most heritable differences in adult kidney function are a result of altered gene expression in these two cell types. Using enhancer assays, allele-specific scATAC-seq and machine learning, we found that many kidney function variants alter tubule epithelial cCRE chromatin accessibility and function. Using CRISPRi, we determined which genes some of these cCREs regulate, implicating NDRG1, CCNB1 and STC1 in human kidney function.

BEATRICE: Bayesian fine-mapping from summary data using deep variational inference

MOTIVATION

We introduce a novel framework BEATRICE to identify putative causal variants from GWAS statistics. Identifying causal variants is challenging due to their sparsity and high correlation in the nearby regions. To account for these challenges, we rely on a hierarchical Bayesian model that imposes a binary concrete prior on the set of causal variants. We derive a variational algorithm for this fine-mapping problem by minimizing the KL divergence between an approximate density and the posterior probability distribution of the causal configurations. Correspondingly, we use a deep neural network as an inference machine to estimate the parameters of our proposal distribution. Our stochastic optimization procedure allows us to sample from the space of causal configurations, which we use to compute the posterior inclusion probabilities and determine credible sets for each causal variant. We conduct a detailed simulation study to quantify the performance of our framework against two state-of-the-art baseline methods across different numbers of causal variants and noise paradigms, as defined by the relative genetic contributions of causal and noncausal variants.

RESULTS

We demonstrate that BEATRICE achieves uniformly better coverage with comparable power and set sizes, and that the performance gain increases with the number of causal variants. We also show the efficacy BEATRICE in finding causal variants from the GWAS study of Alzheimer's disease. In comparison to the baselines, only BEATRICE can successfully find the APOE ϵ2 allele, a commonly associated variant of Alzheimer's.

AVAILABILITY AND IMPLEMENTATION

BEATRICE is available for download at https://github.com/sayangsep/Beatrice-Finemapping.

Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk

Genome-wide association studies of colorectal cancer (CRC) have identified 170 autosomal risk loci. However, for most of these, the functional variants and their target genes are unknown. Here, we perform statistical fine-mapping incorporating tissue-specific epigenetic annotations and massively parallel reporter assays to systematically prioritize functional variants for each CRC risk locus. We identify plausible causal variants for the 170 risk loci, with a single variant for 40. We link these variants to 208 target genes by analyzing colon-specific quantitative trait loci and implementing the activity-by-contact model, which integrates epigenomic features and Micro-C data, to predict enhancer-gene connections. By deciphering CRC risk loci, we identify direct links between risk variants and target genes, providing further insight into the molecular basis of CRC susceptibility and highlighting potential pharmaceutical targets for prevention and treatment.

A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders

Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generate single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. We evaluate enhancer activity for 59 elements using an in vivo transgenic assay and validate 44 (75%), demonstrating that single cell accessibility can be a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieve significant reduction in our variant search space and nominate candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work delivers non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.

Phenome-wide association study of population-differentiating genetic variants around gene ACSL1

Background and objectives

Demographic dynamics and natural selection during human evolution shaped the present-day patterns of genetic variations, and geographically varying genetic factors contribute to different disease prevalences across human populations. This study aims to evaluate the presence of positive selection on the gene encoding long-chain fatty acyl-CoA synthetase 1 (ACSL1) and the phenotypic impacts of population-differentiating genetic variants around this gene.

Methodology

Three types of statistical tests for positive selection, based on site frequency spectrum, extended haplotype homozygosity and population differentiation, were applied to the whole-genome sequencing data from the 1000 Genomes Project. A phenome-wide association study of ACSL1 was performed with published genome-wide association studies (GWAS) and transcriptome-wide association studies, including phenome-wide studies in biobanks.

Results

Genetic variants associated with ACSL1 expression in various tissues exhibit geographically varying allele frequencies. Three types of statistical tests consistently supported the presence of positive selection on the coding and regulatory regions of ACSL1 in African, European, South Asian and East Asian populations. A phenome-wide association study of ACSL1 revealed associations with type 2 diabetes, blood glucose, age at menopause, mean platelet volume and mean reticulocyte volume. The top allele associated with lower diabetes risk has the highest frequency in European populations, whereas the top allele associated with later menopause has the highest frequency in African populations.

Conclusions and implications

Positive selection on ACSL1 resulted in geographically varying genetic variants, which may contribute to differential phenotypes across human populations, including type 2 diabetes and age at menopause.

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

Genetic, transcriptomic, histological, and biochemical analysis of progressive supranuclear palsy implicates glial activation and novel risk genes

Progressive supranuclear palsy (PSP), a rare Parkinsonian disorder, is characterized by problems with movement, balance, and cognition. PSP differs from Alzheimer's disease (AD) and other diseases, displaying abnormal microtubule-associated protein tau by both neuronal and glial cell pathologies. Genetic contributors may mediate these differences; however, the genetics of PSP remain underexplored. Here we conduct the largest genome-wide association study (GWAS) of PSP which includes 2779 cases (2595 neuropathologically-confirmed) and 5584 controls and identify six independent PSP susceptibility loci with genome-wide significant (P < 5 × 10-8) associations, including five known (MAPT, MOBP, STX6, RUNX2, SLCO1A2) and one novel locus (C4A). Integration with cell type-specific epigenomic annotations reveal an oligodendrocytic signature that might distinguish PSP from AD and Parkinson's disease in subsequent studies. Candidate PSP risk gene prioritization using expression quantitative trait loci (eQTLs) identifies oligodendrocyte-specific effects on gene expression in half of the genome-wide significant loci, and an association with C4A expression in brain tissue, which may be driven by increased C4A copy number. Finally, histological studies demonstrate tau aggregates in oligodendrocytes that colocalize with C4 (complement) deposition. Integrating GWAS with functional studies, epigenomic and eQTL analyses, we identify potential causal roles for variation in MOBP, STX6, RUNX2, SLCO1A2, and C4A in PSP pathogenesis.

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Addressing Technical Pitfalls in Pursuit of Molecular Factors That Mediate Immunoglobulin Gene Regulation

The expressed Ab repertoire is a critical determinant of immune-related phenotypes. Ab-encoding transcripts are distinct from other expressed genes because they are transcribed from somatically rearranged gene segments. Human Abs are composed of two identical H and L chain polypeptides derived from genes in IGH locus and one of two L chain loci. The combinatorial diversity that results from Ab gene rearrangement and the pairing of different H and L chains contributes to the immense diversity of the baseline Ab repertoire. During rearrangement, Ab gene selection is mediated by factors that influence chromatin architecture, promoter/enhancer activity, and V(D)J recombination. Interindividual variation in the composition of the Ab repertoire associates with germline variation in IGH, implicating polymorphism in Ab gene regulation. Determining how IGH variants directly mediate gene regulation will require integration of these variants with other functional genomic datasets. In this study, we argue that standard approaches using short reads have limited utility for characterizing regulatory regions in IGH at haplotype resolution. Using simulated and chromatin immunoprecipitation sequencing reads, we define features of IGH that limit use of short reads and a single reference genome, namely 1) the highly duplicated nature of the DNA sequence in IGH and 2) structural polymorphisms that are frequent in the population. We demonstrate that personalized diploid references enhance performance of short-read data for characterizing mappable portions of the locus, while also showing that long-read profiling tools will ultimately be needed to fully resolve functional impacts of IGH germline variation on expressed Ab repertoires.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.

Review of pharmacogenetics of antiseizure medications: focusing on genetic variants of mechanistic targets

Antiseizure medications (ASMs) play a central role in seizure management, however, unpredictability in the response to treatment persists, even among patients with similar seizure manifestations and clinical backgrounds. An objective biomarker capable of reliably predicting the response to ASMs would profoundly impact epilepsy treatment. Presently, clinicians rely on a trial-and-error approach when selecting ASMs, a time-consuming process that can result in delays in receiving alternative non-pharmacological therapies such as a ketogenetic diet, epilepsy surgery, and neuromodulation therapies. Pharmacogenetic studies investigating the correlation between ASMs and genetic variants regarding their mechanistic targets offer promise in predicting the response to treatment. Sodium channel subunit genes have been extensively studied along with other ion channels and receptors as targets, however, the results have been conflicting, possibly due to methodological disparities including inconsistent definitions of drug response, variations in ASM combinations, and diversity of genetic variants/genes studied. Nonetheless, these studies underscore the potential effect of genetic variants on the mechanism of ASMs and consequently the prediction of treatment response. Recent advances in sequencing technology have led to the generation of large genetic datasets, which may be able to enhance the predictive accuracy of the response to ASMs.

Genome-wide fine-mapping improves identification of causal variants

Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 600 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.

Genome-wide fine-mapping improves identification of causal variants

Fine-mapping refines genotype-phenotype association signals to identify causal variants underlying complex traits. However, current methods typically focus on individual genomic segments without considering the global genetic architecture. Here, we demonstrate the advantages of performing genome-wide fine-mapping (GWFM) and develop methods to facilitate GWFM. In simulations and real data analyses, GWFM outperforms current methods in error control, mapping power and precision, replication rate, and trans-ancestry phenotype prediction. For 48 well-powered traits in the UK Biobank, we identify causal variants that collectively explain 17% of the SNP-based heritability, and predict that fine-mapping 50% of that would require 2 million samples on average. We pinpoint a known causal variant, as proof-of-principle, at FTO for body mass index, unveil a hidden secondary variant with evolutionary conservation, and identify new missense causal variants for schizophrenia and Crohn's disease. Overall, we analyse 599 complex traits with 13 million SNPs, highlighting the efficacy of GWFM with functional annotations.

Genetics of chronic respiratory disease

Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), asthma and interstitial lung diseases are frequently occurring disorders with a polygenic basis that account for a large global burden of morbidity and mortality. Recent large-scale genetic epidemiology studies have identified associations between genetic variation and individual respiratory diseases and linked specific genetic variants to quantitative traits related to lung function. These associations have improved our understanding of the genetic basis and mechanisms underlying common lung diseases. Moreover, examining the overlap between genetic associations of different respiratory conditions, along with evidence for gene-environment interactions, has yielded additional biological insights into affected molecular pathways. This genetic information could inform the assessment of respiratory disease risk and contribute to stratified treatment approaches.

Genetic variation across and within individuals

Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.

From genetic associations to genes: methods, applications, and challenges

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.

CoPheScan: phenome-wide association studies accounting for linkage disequilibrium

Phenome-wide association studies (PheWAS) facilitate the discovery of associations between a single genetic variant with multiple phenotypes. For variants which impact a specific protein, this can help identify additional therapeutic indications or on-target side effects of intervening on that protein. However, PheWAS is restricted by an inability to distinguish confounding due to linkage disequilibrium (LD) from true pleiotropy. Here we describe CoPheScan (Coloc adapted Phenome-wide Scan), a Bayesian approach that enables an intuitive and systematic exploration of causal associations while simultaneously addressing LD confounding. We demonstrate its performance through simulation, showing considerably better control of false positive rates than a conventional approach not accounting for LD. We used CoPheScan to perform PheWAS of protein-truncating variants and fine-mapped variants from disease and pQTL studies, in 2275 disease phenotypes from the UK Biobank. Our results identify the complexity of known pleiotropic genes such as APOE, and suggest a new causal role for TGM3 in skin cancer.

Cross-Phenotype GWAS Supports Shared Genetic Susceptibility to Systemic Sclerosis and Primary Biliary Cholangitis

Objective

An increased risk of primary biliary cholangitis (PBC) has been reported in patients with systemic sclerosis (SSc). Our study aims to investigate the shared genetic susceptibility between the two disorders and to define candidate causal genes using cross-phenotype GWAS meta-analysis.

Methods

We performed cross-phenotype GWAS meta-analysis and colocalization analysis for SSc and PBC. We performed both genome-wide and locus-based analysis, including tissue and pathway enrichment analyses, fine-mapping, colocalization analyses with expression quantitative trait loci (eQTL) and protein quantitative trait loci (pQTL) datasets, and phenome-wide association studies (PheWAS). Finally, we used an integrative approach to prioritize candidate causal genes from the novel loci.

Results

We detected a strong genetic correlation between SSc and PBC (rg = 0.84, p = 1.7 × 10-6). In the cross-phenotype GWAS meta-analysis, we identified 44 non-HLA loci that reached genome-wide significance (p < 5 × 10-8). Evidence of shared causal variants between SSc and PBC was found for nine loci, five of which were novel. Integrating multiple sources of evidence, we prioritized CD40, ERAP1, PLD4, SPPL3, and CCDC113 as novel candidate causal genes. The CD40 risk locus colocalized with trans-pQTLs of multiple plasma proteins involved in B cell function.

Conclusion

Our study supports a strong shared genetic susceptibility between SSc and PBC. Through cross-phenotype analyses, we have prioritized several novel candidate causal genes and pathways for these disorders.