Bayesian refinement of association signals for 14 loci in 3 common diseases

Sequence-based genome-wide association study and fine-mapping in German Holstein reveal new quantitative trait loci for health traits

We conducted a large GWAS of 11 health traits belonging to 3 trait complexes: (1) metabolic diseases, (2) infectious and noninfectious feet and claw disorders, and (3) udder-related traits in 100,809 to 180,217 German Holstein cows to investigate the genetic architecture and underlying biological mechanisms behind these complex traits. The GWAS identified 12,306 genome-wide significant variants across 10 traits. The new association signals were inspected with a Bayesian fine-mapping approach, leading to the discovery of 159 novel variants with high potential for causality. Variants were in known and novel regions for the traits studied, leading to a list of 53 novel candidate genes. Our study represents the largest whole-genome sequence GWAS for health traits so far, hence ensuring the power to detect meaningful variants, especially when enhanced with fine-mapping.

Systematic review and independent validation of genetic factors of radiographic outcome in rheumatoid arthritis identifies a genome-wide association with CARD9

OBJECTIVES

This study aimed to investigate non-HLA genetic mechanisms underlying radiographic severity in rheumatoid arthritis (RA).

METHODS

A systematic review of publications reporting non-HLA genetic associations with radiographic severity in RA across ancestries was undertaken. Experimental validation was performed in the Norfolk Arthritis Register, comprising 1407 patients with available genetic and treatment data followed prospectively for up to 10 years, with 2198 longitudinal radiographs. Genome-wide genotyping was performed with Illumina Human Core Exome Array. Radiographic outcomes (presence of erosions; Larsen score) were modelled longitudinally. Fine mapping and functional annotations to refine associations to potential causative loci were undertaken using FUMA, PolyPhen2, and RegulomeDB.

RESULTS

The systematic review identified 102 publications reporting 139 independent associations with radiographic outcome. Association with 15 independent polymorphisms were replicated in the Norfolk Arthritis Register data set, implicating adaptive immune processes (Th1, Th2, and Th17 pathways), cytokine regulation, and osteoclast differentiation. Notably, we refined the association of rs59902911 at the CARD9 locus to an intronic polymorphism within an active enhancer (rs78892335), achieving genome-wide significance and with an effect size exceeding the minimal clinically important difference for each copy of the minor allele (4.78 Larsen units/copy; 95% CI, 3.15-6.41; p = 9.01 × 10-9). This polymorphism is associated with the expression of CARD9 in immune cells, including B cells.

CONCLUSIONS

We provide a comprehensive list of validated genetic associations with RA outcome and demonstrate that non-HLA polymorphisms can associate with radiographic severity independently of disease susceptibility. This highlights the importance of dedicated genetic outcome studies for patient stratification in precision medicine for RA.

Multi-ancestry genome-wide association meta-analysis of buprenorphine treatment response

Although the mu-opioid partial agonist buprenorphine is increasingly being prescribed to treat opioid use disorder, patients' responses to the drug vary and few clinical and no genetic predictors of treatment response have been identified. We conducted a genome-wide association study (GWAS) meta-analysis of buprenorphine treatment response (defined using urine drug screen results) in 4394 Veterans with opioid use disorder from the VA Million Veteran Program (751 of African-like ancestry [AFR] and 3643 of European-like ancestry [EUR]) and 296 participants from a clinical trial of extended-release buprenorphine (nAFR = 104, nEUR = 192). We conducted within-ancestry GWAS in both cohorts, followed by cross-ancestry, fixed-effects GWAS meta-analyses within and across cohorts. We also examined associations between demographic and clinical characteristics and buprenorphine treatment response. The cross-ancestry meta-analysis of both cohorts identified one genome-wide significant locus (rs149319538) that maps to SLC39A10, a gene that encodes a zinc transporter. Phenome-wide association analyses of the lead variant implicated connectivity of the uncinate fasciculus, a limbic white matter fiber tract. Of the clinical characteristics, only the presence of chronic pain and a lower maximum buprenorphine dosage were related to higher odds of treatment response in adjusted models. We report here the first genome-wide significant variant associated with buprenorphine treatment response. Larger samples are needed to replicate these findings and identify additional clinical and genetic factors that predict buprenorphine treatment efficacy to enable the use of a precision approach to OUD treatment.

Mapping disease loci to biological processes via joint pleiotropic and epigenomic partitioning

Genome-wide association studies (GWAS) have identified thousands of disease-associated loci, yet their interpretation remains limited by the heterogeneity of underlying biological processes. We propose Joint Pleiotropic and Epigenomic Partitioning (J-PEP), a clustering framework that integrates pleiotropic SNP effects on auxiliary traits and tissue-specific epigenomic data to partition disease-associated loci into biologically distinct clusters. To benchmark J-PEP against existing methods, we introduce a metric-Pleiotropic and Epigenomic Prediction Accuracy (PEPA)-that evaluates how well the clusters predict SNP-to-trait and SNP-to-tissue associations using off-chromosome data, avoiding overfitting. Applying J-PEP to GWAS summary statistics for 165 diseases/traits (average N = 290 K ), we attained 16-30% higher PEPA than pleiotropic or epigenomic partitioning approaches with larger improvements for well-powered traits, consistent with simulations; these gains arise from J-PEP's tendency to upweight correlated structure-signals present in both auxiliary trait and tissue data-thereby emphasizing shared components. For type 2 diabetes (T2D), J-PEP identified clusters refining canonical pathological processes while revealing underexplored immune and developmental signals. For hypertension (HTN), J-PEP identified stromal and adrenal-endocrine processes that were not identified in prior analyses. For neutrophil count, J-PEP identified hematopoietic, hepatic-inflammatory, and neuroimmune processes, expanding biological interpretation beyond classical immune regulation. Notably, integrating single-cell chromatin accessibility data refined bulk-based clusters, enhancing cell-type resolution and specificity. For T2D, single-cell data refined a bulk endocrine cluster to pancreatic islet β -cells, consistent with established β -cell dysfunction in insulin deficiency; for HTN, single-cell data refined a bulk endocrine cluster to adrenal cortex cells, consistent with a GO enrichment for neutrophil-mediated inflammation that implicates feedback between aldosterone production in the adrenal gland and local immune signaling. In conclusion, J-PEP provides a principled framework for partitioning GWAS loci into interpretable, tissue-informed clusters that provide biological insights on complex disease.

A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features

Facial morphology, a complex trait influenced by genetics, holds great significance in evolutionary research. However, due to limited fossil evidence, the facial characteristics of Neanderthals and Denisovans have remained largely unknown. In this study, we conduct a large-scale multi-ethnic meta-analysis of the genome-wide association study (GWAS), including 9674 East Asians and 10,115 Europeans, quantitatively assessing 78 facial traits using 3D facial images. We identify 71 genomic loci associated with facial features, including 21 novel loci. We develop a facial polygenic score (FPS) that enables the prediction of facial features based on genetic information. Interestingly, the distribution of FPSs among populations from diverse continental groups exhibits relevant correlations with observed facial features. Furthermore, we apply the FPS to predict the facial traits of seven Neanderthals and one Denisovan using ancient DNA and align predictions with the fossil records. Our results suggest that Neanderthals and Denisovans likely share similar facial features, such as a wider but shorter nose and a wider endocanthion distance. The decreased mouth width is characterized specifically in Denisovans. The integration of genomic data and facial trait analysis provides valuable insights into the evolutionary history and adaptive changes in human facial morphology.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

23ME-01473, an Fc Effector-Enhanced Anti-ULBP6/2/5 Antibody, Restores NK Cell-Mediated Antitumor Immunity through NKG2D and FcγRIIIa Activation

SIGNIFICANCE

This study emphasizes the utility of population-based genome-wide assessments for discovering naturally occurring genetic variants associated with lifetime risks for cancer or immune diseases as novel drug targets. We identify ULBP6 as a potential keystone member of the NKG2D pathway, which is important for antitumor immunity. Targeting ULBP6 may hold therapeutic promise for patients with cancer.

The genomic architecture of circulating cytokine levels points to drug targets for immune-related diseases

Circulating cytokines orchestrate immune reactions and are promising drug targets for immune-mediated and inflammatory diseases. Exploring the genetic architecture of circulating cytokine levels could yield key insights into causal mediators of human disease. Here, we performed genome-wide association studies (GWAS) for 40 circulating cytokines in meta-analyses of 74,783 individuals. We detected 359 significant associations between cytokine levels and variants in 169 independent loci, including 150 trans- and 19 cis-acting loci. Integration with transcriptomic data point to key regulatory mechanisms, such as the buffering function of the Atypical Chemokine Receptor 1 (ACKR1) acting as scavenger for multiple chemokines and the role of tumor necrosis factor receptor-associated factor 1 (TRAFD1) in modulating the cytokine storm triggered by TNF signaling. Applying Mendelian randomization (MR), we detected a network of complex cytokine interconnections with TNF-b, VEGF, and IL-1ra exhibiting pleiotropic downstream effects on multiple cytokines. Drug target cis-MR using 2 independent proteomics datasets paired with colocalization revealed G-CSF/CSF-3 and CXCL9/MIG as potential causal mediators of asthma and Crohn's disease, respectively, but also a potentially protective role of TNF-b in multiple sclerosis. Our results provide an overview of the genetic architecture of circulating cytokines and could guide the development of targeted immunotherapies.

Causal association and shared genetics between telomere length and COVID-19 outcomes: New evidence from the latest large-scale summary statistics

BACKGROUND

Observational studies suggested that leukocyte telomere length (LTL) is shortened in COVID-19 patients. However, the genetic association and causality remained unknown.

METHODS

Based on the genome-wide association of LTL (N = 472,174) and COVID-19 phenotypes (N = 1086,211-2597,856), LDSC and SUPERGNOVA were used to estimate the genetic correlation. Cross-trait GWAS meta-analysis, colocalization, fine-mapping analysis, and transcriptome-wide association study were conducted to explore the shared genetic etiology. Mendelian randomization (MR) was utilized to infer the causality. Upstream and downstream two-step MR was performed to investigate the potential mediating effects.

RESULTS

LDSC identified a significant genetic association between LTL and all COVID-19 phenotypes (rG < 0, p < 0.05). Six significant regions were observed for LTL and COVID-19 susceptibility and hospitalization, respectively. Colocalization analysis found rs144204502, rs34517439, and rs56255908 were shared causal variants between LTL and COVID-19 phenotypes. Numerous biological pathways associated with LTL and COVID-19 outcomes were identified, mainly involved in -immune-related pathways. MR showed that longer LTL was significantly associated with a lower risk of COVID-19 severity (OR [95% CI] = 0.81 [0.71-0.92], p = 1.24 ×10-3) and suggestively associated with lower risks of COVID-19 susceptibility (OR [95% CI] = 0.96 [0.92-1.00], p = 3.44 ×10-2) and COVID-19 hospitalization (OR [95% CI] = 0.89 [0.80-0.98], p = 1.89 ×10-2). LTL partially mediated the effects of BMI, smoking, and education on COVID-19 outcomes. Furthermore, six proteins partially mediated the causality of LTL on COVID-19 outcomes, including BNDF, QPCT, FAS, MPO, SFTPB, and APOF.

CONCLUSIONS

Our findings suggested that shorter LTL was genetically associated with a higher risk of COVID-19 phenotypes, with shared genetic etiology and potential causality.

Whole-genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles

ABSTRACT

Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole-genome sequencing (WGS) data provide better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program (n = 32 572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (n = 131 340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, 4 are driven by common variants of small effect with reported minor allele frequency (MAF) at least 10 percentage points higher in African populations. Three signals (SERPINA1, ZFP36L2, and TLR10) contain predicted deleterious missense variants. Two loci, SOCS3 and HPN, each harbor 2 conditionally distinct, noncoding variants. The gene region encoding the fibrinogen protein chain subunits (FGG;FGB;FGA) contains 7 distinct signals, including 1 novel signal driven by rs28577061, a variant common in African ancestry populations but extremely rare in Europeans (MAFAFR = 0.180; MAFEUR = 0.008). Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation.

Integrated systematic functional screen and fine-mapping decipher the role and genetic regulation of RPS19 in colorectal cancer development

Despite genome-wide association studies (GWAS) have identified more than 200 risk loci associated with colorectal cancer (CRC), the causal genes or risk variants within these loci and their biological functions remain not fully revealed. Recently, the genomic locus 19q13.2, with the lead SNP rs1800469 was identified as a crucial CRC risk locus in Asian populations. However, the functional mechanism of this region has not been fully elucidated. Here we employed an RNA interfering-based on-chip approach to screen for the genes essential for cell proliferation in the CRC risk locus 19q13.2. Notably, we found that RPS19 exhibited the most significant effect among the identified genes and acted as a critical oncogene facilitating CRC cell proliferation. Subsequently, combining integrative fine-mapping analysis and a large-scale population study consisting of 6027 cases and 6099 controls, we prioritized rs1025497 as a potential causal candidate for CRC risk, demonstrating that rs1025497[A] allele significantly reduced the risk of CRC (OR 0.70, 95% confidence interval = 0.56-0.83, P = 1.12 × 10-6), which was further validated in UK Biobank cohort comprising 5,313 cases and 21,252 controls. Mechanistically, we experimentally elucidated that variant rs1025497 might acted as an allele-specific silencer, inhibiting the expression level of oncogene RPS19 mediated by the transcription suppressive factor HBP1. Taken together, our sturdy unveils the significant role of RPS19 during CRC pathogenesis and delineates its distal regulatory mechanism mediated by rs1025497, advancing our understanding of the etiology of CRC and provided new insights into the personalized medicine of human cancer.

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.

Atrial fibrillation variant-to-gene prioritization through cross-ancestry eQTL and single-nucleus multiomic analyses

Atrial fibrillation (AF) is the most common arrhythmia in the world. Human genetics can provide strong AF therapeutic candidates, but the identification of the causal genes and their functions remains challenging. Here, we applied an AF fine-mapping strategy that leverages results from a previously published cross-ancestry genome-wide association study (GWAS), expression quantitative trait loci (eQTLs) from left atrial appendages (LAAs) obtained from two cohorts with distinct ancestry, and a paired RNA sequencing (RNA-seq) and ATAC sequencing (ATAC-seq) LAA single-nucleus assay (sn-multiome). At nine AF loci, our co-localization and fine-mapping analyses implicated 14 genes. Data integration identified several candidate causal AF variants, including rs7612445 at GNB4 and rs242557 at MAPT. Finally, we showed that the repression of the strongest AF-associated eQTL gene, LINC01629, in human embryonic stem cell-derived cardiomyocytes using CRISPR inhibition results in the dysregulation of pathways linked to genes involved in the development of atrial tissue and the cardiac conduction system.

Sequence variants influencing the regulation of serum IgG subclass levels

Immunoglobulin G (IgG) is the main isotype of antibody in human blood. IgG consists of four subclasses (IgG1 to IgG4), encoded by separate constant region genes within the Ig heavy chain locus (IGH). Here, we report a genome-wide association study on blood IgG subclass levels. Across 4334 adults and 4571 individuals under 18 years, we discover ten new and identify four known variants at five loci influencing IgG subclass levels. These variants also affect the risk of asthma, autoimmune diseases, and blood traits. Seven variants map to the IGH locus, three to the Fcγ receptor (FCGR) locus, and two to the human leukocyte antigen (HLA) region, affecting the levels of all IgG subclasses. The most significant associations are observed between the G1m (f), G2m(n) and G3m(b*) allotypes, and IgG1, IgG2 and IgG3, respectively. Additionally, we describe selective associations with IgG4 at 16p11.2 (ITGAX) and 17q21.1 (IKZF3, ZPBP2, GSDMB, ORMDL3). Interestingly, the latter coincides with a highly pleiotropic signal where the allele associated with lower IgG4 levels protects against childhood asthma but predisposes to inflammatory bowel disease. Our results provide insight into the regulation of antibody-mediated immunity that can potentially be useful in the development of antibody based therapeutics.

BEATRICE: Bayesian Fine-mapping from Summary Data using Deep Variational Inference

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 simultaneously sample from the space of causal configurations. We use these samples 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 different noise paradigms, as defined by the relative genetic contributions of causal and non-causal variants. 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 ϵ 2 allele, a commonly associated variant of Alzheimer's. Thus, we show that BEATRICE is a valuable tool to identify causal variants from eQTL and GWAS summary statistics across complex diseases and traits.

Genetic architecture of telomere length in 462,666 UK Biobank whole-genome sequences

Telomeres protect chromosome ends from damage and their length is linked with human disease and aging. We developed a joint telomere length metric, combining quantitative PCR and whole-genome sequencing measurements from 462,666 UK Biobank participants. This metric increased SNP heritability, suggesting that it better captures genetic regulation of telomere length. Exome-wide rare-variant and gene-level collapsing association studies identified 64 variants and 30 genes significantly associated with telomere length, including allelic series in ACD and RTEL1. Notably, 16% of these genes are known drivers of clonal hematopoiesis-an age-related somatic mosaicism associated with myeloid cancers and several nonmalignant diseases. Somatic variant analyses revealed gene-specific associations with telomere length, including lengthened telomeres in individuals with large SRSF2-mutant clones, compared with shortened telomeres in individuals with clonal expansions driven by other genes. Collectively, our findings demonstrate the impact of rare variants on telomere length, with larger effects observed among genes also associated with clonal hematopoiesis.

A Genomics England haplotype reference panel and imputation of UK Biobank

We built a reference panel with 342 million autosomal variants using 78,195 individuals from the Genomics England (GEL) dataset, achieving a phasing switch error rate of 0.18% for European samples and imputation quality of r2 = 0.75 for variants with minor allele frequencies as low as 2 × 10-4 in white British samples. The GEL-imputed UK Biobank genome-wide association analysis identified 70% of associations found by direct exome sequencing (P < 2.18 × 10-11), while extending testing of rare variants to the entire genome. Coding variants dominated the rare-variant genome-wide association results, implying less disruptive effects of rare non-coding variants.

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.

Human genetics and epigenetics of alcohol use disorder

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals

T-cells play a critical role in multiple aspects of human health and disease. However, to date the genetic determinants of human T-cell abundance have not been studied at scale because assays quantifying T-cell abundance are not widely used in clinical or research settings. The complete blood count clinical assay quantifies lymphocyte abundance which includes T-cells, B-cells, and NK-cells. To address this gap, we directly estimate T-cell fractions from whole genome sequencing data in over 200,000 individuals from the multi-ethnic TOPMed and All of Us studies. We identified 27 loci associated with T-cell fraction. Interrogating electronic health records identified clinical phenotypes associated with T-cell fraction, including notable changes in T-cell proportions that were highly dynamic over the course of pregnancy. In summary, by estimating T-cell fraction, we obtained new insights into the genetic regulation of T-cells and identified disease consequences of T-cell fractions across the human phenome.

Finemap-MiXeR: A variational Bayesian approach for genetic finemapping

Genome-wide association studies (GWAS) implicate broad genomic loci containing clusters of highly correlated genetic variants. Finemapping techniques can select and prioritize variants within each GWAS locus which are more likely to have a functional influence on the trait. Here, we present a novel method, Finemap-MiXeR, for finemapping causal variants from GWAS summary statistics, controlling for correlation among variants due to linkage disequilibrium. Our method is based on a variational Bayesian approach and direct optimization of the Evidence Lower Bound (ELBO) of the likelihood function derived from the MiXeR model. After obtaining the analytical expression for ELBO's gradient, we apply Adaptive Moment Estimation (ADAM) algorithm for optimization, allowing us to obtain the posterior causal probability of each variant. Using these posterior causal probabilities, we validated Finemap-MiXeR across a wide range of scenarios using both synthetic data, and real data on height from the UK Biobank. Comparison of Finemap-MiXeR with two existing methods, FINEMAP and SuSiE RSS, demonstrated similar or improved accuracy. Furthermore, our method is computationally efficient in several aspects. For example, unlike many other methods in the literature, its computational complexity does not increase with the number of true causal variants in a locus and it does not require any matrix inversion operation. The mathematical framework of Finemap-MiXeR is flexible and may also be applied to other problems including cross-trait and cross-ancestry finemapping.

Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model

We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.

Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation

Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.

Genetic polymorphisms associated with adverse pregnancy outcomes in nulliparas

Adverse pregnancy outcomes (APOs) affect a large proportion of pregnancies and represent an important cause of morbidity and mortality worldwide. Yet the pathophysiology of APOs is poorly understood, limiting our ability to prevent and treat these conditions. To search for genetic markers of maternal risk for four APOs, we performed multi-ancestry genome-wide association studies (GWAS) for pregnancy loss, gestational length, gestational diabetes, and preeclampsia. We clustered participants by their genetic ancestry and focused our analyses on three sub-cohorts with the largest sample sizes: European, African, and Admixed American. Association tests were carried out separately for each sub-cohort and then meta-analyzed together. Two novel loci were significantly associated with an increased risk of pregnancy loss: a cluster of SNPs located downstream of the TRMU gene (top SNP: rs142795512), and the SNP rs62021480 near RGMA. In the GWAS of gestational length we identified two new variants, rs2550487 and rs58548906 near WFDC1 and AC005052.1, respectively. Lastly, three new loci were significantly associated with gestational diabetes (top SNPs: rs72956265, rs10890563, rs79596863), located on or near ZBTB20, GUCY1A2, and RPL7P20, respectively. Fourteen loci previously correlated with preterm birth, gestational diabetes, and preeclampsia were found to be associated with these outcomes as well.

Functional dissection of complex and molecular trait variants at single nucleotide resolution

Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.

Genetic contribution of reproductive traits to risk of uterine leiomyomata: a large-scale, genome-wide, cross-trait analysis

BACKGROUND

Although phenotypic associations between female reproductive characteristics and uterine leiomyomata have long been observed in epidemiologic investigations, the shared genetic architecture underlying these complex phenotypes remains unclear.

OBJECTIVE

We aimed to investigate the shared genetic basis, pleiotropic effects, and potential causal relationships underlying reproductive traits (age at menarche, age at natural menopause, and age at first birth) and uterine leiomyomata.

STUDY DESIGN

With the use of large-scale, genome-wide association studies conducted among women of European ancestry for age at menarche (n=329,345), age at natural menopause (n=201,323), age at first birth (n=418,758), and uterine leiomyomata (ncases/ncontrols=35,474/267,505), we performed a comprehensive, genome-wide, cross-trait analysis to examine systematically the common genetic influences between reproductive traits and uterine leiomyomata.

RESULTS

Significant global genetic correlations were identified between uterine leiomyomata and age at menarche (rg, -0.17; P=3.65×10-10), age at natural menopause (rg, 0.23; P=3.26×10-07), and age at first birth (rg, -0.16; P=1.96×10-06). Thirteen genomic regions were further revealed as contributing significant local correlations (P<.05/2353) to age at natural menopause and uterine leiomyomata. A cross-trait meta-analysis identified 23 shared loci, 3 of which were novel. A transcriptome-wide association study found 15 shared genes that target tissues of the digestive, exo- or endocrine, nervous, and cardiovascular systems. Mendelian randomization suggested causal relationships between a genetically predicted older age at menarche (odds ratio, 0.88; 95% confidence interval, 0.85-0.92; P=1.50×10-10) or older age at first birth (odds ratio, 0.95; 95% confidence interval, 0.90-0.99; P=.02) and a reduced risk for uterine leiomyomata and between a genetically predicted older age at natural menopause and an increased risk for uterine leiomyomata (odds ratio, 1.08; 95% confidence interval, 1.06-1.09; P=2.30×10-27). No causal association in the reverse direction was found.

CONCLUSION

Our work highlights that there are substantial shared genetic influences and putative causal links that underlie reproductive traits and uterine leiomyomata. The findings suggest that early identification of female reproductive risk factors may facilitate the initiation of strategies to modify potential uterine leiomyomata risk.

Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.

Genome-wide association analysis revealed new QTL and candidate genes affecting the teat number in Dutch Large White pigs

Teat number (TNUM) is an important reproductive trait of sows, which affects the weaning survival rate of piglets. In this study, 1166 Dutch Large White pigs with TNUM phenotype were used as the research object. These pigs were genotyped by 50K SNP chip and the chip data were further imputed to the resequencing level. The estimated heritabilities of left teat number (LTN), right teat number (RTN) and total teat number (TTN) were 0.21, 0.19 and 0.3, respectively. Based on chip data, significant SNPs for RTN on SSC2, SSC5, SSC9 and SSC13 were identified using genome-wide association analysis (GWAS). Significant SNPs for TTN were identified on SSC2, SSC5 and SSC7. Based on imputed data, the GWAS identified a significant SNP (rs329158522) for LTN on SSC17, two significant SNPs (rs342855242 and rs80813115) for RTN on SSC2 and SSC9, and two significant SNPs (rs327003548 and rs326943811) for TTN on SSC5 and SSC6. Among them, four novel QTL were discovered. The Bayesian fine-mapping method was used to fine map the QTL identified in the GWAS of the imputed data, and the confidence intervals of QTL affecting LTN (SSC17: 45.22-46.20 Mb), RTN (SSC9: 122.18-122.80 Mb) and TTN (SSC5: 14.01-15.91 Mb, SSC6: 120.06-121.25 Mb) were detected. A total of 52 candidate genes were obtained. Furthermore, we identified five candidate genes, WNT10B, AQP5, FMNL3, NUAK1 and CKAP4, for the first time, which involved in breast development and other related functions by gene annotation. Overall, this study provides new molecular markers for the breeding of teat number in pigs.

Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens

BACKGROUND

Drug targets with genetic evidence are expected to increase clinical success by at least twofold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of drug discovery. A key issue is that the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements found in T-cell-specific open chromatin regions.

RESULTS

To identify genes and molecular programs modulated by these regulatory elements, we develop a CRISPRi-based single-cell functional screening approach in primary human T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimize an efficient CRISPRi protocol in primary CD4+ T cells via CROPseq vectors. Subsequently, we perform a screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profile approximately 250,000 T -cell single-cell transcriptomes. We develop a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrate genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci.

CONCLUSIONS

We describe "primary T cell crisprQTL" - a scalable, single-cell functional genomics approach for mapping regulatory elements to genes in primary human T cells. We show how this framework can facilitate the interrogation of immune disease GWAS hits and propose that the combination of experimental and QTL-based techniques is likely to address the variant-to-function problem.

A Bayesian fine-mapping model using a continuous global-local shrinkage prior with applications in prostate cancer analysis

The aim of fine mapping is to identify genetic variants causally contributing to complex traits or diseases. Existing fine-mapping methods employ Bayesian discrete mixture priors and depend on a pre-specified maximum number of causal variants, which may lead to sub-optimal solutions. In this work, we propose a Bayesian fine-mapping method called h2-D2, utilizing a continuous global-local shrinkage prior. We also present an approach to define credible sets of causal variants in continuous prior settings. Simulation studies demonstrate that h2-D2 outperforms current state-of-the-art fine-mapping methods such as SuSiE and FINEMAP in accurately identifying causal variants and estimating their effect sizes. We further applied h2-D2 to prostate cancer analysis and discovered some previously unknown causal variants. In addition, we inferred 369 target genes associated with the detected causal variants and several pathways that were significantly over-represented by these genes, shedding light on their potential roles in prostate cancer development and progression.

The Impact of Patterns in Linkage Disequilibrium and Sequencing Quality on the Imprint of Balancing Selection

Regions under balancing selection are characterized by dense polymorphisms and multiple persistent haplotypes, along with other sequence complexities. Successful identification of these patterns depends on both the statistical approach and the quality of sequencing. To address this challenge, at first, a new statistical method called LD-ABF was developed, employing efficient Bayesian techniques to effectively test for balancing selection. LD-ABF demonstrated the most robust detection of selection in a variety of simulation scenarios, compared against a range of existing tests/tools (Tajima's D, HKA, Dng, BetaScan, and BalLerMix). Furthermore, the impact of the quality of sequencing on detection of balancing selection was explored, as well, using: (i) SNP genotyping and exome data, (ii) targeted high-resolution HLA genotyping (IHIW), and (iii) whole-genome long-read sequencing data (Pangenome). In the analysis of SNP genotyping and exome data, we identified known targets and 38 new selection signatures in genes not previously linked to balancing selection. To further investigate the impact of sequencing quality on detection of balancing selection, a detailed investigation of the MHC was performed with high-resolution HLA typing data. Higher quality sequencing revealed the HLA-DQ genes consistently demonstrated strong selection signatures otherwise not observed from the sparser SNP array and exome data. The HLA-DQ selection signature was also replicated in the Pangenome samples using considerably less samples but, with high-quality long-read sequence data. The improved statistical method, coupled with higher quality sequencing, leads to more consistent identification of selection and enhanced localization of variants under selection, particularly in complex regions.

Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas

Chronic kidney disease is a leading cause of death and disability globally and impacts individuals of African ancestry (AFR) or with ancestry in the Americas (AMS) who are under-represented in genome-wide association studies (GWASs) of kidney function. To address this bias, we conducted a large meta-analysis of GWASs of estimated glomerular filtration rate (eGFR) in 145,732 AFR and AMS individuals. We identified 41 loci at genome-wide significance (p < 5 × 10-8), of which two have not been previously reported in any ancestry group. We integrated fine-mapped loci with epigenomic and transcriptomic resources to highlight potential effector genes relevant to kidney physiology and disease, and reveal key regulatory elements and pathways involved in renal function and development. We demonstrate the varying but increased predictive power offered by a multi-ancestry polygenic score for eGFR and highlight the importance of population diversity in GWASs and multi-omics resources to enhance opportunities for clinical translation for all.

Unravelling novel and pleiotropic genes for cannon bone circumference and bone mineral density in Yorkshire pigs

Leg weakness is a prevalent health condition in pig farms. The augmentation of cannon bone circumference and bone mineral density can effectively improve limb strength in pigs and alleviate leg weakness. This study measured forelimb cannon bone circumference (fCBC) and rear limb cannon bone circumference (rCBC) using an inelastic tapeline and rear limb metatarsal area bone mineral density (raBMD) using a dual-energy X-ray absorptiometry bone density scanner. The samples of Yorkshire castrated boars were genotyped using a 50K single-nucleotide polymorphism (SNP) array. The SNP-chip data were imputed to the level of whole-genome sequencing data (iWGS). This study used iWGS data to perform genome-wide association studies and identified novel significant SNPs associated with fCBC on SSC6, SSC12, and SSC13, rCBC on SSC12 and SSC14, and raBMD on SSC7. Based on the high phenotypic and genetic correlations between CBC and raBMD, multi-trait meta-analysis was performed to identify pleiotropic SNPs. A significant potential pleiotropic quantitative trait locus (QTL) regulating both CBC and raBMD was identified on SSC15. Bayes fine mapping was used to establish the confidence intervals for these novel QTLs with the most refined confidence interval narrowed down to 56 kb (15.11 to 15.17 Mb on SSC12 for fCBC). Furthermore, the confidence interval for the potential pleiotropic QTL on SSC15 in the meta-analysis was narrowed down to 7.45 kb (137.55 to137.56 Mb on SSC15). Based on the biological functions of genes, the following genes were identified as novel regulatory candidates for different phenotypes: DDX42, MYSM1, FTSJ3, and MECOM for fCBC; SMURF2, and STC1 for rCBC; RGMA for raBMD. Additionally, RAMP1, which was determined to be located 23.68 kb upstream of the confidence interval of the QTL on SSC15 in the meta-analysis, was identified as a potential pleiotropic candidate gene regulating both CBC and raBMD. These findings offered valuable insights for identifying pathogenic genes and elucidating the genetic mechanisms underlying CBC and BMD.

Integration of expression QTLs with fine mapping via SuSiE

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine-mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine-mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine-mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By prioritizing functional variants within the candidate region using eQTL information, SuSiE2 improves SuSiE by increasing the detection rate of causal SNPs and reducing the average size of credible sets. We compared the performance of SuSiE2 with other multi-trait fine-mapping methods with respect to power, coverage, and precision through simulations and applications to the GWAS results of Alzheimer's disease (AD) and body mass index (BMI). Our results demonstrate the better performance of SuSiE2, both when the in-sample linkage disequilibrium (LD) matrix and an external reference panel is used in inference.

Whole-genome sequencing uncovers two loci for coronary artery calcification and identifies ARSE as a regulator of vascular calcification

Coronary artery calcification (CAC) is a measure of atherosclerosis and a well-established predictor of coronary artery disease (CAD) events. Here we describe a genome-wide association study (GWAS) of CAC in 22,400 participants from multiple ancestral groups. We confirmed associations with four known loci and identified two additional loci associated with CAC (ARSE and MMP16), with evidence of significant associations in replication analyses for both novel loci. Functional assays of ARSE and MMP16 in human vascular smooth muscle cells (VSMCs) demonstrate that ARSE is a promoter of VSMC calcification and VSMC phenotype switching from a contractile to a calcifying or osteogenic phenotype. Furthermore, we show that the association of variants near ARSE with reduced CAC is likely explained by reduced ARSE expression with the G allele of enhancer variant rs5982944. Our study highlights ARSE as an important contributor to atherosclerotic vascular calcification, and a potential drug target for vascular calcific disease.

An in-depth association analysis of genetic variants within nicotine-related loci: Meeting in middle of GWAS and genetic fine-mapping

In the last two decades of Genome-wide association studies (GWAS), nicotine-dependence-related genetic loci (e.g., nicotinic acetylcholine receptor - nAChR subunit genes) are among the most replicable genetic findings. Although GWAS results have reported tens of thousands of SNPs within these loci, further analysis (e.g., fine-mapping) is required to identify the causal variants. However, it is computationally challenging for existing fine-mapping methods to reliably identify causal variants from thousands of candidate SNPs based on the posterior inclusion probability. To address this challenge, we propose a new method to select SNPs by jointly modeling the SNP-wise inference results and the underlying structured network patterns of the linkage disequilibrium (LD) matrix. We use adaptive dense subgraph extraction method to recognize the latent network patterns of the LD matrix and then apply group LASSO to select causal variant candidates. We applied this new method to the UK biobank data to identify the causal variant candidates for nicotine addiction. Eighty-one nicotine addiction-related SNPs (i.e.,-log(p) > 50) of nAChR were selected, which are highly correlated (average r2>0.8) although they are physically distant (e.g., >200 kilobase away) and from various genes. These findings revealed that distant SNPs from different genes can show higher LD r2 than their neighboring SNPs, and jointly contribute to a complex trait like nicotine addiction.

Integration of Expression QTLs with fine mapping via SuSiE

Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE2, connects two SuSiE models, one for eQTL analysis and one for genetic fine mapping. This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By leveraging eQTL information, SuSiE2 enhances the power of detecting causal SNPs while reducing false positives and the average size of credible sets by prioritizing functional variants within the candidate region. The advantages of SuSiE2 over SuSiE are demonstrated by simulations and an application to a single-cell epigenomic study for Alzheimer's disease. We also demonstrate that eQTL information can be used by SuSiE2 to compensate for the power loss because of an inaccurate LD matrix.

Integration of genetic fine-mapping and multi-omics data reveals candidate effector genes for hypertension

Genome-wide association studies of blood pressure (BP) have identified >1,000 loci, but the effector genes and biological pathways at these loci are mostly unknown. Using published association summary statistics, we conducted annotation-informed fine-mapping incorporating tissue-specific chromatin segmentation and colocalization to identify causal variants and candidate effector genes for systolic BP, diastolic BP, and pulse pressure. We observed 532 distinct signals associated with ≥2 BP traits and 84 with all three. For >20% of signals, a single variant accounted for >75% posterior probability, 65 were missense variants in known (SLC39A8, ADRB2, and DBH) and previously unreported BP candidate genes (NRIP1 and MMP14). In disease-relevant tissues, we colocalized >80 and >400 distinct signals for each BP trait with cis-eQTLs and regulatory regions from promoter capture Hi-C, respectively. Integrating mouse, human disorder, gene expression and tissue abundance data, and literature review, we provide consolidated evidence for 436 BP candidate genes for future functional validation and discover several potential drug targets.

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Large-scale plasma proteomics comparisons through genetics and disease associations

High-throughput proteomics platforms measuring thousands of proteins in plasma combined with genomic and phenotypic information have the power to bridge the gap between the genome and diseases. Here we performed association studies of Olink Explore 3072 data generated by the UK Biobank Pharma Proteomics Project1 on plasma samples from more than 50,000 UK Biobank participants with phenotypic and genotypic data, stratifying on British or Irish, African and South Asian ancestries. We compared the results with those of a SomaScan v4 study on plasma from 36,000 Icelandic people2, for 1,514 of whom Olink data were also available. We found modest correlation between the two platforms. Although cis protein quantitative trait loci were detected for a similar absolute number of assays on the two platforms (2,101 on Olink versus 2,120 on SomaScan), the proportion of assays with such supporting evidence for assay performance was higher on the Olink platform (72% versus 43%). A considerable number of proteins had genomic associations that differed between the platforms. We provide examples where differences between platforms may influence conclusions drawn from the integration of protein levels with the study of diseases. We demonstrate how leveraging the diverse ancestries of participants in the UK Biobank helps to detect novel associations and refine genomic location. Our results show the value of the information provided by the two most commonly used high-throughput proteomics platforms and demonstrate the differences between them that at times provides useful complementarity.

A genome-wide cross-trait analysis identifies genomic correlation, pleiotropic loci, and causal relationship between sex hormone-binding globulin and rheumatoid arthritis

BACKGROUND

Our study aims to investigate an intrinsic link underlying sex hormone-binding globulin (SHBG) and rheumatoid arthritis (RA), which remains inconclusive in observational settings.

METHODS

Summary statistics were collected from the largest GWAS(s) on SHBG adjusted for BMI (SHBGadjBMI; Noverall = 368,929; Nmen = 180,094; Nwomen = 188,908), crude SHBG (Noverall = 370,125; Nmen = 180,726; Nwomen = 189,473), and RA (Ncase = 22,350; Ncontrol = 74,823). A genome-wide cross-trait design was performed to quantify global and local genetic correlation, identify pleiotropic loci, and infer a causal relationship.

RESULTS

Among the overall population, a significant global genetic correlation was observed for SHBGadjBMI and RA ([Formula: see text] = 0.11, P = 1.0 × 10-4) which was further supported by local signal (1q25.2). A total of 18 independent pleiotropic SNPs were identified, of which three were highly likely causal variants and four were found to have effects on both traits through gene expression mediation. A putative causal association of SHBGadjBMI on RA was demonstrated (OR = 1.20, 95% CI = 1.01-1.43) without evidence of reverse causality (OR = 0.999, 95% CI = 0.997-1.000). Sex-specific analyses revealed distinct shared genetic regions (men: 1q32.1-q32.2 and 5p13.1; women: 1q25.2 and 22q11.21-q11.22) and diverse pleiotropic SNPs (16 in men and 18 in women, nearly half were sex-specific) underlying SHBGadjBMI and RA, demonstrating biological disparities between sexes. Replacing SHBGadjBMI with crude SHBG, a largely similar yet less significant pattern of results was observed.

CONCLUSION

Our cross-trait analysis suggests an intrinsic, as well as a sex-specific, link underlying SHBG and RA, providing novel insights into disease etiology.

Meta-analysis of African ancestry genome-wide association studies identified novel locus and validates multiple loci associated with kidney function

Despite recent efforts to increase diversity in genome-wide association studies (GWASs), most loci currently associated with kidney function are still limited to European ancestry due to the underlying sample selection bias in available GWASs. We set out to identify susceptibility loci associated with estimated glomerular filtration rate (eGFRcrea) in 80027 individuals of African-ancestry from the UK Biobank (UKBB), Million Veteran Program (MVP), and Chronic Kidney Disease genetics (CKDGen) consortia.We identified 8 lead SNPs, 7 of which were previously associated with eGFR in other populations. We identified one novel variant, rs77408001 which is an intronic variant mapped to the ELN gene. We validated three previously reported loci at GATM-SPATA5L1, SLC15A5 and AGPAT3. Fine-mapping analysis identified variants rs77121243 and rs201602445 as having a 99.9% posterior probability of being causal. Our results warrant designing bigger studies within individuals of African ancestry to gain new insights into the pathogenesis of Chronic Kidney Disease (CKD), and identify genomic variants unique to this ancestry that may influence renal function and disease.

Androgen receptor binding sites enabling genetic prediction of mortality due to prostate cancer in cancer-free subjects

Prostate cancer (PrCa) is the second most common cancer worldwide in males. While strongly warranted, the prediction of mortality risk due to PrCa, especially before its development, is challenging. Here, we address this issue by maximizing the statistical power of genetic data with multi-ancestry meta-analysis and focusing on binding sites of the androgen receptor (AR), which has a critical role in PrCa. Taking advantage of large Japanese samples ever, a multi-ancestry meta-analysis comprising more than 300,000 subjects in total identifies 9 unreported loci including ZFHX3, a tumor suppressor gene, and successfully narrows down the statistically finemapped variants compared to European-only studies, and these variants strongly enrich in AR binding sites. A polygenic risk scores (PRS) analysis restricting to statistically finemapped variants in AR binding sites shows among cancer-free subjects, individuals with a PRS in the top 10% have a strongly higher risk of the future death of PrCa (HR: 5.57, P = 4.2 × 10-10). Our findings demonstrate the potential utility of leveraging large-scale genetic data and advanced analytical methods in predicting the mortality of PrCa.

Exploring opportunities for drug repurposing and precision medicine in cannabis use disorder using genetics

Cannabis use disorder (CUD) remains a significant public health issue globally, affecting up to one in five adults who use cannabis. Despite extensive research into the molecular underpinnings of the condition, there are no effective pharmacological treatment options available. Therefore, we sought to further explore genetic analyses to prioritise opportunities to repurpose existing drugs for CUD. Specifically, we aimed to identify druggable genes associated with the disorder, integrate transcriptomic/proteomic data and estimate genetic relationships with clinically actionable biochemical traits. Aggregating variants to genes based on genomic position, prioritised the phosphodiesterase gene PDE4B as an interesting target for drug repurposing in CUD. Credible causal PDE4B variants revealed by probabilistic finemapping in and around this locus demonstrated an association with inflammatory and other substance use phenotypes. Gene and protein expression data integrated with the GWAS data revealed a novel CUD associated gene, NPTX1, in whole blood and supported a role for hyaluronidase, a key enzyme in the extracellular matrix in the brain and other tissues. Finally, genetic correlation with biochemical traits revealed a genetic overlap between CUD and immune-related markers such as lymphocyte count, as well as serum triglycerides.

Interpreting non-coding disease-associated human variants using single-cell epigenomics

Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.

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 ancestries 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, which builds on the single-population fine-mapping framework, Sum of Single Effects (SuSiE). SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics. We comprehensively evaluated SuSiEx using simulations, a range of quantitative traits measured in both UK Biobank and Taiwan Biobank, and schizophrenia GWAS across East Asian and European ancestries. In all evaluations, SuSiEx fine-mapped more association signals, produced smaller credible sets and higher posterior inclusion probability (PIP) for putative causal variants, and captured population-specific causal variants.

Genetic fine-mapping from summary data using a nonlocal prior improves the detection of multiple causal variants

MOTIVATION

Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns.

RESULTS

We present "FiniMOM" (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus.

AVAILABILITY AND IMPLEMENTATION

https://vkarhune.github.io/finimom/.

A review of genetic risk in systemic lupus erythematosus

INTRODUCTION

Systemic Lupus Erythematosus (SLE) is a complex multisystem autoimmune disease with a wide range of signs and symptoms in affected individuals. The utilization of genome-wide association study (GWAS) technology has led to an explosion in the number of genetic risk factors mapped for autoimmune diseases, including SLE.

AREAS COVERED

In this review, we summarize the more recent genetic risk loci mapped in SLE, which bring the total number of loci mapped to approximately 200. We review prioritization analyses of the associated variants and experimental validation of the putative causal variants. This includes the implementation of new bioinformatic techniques to align genomic and functional data and the use of transcriptomics with single-cell RNA-sequencing, CRISPR genome editing, and Massive Parallel Reporter Assays to analyze non-coding regulatory genetics.

EXPERT OPINION

Despite progress in identifying more genetic risk loci and variant-gene pairs for SLE, understanding its pathogenesis and applying findings clinically remains challenging. The polygenic risk score (PRS) has been used as an application of SLE genetics, but with limited performance in non-EUR populations. In the next few years, advancements in proteomics, post-translational modification estimation, and whole-genome sequencing will enhance disease understanding.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

AIMS/HYPOTHESIS

The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population.

METHODS

We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort.

RESULTS

Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10-9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.

CONCLUSIONS/INTERPRETATION

Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

DATA AVAILABILITY

Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal ( https://t2d.hugeamp.org/downloads.html ) and through the GWAS catalog ( https://www.ebi.ac.uk/gwas/ , accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog ( https://www.pgscatalog.org , publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).

Sequence variants affecting voice pitch in humans

The genetic basis of the human vocal system is largely unknown, as are the sequence variants that give rise to individual differences in voice and speech. Here, we couple data on diversity in the sequence of the genome with voice and vowel acoustics in speech recordings from 12,901 Icelanders. We show how voice pitch and vowel acoustics vary across the life span and correlate with anthropometric, physiological, and cognitive traits. We found that voice pitch and vowel acoustics have a heritable component and discovered correlated common variants in ABCC9 that associate with voice pitch. The ABCC9 variants also associate with adrenal gene expression and cardiovascular traits. By showing that voice and vowel acoustics are influenced by genetics, we have taken important steps toward understanding the genetics and evolution of the human vocal system.