Fine-mapping from summary data with the "Sum of Single Effects" model

Psychiatric Genomics 2025: State of the Art and the Path Forward

Psychiatric genetics has evolved from candidate-gene studies to whole-genome sequencing efforts. With hundreds of disease-associated loci now identified, functional interpretation of the associated loci becomes the critical next step toward translational applications. The article discusses achievements, challenges, and opportunities in psychiatric genomics associated with complexity and heterogeneity. Brain expression quantitative trait loci, single-cell ribonucleic acid-sequence, and functional genomics technologies are highlighted. It also covers newly developed techniques with improved spatiotemporal resolution, quality and sensitivity, coupled with advanced analytical methods and artificial intelligence. The power of collaborative research and inclusion of diverse populations will ensure a bright future for precision psychiatry.

Exploring the shared genetic landscape of diabetes and cardiovascular disease: findings and future implications

Diabetes is a rapidly growing global health concern projected to affect one in eight adults by 2045, which translates to roughly 783 million people. The profound metabolic alterations often present in dysglycaemia significantly increase the risk of cardiovascular complications. While genetic susceptibility plays a crucial role in diabetes and its vascular complications, identifying genes and molecular mechanisms that influence both diseases simultaneously has proven challenging. A key reason for this challenge is the pathophysiological heterogeneity underlying these diseases, with multiple processes contributing to different forms of diabetes and specific cardiovascular complications. This molecular heterogeneity has limited the effectiveness of large-scale genome-wide association studies (GWAS) in identifying shared underlying mechanisms. Additionally, our limited knowledge of the causal genes, cell types and disease-relevant states through which GWAS signals operate has hindered the discovery of common molecular pathways. This review highlights recent advances in genetic epidemiology, including studies of causal associations that have uncovered genetic and molecular factors influencing both dysglycaemia and cardiovascular complications. We explore how disease subtyping approaches can be critical in pinpointing the unique molecular signatures underlying both diabetes and cardiovascular complications. Finally, we address critical research gaps and future opportunities to advance our understanding of both diseases and translate these discoveries into tangible benefits for patient care and population health.

Integration of biobank-scale genetics and plasma proteomics reveals evidence for causal processes in asthma risk and heterogeneity

Hundreds of genetic associations for asthma have been identified, yet translating these findings into mechanistic insights remains challenging. We leveraged plasma proteomics from the UK Biobank Pharma Proteomics Project (UKB-PPP) to identify biomarkers and effectors of asthma risk or heterogeneity using genetic causal inference approaches. We identified 609 proteins associated with asthma status (269 proteins after controlling for body mass index [BMI] and smoking). Analysis of genetically predicted protein levels identified 70 proteins with putative causal roles in asthma risk, including known drug targets and proteins without prior genetic evidence in asthma (e.g., GCHFR, TDRKH, and CLEC7A). The genetic architecture of causally associated proteins provided evidence for a Toll-like receptor (TLR)1-interleukin (IL)-27 asthma axis. Lastly, we identified evidence of causal relationships between proteins and heterogeneous aspects of asthma biology, including between TSPAN8 and neutrophil counts. These findings illustrate that integrating biobank-scale genetics and plasma proteomics can provide a framework to identify therapeutic targets and mechanisms underlying disease risk and heterogeneity.

Elucidating the Genetic Underpinnings of Human Musculoskeletal System Aging Through Genomic Structural Equation Modeling

The genetic architecture underlying traits related to Human Musculoskeletal System Aging (MSA) remains largely unexplored. In this study, we conducted a large-scale multivariate genome-wide association study (GWAS) of MSA utilizing Genomic Structural Equation Modeling (Genomic SEM). We estimated causal single nucleotide polymorphisms (SNPs) associated with independent variation and identified 14 genome-wide significant loci (mean.PP > 0.95). We employed multiple transcriptome-wide association methods to analyze tissue, cellular levels, and genomic elements, identifying loci with high relevance to MSA susceptibility, along with associated element information. Our research represents the first comprehensive delineation of the genetic architecture of Musculoskeletal System Aging through a GWAS of unmeasured phenotype.

Human genetic variation determines 24-hour rhythmic gene expression and disease risk

24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients.

Rare genetic variation in PTPRB is associated with central serous chorioretinopathy, varicose veins and glaucoma

Central serous chorioretinopathy is an eye disease characterized by fluid buildup under the central retina whose etiology is not well understood. Abnormal choroidal veins in central serous chorioretinopathy patients have been shown to have similarities with varicose veins. To identify potential mechanisms, we analyzed genotype data from 1,477 patients and 455,449 controls in FinnGen. We identified an association for a low-frequency (allele frequency = 0.5%) missense variant (rs113791087) in PTPRB, the gene encoding vascular endothelial protein tyrosine phosphatase (odds ratio=2.85, P = 4.5 × 10-9). This was confirmed in a meta-analysis of 2,452 patients and 865,767 controls from 4 studies (odds ratio=3.06, P = 7.4 × 10-15). Rs113791087 was associated with a 56% higher prevalence of retinal abnormalities (35.3% vs 22.6%, P = 8.0 × 10-4) in 708 UK Biobank participants and, surprisingly, with increased risk of varicose veins (odds ratio=1.31, P = 2.3 × 10-11) and reduced risk of glaucoma (odds ratio=0.82, P = 6.9 × 10-9). Predicted loss-of-function variants in PTPRB, though rare in number, were associated with central serous chorioretinopathy in All of Us (odds ratio=17.09, P = 0.018). These findings highlight the significance of vascular endothelial protein tyrosine phosphatase in diverse ocular and systemic veno-vascular diseases.

Genetic regulation of gene expression across multiple tissues in chickens

The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues. This atlas reveals millions of regulatory effects on primary expression (protein-coding genes, long non-coding RNA and exons) and post-transcriptional modifications (alternative splicing and 3'-untranslated region alternative polyadenylation). We highlighted distinct molecular mechanisms underlying these regulatory variants, their context-dependent behavior and their utility in interpreting genome-wide associations for 39 chicken complex traits. Finally, our comparative analyses of gene regulation between chickens and mammals demonstrate how this resource can facilitate cross-species gene mapping of complex traits.

The genetic architecture of cell type-specific cis regulation in maize

Gene expression and complex phenotypes are determined by the activity of cis-regulatory elements. However, an understanding of how extant genetic variants affect cis regulation remains limited. Here, we investigated the consequences of cis-regulatory diversity using single-cell genomics of more than 0.7 million nuclei across 172 Zea mays (maize) inbreds. Our analyses pinpointed cis-regulatory elements distinct to domesticated maize and revealed how historical transposon activity has shaped the cis-regulatory landscape. Leveraging population genetics principles, we fine-mapped about 22,000 chromatin accessibility-associated genetic variants with widespread cell type-specific effects. Variants in TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING CELL FACTOR-binding sites were the most prevalent determinants of chromatin accessibility. Finally, integrating chromatin accessibility-associated variants, organismal trait variation, and population differentiation revealed how local adaptation has rewired regulatory networks in unique cellular contexts to alter maize flowering.

A Genome-wide Association study of Buccal Mucosa Cancer in India and Multi-ancestry Meta-analysis Identifies Novel Risk Loci and Gene-environment Interactions

Genome-wide association studies (GWAS) of oral cancers (OC) to date have focused predominantly on European Ancestry (EA) populations. India faces an excess burden of OC, but the most common site of occurrence is the cancer of the buccal mucosa, which is relatively rare in EA populations. We conducted a GWAS of buccal mucosa cancer (BMC) comprising 2,160 BMC cases and 2,325 controls from different geographical locations in India. Single-SNP association tests detected one novel locus (6q27) and one novel signal within the known OC risk locus 5p13.33, at the genome-wide significance level (P-value<5 × 10-8). We additionally conducted a GWAS of 397 BMC cases and 439 controls from Taiwan and performed multi-ancestry GWAS meta-analysis of OC on 5255 cases and 8748 controls across EA, Indian and Taiwanese populations. We identified a novel risk locus harbouring the tumour suppressor gene NOTCH1 through a gene-level analysis of the multi-ancestry GWAS data. Pathway analysis suggested that PD-1 signalling, and Interferon Gamma Signalling may be important in the aetiology of BMC. Within data from the Indian BMC GWAS, we further identified statistically significant evidence of both multiplicative interactions (P-value=0.026) indicating stronger polygenic risk of BMC among individuals with history of chewing tobacco compared to those without. Our study provides insights into the etiologies of BMC in India, highlighting both its similarities and differences with other types of oral cavity cancers, as well as the interactions between polygenic gene score and tobacco chewing.

Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSxEAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSxEAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

BACKGROUND

Genome-wide association studies (GWAS) have identified hundreds of loci underlying adult-onset asthma (AOA) and childhood-onset asthma (COA). However, the causal variants, regulatory elements, and effector genes at these loci are largely unknown.

METHODS

We performed heritability enrichment analysis to determine relevant cell types for AOA and COA, respectively. Next, we fine-mapped putative causal variants at AOA and COA loci. To improve the resolution of fine-mapping, we integrated ATAC-seq data in blood and lung cell types to annotate variants in candidate cis-regulatory elements (CREs). We then computationally prioritized candidate CREs underlying asthma risk, experimentally assessed their enhancer activity by massively parallel reporter assay (MPRA) in bronchial epithelial cells (BECs) and further validated a subset by luciferase assays. Combining chromatin interaction data and expression quantitative trait loci, we nominated genes targeted by candidate CREs and prioritized effector genes for AOA and COA.

RESULTS

Heritability enrichment analysis suggested a shared role of immune cells in the development of both AOA and COA while highlighting the distinct contribution of lung structural cells in COA. Functional fine-mapping uncovered 21 and 67 credible sets for AOA and COA, respectively, with only 16% shared between the two. Notably, one-third of the loci contained multiple credible sets. Our CRE prioritization strategy nominated 62 and 169 candidate CREs for AOA and COA, respectively. Over 60% of these candidate CREs showed open chromatin in multiple cell lineages, suggesting their potential pleiotropic effects in different cell types. Furthermore, COA candidate CREs were enriched for enhancers experimentally validated by MPRA in BECs. The prioritized effector genes included many genes involved in immune and inflammatory responses. Notably, multiple genes, including TNFSF4, a drug target undergoing clinical trials, were supported by two independent GWAS signals, indicating widespread allelic heterogeneity. Four out of six selected candidate CREs demonstrated allele-specific regulatory properties in luciferase assays in BECs.

CONCLUSIONS

We present a comprehensive characterization of causal variants, regulatory elements, and effector genes underlying AOA and COA genetics. Our results supported a distinct genetic basis between AOA and COA and highlighted regulatory complexity at many GWAS loci marked by both extensive pleiotropy and allelic heterogeneity.

Translational genomics of osteoarthritis in 1,962,069 individuals

Osteoarthritis is the third most rapidly growing health condition associated with disability, after dementia and diabetes1. By 2050, the total number of patients with osteoarthritis is estimated to reach 1 billion worldwide2. As no disease-modifying treatments exist for osteoarthritis, a better understanding of disease aetiopathology is urgently needed. Here we perform a genome-wide association study meta-analyses across up to 489,975 cases and 1,472,094 controls, establishing 962 independent associations, 513 of which have not been previously reported. Using single-cell multiomics data, we identify signal enrichment in embryonic skeletal development pathways. We integrate orthogonal lines of evidence, including transcriptome, proteome and epigenome profiles of primary joint tissues, and implicate 700 effector genes. Within these, we find rare coding-variant burden associations with effect sizes that are consistently higher than common frequency variant associations. We highlight eight biological processes in which we find convergent involvement of multiple effector genes, including the circadian clock, glial-cell-related processes and pathways with an established role in osteoarthritis (TGFβ, FGF, WNT, BMP and retinoic acid signalling, and extracellular matrix organization). We find that 10% of the effector genes express a protein that is the target of approved drugs, offering repurposing opportunities, which can accelerate translation.

Polymorphic tandem repeats influence cell type-specific gene expression across the human immune landscape

Tandem repeats (TRs) - highly polymorphic, repetitive sequences dispersed across the human genome - are crucial regulators of gene expression and diverse biological processes, but have remained underexplored relative to other classes of genetic variation due to historical challenges in their accurate calling and analysis. Here, we leverage whole genome and single-cell RNA sequencing from over 5.4 million blood-derived cells from 1,925 individuals to explore the impact of variation in over 1.7 million polymorphic TR loci on blood cell type-specific gene expression. We identify over 62,000 single-cell expression quantitative trait TR loci (sc-eTRs), 16.6% of which are specific to one of 28 distinct immune cell types. Further fine-mapping uncovers 4,283 sc-eTRs as candidate causal drivers of gene expression in 13.6% of genes tested genome-wide. We show through colocalization that TRs are likely mediators of genetic associations with immune-mediated and hematological traits in over 700 genes, and further identify novel TRs warranting investigation in rare disease cohorts. TRs are critical, yet long-overlooked, contributors to cell type-specific gene expression, with implications for understanding rare disease pathogenesis and the genetic architecture of complex traits.

Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders

A meta-genome-wide association study across eight psychiatric disorders has highlighted the genetic architecture of pleiotropy in major psychiatric disorders. However, mechanisms underlying pleiotropic effects of the associated variants remain to be explored. We conducted a massively parallel reporter assay to decode the regulatory logic of variants with pleiotropic and disorder-specific effects. Pleiotropic variants differ from disorder-specific variants by exhibiting chromatin accessibility that extends across diverse cell types in the neuronal lineage and by altering motifs for transcription factors with higher connectivity in protein-protein interaction networks. We mapped pleiotropic and disorder-specific variants to putative target genes using functional genomics approaches and CRISPR perturbation. In vivo CRISPR perturbation of a pleiotropic and a disorder-specific gene suggests that pleiotropy may involve the regulation of genes expressed broadly across neuronal cell types and with higher network connectivity.

Efficient count-based models improve power and robustness for large-scale single-cell eQTL mapping

Population-scale single-cell transcriptomic technologies (scRNA-seq) enable characterizing variant effects on gene regulation at the cellular level (e.g., single-cell eQTLs; sc-eQTLs). However, existing sc-eQTL mapping approaches are either not designed for analyzing sparse counts in scRNA-seq data or can become intractable in extremely large datasets. Here, we propose jaxQTL, a flexible and efficient sc-eQTL mapping framework using highly efficient count-based models given pseudobulk data. Using extensive simulations, we demonstrated that jaxQTL with a negative binomial model outperformed other models in identifying sc-eQTLs, while maintaining a calibrated type I error. We applied jaxQTL across 14 cell types of OneK1K scRNA-seq data (N=982), and identified 11-16% more eGenes compared with existing approaches, primarily driven by jaxQTL ability to identify lowly expressed eGenes. We observed that fine-mapped sc-eQTLs were further from transcription starting site (TSS) than fine-mapped eQTLs identified in all cells (bulk-eQTLs; P=1x10-4) and more enriched in cell-type-specific enhancers (P=3x10-10), suggesting that sc-eQTLs improve our ability to identify distal eQTLs that are missed in bulk tissues. Overall, the genetic effect of fine-mapped sc-eQTLs were largely shared across cell types, with cell-type-specificity increasing with distance to TSS. Lastly, we observed that sc-eQTLs explain more SNP-heritability (h2 ) than bulk-eQTLs (9.90 ± 0.88% vs. 6.10 ± 0.76% when meta-analyzed across 16 blood and immune-related traits), improving but not closing the missing link between GWAS and eQTLs. As an example, we highlight that sc-eQTLs in T cells (unlike bulk-eQTLs) can successfully nominate IL6ST as a candidate gene for rheumatoid arthritis. Overall, jaxQTL provides an efficient and powerful approach using count-based models to identify missing disease-associated eQTLs.

FLT1 and other candidate fetal haemoglobin modifying loci in sickle cell disease in African ancestries

Known fetal haemoglobin (HbF)-modulating loci explain 10-24% variation of HbF level in Africans with Sickle Cell Disease (SCD), compared to 50% among Europeans. Here, we report fourteen candidate loci from a genome-wide association study (GWAS) of HbF level in patients with SCD from Cameroon, Tanzania, and the United States of America. We present results of cell-based experiments for FLT1 candidate, demonstrating expression in early haematopoiesis and a possible involvement in hypoxia associated HbF induction. Our study employed genotyping arrays that capture a broad range of African and non-African genetic variation and replicated known loci (BCL11A and HBS1L-MYB). We estimated the heritability of HbF level in SCD at 94%, higher than estimated in unselected Europeans, and suggesting a robust capture of HbF-associated loci by these arrays. Our approach, which involved genotype imputation against six reference haplotype panels and association analysis with each of the panels, proved superior over selecting a best-performing panel, evidenced by a substantial proportion of panel-specific (up to 18%) and a low proportion of shared (28%) imputed variants across the panels.

Genetic, developmental, and neural changes underlying the evolution of butterfly mate preference

Many studies have linked genetic variation to behavior, but few connect to the intervening neural circuits that underlie the arc from sensation to action. Here, we used a combination of genome-wide association (GWA), developmental gene expression, and photoreceptor electrophysiology to investigate the architecture of mate choice behavior in Heliconius cydno butterflies, a clade where males identify preferred mates based on wing color patterns. We first found that the GWA variants most strongly associated with male mate choice were tightly linked to the gene controlling wing color in the K locus, consistent with previous mapping efforts. RNA-seq across developmental time points then showed that seven genes near the top GWA peaks were differentially expressed in the eyes, optic lobes, or central brain of white and yellow H. cydno males, many of which have known functions in the development and maintenance of synaptic connections. In the visual system of these butterflies, we identified a striking physiological difference between yellow and white males that could provide an evolutionarily labile circuit motif in the eye to rapidly switch behavioral preference. Using single-cell electrophysiology recordings, we found that some ultraviolet (UV)-sensitive photoreceptors receive inhibition from long-wavelength photoreceptors in the male eye. Surprisingly, the proportion of inhibited UV photoreceptors was strongly correlated with male wing color, suggesting a difference in the early stages of visual processing that could plausibly influence courtship decisions. We discuss potential links between candidate genes and this physiological signature, and suggest future avenues for experimental work. Taken together, our results support the idea that alterations to the evolutionarily labile peripheral nervous system, driven by genetic and gene expression differences, can significantly and rapidly alter essential behaviors.

Deciphering the Genetic Architecture of Parkinson's Disease in India

The genomic landscape of the Indian population, particularly for age-related disorders like Parkinson's disease (PD) remains underrepresented in global research. Genetic variability in PD has been studied predominantly in European populations, offering limited insights into its role within the Indian population. To address this gap, we conducted the first pan-India genomic survey of PD involving 4,806 cases and 6,364 controls, complemented by a meta-analysis integrating summary statistics from a multi-ancestry PD meta-analysis (N=611,485). We further leveraged RNA-sequencing data from lymphoblastoid cell lines of 731 individuals from the 1000 Genomes project to evaluate the expression of key loci across global populations. Our findings reveal a higher genetic burden of PD in the Indian population compared to Europeans, accounting for ∼30% of the previously unexplained heritability. Thirteen genome-wide significant loci were identified, including two novel loci, with an additional three loci uncovered through meta-analysis. Polygenic risk score analysis showed moderate transferability from European populations. Our results highlight the importance of genetic loci in immune function, lipid metabolism and SNCA aggregation in PD pathogenesis, with gene expression variability emphasizing population-specific differences. We also established South Asia's largest PD biobank, providing a foundation for patient-centric approaches to PD research and treatment in India.

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize de novo mutations with extreme regulatory effects, we developed FLARE, a context-specific functional genomic model of constraint. FLARE outperformed other methods in prioritizing case mutations from autism-affected families near syndromic autism-associated genes; for example, identifying mutation outliers near CNTNAP2 that would be missed by alternative approaches. Overall, our findings demonstrate the potential of integrating single-cell maps with population genetics and deep learning-based variant effect prediction to elucidate mechanisms of development and disease-ultimately, supporting the notion that genetic contributions to neurodevelopmental disorders are predominantly rare.

A genome-wide association study identified PRKCB as a causal gene and therapeutic target for Mycobacterium avium complex disease

Non-tuberculous mycobacterial pulmonary disease (NTM-PD) is a chronic progressive lung disease that is increasing in incidence. Host genetic factors are associated with NTM-PD susceptibility. However, the heritability of NTM-PD is not well understood. Here, we perform a two-stage genome-wide association study (GWAS) and discover a susceptibility locus at 16p21 associated with NTM-PD, especially with pulmonary Mycobacterium avium complex (MAC) disease. As the lead variant, rs194800 C allele augments protein kinase C beta (PRKCB) gene expression and associates with severer NTM-PD. The functional studies show that PRKCB exacerbates M. avium infection and promotes intracellular survival of M. avium in macrophages by inhibiting phagosomal acidification. Mechanistically, PRKCB interacts with subunit G of the vacuolar-H+-ATPase (V-ATPase) and vacuolar protein sorting-associated protein 16 homolog (VPS16), blocking the fusion between lysosomes and mycobacterial phagosomes. PRKCB inhibitor has therapeutic potential against M. avium infection. These findings provide insights into the genetic etiology of NTM-PD and highlight PRKCB as an attractive target for host-directed therapy of MAC disease.

Integration of functional genomics and statistical fine-mapping systematically characterizes adult-onset and childhood-onset asthma genetic associations

Background

Genome-wide association studies (GWAS) have identified hundreds of loci underlying adult-onset asthma (AOA) and childhood-onset asthma (COA). However, the causal variants, regulatory elements, and effector genes at these loci are largely unknown.

Methods

We performed heritability enrichment analysis to determine relevant cell types for AOA and COA, respectively. Next, we fine-mapped putative causal variants at AOA and COA loci. To improve the resolution of fine-mapping, we integrated ATAC-seq data in blood and lung cell types to annotate variants in candidate cis-regulatory elements (CREs). We then computationally prioritized candidate CREs underlying asthma risk, experimentally assessed their enhancer activity by massively parallel reporter assay (MPRA) in bronchial epithelial cells (BECs) and further validated a subset by luciferase assays. Combining chromatin interaction data and expression quantitative trait loci, we nominated genes targeted by candidate CREs and prioritized effector genes for AOA and COA.

Results

Heritability enrichment analysis suggested a shared role of immune cells in the development of both AOA and COA while highlighting the distinct contribution of lung structural cells in COA. Functional fine-mapping uncovered 21 and 67 credible sets for AOA and COA, respectively, with only 16% shared between the two. Notably, one-third of the loci contained multiple credible sets. Our CRE prioritization strategy nominated 62 and 169 candidate CREs for AOA and COA, respectively. Over 60% of these candidate CREs showed open chromatin in multiple cell lineages, suggesting their potential pleiotropic effects in different cell types. Furthermore, COA candidate CREs were enriched for enhancers experimentally validated by MPRA in BECs. The prioritized effector genes included many genes involved in immune and inflammatory responses. Notably, multiple genes, including TNFSF4, a drug target undergoing clinical trials, were supported by two independent GWAS signals, indicating widespread allelic heterogeneity. Four out of six selected candidate CREs demonstrated allele-specific regulatory properties in luciferase assays in BECs.

Conclusions

We present a comprehensive characterization of causal variants, regulatory elements, and effector genes underlying AOA and COA genetics. Our results supported a distinct genetic basis between AOA and COA and highlighted regulatory complexity at many GWAS loci marked by both extensive pleiotropy and allelic heterogeneity.

ML-MAGES: A machine learning framework for multivariate genetic association analyses with genes and effect size shrinkage

A fundamental goal of genetics is to identify which and how genetic variants are associated with a trait, often using the regression results from genome-wide association (GWA) studies. Important methodological challenges are accounting for inflation in GWA effect estimates as well as investigating more than one trait simultaneously. We leverage machine learning approaches for these two challenges, developing a computationally efficient method called ML-MAGES. First, we shrink the inflation in GWA effect sizes caused by non-independence among variants using neural networks. We then cluster variant associations among multiple traits via variational inference. We compare the performance of shrinkage via neural networks to regularized regression and fine-mapping, two approaches used for addressing inflated effects but dealing with variants in focal regions of different sizes. Our neural network shrinkage outperforms both methods in approximating the true effect sizes in simulated data. Our infinite mixture clustering approach offers a flexible, data-driven way to distinguish different types of associations-trait-specific, shared across traits, or spurious-among multiple traits based on their regularized effects. Clustering applied to our neural network shrinkage results also produces consistently higher precision and recall for distinguishing gene-level associations in simulations. We demonstrate the application of ML-MAGES on association analyses of two quantitative traits and two binary traits in the UK Biobank (genetic and phenotypic data from 500,000 residents of the UK). Our identified associated genes from single-trait enrichment tests overlap with those having known relevant biological processes to the traits. Besides trait-specific associations, ML-MAGES identifies several variants with shared multi-trait associations, suggesting putative shared genetic architecture.

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).

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.

Genetic predisposition to altered blood cell homeostasis is associated with glioma risk and survival

Glioma is a highly fatal and heterogeneous brain tumor with few known risk factors. Our study examines genetically predicted variability in blood cell indices in relation to glioma risk and survival in 3418 cases and 8156 controls. We find that increased platelet to lymphocyte ratio (PLR) confers an increased risk of glioma (odds ratio (OR) = 1.25, p = 0.005), especially tumors with isocitrate dehydrogenase (IDH) mutations (OR = 1.38, p = 0.007) and IDHmut 1p/19q intact (IDHmut-intact OR = 1.53, p = 0.004) tumors. Genetically inferred increased counts of lymphocytes (IDHmut-intact OR = 0.70, p = 0.004) and neutrophils (IDHmut OR = 0.69, p = 0.019; IDHmut-intact OR = 0.60, p = 0.009) show inverse associations with risk, which may reflect enhanced immune-surveillance. Considering survival, we observe higher mortality risk in patients with IDHmut 1p/19q with genetically predicted increased counts of lymphocytes (hazard ratio (HR) = 1.65, 95% CI: 1.24-2.20), neutrophils (HR = 1.49, 1.13-1.97), and eosinophils (HR = 1.59, 1.18-2.14). Polygenic scores for blood cell traits are also differentially associated with 17 tumor immune microenvironment features in a subtype-specific manner, including signatures related to interferon signaling, PD-1 expression, and T-cell/Cytotoxic responses. Our findings highlight immune-mediated susceptibility mechanisms with potential disease management implications.

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.

GWAShug: a comprehensive platform for decoding the shared genetic basis between complex traits based on summary statistics

The shared genetic basis offers very valuable insights into the etiology, diagnosis and therapy of complex traits. However, a comprehensive resource providing shared genetic basis using the accessible summary statistics is currently lacking. It is challenging to analyze the shared genetic basis due to the difficulty in selecting parameters and the complexity of pipeline implementation. To address these issues, we introduce GWAShug, a platform featuring a standardized best-practice pipeline with four trait level methods and three molecular level methods. Based on stringent quality control, the GWAShug resource module includes 539 high-quality GWAS summary statistics for European and East Asian populations, covering 54 945 pairs between a measurement-based and a disease-based trait and 43 902 pairs between two disease-based traits. Users can easily search for shared genetic basis information by trait name, MeSH term and category, and access detailed gene information across different trait pairs. The platform facilitates interactive visualization and analysis of shared genetic basic results, allowing users to explore data dynamically. Results can be conveniently downloaded via FTP links. Additionally, we offer an online analysis module that allows users to analyze their own summary statistics, providing comprehensive tables, figures and interactive visualization and analysis. GWAShug is freely accessible at http://www.gwashug.com.

regionalpcs improve discovery of DNA methylation associations with complex traits

We have developed the regionalpcs method, an approach for summarizing gene-level methylation. regionalpcs addresses the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease. In contrast to averaging, regionalpcs uses principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrates a 54% improvement in sensitivity over averaging in simulations, providing a robust framework for identifying subtle epigenetic variations. Applying regionalpcs to Alzheimer's disease brain methylation data, combined with cell type deconvolution, we uncover 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci with genome-wide association studies identified 17 genes with potential causal roles in Alzheimer's disease risk, including MS4A4A and PICALM. Available in the Bioconductor package regionalpcs, our approach facilitates a deeper understanding of the epigenetic landscape in Alzheimer's disease and opens avenues for research into complex diseases.

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.

Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes

Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, mr.mash, a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy, was introduced. However, a drawback of mr.mash is that it requires individual-level data, which are often not publicly available. In this work, we introduce mr.mash-rss, an extension of the mr.mash model that requires only summary statistics from Genome-Wide Association Studies (GWAS) and linkage disequilibrium (LD) estimates from a reference panel. By using summary data, we achieve the twin goal of increasing the applicability of the mr.mash model to data sets that are not publicly available and making it scalable to biobank-size data. Through simulations, we show that mr.mash-rss is competitive with, and often outperforms, current state-of-the-art methods for single- and multi-phenotype polygenic prediction in a variety of scenarios that differ in the pattern of effect sharing across phenotypes, the number of phenotypes, the number of causal variants, and the genomic heritability. We also present a real data analysis of 16 blood cell phenotypes in the UK Biobank, showing that mr.mash-rss achieves higher prediction accuracy than competing methods for the majority of traits, especially when the data set has smaller sample size.

Fine-Mapping the Results From Genome-Wide Association Studies of Primary Biliary Cholangitis Using SuSiE and h2-D2

The main goal of fine-mapping is the identification of relevant genetic variants that have a causal effect on some trait of interest, such as the presence of a disease. From a statistical point of view, fine mapping can be seen as a variable selection problem. Fine-mapping methods are often challenging to apply because of the presence of linkage disequilibrium (LD), that is, regions of the genome where the variants interrogated have high correlation. Several methods have been proposed to address this issue. Here we explore the 'Sum of Single Effects' (SuSiE) method, applied to real data (summary statistics) from a genome-wide meta-analysis of the autoimmune liver disease primary biliary cholangitis (PBC). Fine-mapping in this data set was previously performed using the FINEMAP program; we compare these previous results with those obtained from SuSiE, which provides an arguably more convenient and principled way of generating 'credible sets', that is set of predictors that are correlated with the response variable. This allows us to appropriately acknowledge the uncertainty when selecting the causal effects for the trait. We focus on the results from SuSiE-RSS, which fits the SuSiE model to summary statistics, such as z-scores, along with a correlation matrix. We also compare the SuSiE results to those obtained using a more recently developed method, h2-D2, which uses the same inputs. Overall, we find the results from SuSiE-RSS and, to a lesser extent, h2-D2, to be quite concordant with those previously obtained using FINEMAP. The resulting genes and biological pathways implicated are therefore also similar to those previously obtained, providing valuable confirmation of these previously reported results. Detailed examination of the credible sets identified suggests that, although for the majority of the loci (33 out of 56) the results from SuSiE-RSS seem most plausible, there are some loci (5 out of 56 loci) where the results from h2-D2 seem more compelling. Computer simulations suggest that, overall, SuSiE-RSS generally has slightly higher power, better precision, and better ability to identify the true number of causal variants in a region than h2-D2, although there are some scenarios where the power of h2-D2 is higher. Thus, in real data analysis, the use of complementary approaches such as both SuSiE and h2-D2 is potentially warranted.

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.

Statistical examination of shared loci in neuropsychiatric diseases using genome-wide association study summary statistics

Continued methodological advances have enabled numerous statistical approaches for the analysis of summary statistics from genome-wide association studies. Genetic correlation analysis within specific regions enables a new strategy for identifying pleiotropy. Genomic regions with significant 'local' genetic correlations can be investigated further using state-of-the-art methodologies for statistical fine-mapping and variant colocalisation. We explored the utility of a genome-wide local genetic correlation analysis approach for identifying genetic overlaps between the candidate neuropsychiatric disorders, Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia, Parkinson's disease, and schizophrenia. The correlation analysis identified several associations between traits, the majority of which were loci in the human leukocyte antigen region. Colocalisation analysis suggested that disease-implicated variants in these loci often differ between traits and, in one locus, indicated a shared causal variant between ALS and AD. Our study identified candidate loci that might play a role in multiple neuropsychiatric diseases and suggested the role of distinct mechanisms across diseases despite shared loci. The fine-mapping and colocalisation analysis protocol designed for this study has been implemented in a flexible analysis pipeline that produces HTML reports and is available at: https://github.com/ThomasPSpargo/COLOC-reporter.

Multi-ancestry GWAS of severe pregnancy nausea and vomiting identifies risk loci associated with appetite, insulin signaling, and brain plasticity

While most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel (SLITRK1, SYN3, IGSF11, FSHB, TCF7L2, and CDH9), and confirmed previous genome-wide significant associations with risk genes GDF15, IGFBP7, PGR, and GFRAL. In a spatiotemporal analysis of placental development, GDF15 and TCF7L2 were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects for GDF15 between maternal and fetal genotype. Conversely, IGFBP7 and PGR were primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues.

Cell-type-resolved chromatin accessibility in the human intestine identifies complex regulatory programs and clarifies genetic associations in Crohn's disease

Crohn's disease (CD) is a complex inflammatory bowel disease resulting from an interplay of genetic, microbial, and environmental factors. Cell-type-specific contributions to CD etiology and genetic risk are incompletely understood. Here we built a comprehensive atlas of cell-type- resolved chromatin accessibility comprising 557,310 candidate cis-regulatory elements (cCREs) in terminal ileum and ascending colon from patients with active and inactive CD and healthy controls. Using this atlas, we identified cell-type-, anatomic location-, and context-specific cCREs and characterized the regulatory programs underlying inflammatory responses in the intestinal mucosa of CD patients. Genetic variants that disrupt binding motifs of cell-type-specific transcription factors significantly affected chromatin accessibility in specific mucosal cell types. We found that CD heritability is primarily enriched in immune cell types. However, using fine- mapped non-coding CD variants we identified 29 variants located within cCREs several of which were accessible in epithelial and stromal cells implicating cell types from additional lineages in mediating CD risk in some loci. Our atlas provides a comprehensive resource to study gene regulatory effects in CD and health, and highlights the cellular complexity underlying CD risk.

Uncovering causal gene-tissue pairs and variants: A multivariable TWAS method controlling for infinitesimal effects

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariable TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariable TWAS method named Tissue-Gene pairs, direct causal Variants, and Infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci (eQTL/sQTL) from 31 tissues, TGVIS improves causal gene prioritization and enhances the biological interpretability over existing methods.

Uncovering causal gene-tissue pairs and variants: A multivariable TWAS method controlling for infinitesimal effects

Transcriptome-wide association studies (TWAS) are commonly used to prioritize causal genes underlying associations found in genome-wide association studies (GWAS) and have been extended to identify causal genes through multivariable TWAS methods. However, recent studies have shown that widespread infinitesimal effects due to polygenicity can impair the performance of these methods. In this report, we introduce a multivariable TWAS method named Tissue-Gene pairs, direct causal Variants, and Infinitesimal effects selector (TGVIS) to identify tissue-specific causal genes and direct causal variants while accounting for infinitesimal effects. In simulations, TGVIS maintains an accurate prioritization of causal gene-tissue pairs and variants and demonstrates comparable or superior power to existing approaches, regardless of the presence of infinitesimal effects. In the real data analysis of GWAS summary data of 45 cardiometabolic traits and expression/splicing quantitative trait loci (eQTL/sQTL) from 31 tissues, TGVIS is able to improve causal gene prioritization and identifies novel genes that were missed by conventional TWAS.

Whole-genome sequencing study in Koreans identifies novel loci for Alzheimer's disease

INTRODUCTION

The genetic basis of Alzheimer's disease (AD) in Koreans is poorly understood.

METHODS

We performed an AD genome-wide association study using whole-genome sequence data from 3540 Koreans (1583 AD cases, 1957 controls) and single-nucleotide polymorphism array data from 2978 Japanese (1336 AD cases, 1642 controls). Significant findings were evaluated by pathway enrichment and differential gene expression analysis in brain tissue from controls and AD cases with and without dementia prior to death.

RESULTS

We identified genome-wide significant associations with APOE in the total sample and ROCK2 (rs76484417, p = 2.71×10-8) among APOE ε4 non-carriers. A study-wide significant association was found with aggregated rare variants in MICALL1 (MICAL like 1) (p = 9.04×10-7). Several novel AD-associated genes, including ROCK2 and MICALL1, were differentially expressed in AD cases compared to controls (p < 3.33×10-3). ROCK2 was also differentially expressed between AD cases with and without dementia (p = 1.34×10-4).

DISCUSSION

Our results provide insight into genetic mechanisms leading to AD and cognitive resilience in East Asians.

HIGHLIGHTS

Novel genome-wide significant associations for AD identified with ROCK2 and MICALL1. ROCK2 and MICALL1 are differentially expressed between AD cases and controls in the brain. This is the largest whole-genome-sequence study of AD in an East Asian population.

Multi-ancestry GWAS of severe pregnancy nausea and vomiting identifies risk loci associated with appetite, insulin signaling, and brain plasticity

While most pregnancies are affected by nausea and vomiting, hyperemesis gravidarum (HG) is at the severe end of the clinical spectrum and is associated with dehydration, undernutrition, and adverse maternal, fetal, and child outcomes. Herein we performed a multi-ancestry genome-wide association study (GWAS) of severe nausea and vomiting of pregnancy of 10,974 cases and 461,461 controls across European, Asian, African, and Latino ancestries. We identified ten significantly associated loci, of which six were novel (SLITRK1, SYN3, IGSF11, FSHB, TCF7L2, and CDH9), and confirmed previous genome-wide significant associations with risk genes GDF15, IGFBP7, PGR, and GFRAL. In a spatiotemporal analysis of placental development, GDF15 and TCF7L2 were expressed primarily in extra villous trophoblast, and using a weighted linear model of maternal, paternal, and fetal effects, we confirmed opposing effects for GDF15 between maternal and fetal genotype. Conversely, IGFBP7 and PGR were primarily expressed in developing maternal spiral arteries during placentation, with effects limited to the maternal genome. Risk loci were found to be under significant evolutionary selection, with the strongest effects on nausea and vomiting mid-pregnancy. Selected loci were associated with abnormal pregnancy weight gain, pregnancy duration, birth weight, head circumference, and pre-eclampsia. Potential roles for candidate genes in appetite, insulin signaling, and brain plasticity provide new pathways to explore etiological mechanisms and novel therapeutic avenues.

Diverse ancestry GWAS for advanced age-related macular degeneration in TOPMed-imputed and Ophthalmologically-confirmed 16,108 cases and 18,038 controls

Age-related macular degeneration (AMD) is a leading cause of blindness with $344 billion dollars global costs. In 2016, the International Age-related Macular Degeneration Genomics Consortium devised genomic data on ∼50,000 individuals (IAMDGC 1.0) and identified 52 variants across 34 loci associated with advanced AMD in European ancestry. We have now analyzed a more densely imputed version (IAMDGC 2.0) and performed cross-ancestry GWAS in 16,108 advanced AMD cases and 18,038 AMD-free controls. This identified 28 loci at P<5×10 -8 , including two additional AMD loci compared to IAMDGC 1.0 ( SERPINA1 and CPN1 ). Fine-mapping supported one ancestry-shared signal around HTRA1/ARMS2 and nine signals around CFH without African ancestry contribution. The 52-variant genetic risk score with and the 44-variant score without CFH -variants predicted advanced AMD not only in EUR, but also in AFR and ASN (AUC=0.80/0.75, 0.65/0.64, 0.80/0.79, respectively). Our results indicate that the genetic underpinning of advanced AMD is mostly shared between ancestries.

Loss of CFHR5 function reduces the risk for age-related macular degeneration

Age-related macular degeneration (AMD) is a prevalent cause of vision loss in the elderly with limited therapeutic options. A single chromosomal region around the complement factor H gene (CFH) is reported to explain nearly 25% of genetic AMD risk. Here, we used association testing, statistical finemapping and conditional analyses in 12,495 AMD cases and 461,686 controls to deconvolute four major CFH haplotypes that convey protection from AMD. We show that beyond CFH, two of these are explained by Finn-enriched frameshift and missense variants in the CFH modulator CFHR5. We demonstrate through a FinnGen sample recall study that CFHR5 variant carriers exhibit dose-dependent reductions in serum levels of the CFHR5 gene product FHR-5 and two functionally related proteins at the locus. Genetic reduction in FHR-5 correlates with higher preserved activities of the classical and alternative complement pathways. Our results propose therapeutic downregulation of FHR-5 as promising to prevent or treat AMD.

Cross-ancestry analysis of brain QTLs enhances interpretation of schizophrenia genome-wide association studies

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet most of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n = 158), Europeans (EUR, n = 408), and East Asians (EAS, n = 217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs linked to 1,276 genes and 198,769 SNPs were found to be specific to non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified five risk genes (SFXN2, VPS37B, DENR, FTCDNL1, and NT5DC2) and three potential regulatory variants in known risk genes (CNNM2, MTRFR, and MPHOSPH9) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of risk genes in SCZ.

A genome-wide association study of adults with community-acquired pneumonia

BACKGROUND

Community-acquired pneumonia (CAP) is associated with high morbidity and hospitalization rate. In infectious diseases, host genetics plays a critical role in susceptibility and immune response, and the immune pathways involved are highly dependent on the microorganism and its route of infection. Here we aimed to identify genetic risk loci for CAP using a case-control genome-wide association study (GWAS).

METHODS

We performed a GWAS on 3,765 Spanish individuals, including 257 adult patients hospitalized with CAP and 3,508 population controls. Pneumococcal CAP was documented in 30% of patients; the remaining 70% were selected among patients with unidentified microbiological etiology. We tested 7,6 million imputed genotypes using logistic regressions. UK Biobank GWAS of bacterial pneumonia were used for results validation. Subsequently, we prioritized genes and likely causal variants based on Bayesian fine mapping and functional evidence. Imputation and association of classical HLA alleles and amino acids were also conducted.

RESULTS

Six independent sentinel variants reached the genome-wide significance (p < 5 × 10-8), three on chromosome 6p21.32, and one for each of the chromosomes 4q28.2, 11p12, and 20q11.22. Only one variant at 6p21.32 was validated in independent GWAS of bacterial and pneumococcal pneumonia. Our analyses prioritized C4orf33 on 4q28.2, TAPBP on 6p21.32, and ZNF341 on 20q11.22. Interestingly, genetic defects of TAPBP and ZNF341 are previously known inborn errors of immunity predisposing to bacterial pneumonia, including pneumococcus and Haemophilus influenzae. Associations were all non-significant for the classical HLA alleles.

CONCLUSIONS

We completed a GWAS of CAP and identified four novel risk loci involved in CAP susceptibility.

A compendium of genetic variations associated with promoter usage across 49 human tissues

Promoters play a crucial role in regulating gene transcription. However, our understanding of how genetic variants influence alternative promoter selection is still incomplete. In this study, we implement a framework to identify genetic variants that affect the relative usage of alternative promoters, known as promoter usage quantitative trait loci (puQTLs). By constructing an atlas of human puQTLs across 49 different tissues from 838 individuals, we have identified approximately 76,856 independent loci associated with promoter usage, encompassing 602,009 genetic variants. Our study demonstrates that puQTLs represent a distinct type of molecular quantitative trait loci, effectively uncovering regulatory targets and patterns. Furthermore, puQTLs are regulating in a tissue-specific manner and are enriched with binding sites of epigenetic marks and transcription factors, especially those involved in chromatin architecture formation. Notably, we have also found that puQTLs colocalize with complex traits or diseases and contribute to their heritability. Collectively, our findings underscore the significant role of puQTLs in elucidating the molecular mechanisms underlying tissue development and complex diseases.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

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.

Powerful mapping of cis-genetic effects on gene expression across diverse populations reveals novel disease-critical genes

While disease-associated variants identified by genome-wide association studies (GWAS) most likely regulate gene expression levels, linking variants to target genes is critical to determining the functional mechanisms of these variants. Genetic effects on gene expression have been extensively characterized by expression quantitative trait loci (eQTL) studies, yet data from non-European populations is limited. This restricts our understanding of disease to genes whose regulatory variants are common in European populations. While previous work has leveraged data from multiple populations to improve GWAS power and polygenic risk score (PRS) accuracy, multi-ancestry data has not yet been used to better estimate cis-genetic effects on gene expression. Here, we present a new method, Multi-Ancestry Gene Expression Prediction Regularized Optimization (MAGEPRO), which constructs robust genetic models of gene expression in understudied populations or cell types by fitting a regularized linear combination of eQTL summary data across diverse cohorts. In simulations, our tool generates more accurate models of gene expression than widely-used LASSO and the state-of-the-art multi-ancestry PRS method, PRS-CSx, adapted to gene expression prediction. We attribute this improvement to MAGEPRO's ability to more accurately estimate causal eQTL effect sizes (p < 3.98 × 10-4, two-sided paired t-test). With real data, we applied MAGEPRO to 8 eQTL cohorts representing 3 ancestries (average n = 355) and consistently outperformed each of 6 competing methods in gene expression prediction tasks. Integration with GWAS summary statistics across 66 complex traits (representing 22 phenotypes and 3 ancestries) resulted in 2,331 new gene-trait associations, many of which replicate across multiple ancestries, including PHTF1 linked to white blood cell count, a gene which is overexpressed in leukemia patients. MAGEPRO also identified biologically plausible novel findings, such as PIGB, an essential component of GPI biosynthesis, associated with heart failure, which has been previously evidenced by clinical outcome data. Overall, MAGEPRO is a powerful tool to enhance inference of gene regulatory effects in underpowered datasets and has improved our understanding of population-specific and shared genetic effects on complex traits.

Genetic and functional analysis of Raynaud's syndrome implicates loci in vasculature and immunity

Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.