Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases

Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders

The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

Theory guided engineering of zeolite adsorbents for acaricide residue adsorption from the environment

CONTEXT

Zeolites have attracted attention for their potential in adsorbing environmental contaminants. However, contaminants, such as acaricides used extensively in livestock production to control ticks and mites, have received limited exploration regarding their adsorption onto zeolite surfaces. This study aimed to identify the most appropriate zeolite frameworks for the adsorption of acaricide residues, deduce the mechanism underlying the adsorption process, and evaluate the impact of surface modification on the adsorption capabilities of zeolites.

METHODS

Grand Canonical Monte Carlo (GCMC) was used to screen the entire zeolite database to analyze their adsorption properties, where the cloverite zeolite framework (CLO) exhibits the highest adsorption capacity (percentage weight, 54%). Machine learning was employed to rank structural feature importance on adsorption. Density and helium void fraction appeared to be the most important structural features. Thus, engineering these features is of utmost significance in harvesting the desired acaricides. The second step involved engineering the structural and electronic properties of the shortlisted zeolite frameworks via cation substitution with suitable atoms. DFT calculations involving natural bond orbital (NBO) analysis and quantum theory of atoms in molecules (QTAIM) have been done to understand the influence of cation substitution on the electronic structure.

Genetic drivers and cellular selection of female mosaic X chromosome loss

Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.

STPDA: Leveraging spatial-temporal patterns for downstream analysis in spatial transcriptomic data

Spatial transcriptomics, a groundbreaking field in cellular biology, faces the challenge of effectively deciphering complex spatial-temporal gene expression patterns. Traditional data analysis methods often fail to capture the intricate nuances of this data, limiting the depth of understanding in spatial distribution and gene interactions. In response, we present Spatial-Temporal Patterns for Downstream Analysis (STPDA), a sophisticated computational framework tailored for spatial transcriptomic data analysis. STPDA leverages high-resolution mapping to bridge the gap between genomics and histopathology, offering a comprehensive perspective on the spatial dynamics of gene expression within tissues. This approach enables a view of cellular function and organization, marking a paradigm shift in our comprehension of biological systems. By employing Autoregressive Moving Average (ARMA) and Long Short-Term Memory (LSTM) models, STPDA effectively deciphers both global and local spatio-temporal dynamics in cellular environments. This integration of spatial-temporal patterns for downstream analysis offers a transformative approach to spatial transcriptomics data analysis. STPDA excels in various single-cell analytical tasks, including the identification of ligand-receptor interactions and cell type classification. Its ability to harness spatial-temporal patterns not only matches but frequently surpasses the performance of existing state-of-the-art methods. To ensure widespread usability and impact, we have encapsulated STPDA in a scalable and accessible Python package, addressing single-cell tasks through advanced spatial-temporal pattern analysis. This development promises to enhance our understanding of cellular biology, offering novel insights and therapeutic strategies, and represents a substantial advancement in the field of spatial transcriptomics.

An integrative framework to prioritize genes in more than 500 loci associated with body mass index

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

Decoding polygenic diseases: advances in noncoding variant prioritization and validation

Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.

From GWASs toward Mechanistic Understanding with Case Studies in Dermatogenetics

Many human skin diseases result from the complex interplay of genetic and environmental mechanisms that are largely unknown. GWASs have yielded insight into the genetic aspect of complex disease by highlighting regions of the genome or specific genetic variants associated with disease. Leveraging this information to identify causal genes and cell types will provide insight into fundamental biology, inform diagnostics, and aid drug discovery. However, the etiological mechanisms from genetic variant to disease are still unestablished in most cases. There now exists an unprecedented wealth of data and computational methods for variant interpretation in a functional context. It can be challenging to decide where to start owing to a lack of consensus on the best way to identify causal genetic mechanisms. This article highlights 3 key aspects of genetic variant interpretation: prioritizing causal genes, cell types, and pathways. We provide a practical overview of the main methods and datasets, giving examples from recent atopic dermatitis studies to provide a blueprint for variant interpretation. A collection of resources, including brief description and links to the packages and web tools, is provided for researchers looking to start in silico follow-up genetic analysis of associated genetic variants.

Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance

Methods that analyze single-cell paired RNA-seq and ATAC-seq multiome data have shown great promise in linking regulatory elements to genes. However, existing methods differ in their modeling assumptions and approaches to account for biological and technical noise-leading to low concordance in their linking scores-and do not capture the effects of genomic distance. We propose pgBoost, an integrative modeling framework that trains a non-linear combination of existing linking strategies (including genomic distance) on fine-mapped eQTL data to assign a probabilistic score to each candidate SNP-gene link. We applied pgBoost to single-cell multiome data from 85k cells representing 6 major immune/blood cell types. pgBoost attained higher enrichment for fine-mapped eSNP-eGene pairs (e.g. 21x at distance >10kb) than existing methods (1.2-10x; p-value for difference = 5e-13 vs. distance-based method and < 4e-35 for each other method), with larger improvements at larger distances (e.g. 35x vs. 0.89-6.6x at distance >100kb; p-value for difference < 0.002 vs. each other method). pgBoost also outperformed existing methods in enrichment for CRISPR-validated links (e.g. 4.8x vs. 1.6-4.1x at distance >10kb; p-value for difference = 0.25 vs. distance-based method and < 2e-5 for each other method), with larger improvements at larger distances (e.g. 15x vs. 1.6-2.5x at distance >100kb; p-value for difference < 0.009 for each other method). Similar improvements in enrichment were observed for links derived from Activity-By-Contact (ABC) scores and GWAS data. We further determined that restricting pgBoost to features from a focal cell type improved the identification of SNP-gene links relevant to that cell type. We highlight several examples where pgBoost linked fine-mapped GWAS variants to experimentally validated or biologically plausible target genes that were not implicated by other methods. In conclusion, a non-linear combination of linking strategies, including genomic distance, improves power to identify target genes underlying GWAS associations.

Identifying drug targets for schizophrenia through gene prioritization

Background

Schizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome.

Methods

To more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool.

Results

We prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition to DRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2A and GRM3), calcium channels (CACNA1C and CACNB2), and GABAB receptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4B and VRK2).

Conclusions

We curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.

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

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

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.

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity

Importance

Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects.

Objective

To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort.

Design, Setting, and Participants

A deep learning model was trained to quantify EPAT area from 4-chamber magnetic resonance images using semantic segmentation. Cross-sectional and prospective cardiovascular disease associations were evaluated, controlling for sex and age. Prospective associations were additionally controlled for abdominal visceral adipose tissue (VAT) volumes. A genome-wide association study was performed, and a polygenic score (PGS) for EPAT was examined in independent FinnGen cohort study participants. Data analyses were conducted from March 2022 to December 2023.

Exposures

The primary exposures were magnetic resonance imaging-derived continuous measurements of epicardial and pericardial adipose tissue area and visceral adipose tissue volume.

Main Outcomes and Measures

Prevalent and incident CAD, AF, heart failure (HF), stroke, and type 2 diabetes (T2D).

Results

After exclusions, this study included 44 475 participants (mean [SD] age, 64.1 [7.7] years; 22 972 female [51.7%]) from the UK Biobank. Cross-sectional and prospective cardiovascular disease associations were evaluated for a mean (SD) of 3.2 (1.5) years of follow-up. Prospective associations were additionally controlled for abdominal VAT volumes for 38 527 participants. A PGS for EPAT was examined in 453 733 independent FinnGen cohort study participants. EPAT was positively associated with male sex (β = +0.78 SD in EPAT; P < 3 × 10-324), age (Pearson r = 0.15; P = 9.3 × 10-229), body mass index (Pearson r = 0.47; P < 3 × 10-324), and VAT (Pearson r = 0.72; P < 3 × 10-324). EPAT was more elevated in prevalent HF (β = +0.46 SD units) and T2D (β = +0.56) than in CAD (β = +0.23) or AF (β = +0.18). EPAT was associated with incident HF (hazard ratio [HR], 1.29 per +1 SD in EPAT; 95% CI, 1.17-1.43), T2D (HR, 1.63; 95% CI, 1.51-1.76), and CAD (HR, 1.19; 95% CI, 1.11-1.28). However, the associations were no longer significant when controlling for VAT. Seven genetic loci were identified for EPAT, implicating transcriptional regulators of adipocyte morphology and brown adipogenesis (EBF1, EBF2, and CEBPA) and regulators of visceral adiposity (WARS2 and TRIB2). The EPAT PGS was associated with T2D (odds ratio [OR], 1.06; 95% CI, 1.05-1.07; P =3.6 × 10-44), HF (OR, 1.05; 95% CI, 1.04-1.06; P =4.8 × 10-15), CAD (OR, 1.04; 95% CI, 1.03-1.05; P =1.4 × 10-17), AF (OR, 1.04; 95% CI, 1.03-1.06; P =7.6 × 10-12), and stroke in FinnGen (OR, 1.02; 95% CI, 1.01-1.03; P =3.5 × 10-3) per 1 SD in PGS.

Conclusions and Relevance

Results of this cohort study suggest that epicardial and pericardial adiposity was associated with incident cardiovascular diseases, but this may largely reflect a metabolically unhealthy adiposity phenotype similar to abdominal visceral adiposity.

Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis

We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases

Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Genetics of chronic respiratory disease

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

Biological basis of extensive pleiotropy between blood traits and cancer risk

BACKGROUND

The immune system has a central role in preventing carcinogenesis. Alteration of systemic immune cell levels may increase cancer risk. However, the extent to which common genetic variation influences blood traits and cancer risk remains largely undetermined. Here, we identify pleiotropic variants and predict their underlying molecular and cellular alterations.

METHODS

Multivariate Cox regression was used to evaluate associations between blood traits and cancer diagnosis in cases in the UK Biobank. Shared genetic variants were identified from the summary statistics of the genome-wide association studies of 27 blood traits and 27 cancer types and subtypes, applying the conditional/conjunctional false-discovery rate approach. Analysis of genomic positions, expression quantitative trait loci, enhancers, regulatory marks, functionally defined gene sets, and bulk- and single-cell expression profiles predicted the biological impact of pleiotropic variants. Plasma small RNAs were sequenced to assess association with cancer diagnosis.

RESULTS

The study identified 4093 common genetic variants, involving 1248 gene loci, that contributed to blood-cancer pleiotropism. Genomic hotspots of pleiotropism include chromosomal regions 5p15-TERT and 6p21-HLA. Genes whose products are involved in regulating telomere length are found to be enriched in pleiotropic variants. Pleiotropic gene candidates are frequently linked to transcriptional programs that regulate hematopoiesis and define progenitor cell states of immune system development. Perturbation of the myeloid lineage is indicated by pleiotropic associations with defined master regulators and cell alterations. Eosinophil count is inversely associated with cancer risk. A high frequency of pleiotropic associations is also centered on the regulation of small noncoding Y-RNAs. Predicted pleiotropic Y-RNAs show specific regulatory marks and are overabundant in the normal tissue and blood of cancer patients. Analysis of plasma small RNAs in women who developed breast cancer indicates there is an overabundance of Y-RNA preceding neoplasm diagnosis.

CONCLUSIONS

This study reveals extensive pleiotropism between blood traits and cancer risk. Pleiotropism is linked to factors and processes involved in hematopoietic development and immune system function, including components of the major histocompatibility complexes, and regulators of telomere length and myeloid lineage. Deregulation of Y-RNAs is also associated with pleiotropism. Overexpression of these elements might indicate increased cancer risk.

Convergence of coronary artery disease genes onto endothelial cell programs

Linking variants from genome-wide association studies (GWAS) to underlying mechanisms of disease remains a challenge1-3. For some diseases, a successful strategy has been to look for cases in which multiple GWAS loci contain genes that act in the same biological pathway1-6. However, our knowledge of which genes act in which pathways is incomplete, particularly for cell-type-specific pathways or understudied genes. Here we introduce a method to connect GWAS variants to functions. This method links variants to genes using epigenomics data, links genes to pathways de novo using Perturb-seq and integrates these data to identify convergence of GWAS loci onto pathways. We apply this approach to study the role of endothelial cells in genetic risk for coronary artery disease (CAD), and discover 43 CAD GWAS signals that converge on the cerebral cavernous malformation (CCM) signalling pathway. Two regulators of this pathway, CCM2 and TLNRD1, are each linked to a CAD risk variant, regulate other CAD risk genes and affect atheroprotective processes in endothelial cells. These results suggest a model whereby CAD risk is driven in part by the convergence of causal genes onto a particular transcriptional pathway in endothelial cells. They highlight shared genes between common and rare vascular diseases (CAD and CCM), and identify TLNRD1 as a new, previously uncharacterized member of the CCM signalling pathway. This approach will be widely useful for linking variants to functions for other common polygenic diseases.

Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health

The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.

Evaluation of input data modality choices on functional gene embeddings

Functional gene embeddings, numerical vectors capturing gene function, provide a promising way to integrate functional gene information into machine learning models. These embeddings are learnt by applying self-supervised machine-learning algorithms on various data types including quantitative omics measurements, protein-protein interaction networks and literature. However, downstream evaluations comparing alternative data modalities used to construct functional gene embeddings have been lacking. Here we benchmarked functional gene embeddings obtained from various data modalities for predicting disease-gene lists, cancer drivers, phenotype-gene associations and scores from genome-wide association studies. Off-the-shelf predictors trained on precomputed embeddings matched or outperformed dedicated state-of-the-art predictors, demonstrating their high utility. Embeddings based on literature and protein-protein interactions inferred from low-throughput experiments outperformed embeddings derived from genome-wide experimental data (transcriptomics, deletion screens and protein sequence) when predicting curated gene lists. In contrast, they did not perform better when predicting genome-wide association signals and were biased towards highly-studied genes. These results indicate that embeddings derived from literature and low-throughput experiments appear favourable in many existing benchmarks because they are biased towards well-studied genes and should therefore be considered with caution. Altogether, our study and precomputed embeddings will facilitate the development of machine-learning models in genetics and related fields.

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

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316 K ) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the well-established role of CTLA4 in CD8+ T cells for All autoimmune disease. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Genome-wide association meta-analysis identifies risk loci for abdominal aortic aneurysm and highlights PCSK9 as a therapeutic target

Abdominal aortic aneurysm (AAA) is a common disease with substantial heritability. In this study, we performed a genome-wide association meta-analysis from 14 discovery cohorts and uncovered 141 independent associations, including 97 previously unreported loci. A polygenic risk score derived from meta-analysis explained AAA risk beyond clinical risk factors. Genes at AAA risk loci indicate involvement of lipid metabolism, vascular development and remodeling, extracellular matrix dysregulation and inflammation as key mechanisms in AAA pathogenesis. These genes also indicate overlap between the development of AAA and other monogenic aortopathies, particularly via transforming growth factor β signaling. Motivated by the strong evidence for the role of lipid metabolism in AAA, we used Mendelian randomization to establish the central role of nonhigh-density lipoprotein cholesterol in AAA and identified the opportunity for repurposing of proprotein convertase, subtilisin/kexin-type 9 (PCSK9) inhibitors. This was supported by a study demonstrating that PCSK9 loss of function prevented the development of AAA in a preclinical mouse model.

Genome-wide association study of thyroid-stimulating hormone highlights new genes, pathways and associations with thyroid disease

Thyroid hormones play a critical role in regulation of multiple physiological functions and thyroid dysfunction is associated with substantial morbidity. Here, we use electronic health records to undertake a genome-wide association study of thyroid-stimulating hormone (TSH) levels, with a total sample size of 247,107. We identify 158 novel genetic associations, more than doubling the number of known associations with TSH, and implicate 112 putative causal genes, of which 76 are not previously implicated. A polygenic score for TSH is associated with TSH levels in African, South Asian, East Asian, Middle Eastern and admixed American ancestries, and associated with hypothyroidism and other thyroid disease in South Asians. In Europeans, the TSH polygenic score is associated with thyroid disease, including thyroid cancer and age-of-onset of hypothyroidism and hyperthyroidism. We develop pathway-specific genetic risk scores for TSH levels and use these in phenome-wide association studies to identify potential consequences of pathway perturbation. Together, these findings demonstrate the potential utility of genetic associations to inform future therapeutics and risk prediction for thyroid diseases.

Bayesian multivariate genetic analysis improves translational insights

While lipid traits are known essential mediators of cardiovascular disease, few approaches have taken advantage of their shared genetic effects. We apply a Bayesian multivariate size estimator, mash, to GWAS of four lipid traits in the Million Veterans Program (MVP) and provide posterior mean and local false sign rates for all effects. These estimates borrow information across traits to improve effect size accuracy. We show that controlling local false sign rates accurately and powerfully identifies replicable genetic associations and that multivariate control furthers the ability to explain complex diseases. Our application yields high concordance between independent datasets, more accurately prioritizes causal genes, and significantly improves polygenic prediction beyond state-of-the-art methods by up to 59% for lipid traits. The use of Bayesian multivariate genetic shrinkage has yet to be applied to human quantitative trait GWAS results, and we present a staged approach to prediction on a polygenic scale.

Translating GWAS Findings to Inform Drug Repositioning Strategies for COVID-19 Treatment

We developed a computational framework that integrates Genome-Wide Association Studies (GWAS) and post-GWAS analyses, designed to facilitate drug repurposing for COVID-19 treatment. The comprehensive approach combines transcriptomic-wide associations, polygenic priority scoring, 3D genomics, viral-host protein-protein interactions, and small-molecule docking. Through GWAS, we identified nine druggable host genes associated with COVID-19 severity and SARS-CoV-2 infection, all of which show differential expression in COVID-19 patients. These genes include IFNAR1, IFNAR2, TYK2, IL10RB, CXCR6, CCR9, and OAS1. We performed an extensive molecular docking analysis of these targets using 553 small molecules derived from five therapeutically enriched categories, namely antibacterials, antivirals, antineoplastics, immunosuppressants, and anti-inflammatories. This analysis, which comprised over 20,000 individual docking analyses, enabled the identification of several promising drug candidates. All results are available via the DockCoV2 database (https://dockcov2.org/drugs/). The computational framework ultimately identified nine potential drug candidates: Peginterferon alfa-2b, Interferon alfa-2b, Interferon beta-1b, Ruxolitinib, Dactinomycin, Rolitetracycline, Irinotecan, Vinblastine, and Oritavancin. While its current focus is on COVID-19, our proposed computational framework can be applied more broadly to assist in drug repurposing efforts for a variety of diseases. Overall, this study underscores the potential of human genetic studies and the utility of a computational framework for drug repurposing in the context of COVID-19 treatment, providing a valuable resource for researchers in this field.

Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease

Genetic variants associated with complex traits are primarily noncoding, and their effects on gene-regulatory activity remain largely uncharacterized. To address this, we profile epigenomic variation of histone mark H3K27ac across 387 brain, heart, muscle and lung samples from Genotype-Tissue Expression (GTEx). We annotate 282 k active regulatory elements (AREs) with tissue-specific activity patterns. We identify 2,436 sex-biased AREs and 5,397 genetically influenced AREs associated with 130 k genetic variants (haQTLs) across tissues. We integrate genetic and epigenomic variation to provide mechanistic insights for disease-associated loci from 55 genome-wide association studies (GWAS), by revealing candidate tissues of action, driver SNPs and impacted AREs. Lastly, we build ARE-gene linking scores based on genetics (gLink scores) and demonstrate their unique ability to prioritize SNP-ARE-gene circuits. Overall, our epigenomic datasets, computational integration and mechanistic predictions provide valuable resources and important insights for understanding the molecular basis of human diseases/traits such as schizophrenia.

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.

Integrative GWAS and co-localisation analysis suggests novel genes associated with age-related multimorbidity

Advancing age is the greatest risk factor for developing multiple age-related diseases. Therapeutic approaches targeting the underlying pathways of ageing, rather than individual diseases, may be an effective way to treat and prevent age-related morbidity while reducing the burden of polypharmacy. We harness the Open Targets Genetics Portal to perform a systematic analysis of nearly 1,400 genome-wide association studies (GWAS) mapped to 34 age-related diseases and traits, identifying genetic signals that are shared between two or more of these traits. Using locus-to-gene (L2G) mapping, we identify 995 targets with shared genetic links to age-related diseases and traits, which are enriched in mechanisms of ageing and include known ageing and longevity-related genes. Of these 995 genes, 128 are the target of an approved or investigational drug, 526 have experimental evidence of binding pockets or are predicted to be tractable, and 341 have no existing tractability evidence, representing underexplored genes which may reveal novel biological insights and therapeutic opportunities. We present these candidate targets for exploration and prioritisation in a web application.

GWAS meta-analysis of over 29,000 people with epilepsy identifies 26 risk loci and subtype-specific genetic architecture

Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment.

Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.