An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

Genetic Distinctions Between Reticular Pseudodrusen and Drusen: A Genome-Wide Association Study

OBJECTIVE

To identify genetic determinants specific to reticular pseudodrusen (RPD) compared with drusen.

DESIGN

Genome-wide association study (GWAS) SUBJECTS: Participants with RPD, drusen, and controls from the UK Biobank (UKBB), a large, multisite, community-based cohort.

METHODS

Participants with RPD, drusen, and controls from the UK Biobank (UKBB), a large, multisite, community-based cohort, were included. A deep learning framework analyzed 169,370 optical coherence tomography (OCT) volumes to identify cases and controls within the UKBB. Five retina specialists validated the cohorts using OCT and color fundus photographs. Several GWAS were undertaken utilizing the quantity and presence of RPD and drusen. Genome-wide significance was defined as P < 5e-8.

MAIN OUTCOMES MEASURES

Genetic associations were examined with the number of RPD and drusen within 'pure' cases, where only RPD or drusen were present in either eye. A candidate approach assessed 46 previously known AMD loci. Secondary GWAS were conducted for number of RPD and drusen in mixed cases, and binary case-control analyses for pure RPD and pure drusen.

RESULTS

The study included 1787 participants: 1037 controls, 361 pure drusen, 66 pure RPD, and 323 mixed cases. The primary pure RPD GWAS identified four genome-wide significant loci: rs11200630 near ARMS2-HTRA1 (P = 1.9e-09), rs79641866 at PARD3B (P = 1.3e-08), rs143184903 near ITPR1 (P = 8.1e-09), and rs76377757 near SLN (P = 4.3e-08). The latter three are uncommon variants (minor allele frequency <5%). A significant association at the CFH locus was also observed using a candidate approach (P = 1.8e-04). For pure drusen, two loci reached genome-wide significance: rs10801555 at CFH (P = 6.0e-33) and rs61871744 at ARMS2-HTRA1 (P = 4.2e-20).

CONCLUSIONS

The study highlights a clear association between the ARMS2-HTRA1 locus and higher RPD load. Although the CFH locus association did not achieve genome-wide significance, a suggestive link was observed. Three novel associations unique to RPD were identified, albeit for uncommon genetic variants. Further studies with larger sample sizes are needed to explore these findings.

Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers

Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

Role of multi-omics in aquaculture genetics and breeding: current status and future perspective

Aquaculture, a fast-growing sector, plays an important role in the supply of nutrient-rich food for humans. Selective breeding is a promising approach to ensure the development and sustainability of intensive aquaculture systems by achieving cumulative and permanent improvements in desirable traits. The advancement of omics technologies offers unprecedented opportunities for genetic improvement, especially in the prioritization of SNPs to be used in the genomic selection and editing of economically important traits. This review highlights novel breeding strategies in aquaculture, emphasizing how multi-omics data can be integrated into selective breeding programs. Specifically, we discuss the current achievements in integrating functional data into conventional genomic prediction models and highlight the potential of artificial intelligence to efficiently map genes and predict phenotypes or genetic merit using multi-omics data. Ultimately, we discuss genome editing methods for their potential to fix existing alleles, introduce alleles from wild populations or related species, and create de novo alleles, with the general goal of improving commercially important traits in aquaculture species.

Genome-wide association studies in a large Korean cohort identify quantitative trait loci for 36 traits and illuminate their genetic architectures

Genome-wide association studies (GWAS) have predominantly focused on European ancestry populations, limiting biological discoveries across diverse populations. Here we report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 301 previously unreported genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 4588 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for multiple traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.

Variant-specific priors clarify colocalisation analysis

Linking GWAS variants to their causal gene and context remains an ongoing challenge. A widely used method for performing this analysis is the coloc package for statistical colocalisation analysis, which can be used to link GWAS and eQTL associations. Currently, coloc assumes that all variants in a region are equally likely to be causal, despite the success of fine-mapping methods that use additional information to adjust their prior probabilities. In this paper we propose and implement an approach for specifying variant-specific prior probabilities in the coloc method. We describe and compare six source of information for specifying prior probabilities: non-coding constraint, enhancer-gene link scores, the output of the PolyFun method and three estimates of eQTL-TSS distance densities. Using simulations and analysis of ground-truth pQTL-eQTL colocalisations we show that variant-specific priors, particularly the eQTL-TSS distance density priors, can improve colocalisation performance. Furthermore, across GWAS-eQTL colocalisations variant-specific priors changed colocalisation significance in up to 14.1% of colocalisations, at some loci revealing the likely causal gene.

Genetic predictors of longevity and survival in cellular homeostasis genes: A case-control study

BACKGROUND

Longevity is defined by the ability to maintain both physical and mental health throughout a long life and may results from adaptive mechanisms that mitigate aging's detrimental effects.

METHODS

The 20-year follow-up study of 3,312 unrelated individuals aged 18-114 years from the Volga-Ural region of Eurasia investigated variants in cellular homeostasis genes IGF1, PIK3R1, AKT1, MTOR, NFE2L2, KEAP1, HIF1A, TP53, and SIRT1, to identify associations with clinical aging phenotypes and healthy longevity.

RESULTS

In men, KEAP1 (rs1048290) CC genotype was a longevity and survival marker (OR = 2.39, P = 3E-05, HR = 0.54, P = 2.4E-03). NFE2L2 (rs6721961) TT genotype was linked to higher mortality (HR = 1.77, P = 0.031), particularly combined with KEAP1 (rs1048290) G and AKT1 (rs3803304) C alleles (HR = 2.8, P = 0.023). In women, AKT1 (rs3803304) C allele interacted with NFE2L2 (rs6721961) TT genotype (SF = 0.13, P = 3.6E-03), and was linked to longevity (OR = 2.22, P = 6.3E-03) and protection against cerebrovascular diseases (OR = 0.62, P = 5.1E-03). AKT1 (rs3803304) GG genotype, along with HIF1A (rs11549465) T and SIRT1 (rs3758391) T alleles (SF = 2.52, P = 1.5E-03), promoted survival (HR = 0.71, P = 0.014). In men, HIF1A (rs11549465) TT genotype predicted cardiovascular mortality (HR = 7.5, P = 5.5E-03). SIRT1 (rs3758391) TT genotype was associated with improved survival in individuals with diabetes (HR = 0.4, P = 5.8E-03) and multimorbidity (HR = 0.48, P = 0.025).

CONCLUSION

Variants in NFE2L2, KEAP1, SIRT1, AKT1, and HIF1A, along with their interactions, were significantly associated with survival in age-related diseases and healthy longevity.

Improved genetic discovery and fine-mapping resolution through multivariate latent factor analysis of high-dimensional traits

Genome-wide association studies (GWASs) of high-dimensional traits, such as blood cell or metabolic traits, often use univariate approaches, ignoring trait relationships. Biological mechanisms generating variation in high-dimensional traits can be captured parsimoniously through a GWAS of latent factors. Here, we introduce flashfmZero, a zero-correlation latent-factor-based multi-trait fine-mapping approach. In an application to 25 latent factors derived from 99 blood cell traits in the INTERVAL cohort, we show that latent factor GWASs enable the detection of signals generating sub-threshold associations with several blood cell traits. The 99% credible sets (CS99) from flashfmZero were equal to or smaller in size than those from univariate fine-mapping of blood cell traits in 87% of our comparisons. In all cases univariate latent factor CS99 contained those from flashfmZero. Our latent factor approaches can be applied to GWAS summary statistics and will enhance power for the discovery and fine-mapping of associations for many traits.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A tissue-specific atlas of protein-protein associations enables prioritization of candidate disease genes

Despite progress in mapping protein-protein interactions, their tissue specificity is understudied. Here, given that protein coabundance is predictive of functional association, we compiled and analyzed protein abundance data of 7,811 proteomic samples from 11 human tissues to produce an atlas of tissue-specific protein associations. We find that this method recapitulates known protein complexes and the larger structural organization of the cell. Interactions of stable protein complexes are well preserved across tissues, while cell-type-specific cellular structures, such as synaptic components, are found to represent a substantial driver of differences between tissues. Over 25% of associations are tissue specific, of which <7% are because of differences in gene expression. We validate protein associations for the brain through cofractionation experiments in synaptosomes, curation of brain-derived pulldown data and AlphaFold2 modeling. We also construct a network of brain interactions for schizophrenia-related genes, indicating that our approach can functionally prioritize candidate disease genes in loci linked to brain disorders.

A regulatory variant rs9379874 in T1D risk region 6p22.2 affects BTN3A1 expression regulating T cell function

OBJECTIVE

Genome-wide association studies (GWAS) have identified that 6p22.2 region is associated with type 1 diabetes (T1D) risk in the Chinese Han population. This study aims to reveal associations between this risk region and T1D subgroups and related clinical features, and further identify causal variant(s) and target gene(s) in this region.

METHODS

2608 T1D and 4814 healthy controls were recruited from East, Central, and South China. Baseline data and genotyping for rs4320356 were collected. The most likely causal variant and gene were identified by bioinformatics analysis, dual-luciferase reporter assays, expression quantitative trait loci (eQTL), and functional annotation of the non-coding region within the 6p22.2 region.

RESULTS

The leading variant rs4320356 in the 6p22.2 region was associated with T1D risk in the Chinese and Europeans. However, this variant was not significantly associated with islet function or autoimmunity. In silico analysis suggested rs9379874 was the most potential causal variant for T1D risk among thymus, spleen, and T cells, overlapping with the enhancer-related histone mark in multiple T cell subsets. Dual luciferase reporter assay and eQTL showed that the T allele of rs9379874 increased BTN3A1 expression by binding to FOXA1. Public single-cell RNA sequencing analysis indicated that BTN3A1 was related to T-cell activation, ATP metabolism, and cytokine metabolism pathways, which might contribute to T1D development.

CONCLUSION

This study indicates that a functional variant rs9379874 regulates BTN3A1 expression, expanding the genomic landscape of T1D risk and offering a potential target for developing novel therapies.

Unravelling novel and closely linked association signals for fat-related traits in pigs using prioritised variants from whole-genome sequence data

For most production traits, the largest proportions of genetic variance remain unmapped. Dense whole-genome sequence (WGS) data enable the possibility of discovering novel associations as well as unravelling closely linked association signals with a resolution that marker arrays cannot reach. However, the identification of variants from WGS data that are causal of the variation of complex traits is hindered by the high dimensionality and linkage disequilibrium. Thus, at best, we can narrow the circle around the causal variants to prioritise a set of variants for their posterior validation. In this study, we assessed the utility of WGS data for uncovering associations of weaker effects using, as a model, fat content and composition traits in a Duroc pig population where we previously described major effects of the LEPR and SCD genes. We genotyped 971 pigs for a set of 182 variants from 154 candidate genes that were prioritised from amongst the WGS variants discovered in 205 sequenced individuals. These variants were prioritised conditional to LEPR and SCD. The association of the prioritised variants with the target traits was then tested in the confirmation set of 971 pigs. A total of 17 potentially independent quantitative trait loci (8.4% of the total number of studied genes) were significantly associated (q-value < 0.05) with at least one of the studied traits. We identified novel associations attributable to genes such as ABCC2, MOGAT2, or PLPP1 for backfat thickness, myristic acid content, and monounsaturated fatty acid content, respectively. Our results also revealed a finer granularity of weaker genetic effects in loci such as those around the DGAT2 and FADS2 genes, which may mask the effects of closely located genes like MOGAT2 and DAGLA, respectively. To refine the prioritisation of variants for validation studies, especially when targeting those of weaker effects, we recommend larger and more diverse discovery sets, more precise and complete functional gene annotation, and the integration of other omics data.

The retrotransposon-derived capsid genes PNMA1 and PNMA4 maintain reproductive capacity

Almost half of the human genome consists of retrotransposons-'parasitic' sequences that insert themselves into the host genome via an RNA intermediate. Although most of these sequences are silenced or mutationally deactivated, they can present opportunities for evolutionary innovation: mutation of a deteriorating retrotransposon can result in a gene that provides a selective advantage to the host in a process termed 'domestication'1-3. The PNMA family of gag-like capsid genes was domesticated from an ancient vertebrate retrotransposon of the Metaviridae clade at least 100 million years ago4,5. PNMA1 and PNMA4 are positively regulated by the master germ cell transcription factors MYBL1 and STRA8, and their transcripts are bound by the translational regulator DAZL during gametogenesis6. This developmental regulation of PNMA1 and PNMA4 expression in gonadal tissue suggested to us that they might serve a reproductive function. Through the analysis of donated human ovaries, genome-wide association studies (GWASs) and mouse models, we found that PNMA1 and PNMA4 are necessary for the maintenance of a normal reproductive lifespan. These proteins self-assemble into capsid-like structures that exit human cells, and we observed large PNMA4 particles in mouse male gonadal tissue that contain RNA and are consistent with capsid formation.

Fine-mapping in admixed populations using CARMA-X, with applications to Latin American studies

Genome-wide association studies (GWASs) in ancestrally diverse populations are rapidly expanding, opening up unique opportunities for novel gene discoveries and increased utility of genetic findings in non-European individuals. A popular technique to identify putative causal variants at GWAS loci is via statistical fine-mapping. Despite tremendous efforts, fine-mapping remains a very challenging task, even in the relatively simple scenario of studies with a single, homogeneous population. For studies with admixed individuals, such as within Latin America and the Caribbean, methods for gene discovery are still limited. Here, we propose a Bayesian model for fine-mapping in admixed populations, CARMA-X, that addresses some of the unique challenges of admixed individuals. The proposed method includes an estimation method for the linkage disequilibrium (LD) matrix that accounts for small reference panels for admixed individuals, heterogeneity across populations and cross-ancestry LD, and a Bayesian hypothesis test that leads to robust fine-mapping when relying on external reference panels of modest size for LD estimation. Using simulations, we compare performance with recently proposed fine-mapping methods for multi-ancestry studies and show that the proposed model provides higher power while controlling false discoveries, especially when using an out-of-sample LD matrix. We further illustrate our approach through applications to two Latin American genetic studies, the Estudio Familiar de Influencia Genética en Alzheimer (EFIGA) study in the Dominican Republic and the Mexican Biobank, where we show the benefit of modeling ancestry-specific effects by prioritizing putative causal variants and genes, including several findings driven by ancestry-specific effects in the African and Native American ancestries.

Brain-based gene expression and corresponding behavioural relevance of risk genes for broad antisocial behaviour

Antisocial behaviour (ASB) involves persistent irresponsible, delinquent activities violating rights and safety of others. A meta-analysis of genome-wide association studies revealed significant genetic associations with ASB, yet their brain expression patterns and behavioural relevance remain unclear. Our investigation of fifteen genes associated with ASB examined their biological role and distribution across tissues, integrating post-mortem brain sample data from the Allen-Human-Brain Atlas and the Genotype-Tissue Expression project. We found that these genes were differentially expressed in the brain, particularly in regions like the cerebellum, putamen, and caudate, and were notably downregulated in the pancreas. Single cell type expression analysis revealed that ASB-associated genes had strong correlations with ductal and endothelial cells in the pancreas, indicating a possible metabolic influence on ASB. Certain genes like NTN1, SMAD5, NCAM2, and CDC42EP3 displayed specificity for cognitive terms including chronic pain, heart rate, and aphasia. These expression patterns aligned with neurocognitive domains related to thinking, and learning, distress, motor skills, as determined by fMRI analysis. This study connects specific brain gene expression with potential genetic and metabolic factors in ASB, offering novel insights into its biological basis and possible interdisciplinary approaches to understanding and addressing aggressive behaviours.

Exploring the comorbidity association and biological mechanisms of chronic rhinosinusitis and chronic obstructive pulmonary disease

Although chronic rhinosinusitis (CRS) and chronic obstructive pulmonary disease (COPD) are both common chronic inflammatory diseases, the interaction between their comorbidities remains poorly understood within the unified airway disease framework. This study, for the first time, integrated multi-omics analysis and large-scale epidemiological data to explore their common mechanisms and clinical significance. The NHANES database was used for analysis, and multivariate logistic regression was employed to assess the comorbidity risk of CRS and COPD. Machine-learning models (glmnet, ranger, and xgboost) were used to analyze the NHANES data to determine the best model. Subsequently, Mendelian randomization(MR) was applied to explore relevant associations. Additionally, CRS and COPD datasets from GEO were further analyzed to identify potential targets. The NHANES analysis showed a significant association between CRS and COPD, with MR results indicating that CRS significantly increased the risk of COPD. Multi-omics integration revealed that C3 and CD163 are core targets in CRS/COPD patients. The ranger model was identified as the most suitable in this study. This study provides new evidence that CRS is an independent risk factor for COPD and establishes a unified airway mechanism centered on C3-CD163-mediated inflammation. These findings advance the "one airway, one disease" paradigm and support a dual-target therapeutic strategy.

Unveiling Novel Genetic Loci and Superior Alleles for Nickel Accumulation in Wheat via Genome-Wide Association Study

Nickel (Ni) pollution poses significant threats to human health and crop development through the food chain. This study aimed to identify the novel genomic regions and superior alleles associated with Ni accumulation in wheat (Triticum aestivum L.) grains using genome-wide association analysis (GWAS) with a diversity panel of 207 bread wheat varieties. In total, five unique genetic loci associated with Ni accumulation were identified and they explained, on average, 8.20-11.29% of the phenotypic variation. Among them, three unique genetic loci were mutually verified by different statistical models in at least two environments, indicating their stability across different environments. Moreover, the highest effect quantitative trait nucleotide (QTN) AX-111126872 with a quantitative trait locus (QTL) hotspot on chromosome 6B identified in this study was not reported previously. Three putative candidate genes linked to Ni accumulation were revealed from the stable genetic loci. Among them, one gene associated with the stable genetic locus on chromosome 6B (AX-111126872) encodes the glycine-rich proteins (GRPs) as a critical factor influencing Ni accumulation in wheat grains. This study increases our understanding of the genetic architecture of Ni accumulation in wheat grains, which is potentially helpful for breeding wheat varieties without Ni toxicity.

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.

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

Many drug targets in ongoing Parkinson's disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.

Genome-wide analyses identify 25 infertility loci and relationships with reproductive traits across the allele frequency spectrum

Genome-wide association studies (GWASs) may help inform the etiology of infertility. Here, we perform GWAS meta-analyses across seven cohorts in up to 42,629 cases and 740,619 controls and identify 25 genetic risk loci for male and female infertility. We additionally identify up to 269 genetic loci associated with follicle-stimulating hormone, luteinizing hormone, estradiol and testosterone through sex-specific GWAS meta-analyses (n = 6,095-246,862). Exome sequencing analyses reveal that women carrying testosterone-lowering rare variants in some genes are at risk of infertility. However, we find no local or genome-wide genetic correlation between female infertility and reproductive hormones. While infertility is genetically correlated with endometriosis and polycystic ovary syndrome, we find limited genetic overlap between infertility and obesity. Finally, we show that the evolutionary persistence of infertility-risk alleles may be explained by directional selection. Taken together, we provide a comprehensive view of the genetic determinants of infertility across multiple diagnostic criteria.

Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

Caution when using network partners for target identification in drug discovery

Identifying novel, high-yield drug targets is challenging and often results in a high failure rate. However, recent data indicate that leveraging human genetic evidence to identify and validate these targets significantly increases the likelihood of success in drug development. Two recent papers from Open Targets claimed that around half of US Food and Drug Administration-approved drugs had targets with direct human genetic evidence. By expanding target identification to include protein network partners-molecules in physical contact-the proportion of drug targets with genetic evidence support increased to two-thirds. However, the efficacy of using these network partners for target identification was not formally tested. To address this, we tested the approach on a list of robust positive control genes. We used the IntAct database to find physically interacting proteins of genes identified by exome-wide association studies (ExWASs), genome-wide association studies (GWASs) combined with a locus-to-gene mapping algorithm called the Effector Index, and Genetic Priority Score (GPS), which integrated eight genetic features with drug indications from the Open Targets and SIDER databases. We assessed how accurately including interacting genes with the ExWAS-, Effector Index-, and GPS-selected genes identified positive controls, focusing on precision, sensitivity, and specificity. Our results indicated that although molecular interactions led to higher sensitivity in identifying positive control genes, their practical application is limited by low precision. Expanding genetically identified targets to include network partners using IntAct did not increase the likelihood of identifying drug targets across the 412 tested traits, suggesting that such results should be interpreted with caution.

A disease-specific convergence of host and Epstein-Barr virus genetics in multiple sclerosis

Recent sero-epidemiological studies have strengthened the hypothesis that Epstein-Barr virus (EBV) may be a causal factor in multiple sclerosis (MS). Given the complexity of the EBV-host interaction, various mechanisms may be responsible for the disease pathogenesis. Furthermore, it remains unclear whether this is a disease-specific process. Here, we showed that genes encoding EBV interactors are enriched in loci associated with MS but not with other diseases and in prioritized therapeutic targets. Analyses of MS blood and brain transcriptomes confirmed a dysregulation of MS-associated EBV interactors affecting the CD40 pathway. Such interactors were strongly enriched in binding sites for the EBV nuclear antigen 2 (EBNA2) viral transcriptional regulator, often in colocalization with CCCTC binding factor (CTCF) and RNA Polymerase II Subunit A (POLR2A). EBNA2 was expressed in the MS brain. The 1.2 EBNA2 allele downregulated the expression of the CD40 MS-associated gene analogously to the CD40 MS-risk variant. Finally, we showed that the 1.2 EBNA2 allele associates with the risk of MS. This study delineates how host and viral genetic variability converge in MS-specific pathogenetic mechanisms.

Warfarin use and vestibular dysfunction insights from NHANES data, network pharmacology, Mendelian randomization, and molecular docking

Despite numerous anticoagulants available, warfarin is widely used due to its efficacy and cost-effectiveness in treating thromboembolic diseases. However, its potential impact on vestibular function remains unexplored. This study investigates the association between warfarin use and vestibular dysfunction using data from the NHANES database and examines underlying mechanisms through network pharmacology, Mendelian randomization (MR), and molecular docking. We conducted a cross-sectional analysis of NHANES data (1999-2004) to evaluate the prevalence of vestibular dysfunction among warfarin users. Network pharmacology identified overlapping genes between warfarin targets and vestibular dysfunction-related genes. MR analysis assessed the causal relationship, and molecular docking examined interactions between warfarin and significant genes. The study included 1681 participants, revealing a higher prevalence of vestibular dysfunction in warfarin users. Multiple regression analysis confirmed a significant association between warfarin use and vestibular dysfunction. Network pharmacology identified 31 overlapping genes, with MAPK8 emerging as a key gene through MR analysis. Molecular docking showed a strong binding affinity between warfarin and MAPK8. Findings suggest that warfarin use is significantly associated with vestibular dysfunction, potentially through interactions with MAPK8. This highlights the importance of monitoring vestibular function in patients on warfarin therapy and considering genetic factors to personalize treatment. Future research should explore these mechanisms further and validate findings in broader populations.

Decomposing the genetic background of chronic back pain

Chronic back pain (CBP) is a disabling condition with a lifetime prevalence of 40% and a substantial socioeconomic burden. Because of the high heterogeneity of CBP, subphenotyping may help to improve prediction and support personalized treatment of CBP. To investigate CBP subphenotypes, we decomposed its genetic background into a shared one common to other chronic pain conditions (back, neck, hip, knee, stomach, and head pain) and unshared genetic background specific to CBP. We identified and replicated 18 genes with shared impact across different chronic pain conditions and two genes that were specific for CBP. Among people with CBP, we demonstrated that polygenic risk scores accounting for the shared and unshared genetic backgrounds of CBP may underpin different CBP subphenotypes. These subphenotypes are characterized by varying genetic predisposition to diverse medical conditions and interventions such as diabetes mellitus, myocardial infarction, diagnostic endoscopic procedures, and surgery involving muscles, bones, and joints.

CXCL12 drives natural variation in coronary artery anatomy across diverse populations

Coronary arteries have a specific branching pattern crucial for oxygenating heart muscle. Among humans, there is natural variation in coronary anatomy with respect to perfusion of the inferior/posterior left heart, which can branch from either the right arterial tree, the left, or both-a phenotype known as coronary dominance. Using angiographic data for >60,000 US veterans of diverse ancestry, we conducted a genome-wide association study of coronary dominance, revealing moderate heritability and identifying ten significant loci. The strongest association occurred near CXCL12 in both European- and African-ancestry cohorts, with downstream analyses implicating effects on CXCL12 expression. We show that CXCL12 is expressed in human fetal hearts at the time dominance is established. Reducing Cxcl12 in mice altered coronary dominance and caused septal arteries to develop away from Cxcl12 expression domains. These findings indicate that CXCL12 patterns human coronary arteries, paving the way for "medical revascularization" through targeting developmental pathways.

An evolving understanding of multiple causal variants underlying genetic association signals

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

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

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

Integrative Proteogenomic Analyses Provide Novel Interpretations of Type 1 Diabetes Risk Loci Through Circulating Proteins

ARTICLE HIGHLIGHTS

Identification of circulating proteins that may play a role in the pathogenesis of type 1 diabetes can provide promising targets for biomarker and drug target identification. Supported by multiple lines of evidence, circulating abundances of CTSH, IL27RA, SIRPG, and PGM1 were associated with the risk of type 1 diabetes. Tissues and cell types with enrichment of target protein-coding gene expression were identified. CTSH, IL27RA, SIRPG, and PGM1 may be explored as biomarkers or drug targets for type 1 diabetes.

PLATELET TRAITS AND SEPSIS RISK AND PROGNOSIS: A BIDIRECTIONAL TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY

ABSTRACT

Background: Sepsis is a critical medical condition characterized by a dysregulated host response to infection. Platelet abnormalities frequently manifest in sepsis patients, but the causal role of platelets in sepsis remains unclear. This study employed a bidirectional two-sample Mendelian randomization (MR) approach to investigate the causal direction between platelets and sepsis. Methods: MR analysis was used to investigate the causal effect of four platelet traits-platelet count (PLT), platelet crit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW)-on sepsis risk and prognosis. Additionally, the study explored the reverse causality, assessing the impact of sepsis on these platelet traits. Genetic variants from large-scale genome-wide association studies served as instrumental variables to infer causality. Sensitivity analyses and heterogeneity tests were conducted to ensure the validity and robustness of the results. Results: Genetically predicted decreased PCT (OR = 0.938, P = 0.044) and MPV (OR = 0.410, P = 0.006) were associated with an increased risk of sepsis. In the reverse direction, 28-day sepsis mortality was significantly associated with decreased PLT (OR = 0.986, P = 0.034). No significant causal relationships were observed between sepsis and other platelet traits. Conclusions: This study suggests a causal association between low PCT and MPV levels and increased risk of sepsis. Additionally, sepsis with a poor prognosis was causally linked to decreased PLT. These findings provide novel evidence for the causal relationship between platelet traits and sepsis.

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Genotype inference from aggregated chromatin accessibility data reveals genetic regulatory mechanisms

BACKGROUND

Understanding the genetic causes underlying variability in chromatin accessibility can shed light on the molecular mechanisms through which genetic variants may affect complex traits. Thousands of ATAC-seq samples have been collected that hold information about chromatin accessibility across diverse cell types and contexts, but most of these are not paired with genetic information and come from distinct projects and laboratories.

RESULTS

We report here joint genotyping, chromatin accessibility peak calling, and discovery of quantitative trait loci which influence chromatin accessibility (caQTLs), demonstrating the capability of performing caQTL analysis on a large scale in a diverse sample set without pre-existing genotype information. Using 10,293 profiling samples representing 1454 unique donor individuals across 653 studies from public databases, we catalog 24,159 caQTLs in total. After joint discovery analysis, we cluster samples based on accessible chromatin profiles to identify context-specific caQTLs. We find that caQTLs are strongly enriched for annotations of gene regulatory elements across diverse cell types and tissues and are often linked with genetic variation associated with changes in expression (eQTLs), indicating that caQTLs can mediate genetic effects on gene expression. We demonstrate sharing of causal variants for chromatin accessibility across human traits, enabling a more complete picture of the genetic mechanisms underlying complex human phenotypes.

CONCLUSIONS

Our work provides a proof of principle for caQTL calling from previously ungenotyped samples and represents one of the largest, most diverse caQTL resources currently available, informing mechanisms of genetic regulation of gene expression and contribution to disease.

Rethinking GWAS: how lessons from genetic screens and artificial intelligence could reveal biological mechanisms

MOTIVATION

Modern single-cell omics data are key to unraveling the complex mechanisms underlying risk for complex diseases revealed by genome-wide association studies (GWAS). Phenotypic screens in model organisms have several important parallels to GWAS which the author explores in this essay.

RESULTS

The author provides the historical context of such screens, comparing and contrasting similarities to association studies, and how these screens in model organisms can teach us what to look for. Then the author considers how the results of GWAS might be exhaustively interrogated to interpret the biological mechanisms underpinning disease processes. Finally, the author proposes a general framework for tackling this problem computationally, and explore the data, mechanisms, and technology (both existing and yet to be invented) that are necessary to complete the task.

AVAILABILITY AND IMPLEMENTATION

There are no data or code associated with this article.

Genome-wide association meta-analysis identifies 126 novel loci for diverticular disease and implicates connective tissue and colonic motility

Diverticular disease is a common and morbid complex phenotype influenced by both innate and environmental risk factors. We performed the largest genome-wide association study meta-analysis for diverticular disease, identifying 126 novel loci. Employing multiple downstream analytic strategies, including tissue and pathway enrichment, statistical fine-mapping, allele-specific expression, protein quantitative trait loci and drug-target investigations, and linkage disequilibrium score regression, we prioritized causal genes and produced several lines of evidence linking diverticular disease to connective tissue biology and colonic motility. We substantiated these findings by integrating single-cell RNA sequencing data, showing that prioritized diverticular disease-associated genes are enriched for expression in colonic smooth muscle, fibroblasts, and interstitial cells of Cajal. In quantitative analysis of surgical specimens, we found a substantial reduction in the density of elastin present in the sigmoid colon in severe diverticulitis.

Assessing the influence of plasma metabolites on chronic skin ulcer risk: a two-sample Mendelian randomization study

Chronic skin ulcers, although rare, pose severe and debilitating challenges. The identification of causal metabolite biomarkers presents an opportunity to refine effective risk assessment strategies for this condition. In this study, we conducted a comprehensive Two-Sample Mendelian Randomization (TSMR) investigation to delineate the potential causal effects of plasma metabolites on chronic skin ulcer risk. Exposure data comprised 14,296 participants with 913 metabolites from INTERVAL/EPIC-Norfolk, and 8,299 participants with 1,091 metabolites and 309 ratios from the Canadian Longitudinal Study on Aging (CLSA). Outcome data came from the finngen_R9_L12_CHRONICULCEROFSKIN (1,840 cases, 353,088 controls) and UK Biobank Chronic ulcer of skin (495 cases, 455,853 controls) cohorts. Leveraging the inverse-variance weighted (IVW) method, alongside MR-Egger and MR-PRESSO sensitivity analyses, we evaluated metabolite associations with chronic skin ulcer risk. Further assessment involved a phenome-wide MR (Phe-MR) analysis to explore potential repercussions of targeting identified metabolites for intervention. Our study identified 12 distinct metabolites significantly associated with chronic skin ulcers, demonstrating consistent and replicable results. Notably, X-19,141 exhibited the highest reproducibility. These findings highlight novel plasma metabolites relevant to chronic skin ulcers, offering theoretical underpinnings for mechanistic research and clinical strategies in prevention and treatment.

Trans-ancestry genome-wide association meta-analysis of gallstone disease

Gallstone disease is a highly prevalent and costly gastrointestinal disease. Yet, genetic variation in susceptibility to gallstone disease and its implication in metabolic regulatory pathways remain to be explored. We report a trans-ancestry genome-wide association meta-analysis of gallstone disease including 88,063 cases and 1,490,087 controls in the UK Biobank, FinnGen, Biobank Japan, and Million Veteran Program. We identified 91 (37 novel) risk loci across the meta-analysis and found replication in statistically compelling signals in the All of Us Research Program. A polygenic risk score constructed from trans-ancestry lead variants was positively associated with liver chemistry and alpha-1-antitrypsin deficiency and negatively associated with total cholesterol and low-density lipoprotein levels among trans-ancestry and European ancestry groups in the Penn Medicine BioBank. Cross-trait colocalization analysis between risk loci and 44 liver, metabolic, renal, and inflammatory traits yielded 350 significant colocalizations as well as 97 significant colocalizations and 65 prioritized genes from expression quantitative trait loci from eight tissues. These findings broaden our understanding of the genetic architecture of gallstone disease.

Causal effect between circulating metabolic markers and glioma: a bidirectional, two-sample, Bayesian weighted Mendelian randomization

Glioma is the most common malignant tumor in the central nervous system with significant challenges for its treatment and prognosis. Based on publicly available genome-wide association study data, this study employed a bidirectional, two-sample Mendelian randomization (MR) analysis, combined with Bayesian weighted MR, to investigate the causal effect of 233 circulating metabolic traits on glioma and its subtypes, with the expectation of discovering new diagnostic and therapeutic targets. The MR study revealed that the Total cholesterol to total lipids ratio in large VLDL and the Ratio of polyunsaturated fatty acids to total fatty acids (PUFAbyFA) are risk factors for glioma, while free cholesterol or phospholipids in small HDL are protective against glioma. The free cholesterol to total lipids ratio in IDL, the ratios of total cholesterol or cholesteryl esters to total lipids in small or medium VLDL, as well as linoleic acid (LA 18:2), phosphatidylcholine, and sphingomyelins levels are risk factors for non-GBM glioma. Free cholesterol in small HDL is identified as a protective factor for non-GBM glioma. The results were robust to sensitivity analyses and Bayesian weighted mendelian randomization. The causal effects of glioma on circulating metabolites were explored through reverse mendelian randomization. The results provide potential biomarkers for the early diagnosis and treatment of glioma.

Atlas of genetic and phenotypic associations across 42 female reproductive health diagnoses

The genetic background of many female reproductive health diagnoses remains uncharacterized, compromising our understanding of the underlying biology. Here, we map the genetic architecture across 42 female-specific health conditions using data from up to 293,618 women from two large population-based cohorts, the Estonian Biobank and the FinnGen study. Our study illustrates the utility of genetic analyses in understanding women's health better. As specific examples, we describe genetic risk factors for ovarian cysts that elucidate the genetic determinants of folliculogenesis and, by leveraging population-specific variants, uncover new candidate genes for uterine fibroids. We find that most female reproductive health diagnoses have a heritable component, with varying degrees of polygenicity and discoverability. Finally, we identify pleiotropic loci and genes that function in genital tract development (WNT4, PAX8, WT1, SALL1), hormonal regulation (FSHB, GREB1, BMPR1B, SYNE1/ESR1) and folliculogenesis (CHEK2), underlining their integral roles in female reproductive health.

Genome-wide meta-analysis identifies novel risk loci for uterine fibroids within and across multiple ancestry groups

Uterine leiomyomata or fibroids are highly heritable, common, and benign tumors of the uterus with poorly understood etiology. Previous GWAS have reported 72 associated genes but included limited numbers of non-European individuals. Here, we identify 11 novel genes associated with fibroids across multi-ancestry and ancestry-stratified GWAS analyses. We replicate a known fibroid GWAS gene in African ancestry individuals and estimate the SNP-based heritability of fibroids in African ancestry populations as 15.9%. Using genetically predicted gene expression and colocalization analyses, we identify 46 novel genes associated with fibroids. These genes are significantly enriched in cancer, cell death and survival, reproductive system disease, and cellular growth and proliferation networks. We also find that increased predicted expression of HEATR3 in uterine tissue is associated with fibroids across ancestry strata. Overall, we report genetic variants associated with fibroids coupled with functional and gene pathway enrichment analyses.

RBM43 controls PGC1α translation and a PGC1α-STING signaling axis

Obesity is associated with systemic inflammation that impairs mitochondrial function. This disruption curtails oxidative metabolism, limiting adipocyte lipid metabolism and thermogenesis, a metabolically beneficial program that dissipates chemical energy as heat. Here, we show that PGC1α, a key governor of mitochondrial biogenesis, is negatively regulated at the level of its mRNA translation by the RNA-binding protein RBM43. RBM43 is induced by inflammatory cytokines and suppresses mitochondrial biogenesis in a PGC1α-dependent manner. In mice, adipocyte-selective Rbm43 disruption elevates PGC1α translation and oxidative metabolism. In obesity, Rbm43 loss improves glucose tolerance, reduces adipose inflammation, and suppresses activation of the innate immune sensor cGAS-STING in adipocytes. We further identify a role for PGC1α in safeguarding against cytoplasmic accumulation of mitochondrial DNA, a cGAS ligand. The action of RBM43 defines a translational regulatory axis by which inflammatory signals dictate cellular energy metabolism and contribute to metabolic disease pathogenesis.

Genome-wide association testing beyond SNPs

Decades of genetic association testing in human cohorts have provided important insights into the genetic architecture and biological underpinnings of complex traits and diseases. However, for certain traits, genome-wide association studies (GWAS) for common SNPs are approaching signal saturation, which underscores the need to explore other types of genetic variation to understand the genetic basis of traits and diseases. Copy number variation (CNV) is an important source of heritability that is well known to functionally affect human traits. Recent technological and computational advances enable the large-scale, genome-wide evaluation of CNVs, with implications for downstream applications such as polygenic risk scoring and drug target identification. Here, we review the current state of CNV-GWAS, discuss current limitations in resource infrastructure that need to be overcome to enable the wider uptake of CNV-GWAS results, highlight emerging opportunities and suggest guidelines and standards for future GWAS for genetic variation beyond SNPs at scale.

Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases

Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell-cell communication at 139 loci. Furthermore, we assayed chromatin accessibility using assay for transposase-accessible chromatin with sequencing and histone H3 lysine 4 trimethylation in stem cell-derived atrial cardiomyocytes. We observed a marked increase in chromatin accessibility for our sentinel variants and prioritized genes in atrial cardiomyocytes. Finally, a polygenic risk score (PRS) based on our updated effect estimates improved AF risk prediction compared to the CHARGE-AF clinical risk score and a previously reported PRS for AF. The doubling of known risk loci will facilitate a greater understanding of the pathways underlying AF.

Large-scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. Here, we report results from a large genome-wide association study and multitrait analysis including 5,900 HCM cases, 68,359 controls and 36,083 UK Biobank participants with cardiac magnetic resonance imaging. We identified 70 loci (50 novel) associated with HCM and 62 loci (20 novel) associated with relevant left ventricular traits. Among the prioritized genes in the HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants confer a roughly tenfold increased risk of HCM. Mendelian randomization analyses support a causal role of increased left ventricular contractility in both obstructive and nonobstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, these findings increase our understanding of the genetic basis of HCM, with potential implications for disease management.

Human genetic evidence enriched for side effects of approved drugs

Safety failures are an important factor in low drug development success rates. Human genetic evidence can select drug targets causal in disease and enrich for successful programs. Here, we sought to determine whether human genetic evidence can also enrich for labeled side effects (SEs) of approved drugs. We combined the SIDER database of SEs with human genetic evidence from genome-wide association studies, Mendelian disease, and somatic mutations. SEs were 2.0 times more likely to occur for drugs whose target possessed human genetic evidence for a trait similar to the SE. Enrichment was highest when the trait and SE were most similar to each other, and was robust to removing drugs where the approved indication was also similar to the SE. The enrichment of genetic evidence was greatest for SEs that were more drug specific, affected more people, and were more severe. There was significant heterogeneity among disease areas the SEs mapped to, with the highest positive predictive value for cardiovascular SEs. This supports the integration of human genetic evidence early in the drug discovery process to identify potential SE risks to be monitored or mitigated in the course of drug development.

LRP5 promotes adipose progenitor cell fitness and adipocyte insulin sensitivity

BACKGROUND

WNT signaling plays a key role in postnatal bone formation. Individuals with gain-of-function mutations in the WNT co-receptor LRP5 exhibit increased lower-body fat mass and potentially enhanced glucose metabolism, alongside high bone mass. However, the mechanisms by which LRP5 regulates fat distribution and its effects on systemic metabolism remain unclear. This study aims to explore the role of LRP5 in adipose tissue biology and its impact on metabolism.

METHODS

Metabolic assessments and imaging were conducted on individuals with gain- and loss-of-function LRP5 mutations, along with age- and BMI-matched controls. Mendelian randomization analyses were used to investigate the relationship between bone, fat distribution, and systemic metabolism. Functional studies and RNA sequencing were performed on abdominal and gluteal adipose cells with LRP5 knockdown.

RESULTS

Here we show that LRP5 promotes lower-body fat distribution and enhances systemic and adipocyte insulin sensitivity through cell-autonomous mechanisms, independent of its bone-related functions. LRP5 supports adipose progenitor cell function by activating WNT/β-catenin signaling and preserving valosin-containing protein (VCP)-mediated proteostasis. LRP5 expression in adipose progenitors declines with age, but gain-of-function LRP5 variants protect against age-related fat loss in the lower body.

CONCLUSIONS

Our findings underscore the critical role of LRP5 in regulating lower-body fat distribution and insulin sensitivity, independent of its effects on bone. Pharmacological activation of LRP5 in adipose tissue may offer a promising strategy to prevent age-related fat redistribution and metabolic disorders.

EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell lines

Enhancer-gene maps are essential to understand gene regulation. The identification of enhancers contributing to regulating a given gene, however, is challenging as they may lie far from the gene and act in a tissue-specific manner. Here we present EPIraction, an enhancer-gene atlas in the human genome. As H3K27ac is a typical mark of enhancer function, EPIraction relies on an exhaustive collection of 1,538 H3K27ac ChIP-Seq datasets. We used these data together with other epigenomic data to measure enhancer activity. To predict enhancer-gene interactions we first applied the conventional Activity By Contact algorithm to score enhancer-gene pairs in individual tissues. Then, we re-scored the interaction rewarding those present in biologically similar tissues. We used our predictions to annotate the candidate target genes for 1,664,956 SNPs from 3,693 UK Biobank and 760 FinnGen GWAS traits. The EPIraction predictions can be accessed through the portal at https://epiraction.crg.es . The portal can be employed to produce annotations for user-provided lists of SNPs or genomic intervals.

Genomics-Informed Drug Repurposing Strategy Identifies Novel Therapeutic Targets for Metabolic Dysfunction-Associated Steatotic Liver Disease

Identification of drug-repurposing targets with genetic and biological support is an economically and temporally efficient strategy for improving treatment of diseases. We employed a cross-disciplinary approach to identify potential treatments for metabolic dysfunction associated steatotic liver disease (MASLD) using humans as a model organism. We identified 212 putative causal genes associated with MASLD using data from a large multi-ancestry genetic association study, of which 158 (74.5%) are novel. From this set we identified 57 genes that encode for druggable protein targets, and where the effects of increasing genetically predicted gene expression on MASLD risk align with the function of that drug on the protein target. These potential targets were then evaluated for evidence of efficacy using Mendelian randomization, pathway analysis, and protein structural modeling. Using these approaches, we present compelling evidence to suggest activation of FADS1 by icosopent ethyl as well as S1PR2 by fingolimod could be promising therapeutic strategies for MASLD.

Genetically predicted inflammatory proteins mediate the association between gut microbiota and renal cell carcinoma

BACKGROUND

Studies have indicated a potential relationship between gut microbiota and renal cell carcinoma. However, the causal relationship between various types of gut microbiota and renal cell carcinoma, as well as the role of inflammatory protein as mediators, remains unclear.

METHODS

This study aimed to identify the relationship between gut microbiota, inflammatory protein, and renal cell cancer through a large-scale genome-wide association study (GWAS) utilizing pooled data. We employed Mendelian randomization (MR) to investigate the causal relationship among these variables. Inverse variance weighting (IVW) was utilized as the primary statistical method. Furthermore, we examined the mediating role of inflammatory protein in the pathway through which gut microbiota influences the development of renal cell cancer.

RESULTS

The analysis revealed 12 positive causal relationships and 15 negative causal relationships between the genetic liability of gut microbiota and renal cell cancer. Furthermore, there were three positive causal relationships and one negative causal relationship between inflammatory proteins and renal cell cancer. There were two axes of relationships in which gut microbiota promote the development of renal cell cancer. through inflammatory proteins acting as mediators.

CONCLUSIONS

Gut microbiota and inflammatory protein were causally related to renal cell cancer, and inflammatory protein were intermediary factors in the pathway between gut microbiota and renal cell cancer.

Genetic modifiers of asthma response to air pollution in children: An African ancestry GWAS and PM2.5 polygenic risk score study

RATIONALE

Ambient air pollution (AAP) is linked to asthma outcomes, but predicting individual risk remains challenging. Understanding genetic contributors to AAP sensitivity may help overcome this gap.

OBJECTIVES

To determine if single nucleotide polymorphisms (SNPs) are associated with AAP sensitivity in children with asthma.

METHODS

We complete a GWAS in pediatric patients with asthma frequently exposed to AAP, comparing patients with exacerbations following spikes in AAP to patients without this temporal association and calculate a polygenic risk score (PRS) for PM2.5. This PRS was validated using internal data and data from the All of Us cohort.

MEASUREMENTS AND RESULTS

We included 6023 patients in the GWAS, restricted to the African ancestry cohort due to the association between AAP exposure and race. Three loci reached genome-wide significance, including rs111970601, associated with CO sensitivity (odds ratio [OR] 6.58; P = 1.63 × 10-8) and rs9836522 with PM2.5 sensitivity (OR 0.75; P = 3,87 × 10-9), both externally validated. PRS z-scores were associated with increased asthma exacerbations in patients frequently exposed to poor air quality (β = 0.15; P = 2.67 × 10⁻⁵). Spirometry data from 4138 patients showed that having a high PRS was associated with lower FVC z-scores in patients frequently exposed to AAP (β = -0.44; P = 0.035). External validation confirmed a significant interaction between high PRS and frequent AAP exposure (β = 0.30; P = 0.012) CONCLUSIONS: We associate specific SNPs with AAP-related asthma exacerbations and introduce a PM2.5 sensitivity PRS, paving the way for future research aimed at protecting genetically predisposed patients.

The mediating role of thyroid-related hormones between thyroid dysfunction diseases and osteoporosis: a mediation mendelian randomization study

The links between Thyroid dysfunction diseases (TDFDs) and osteoporosis (OP) has received widespread attention, but the causal relationships and mediating factors have not been systematically studied. We used two-sample Mendelian randomization (MR) analysis to elucidate the causal relationship between TDFDs and OP. Moreover, we performed mediation MR analyses to explore the role of thyroid-related hormones and OP risk factors in the association between TDFDs and OP. Two sample MR analyses showed that hyperthyroidism increased OP (OR = 1.080, 95% CI 1.026 to 1.137; P = 0.0032) risk. Hypothyroidism increases OP (OR = 1.183, 95% CI 1.125 to 1.244; P < 0.0001) risk. Furthermore, mediation analysis revealed that TSH mediated 5.314% of the relationship between hypothyroidism and OP. In contrast, FT4 mediated 9.670% of the relationship between hyperthyroidism and OP. In European populations, TDFDs may increase OP risk. TSH mediates in the causal association between hypothyroidism and OP, and similarly, FT4 mediates in the causal link between hyperthyroidism and OP. Our findings underscore the significance of improving integrative care for individuals with TDFDs to mitigate the risk of OP. It is essential to maintain stable levels of thyroid hormones and closely monitor bone health to effectively mitigate and prevent OP.

MorPhiC Consortium: towards functional characterization of all human genes

Recent advances in functional genomics and human cellular models have substantially enhanced our understanding of the structure and regulation of the human genome. However, our grasp of the molecular functions of human genes remains incomplete and biased towards specific gene classes. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) Consortium aims to address this gap by creating a comprehensive catalogue of the molecular and cellular phenotypes associated with null alleles of all human genes using in vitro multicellular systems. In this Perspective, we present the strategic vision of the MorPhiC Consortium and discuss various strategies for generating null alleles, as well as the challenges involved. We describe the cellular models and scalable phenotypic readouts that will be used in the consortium's initial phase, focusing on 1,000 protein-coding genes. The resulting molecular and cellular data will be compiled into a catalogue of null-allele phenotypes. The methodologies developed in this phase will establish best practices for extending these approaches to all human protein-coding genes. The resources generated-including engineered cell lines, plasmids, phenotypic data, genomic information and computational tools-will be made available to the broader research community to facilitate deeper insights into human gene functions.