Single-cell mapping of cis-expression quantitative trait loci in inflammatory bowel disease revealed distal, enhancer-enriched variants detected at the cell-type level more frequently co-loc...

Author summary Most of the genetic variants associated with complex traits are located outside genes, limiting functional interpretation. Genetic colocalisation helps identify candidate causal genes b...

Inflammatory bowel disease (IBD) is a chronic immune-mediated disorder of the gastrointestinal tract whose genetic basis is only partly resolved because most risk variants identified by genome-wide as...

Author summary Most of the genetic variants associated with complex traits are located outside genes, limiting functional interpretation. Genetic colocalisation helps identify candidate causal genes b...

The phenotypic effects of germline variants are often mediated through gene regulation. Expression quantitative trait loci (eQTLs) are genetic variants associated with changes in gene expression. Understanding how eQTLs vary across populations is essential for characterising the genetic and regulatory drivers of trait diversity. Meta-analysing eQTL studies from multiple populations enables more robust detection of eQTLs and can reveal regulatory mechanisms shaped by population-specific environmental or ancestry-related factors. However, across the multi-ancestry eQTL literature, a wide range of methods have been used to quantify eQTL portability across ancestry groups. Because different studies employ different portability metrics, it is challenging to form a coherent view of the regulatory landscape across populations. In this work, we analyse eQTL summary statistics from ten datasets matched on tissue type and sequencing technology. We compare portability metrics used previously and show that they can yield markedly different patterns of apparent regulatory conservation or divergence. We then examine the statistical determinants of portability across metrics and demonstrate that sample size, minor allele frequency, and linkage disequilibrium are major drivers of the observed differences in eQTL portability across studies. These findings highlight that differences in statistical power stemming from factors such as population size and allele frequency must be accounted for when evaluating eQTL portability. To address this issue, we introduce a new approach designed to correct for these factors when calling eQTL portability. Finally, we show that empirical Bayes multivariate adaptive shrinkage provides a powerful framework for meta-analysing multiple eQTL studies, with the ability to pool signals across populations to produce more robust effect-size estimates within each population. ### Competing Interest Statement The authors have declared no competing interest. National Health and Medical Research Council, https://ror.org/011kf5r70, Ideas Grant 2020501, Investigator Grant 1195595

Transcriptome-wide association studies (TWAS) are widely used to identify genes involved in complex traits and to infer the direction of gene effects on traits. However, despite their popularity, it r...

Gene regulatory networks encode the fundamental logic of cellular functions, but systematic network mapping remains challenging, especially in cell states relevant to human biology and disease. Here, ...

Image-based spatial transcriptomics (IST) technologies provide unprecedented resolution of gene expression in tissue sections, but suffer from contamination of cells' gene expression profiles due to i...

WGS data were used from 347,630 individuals with European ancestry in the UK Biobank to obtain high-precision estimates of coding and non-coding rare variant heritability for 34 co...

Colocalization is frequently performed as a step to triage findings from genetic investigations linking molecular and disease data. However, the reliability and consistency of the various colocalizati...

Antibodies are the principal effector proteins of humoral immunity. Dysregulated antibody production is a feature of a number of heritable immune-mediated diseases, such as the antibody deficiencies a...

Out now in Nature Comms: the largest trans-eQTL meta-analysis in a single cell type! An Open Targets team led by Krista Freimann and Kaur Alasoo analysed 3,734 lymphoblastoid cell line samples across...

Fanconi anemia (FA) is a rare genetic condition that predisposes to progressive bone marrow failure, a specific spectrum of malignancies, including he…

Multi-ancestry GWASs enhance discovery in diverse populations, but optimal methods remain debated. Using theory, simulations, and analyses from the UK Biobank and All of Us, we show that pooled analys...

Understanding how genetic variation influences gene regulation at the single-cell level is crucial for elucidating the mechanisms underlying complex diseases. However, limited large-scale single-cell multi-omics data have constrained our understanding of the regulatory pathways that link variants to cell type-specific gene expression. Here we present chromatin accessibility profiles from 3.5 million peripheral blood mononuclear cells (PBMCs) across 1,042 donors, generated using single-cell ATAC-seq and multiome (RNA+ATAC) sequencing, with matched whole-genome sequencing, generated as part of the TenK10K program. We characterized 440,996 chromatin peaks across 28 immune cell types and mapped 243,273 chromatin accessibility quantitative trait loci (caQTLs), 60% of which are cell type-specific. Integration with TenK10K scRNA-seq data (5.4 million PBMCs) identified 31,688 candidate cis-regulatory elements colocalized with eQTLs; over half (52.5%) show evidence of causal effects mediated via chromatin accessibility. Integrating caQTLs with GWAS summary statistics for 16 diseases and 44 blood traits uncovered 9.8% - 30.0% more colocalized signals compared with using eQTLs alone, many of which have not been reported in prior studies. We demonstrate cell type-specific mechanisms, such as a regulatory effect on IRGM acting through altered promoter chromatin accessibility in CD8 effector memory T cells but not in naive cells. Using a graph neural network, we inferred peak-to-gene relationships from unpaired multiome data by incorporating caQTL and eQTL signals, achieving up to 80% higher accuracy compared to using paired multiome data without QTL information. This improvement further enhanced gene regulatory network inference, leading to the identification of 128 additional transcription factor (TF)-target gene pairs (a 22% increase). These findings provide an unprecedented single-cell map of chromatin accessibility and genetic variation in human circulating immune cells, establishing a powerful resource for dissecting cell type-specific regulation and advancing our understanding of genetic risk for complex diseases. ### Competing Interest Statement L.C., E.B.D., and K.K.H.F. are employed at Illumina Inc. D.G.M. is a paid advisor to Insitro and GSK, and receives research funding from Google and Microsoft, unrelated to the work described in this manuscript. G.A.F reports grants from National Health and Medical Research Council (Australia), grants from Abbott Diagnostic, Sanofi, Janssen Pharmaceuticals, and NSW Health. G.A.F reports honorarium from CSL, CPC Clinical Research, Sanofi, Boehringer-Ingelheim, Heart Foundation, and Abbott. G.A.F serves as Board Director for the Australian Cardiovascular Alliance (past President), Executive Committee Member for CPC Clinical Research, Founding Director and CMO for Prokardia and Kardiomics, and Executive Committee member for the CAD Frontiers A2D2 Consortium. In addition, G.A.F serves as CMO for the non-profit, CAD Frontiers, with industry partners including, Novartis, Amgen, Siemens Healthineers, ELUCID, Foresite Labs LLC, HeartFlow, Canon, Cleerly, Caristo, Genentech, Artyra, and Bitterroot Bio, Novo Nordisk and Allelica. In addition, G.A.F has the following patents: "Patent Biomarkers and Oxidative Stress" awarded USA May 2017 (US9638699B2) issued to Northern Sydney Local Health District, "Use of P2X7R antagonists in cardiovascular disease" PCT/AU2018/050905 licensed to Prokardia, "Methods for treatment and prevention of vascular disease" PCT/AU2015/000548 issued to The University of Sydney/Northern Sydney Local Health District, "Methods for predicting coronary artery disease" AU202290266 issued to The University of Sydney, and the patent "Novel P2X7 Receptor Antagonists" PCT/AU2022/051400 (23.11.2022), International App No: WO/2023/092175 (01.06.2023), issued to The University of Sydney. ### Funding Statement A.X. is supported by NHMRC Investigator grant 2033018. J.E.P. is supported by NHMRC Investigator grant 2034556, and a Fok Family Fellowship; D.G.M. is supported by an NHMRC investigator grant (2009982). G.A.F. and the BioHEART Study have been supported by NHMRC Investigator Grant, NSW Health Office of Health and Medical Research, and the NSW Health Statewide Biobank scheme. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Human Research Ethics Committee of St Vincent's Hospital gave ethical approval for this work. The National Statement on Ethical Conduct in Human Research of the National Health and Medical Research Council gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Raw caQTL summary statistics will be available at Zenodo website prior to acceptance. [https://github.com/powellgenomicslab/tenk10k\_phase1\_multiome][1] [1]: https://github.com/powellgenomicslab/tenk10k_phase1_multiome

Colocalisation is a powerful approach to assess if two genetic association signals are likely to share a causal variant. However, association analyses in large biobanks and molecular quantitative trai...

This study introduces a cost-effective strategy called surrogate functional false discovery rates to increase power in genome-wide association studies by leveraging genetic correlations (or pleiotropy...

Genetic variants in RNU4-2, which encodes U4, a key non-coding small nuclear RNA (snRNA) component of the major spliceosome, were recently shown to cause a prevalent neurodevelopmental disorder (NDD) ...

LDAK-KVIK is a mixed-model association method for genome-wide studies that optimizes computational performance and power. LDAK-KVIK can also perform gene-based tests and produces state-of-the-art poly...

Expression quantitative trait locus (eQTL) studies in human cohorts typically detect at least one regulatory signal per gene, and have been proposed as a way to explain mechanisms of genetic liability...

Identifying genetic variants that affect gene expression, expression quantitative trait loci (eQTLs), is a major focus of modern genomics. Today, various methods exist for eQTL mapping, each using dif...

Identifying genetic variants that affect gene expression, expression quantitative trait loci (eQTLs), is a major focus of modern genomics. Today, various methods exist for eQTL mapping, each using dif...

Genome-wide association studies (GWAS) have identified thousands of trait-associated loci. Prioritizing causal variants within these loci is critical for characterizing trait biology. Statistical fine...

Author summary Evaluating whether two traits, such as disease risk and gene expression, are affected by the same genetic variants is crucial for understanding the molecular mechanisms through which ge...

Author summary Evaluating whether two traits, such as disease risk and gene expression, are affected by the same genetic variants is crucial for understanding the molecular mechanisms through which ge...

Standard QTL mapping approaches consider variant effects on a single gene at a time, despite abundant evidence for allelic pleiotropy, where a single variant can affect multiple genes simultaneously. ...