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Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease.

ABSTRACT: The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.

SUBMITTER: D'Antonio M

PROVIDER: S-EPMC9975214 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease.

D'Antonio Matteo M Nguyen Jennifer P JP Arthur Timothy D TD Matsui Hiroko H D'Antonio-Chronowska Agnieszka A Frazer Kelly A KA

Nature communications 20230228 1

The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high pos ...[more]

PMID: 36854752

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Project description:Coronary artery disease (CAD) is the leading cause of mortality and morbidity driven by both genetic and environmental risk factors. Meta-analyses of genome-wide association studies (GWAS) have identified multiple single nucleotide polymorphisms (SNPs) associated with CAD and myocardial infarction (MI) susceptibility in multi-ethnic populations. The majority of these variants reside in non-coding regulatory regions and are co-inherited with hundreds of candidate regulatory SNPs. Herein, we use integrative genomic, epigenomic, and transcriptomic fine-mapping in human coronary artery smooth muscle cells (HCASMC) and tissues to identify causal regulatory variation and mechanisms responsible for CAD associations. Using these genome-wide maps we prioritize 65 candidate variants and perform allele-specific binding and expression analyses on 7 top candidates. We validate our findings in two independent cohorts of diseased human arterial expression quantitative trait loci (eQTL), which together demonstrate fundamental links between CAD associations and regulatory function in the appropriate disease context. We performed ATAC-seq, ChIP-seq, and RNA-seq on human coronary artery smooth muscle cells grown in SmGM-2 Smooth Muscle Growth Medium-2 including hEGF, insulin, hFGF-B and FBS, but without antibiotics (Lonza, #CC-3182). For ATAC-seq and RNA-seq we performed stimulations with growth factors (TGF-B1, PDGF-BB, PDGF-DD) versus serum-free control. We conducted two biological replicates for each condition using independent donors. For ATAC-seq experiments, sequencing was completed on an Illumina Hiseq 2500, paired-end 50bp reads. For ChIP-seq we performed immunoprecipitations using H3K27ac (Abcam ab4729). We conducted two biological replicates using HCASMC from independent donors, and also did an IgG control for these studies. For RNA-seq we also conducted two replicates using HCASMC from independent donors. For both ChIP-seq and RNA-seq experiments, sequencing was completed on an Illumina HiSeq 2500, paired-end 100bp reads. We also performed ex-vivo ATAC-seq on frozen tissues (isolated media) from normal and atherosclerotic human coronary arteries, using three independent donors for each. Sequencing was also completed on an Illumina HiSeq 2500, paired end 50bp reads.

Project description:BackgroundGenome-wide association studies can have limited power to identify QTL, partly due to the stringent correction for multiple testing and low linkage-disequilibrium between SNPs and QTL. Regional Heritability Mapping (RHM) has been advanced as an alternative approach to capture underlying genetic effects. In this study, RHM was used to identify loci underlying variation in the 16(th) QTLMAS workshop simulated traits.MethodsThe method was implemented by fitting a mixed model where a genomic region and the overall genetic background were added as random effects. Heritabilities for the genetic regional effects were estimated, and the presence of a QTL in the region was tested using a likelihood ratio test (LRT). Several region sizes were considered (100, 50 and 20 adjacent SNPs). Bonferroni correction was used to calculate the LRT thresholds for genome-wide (p < 0.05) and suggestive (i.e., one false positive per genome scan) significance.ResultsGenomic heritabilities (0.31, 0.32 and 0.48, respectively) and genetic correlations (0.80, -0.42 and 0.19, between trait-pairs 1&2, 1&3 and 2&3) were similar to the simulated ones. RHM identified 7 QTL (4 at genome-wide and 3 at suggestive level) for Trait1; 4 (2 genome-wide and 2 suggestive) for Trait2; and 7 (6 genome-wide and 1 suggestive) for Trait3. Only one of the identified suggestive QTL was a false-positive. The position of these QTL tended to coincide with the position where the largest QTL (or several of them) were simulated. Several signals were detected for the simulated QTL with smaller effect. A combined analysis including all significant regions showed that they explain more than half of the total genetic variance of the traits. However, this might be overestimated, due to Beavis effect. All QTL affecting traits 1&2 and 2&3 had positive correlations, following the trend of the overall correlation of both trait-pairs. All but one QTL affecting traits 1&3 were negatively correlated, in agreement with the simulated situation. Moreover, RHM identified extra loci that were not found by association and linkage analysis, highlighting the improved power of this approach.ConclusionsRHM identified the largest QTL among the simulated ones, with some signals for the ones with small effect. Moreover, RHM performed better than association and linkage analysis, in terms of both power and resolution.

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Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease.

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Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease.

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