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.

eQTL mapping in fetal-like pancreatic progenitor cells reveals early developmental insights into diabetes risk

“…We additionally estimated the transcription factor (TF) binding score for each variant using the Genetic Variants Allelic TF Binding Database 42 and found that, at increasing posterior probability (PP, probability that the variant is causal for the association) thresholds, the candidate causal variants underlying egQTLs were more likely to affect TF binding compared to those underlying eiQTLs (Figure 1F, Supplementary Data 9, Supplementary Data 10). These results corroborate similar findings from previous studies 10,12,43 , showing that the genetic variants underlying egQTLs and eiQTLs primarily affect gene regulation and coding regions or alternative splicing, respectively.…”

“…We additionally estimated the transcription factor (TF) binding score for each variant using the Genetic Variants Allelic TF Binding Database 42 and found that, at increasing posterior probability (PP, probability that the variant is causal for the association) thresholds, the candidate causal variants underlying egQTLs were more likely to affect TF binding compared to those underlying eiQTLs (Figure 1F, Supplementary Data 9, Supplementary Data 10). These results corroborate similar findings from previous studies 10,12,43 , showing that the genetic variants underlying egQTLs and eiQTLs primarily affect gene regulation and coding regions or alternative splicing, respectively.To further characterize the function of genetic variants associated with the fetal-like iPSC-PPC transcriptome, we examined the distributions of egQTLs and eiQTLs per gene. Of the 5,619 genes whose phenotype was affected by genetic variation, 1,008 were impacted through both gene expression and isoform usage (i.e., had both egQTL and eiQTLs, 17.9%) while 3,057 were impacted through only gene expression (i.e., had only egQTLs, 54.4%) and 1,554 through only isoform usage (i.e., had only eiQTLs, 27.7%, Figure 1G, Supplementary Data 7).…”

“…We calculated TPM and estimated relative isoform usage for each gene in each RNA-seq sample using RSEM (version 1.2.20) 106 with the following options –seed 3272015 –estimate-rspd –paired-end –forward-prob . To identify expressed autosomal genes and isoforms to use for eQTL analyses, we used the same approach previously described 12 . Briefly, autosomal genes were considered expressed if TPM ≥ 1 in at least 10% of samples.…”

Analysis of regulatory network modules in hundreds of human stem cell lines reveals complex epigenetic and genetic factors contribute to pluripotency state differences between subpopulations

“…We previously performed principal component analysis (PCA) on WGS variants to determine the global ancestry of each individual in this study 18 . Briefly, we used the genotypes of 1,634,010 SNPs that had allele frequencies between 30% and 60% in the 1000 Genomes Phase 3 Project and genotyped in both iPSCORE and GTEx.…”

“…We generated 150 ATAC-seq samples from 143 hiPSC lines in iPSCORE and applied an unsupervised machine learning algorithm that enabled us to discover genome-wide regulatory network modules (RNMs) comprised of co-accessible regulatory elements. We integrated these data with gene network modules (GNMs) that we similarly generated from the RNA-seq dataset 18,19 for 213 hiPSC lines also in iPSCORE. We demonstrated that both the RNMs and GNMs were associated with the differential expression of marker genes defining hiPSC pluripotency cell states, and showed that their discovery is due to differences in the proportion of these transitory pluripotency states across the hiPSC samples.…”