Fine-mapping cis-regulatory variants in diverse human populations

Abstract

9Genome-wide association studies (GWAS) are a powerful approach for connecting genotype to 10 phenotype. Most GWAS hits are located in cis-regulatory regions, but the underlying causal 11 variants and their molecular mechanisms remain unknown. To better understand human cis-12 regulatory variation, we mapped quantitative trait loci for chromatin accessibility (caQTLs)-a 13 key step in cis-regulation-in 1000 individuals from 10 diverse populations. Most caQTLs were 14 shared across populations, allowing us to leverage the genetic diversity to fine-map candidate 15 causal regulatory variants, several thousand of which have been previously implicated in GWAS. 16In addition, many caQTLs that affect the expression of distal genes also alter the landscape of 17 long-range chromosomal interactions, suggesting a mechanism for long-range expression QTLs. 18In sum, our results show that molecular QTL mapping integrated across diverse populations 19 provides a high-resolution view of how worldwide human genetic variation affects chromatin 20 accessibility, gene expression, and phenotype. was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Quantitative trait loci mapped for TCF21 binding, chromatin accessibility and chromosomal looping in coronary artery smooth muscle cells reveal molecular mechanisms of coronary disease loci

“…To call the TCF21 binding (bQTL), chromatin accessibility (caQTL) and chromosomal looping (clQTL) quantitative trait loci, we employed a published regression-based approach which uses post-assay allele frequencies together with genotypes of each sample to infer the proportion of each sample in the pool [9,13]. Comparing the pre-assay vs. post-assay allele frequencies allows the identification of outlier SNPs where these frequencies are significantly different from one another, which indicates a cis-acting QTL variant.…”

“…1d, 1e and 1f). With fold enrichment-optimized [9] at P value cutoff 10 -4 , we obtained 5315 significant bQTLs, 8346 caQTLs and 7084 clQTLs with the regression analysis (Additional file 2: Suppl Table. 1).…”

“…Variants that regulate gene expression, eQTLs, have been the most thoroughly investigated, in cell lines and disease related tissues, and have accelerated the identification of causal genes in associated loci [7,[37][38][39][40]. Mapping of QTLs that regulate genomic molecular phenotypes that likely are responsible for eQTL effects on gene expression, such as caQTLs [9] and clQTLs [41], and histone modification, hQTLs [42], have been more limited and restricted primarily to collections of immortalized lymphoblastoid cell lines (LCLs). Studies reported here have significantly expanded our understanding of how these features of the genome regulate gene expression, and how perturbation of DNA sequences that determine these features might contribute to variation in risk for complex human diseases.…”

“…QTLs for histone modification and chromatin accessibility, and more recently chromosomal looping have also been mapped [9][10][11][12]. Identification of genomic QTLs has been accelerated by use of a pooling approach that significantly reduces the number of individuals that have to be studied, and promoted the mapping of allelic variation that regulates transcription factor binding [13] and chromatin accessibility in a large number of ethnically diverse individuals [9]. Such efforts have provided important information regarding the regulatory structure of the non-coding sequence in the genome and identified how such variation may regulate the risk for complex human disease.…”

Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population

“…For quantitative traits, because of their complexity, mapping precision is usually much less than for Mendelian traits. This is particularly true when mapping in human populations (Chen et al., ; Tehranchi et al., ; Tziastoudi, Stefanidis, Stravodimos, & Zintzaras, ; Yang et al., ), due to their complex family and population structure. Consequently, very large human mapping populations are required to map causative genetic elements to narrow QTL.…”