Mapping cis- and trans-regulatory effects across multiple tissues in twins

Abstract

Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many expression quantitative trait locus (eQTL) studies, typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis effect on expression cannot be accounted for by common cis variants, a finding that reveals the contribution of low-frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene, and we identify several replicating trans variants that act predominantly in a tissue-restricted manner and may regulate the transcription of many genes

Genetic effects on gene expression across human tissues

“…The extensive tissue-specificity of trans -eQTLs was also supported by a hierarchical approach for FDR control 17 , where we found no trans -eQTLs shared across more than one tissue (Extended Data Table 3). Our estimate of increased tissue specificity for trans -eQTLs agreed with the minimal sharing of trans effects reported in previous eQTL studies with fewer tissues 4,19 , and greatly exceeds what would be expected on the basis of replication between tissues for cis -eQTLs of matched minor allele frequency (MAF) and effect size (Wilcoxon rank sum test; P ≤ 2.2×10 −16 for all choices of replication FDR; Extended Data Fig. 8h).…”

“…Recent large-scale studies have characterized the regulatory function of the genome across a diverse array of cell types, each from a small number of samples 1–3 . Measuring how gene regulation and expression vary across individuals has further expanded our understanding of the functions of healthy tissues and the molecular origins of complex traits and diseases 4–9 . However, these studies have been conducted in limited, accessible cell types, thus restricting the utility of these studies in informing regulatory biology and human health.…”

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

“…Additionally, using a similar strategy we performed lookup in the MuTHER consortium (Grundberg et al ., 2012) for cis eQTL associations found in fat cell, skin cell, and lymphoblastic cell lines (LCL). After Bonferroni correction for 297 lookups of three different traits ( P  < 5.6 × 10 −5 ), rs1065656 ( NUBP2 ) showed significant associations, specifically with FAHD1 (all tissues) and HAGH (fat cells and LCL) (Table S6, Supporting information).…”

“…Taking into account the eQTL association of the lead SNP rs1065656 with HAGH and the correlation with the mRNA and IGFBP‐3, this SNP might influence the IGFBP‐3 levels by modulating the amount of HAGH mRNA, whereas both the levels of mRNA and IGFBP‐3 are reduced per copy of the minor allele. Although this chain of associations was revealed in whole blood, it might be present in other tissues as well because significant eQTL for rs1065656 with HAGH were observed in other tissues studied in the MuTHER dataset (Grundberg et al ., 2012). Given the more pronounced association with ALS and the less significant eQTL with HAGH of the second signal compared with the lead SNP, rs11644716 might reduce the level of circulating IGFBP‐3 indirectly by reducing the amount of ALS per minor allele.…”

“…For each of the lead SNPs of the significant loci after final stage, significant cis eQTL associations in whole blood, lymphocytes, subcutaneous fat, muscle, and skin were looked up in the publically available association result databases (Grundberg et al ., 2012; Westra et al ., 2013). Association analysis of whole blood gene expression data with serum IGF‐I and IGFBP‐3 levels was conducted in 986 samples of the SHIP‐TREND cohort (Schurmann et al ., 2012).…”