Cross-tissue and tissue-specific eQTLs: partitioning the heritability of a complex trait - PubMed (original) (raw)
Cross-tissue and tissue-specific eQTLs: partitioning the heritability of a complex trait
Jason M Torres et al. Am J Hum Genet. 2014.
Abstract
Top signals from genome-wide association studies (GWASs) of type 2 diabetes (T2D) are enriched with expression quantitative trait loci (eQTLs) identified in skeletal muscle and adipose tissue. We therefore hypothesized that such eQTLs might account for a disproportionate share of the heritability estimated from all SNPs interrogated through GWASs. To test this hypothesis, we applied linear mixed models to the Wellcome Trust Case Control Consortium (WTCCC) T2D data set and to data sets representing Mexican Americans from Starr County, TX, and Mexicans from Mexico City. We estimated the proportion of phenotypic variance attributable to the additive effect of all variants interrogated in these GWASs, as well as a much smaller set of variants identified as eQTLs in human adipose tissue, skeletal muscle, and lymphoblastoid cell lines. The narrow-sense heritability explained by all interrogated SNPs in each of these data sets was substantially greater than the heritability accounted for by genome-wide-significant SNPs (∼10%); GWAS SNPs explained over 50% of phenotypic variance in the WTCCC, Starr County, and Mexico City data sets. The estimate of heritability attributable to cross-tissue eQTLs was greater in the WTCCC data set and among lean Hispanics, whereas adipose eQTLs significantly explained heritability among Hispanics with a body mass index ≥ 30. These results support an important role for regulatory variants in the genetic component of T2D susceptibility, particularly for eQTLs that elicit effects across insulin-responsive peripheral tissues.
Copyright © 2014 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Figures
Figure 1
Study Overview (A) Delineation of the eQTL subsets evaluated in the decomposition of T2D heritability. (B) Diagram showing the constitution of each of the major partitions evaluated in this study. (C) Schematic of the heritability analyses performed on the WTCCC, SCT, and MCM data sets.
Figure 2
Estimates of Narrow-Sense T2D Heritability Explained by GWAS-Interrogated SNPs (A) The REML estimates of phenotypic variance explained by the additive effect of SNPs interrogated in GWASs (V A / V P) on T2D are shown for the WTCCC, SCT, and MCM data sets. (B–D) Heritability estimates for SNP subsets composed of T2D-associated variants from the NHGRI GWAS catalog and HapMap2 SNPs within 1 kb, 10 kb, 100 kb, 500 kb, and 1Mb are shown for the WTCCC (B), SCT (C), and MCM (D) data sets. Total chip heritability and SE for each GWAS are given by the solid and dashed black lines, respectively. The color corresponds to the significance of each heritability estimate determined by the test statistic from the likelihood-ratio test (LRT).
Figure 3
Heritability of T2D Explained by Metabolic-Tissue eQTLs in the WTCCC GWAS Data Set The narrow-sense heritability estimates (V A / V P) attributable to nonoverlapping SNP subsets (top panels). The proportion of chip heritability explained by each subset is plotted with SNP-set proportion (bottom panels). Color is designated by the −log10 of the LRT p value, and estimates are shown with SE. (A and B) IRPT-LCL analysis. (C and D) Expanded IRPT-LCL analysis. (E and F) Index-subset analysis with muscle-specific (M) and cross-tissue (CT) eQTLs as index sets. (G and H) _cis_-trans analysis of cross-tissue eQTLs.
Figure 4
Heritability of T2D Explained by Metabolic-Tissue eQTLs in the Merged Hispanic Data Set The narrow-sense heritability estimates (V A / V P) attributable to nonoverlapping SNP subsets (top panels). The proportion of chip heritability explained by each subset is plotted with SNP-set proportion (bottom panels). Color is designated by the −log10 of the LRT p value, and estimates are shown with SE. (A and B) IRPT-LCL analysis. (C and D) Expanded IRPT-LCL analysis. (E and F) Index-subset analysis with muscle-specific (M) and cross-tissue (CT) eQTLs as index sets. (G and H) Index-subset analysis with adipose-specific (A) and cross-tissue (CT) eQTLs as index sets.
Figure 5
Partition of Metabolic-Tissue Heritability among Hispanics in Low- and High-BMI Subgroups The narrow-sense heritability estimates (V A / V P) attributable to nonoverlapping SNP subsets are shown with SE and color corresponding to LRT (top panels). The proportion of chip heritability explained by each subset is plotted with SNP-set proportion and is color coded by the −log10 of the LRT p value (bottom panels). Results from an index-subset analysis with muscle-specific (M) and cross-tissue (CT) eQTLs as the index sets are shown for Hispanic subjects with a BMI < 30 (A and B) and subjects with a BMI ≥ 30 (E and F). Results from an index-subset analysis with adipose-specific (A) and cross-tissue (CT) eQTLs as the index sets are shown for Hispanic subjects with a BMI < 30 (C and D) and subjects with a BMI ≥ 30 (F and G).
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