Single-Tissue and Cross-Tissue Heritability of Gene Expression Via Identity-by-Descent in Related or Unrelated Individuals

Georgia Institute of Technology, United States of America
PLoS Genetics (Impact Factor: 8.17). 02/2011; 7(2):e1001317. DOI: 10.1371/journal.pgen.1001317
Source: PubMed

ABSTRACT Author Summary
An important goal in biology is to understand how genotype affects gene expression. Because gene expression varies across tissues, the relationship between genotype and gene expression may be tissue-specific. In this study, we used heritability approaches to study the regulation of gene expression in two tissue types, blood and adipose tissue, as well as the regulation of gene expression that is shared across these tissues. Heritability can be partitioned into cis and trans effects by assessing identity-by-descent (IBD) at the genomic location close to the expressed gene or genome-wide, respectively, and applying variance-components methods to partition the heritability of each gene. We estimated the proportion of gene expression heritability explained by cis regulation as 37% in blood and 24% in adipose tissue. Notably, the heritability shared across tissue types was primarily due to cis regulation. Thus, the relative contribution of cis versus trans regulation is expected to increase with the number of cell types present in the tissue being assayed, just as observed in our study and in a comparison to previous work on lymphoblastoid cell lines (LCL). We specifically ruled out a substantial contribution of transgenerational epigenetic inheritance to heritability of gene expression in these cohorts by repeating our heritability analyses using segments shared IBD in distantly related Icelanders.

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    • "Genome-wide expression quantitative trait locus (eQTL) mapping has been widely used to identify common genetic variants regulating gene expression (Myers et al. 2007; Schadt et al. 2008; Stranger et al. 2005, 2007; Zeller et al. 2010). Although numerous studies (Myers et al. 2007; Schadt et al. 2008; Stranger et al. 2005, 2007; Westra et al. 2013; Zeller et al. 2010) have revealed tens of thousands of cis and trans eQTLs, many cis and trans eQTLs remain unrecognized due to sample size limitations and tissue specificity (Grundberg et al. 2012; Price et al. 2011). "
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    ABSTRACT: Genome-wide expressio n quantitative trait locus (eQTL) mapping may reveal common genetic variants regulating gene expression. In addition to mapping eQTLs, we systematically evaluated the heritability of the whole blood transcriptome in 5,626 participants from the Framingham Heart Study. Of all gene expression measurements, about 40 % exhibit evidence of being heritable [\(h_{\text{geneExp}}^{ 2}\) > 0, (p 0.2. To identify the role of eQTLs in promoting phenotype differences and disease susceptibility, we investigated the proportion of cis/trans eQTLs in different heritability categories and discovered that genes with higher heritability are more likely to have cis eQTLs that explain large proportions of variance in the expression of the corresponding genes. Single cis eQTLs explain 0.33–0.53 of variance in transcripts on average, whereas single trans eQTLs only explain 0.02–0.07. The top cis eQTLs tend to explain more variance in the corresponding gene when its \(h_{\text{geneExp}}^{ 2}\) is greater. Taking body mass index (BMI) as a case study, we cross-linked cis/trans eQTLs with both GWAS SNPs and differentially expressed genes for BMI. We discovered that BMI GWAS SNPs in 16p11.2 (e.g., rs7359397) are associated with several BMI differentially expressed genes in a cis manner (e.g. SULT1A1, SPNS1, and TUFM). These BMI signature genes explain a much larger proportion of variance in BMI than do the GWAS SNPs. Our results shed light on the impact of eQTLs on the heritability of the human whole blood transcriptome and its relations to phenotype differences.
    Human Genetics 01/2015; 134(3). DOI:10.1007/s00439-014-1524-3 · 4.52 Impact Factor
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    • "A median heritability of 0.35 for transcript expression has been reported for human lymphoblastoid cell lines, though this estimate may be upwardly biased given that heritability was estimated only for differentially expressed transcripts (Monks et al. 2004). Other human studies show that average transcript heritability differs across tissues: 0.37 in blood and only 0.24 in adipose (Price et al. 2011)—95% quantiles of nonzero estimates from this study overlap (blood = 0.018–0.497; adipose = 0.042–0.539), "
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    ABSTRACT: Evidence implicating differential gene expression as a significant driver of evolutionary novelty continues to accumulate, but our understanding of the underlying sources of variation in expression, both environmental and genetic, are wanting. Heritability in particular may be underestimated when inferred from genetic mapping studies, the predominant 'genetical genomics' approach to the study of expression variation. Such uncertainty represents a fundamental limitation to testing for adaptive evolution at the transcriptomic level. By studying the inheritance of expression levels in 10,495 genes (10,527 splice variants) in a threespine stickleback pedigree consisting of 563 individuals, half of which were subjected to a thermal treatment, we show that 74-98% of transcripts exhibit significant additive genetic variance. Dominance variance is also prevalent (41-99% of transcripts), and genetic sources of variation seem to play a more significant role in expression variance in the liver than a key environmental variable, temperature. Among-population comparisons suggest that the majority of differential expression in the liver is likely due to neutral divergence; however, we also show that signatures of directional selection may be more prevalent than those of stabilizing selection. This predominantly aligns with the neutral model of evolution for gene expression, but also suggests that natural selection may still act on transcriptional variation in the wild. Since genetic variation both within- and among-populations ultimately defines adaptive potential, these results indicate that broad adaptive potential may be found within the transcriptome. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
    Molecular Biology and Evolution 11/2014; 32(3). DOI:10.1093/molbev/msu328 · 14.31 Impact Factor
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    • "Multivariate linear mixed models (mvLMMs) [2] have been applied to many areas of genetics, including, for example, estimating the cross-tissue heritability of gene expression [3], assessing the pleiotropy and genetic correlation between complex phenotypes [1] [4] [5] [6], detecting quantitative * † "
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    ABSTRACT: Multivariate linear mixed models (mvLMMs) are powerful tools for testing associations between single-nucleotide polymorphisms and multiple correlated phenotypes while controlling for population stratification in genome-wide association studies. We present efficient algorithms in the genome-wide efficient mixed model association (GEMMA) software for fitting mvLMMs and computing likelihood ratio tests. These algorithms offer improved computation speed, power and P-value calibration over existing methods, and can deal with more than two phenotypes.
    Nature Methods 02/2014; 11(4). DOI:10.1038/nmeth.2848 · 25.95 Impact Factor
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