Abstract
BackgroundWhile the possible sources underlying the so-called ‘missing heritability’ evident in current genome-wide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Studying heritability of genome-wide gene expression traits can shed light on the goal of understanding the relationship between phenotype and genotype. Here we used microarray gene expression measurements of lymphoblastoid cell lines and genome-wide SNP genotype data from 210 HapMap individuals to examine the heritability of gene expression traits.ResultsHeritability levels for expression of 10,720 genes were estimated by applying variance component model analyses and 1,043 expression quantitative loci (eQTLs) were detected. Our results indicate that gene expression traits display a bimodal distribution of heritability, one peak close to 0% and the other summit approaching 100%. Such a pattern of the within-population variability of gene expression heritability is common among different HapMap populations of unrelated individuals but different from that obtained in the CEU and YRI trio samples. Higher heritability levels are shown by housekeeping genes and genes associated with cis eQTLs. Both cis and trans eQTLs make comparable cumulative contributions to the heritability. Finally, we modelled gene-gene interactions (epistasis) for genes with multiple eQTLs and revealed that epistasis was not prevailing in all genes but made a substantial contribution in explaining total heritability for some genes analysed.ConclusionsWe utilised a mixed effect model analysis for estimating genetic components from population based samples. On basis of analyses of genome-wide gene expression from four HapMap populations, we demonstrated detailed exploitation of the distribution of genetic heritabilities for expression traits from different populations, and highlighted the importance of studying interaction at the gene expression level as an important source of variation underlying missing heritability.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-13) contains supplementary material, which is available to authorized users.
Highlights
While the possible sources underlying the so-called ‘missing heritability’ evident in current genomewide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task
Separation of the four geographically isolated HapMap populations indicates it would not be appropriate to treat the four populations as a homogeneous sample, but instead there should be proper control of the heterogeneity caused by the population structure effect in both the genetic relationship inference and association analysis
We used publicly available datasets of microarray gene expression measurements and genome-wide SNPs from 210 HapMap individuals to examine the heritability of gene expression traits in lymphoblastoid cell lines (LCLs) samples by using restricted maximum likelihood (REML) analysis of a variance component model
Summary
While the possible sources underlying the so-called ‘missing heritability’ evident in current genomewide association studies (GWAS) of complex traits have been actively pursued in recent years, resolving this mystery remains a challenging task. Price et al [12] analysed microarray gene expression data from blood and adipose samples of Icelandic family cohorts and began to partition the heritability into cis and trans components using a variance component model composed of polygenic effects estimated using identity by descent (IBD) for chromosome segments both proximal (cis) and distal (trans) to the gene of interest. Their method implicitly assumed the sum of variance components to be unity after normalising gene expression values to have mean 0 and variance 1, and only genetic variance component parameters were estimated using a binary search algorithm. While it is possible that variation noise caused the negative estimates of heritabilities as discussed in Price et al [12], a robust statistical approach which enables to prevent such negative heritability estimate is highly desirable
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