Academic journal article Genetics

Genetic Variants Contribute to Gene Expression Variability in Humans

Academic journal article Genetics

Genetic Variants Contribute to Gene Expression Variability in Humans

Article excerpt

ABSTRACT Expression quantitative trait loci (eQTL) studies have established convincing relationships between genetic variants and gene expression. Most of these studies focused on the mean of gene expression level, but not the variance of gene expression level (i.e., gene expression variability). In the present study, we systematically explore genome-wide association between genetic variants and gene expression variability in humans. We adapt the double generalized linear model (dglm) to simultaneously fit the means and the variances of gene expression among the three possible genotypes of a biallelic SNP. The genomic loci showing significant association between the variances of gene expression and the genotypes are termed expression variability QTL (evQTL). Using a data set of gene expression in lymphoblastoid cell lines (LCLs) derived from 210 HapMap individuals, we identify cis-acting evQTL involving 218 distinct genes, among which 8 genes, ADCY1, CTNNA2, DAAM2, FERMT2, IL6, PLOD2, SNX7, and TNFRSF11B, are cross-validated using an extra expression data set of the same LCLs. We also identify ~300 trans-acting evQTL between .13,000 common SNPs and 500 randomly selected representative genes. We employ two distinct scenarios, emphasizing single-SNP and multiple-SNP effects on expression variability, to explain the formation of evQTL. We argue that detecting evQTL may represent a novel method for effectively screening for genetic interactions, especially when the multiple-SNP influence on expression variability is implied. The implication of our results for revealing genetic mechanisms of gene expression variability is discussed.

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QUANTITATIVE genetic analysis has long focused on detecting genetic variants that affect organismal phenotypes. This is often done by contrasting mean differences in phenotypes among genotypes. Despite increasing evidence across several species for genetic control of phenotypic variance (Ansel et al. 2008; Hill and Mulder 2010; Jimenez- Gomez et al. 2011), variance differences in phenotypes have been largely ignored. Recently, however, the fact that variance of phenotypes is genotype dependent has inspired the detection of genetic variants associated with phenotypic variability (Pare et al. 2010; Sudmant et al. 2010; Yang et al. 2012).

When gene expression level is considered as a heritable, quantitative trait, statistical associations between mean gene expression and genotype can be established to identify those genomic loci associated with or linked to gene expression level (i.e., expression QTL, eQTL) (Montgomery and Dermitzakis 2011). The difference in mean gene expression and its genetic control have been extensively examined in humans (Stranger et al. 2005, 2007b; Choy et al. 2008; Montgomery et al. 2010; Pickrell et al. 2010). The difference in variance of gene expression (i.e., gene expression variability) is genetically controlled and likely to be selectable (Raser and O'Shea 2005; Blake et al. 2006; Maheshri and O'Shea 2007; Cheung and Spielman 2009; Zhang et al. 2009). A small number of initial efforts have been made to quantify the difference in gene expression variability (that is, variance of gene expression) (Ho et al. 2008; Li et al. 2010a; Mar et al. 2011; Xu et al. 2011b). Yet, little attention has been paid to the genetic control of gene expression variability in humans.

In the present study, we seek to discover genome-wide genetic variants (i.e., SNPs) associated with differences in the variance of gene expression among individuals. We adapt the double generalized linear model (dglm) (Verbyla and Smyth 1998) to test for the inequality of expression variances and measure the contribution of genetic variants to the expression heteroscedasticity. The model has been recently used to detect genetic loci controlling phenotypic variability in chicken F2 crosses (Ronnegard and Valdar 2011). A likelihood ratio test (LRT) of the dglm method allows us to compare the fit of a "full model" and a "mean model. …

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