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Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

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Journal Genetics
Specialty Genetics
Date 2013 Dec 4
PMID 24298061
Citations 24
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Abstract

The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)--i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.

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References
1.
Pare G, Cook N, Ridker P, Chasman D . On the use of variance per genotype as a tool to identify quantitative trait interaction effects: a report from the Women's Genome Health Study. PLoS Genet. 2010; 6(6):e1000981. PMC: 2887471. DOI: 10.1371/journal.pgen.1000981. View

2.
Hulse A, Cai J . Genetic variants contribute to gene expression variability in humans. Genetics. 2012; 193(1):95-108. PMC: 3527258. DOI: 10.1534/genetics.112.146779. View

3.
Ronnegard L, Valdar W . Recent developments in statistical methods for detecting genetic loci affecting phenotypic variability. BMC Genet. 2012; 13:63. PMC: 3493319. DOI: 10.1186/1471-2156-13-63. View

4.
Ansel J, Bottin H, Rodriguez-Beltran C, Damon C, Nagarajan M, Fehrmann S . Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. 2008; 4(4):e1000049. PMC: 2289839. DOI: 10.1371/journal.pgen.1000049. View

5.
Pickrell J, Marioni J, Pai A, Degner J, Engelhardt B, Nkadori E . Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature. 2010; 464(7289):768-72. PMC: 3089435. DOI: 10.1038/nature08872. View