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Genetics. 2016 Apr;202(4):1563-74. doi: 10.1534/genetics.115.183624. Epub 2016 Feb 2.

The Dissection of Expression Quantitative Trait Locus Hotspots.

Author information

1
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.
2
Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706.
3
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
4
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706 Department of Horticulture, University of Wisconsin, Madison, Wisconsin 53706.
5
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706 kbroman@biostat.wisc.edu.

Abstract

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.

KEYWORDS:

data visualization; eQTL; gene expression; multivariate analysis; pleiotropy

PMID:
26837753
PMCID:
PMC4905536
DOI:
10.1534/genetics.115.183624
[Indexed for MEDLINE]
Free PMC Article

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