title: The Dissection of Expression Quantitative Trait Locus Hotspots.
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.
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Jianan
authors.identifier: http://orcid.org/0000-0002-5896-2515
authors.initials: J
authors.lastname: Tian
authors.affiliation: Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Mark P
authors.identifier: http://orcid.org/0000-0002-7405-5552
authors.initials: MP
authors.lastname: Keller
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Aimee Teo
authors.identifier: http://orcid.org/0000-0003-3783-2807
authors.initials: AT
authors.lastname: Broman
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Christina
authors.identifier: http://orcid.org/0000-0002-0700-6267
authors.initials: C
authors.lastname: Kendziorski
authors.affiliation: Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706 Department of Horticulture, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Brian S
authors.identifier: http://orcid.org/0000-0002-8774-9377
authors.initials: BS
authors.lastname: Yandell
authors.affiliation: Department of Biochemistry, University of Wisconsin, Madison, Wisconsin 53706.
authors.forename: Alan D
authors.identifier: http://orcid.org/0000-0002-0568-2261
authors.initials: AD
authors.lastname: Attie
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706 kbroman@biostat.wisc.edu.
authors.forename: Karl W
authors.identifier: http://orcid.org/0000-0002-4914-6671
authors.initials: KW
authors.lastname: Broman
mesh_terms: D000818:Animals; D002874:Chromosome Mapping; D003198:Computer Simulation; D003433:Crosses, Genetic; D015870:Gene Expression; D007178:Inbreeding; D008126:Lod Score; D051379:Mice; D008957:Models, Genetic; D015999:Multivariate Analysis; D040641:Quantitative Trait Loci; D019655:Quantitative Trait, Heritable; D011995:Recombination, Genetic
keywords: data visualization; eQTL; gene expression; multivariate analysis; pleiotropy
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