Accounting for genetic interactions improves modeling of individual quantitative trait phenotypes in yeast.

Autor: Forsberg SK; Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden., Bloom JS; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA.; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, California, USA.; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, California, USA., Sadhu MJ; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA.; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, California, USA.; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, California, USA., Kruglyak L; Department of Human Genetics, University of California, Los Angeles, Los Angeles, California, USA.; Howard Hughes Medical Institute, University of California, Los Angeles, Los Angeles, California, USA.; Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, California, USA., Carlborg Ö; Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
Jazyk: angličtina
Zdroj: Nature genetics [Nat Genet] 2017 Apr; Vol. 49 (4), pp. 497-503. Date of Electronic Publication: 2017 Feb 27.
DOI: 10.1038/ng.3800
Abstrakt: Experiments in model organisms report abundant genetic interactions underlying biologically important traits, whereas quantitative genetics theory predicts, and data support, the notion that most genetic variance in populations is additive. Here we describe networks of capacitating genetic interactions that contribute to quantitative trait variation in a large yeast intercross population. The additive variance explained by individual loci in a network is highly dependent on the allele frequencies of the interacting loci. Modeling of phenotypes for multilocus genotype classes in the epistatic networks is often improved by accounting for the interactions. We discuss the implications of these results for attempts to dissect genetic architectures and to predict individual phenotypes and long-term responses to selection.
Databáze: MEDLINE
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