Misspecification in Mixed-Model-Based Association Analysis
Author(s) -
Willem Kruijer
Publication year - 2015
Publication title -
genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.792
H-Index - 246
eISSN - 1943-2631
pISSN - 0016-6731
DOI - 10.1534/genetics.115.177212
Subject(s) - biology , epistasis , variance (accounting) , similarity (geometry) , covariance , mixed model , genetic model , genetic variation , statistics , genetic similarity , genetic association , genetics , population , set (abstract data type) , evolutionary biology , mathematics , genotype , genetic diversity , computer science , single nucleotide polymorphism , gene , artificial intelligence , demography , accounting , sociology , business , image (mathematics) , programming language
Additive genetic variance in natural populations is commonly estimated using mixed models, in which the covariance of the genetic effects is modeled by a genetic similarity matrix derived from a dense set of markers. An important but usually implicit assumption is that the presence of any nonadditive genetic effect increases only the residual variance and does not affect estimates of additive genetic variance. Here we show that this is true only for panels of unrelated individuals. In the case that there is genetic relatedness, the combination of population structure and epistatic interactions can lead to inflated estimates of additive genetic variance.
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