Li Jun, Reynolds Richard, Pomp Daniel, Allison David B, Yi Nengjun
Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Genet Res (Camb). 2010 Feb;92(1):13-23. doi: 10.1017/S0016672310000029. Epub 2010 Mar 4.
We proposed hierarchical Poisson and binomial models for mapping multiple interacting quantitative trait loci (QTLs) for count traits in experimental crosses. We applied our methods to two counted reproductive traits, live fetuses (LF) and dead fetuses (DF) at 17 days gestation, in an F2 female mouse population. We treated observed number of corpora lutea (ovulation rate) as the baseline and the total trials in our Poisson and binomial models, respectively. We detected more than 10 QTLs for LF and DF, most having epistatic and pleiotropic effects. The epistatic effects were larger, involved more QTLs, and explained a larger proportion of phenotypic variance than the main effects. Our analyses revealed a complex network of multiple interacting QTLs for the reproductive traits, and increase our understanding of the genetic architecture of reproductive characters. The proposed statistical models and methods provide valuable tools for detecting multiple interacting QTLs for complex count phenotypes.
我们提出了分层泊松模型和二项式模型，用于在实验杂交中对计数性状的多个相互作用的数量性状基因座（QTL）进行定位。我们将我们的方法应用于一个F2雌性小鼠群体中17天妊娠期的两个计数生殖性状，即活胎（LF）和死胎（DF）。在我们的泊松模型和二项式模型中，我们分别将观察到的黄体数量（排卵率）作为基线和总试验次数。我们检测到了超过10个与LF和DF相关的QTL，其中大多数具有上位性和多效性效应。上位性效应更大，涉及更多的QTL，并且比主效应解释了更大比例的表型变异。我们的分析揭示了一个用于生殖性状的多个相互作用QTL的复杂网络，并增进了我们对生殖性状遗传结构的理解。所提出的统计模型和方法为检测复杂计数表型的多个相互作用QTL提供了有价值的工具。
