Complex Genetic Effects in Quantitative Trait Locus Identification: A Computationally Tractable Random Model for Use in F2 Populations
Complex Genetic Effects in Quantitative Trait Locus Identification: A Computationally Tractable Random Model for Use in F2 Populations
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DOI：
10.1534/genetics.110.122333
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发表时间：
2011-01
期刊：
影响因子：
3.3
通讯作者：
D. Zimmer;M. Mayer;N. Reinsch
中科院分区：
文献类型：
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作者：
D. Zimmer;M. Mayer;N. Reinsch
定位数量性状基因座（QTL）的方法主要集中在将QTL视为固定效应。这些方法不同于将遗传效应视为随机的遗传变异的通常模型。计算昂贵的方法，允许QTL被视为随机已明确开发的加性遗传和显性效应。通过扩展这些方法与方差分量法（VCM），可以定位多个QTL。我们专注于F2杂交群体来自自交系和估计的影响，每个人和他们相应的标记衍生的遗传协方差。我们提出了成对上位效应的扩展，这是计算密集型的，因为必须估计大量的个体效应。但是，通过用每个标记类别的平均遗传效应代替个体遗传效应，遗传协方差被近似。这大大减少了计算负担，通过减少遗传效应的协方差矩阵的维数，从而在估计方差分量和评估剩余对数似然的速度显着增益。从模拟的初步结果表明，减少模型的竞争力与多区间作图，回归区间作图，和VCM与个别的遗传效应在其估计的QTL位置和实验功率。
Methodology for mapping quantitative trait loci (QTL) has focused primarily on treating the QTL as a fixed effect. These methods differ from the usual models of genetic variation that treat genetic effects as random. Computationally expensive methods that allow QTL to be treated as random have been explicitly developed for additive genetic and dominance effects. By extending these methods with a variance component method (VCM), multiple QTL can be mapped. We focused on an F2 crossbred population derived from inbred lines and estimated effects for each individual and their corresponding marker-derived genetic covariances. We present extensions to pairwise epistatic effects, which are computationally intensive because a great many individual effects must be estimated. But by replacing individual genetic effects with average genetic effects for each marker class, genetic covariances are approximated. This substantially reduces the computational burden by reducing the dimensions of covariance matrices of genetic effects, resulting in a remarkable gain in the speed of estimating the variance components and evaluating the residual log-likelihood. Preliminary results from simulations indicate competitiveness of the reduced model with multiple-interval mapping, regression interval mapping, and VCM with individual genetic effects in its estimated QTL positions and experimental power.