Jannink Jean-Luc
Department of Agronomy, Iowa State University, Ames, Iowa 50011-1010, USA.
Genetics. 2007 May;176(1):553-61. doi: 10.1534/genetics.106.062992. Epub 2006 Dec 18.
Association studies are designed to identify main effects of alleles across a potentially wide range of genetic backgrounds. To control for spurious associations, effects of the genetic background itself are often incorporated into the linear model, either in the form of subpopulation effects in the case of structure or in the form of genetic relationship matrices in the case of complex pedigrees. In this context epistatic interactions between loci can be captured as an interaction effect between the associated locus and the genetic background. In this study I developed genetic and statistical models to tie the locus by genetic background interaction idea back to more standard concepts of epistasis when genetic background is modeled using an additive relationship matrix. I also simulated epistatic interactions in four-generation randomly mating pedigrees and evaluated the ability of the statistical models to identify when a biallelic associated locus was epistatic to other loci. Under additive-by-additive epistasis, when interaction effects of the associated locus were quite large (explaining 20% of the phenotypic variance), epistasis was detected in 79% of pedigrees containing 320 individuals. The epistatic model also predicted the genotypic value of progeny better than a standard additive model in 78% of simulations. When interaction effects were smaller (although still fairly large, explaining 5% of the phenotypic variance), epistasis was detected in only 9% of pedigrees containing 320 individuals and the epistatic and additive models were equally effective at predicting the genotypic values of progeny. Epistasis was detected with the same power whether the overall epistatic effect was the result of a single pairwise interaction or the sum of nine pairwise interactions, each generating one ninth of the epistatic variance. The power to detect epistasis was highest (94%) at low QTL minor allele frequency, fell to a minimum (60%) at minor allele frequency of about 0.2, and then plateaued at about 80% as alleles reached intermediate frequencies. The power to detect epistasis declined when the linkage disequilibrium between the DNA marker and the functional polymorphism was not complete.
关联研究旨在识别在潜在广泛遗传背景下等位基因的主效应。为了控制虚假关联，遗传背景本身的效应通常会以线性模型的形式纳入，在存在结构的情况下以亚群体效应的形式，或者在复杂家系的情况下以遗传关系矩阵的形式。在这种情况下，基因座之间的上位性相互作用可以作为相关基因座与遗传背景之间的相互作用效应来捕捉。在本研究中，我开发了遗传和统计模型，当使用加性关系矩阵对遗传背景进行建模时，将基因座与遗传背景相互作用的概念与更标准的上位性概念联系起来。我还在四代随机交配家系中模拟了上位性相互作用，并评估了统计模型识别双等位基因相关基因座与其他基因座之间上位性的能力。在加性×加性上位性情况下，当相关基因座的相互作用效应相当大（解释20%的表型变异）时，在包含320个个体的79%的家系中检测到上位性。在78%的模拟中，上位性模型比标准加性模型能更好地预测后代的基因型值。当相互作用效应较小时（尽管仍然相当大，解释5%的表型变异），在包含320个个体的家系中仅9%检测到上位性，并且上位性模型和加性模型在预测后代基因型值方面同样有效。无论总体上位性效应是单个成对相互作用的结果还是九个成对相互作用之和（每个相互作用产生九分之一的上位性变异），检测上位性的能力相同。在低QTL小等位基因频率时检测上位性的能力最高（94%），在小等位基因频率约为0.2时降至最低（60%），然后随着等位基因频率达到中等水平稳定在约80%。当DNA标记与功能多态性之间的连锁不平衡不完全时，检测上位性的能力下降。
