Coffman Cynthia J, Doerge R W, Simonsen Katy L, Nichols Krista M, Duarte Christine K, Wolfinger Russell D, McIntyre Lauren M
Institute for Clinical and Epidemiological Research Biostatistics Unit, Durham VA Medical Center (152), Durham, North Carolina 27705, USA.
Genetics. 2005 Jul;170(3):1281-97. doi: 10.1534/genetics.104.033910. Epub 2005 Apr 16.
Quantitative trait locus (QTL) mapping methodology for continuous normally distributed traits is the subject of much attention in the literature. Binary trait locus (BTL) mapping in experimental populations has received much less attention. A binary trait by definition has only two possible values, and the penetrance parameter is restricted to values between zero and one. Due to this restriction, the infinitesimal model appears to come into play even when only a few loci are involved, making selection of an appropriate genetic model in BTL mapping challenging. We present a probability model for an arbitrary number of BTL and demonstrate that, given adequate sample sizes, the power for detecting loci is high under a wide range of genetic models, including most epistatic models. A novel model selection strategy based upon the underlying genetic map is employed for choosing the genetic model. We propose selecting the "best" marker from each linkage group, regardless of significance. This reduces the model space so that an efficient search for epistatic loci can be conducted without invoking stepwise model selection. This procedure can identify unlinked epistatic BTL, demonstrated by our simulations and the reanalysis of Oncorhynchus mykiss experimental data.
连续正态分布性状的数量性状基因座（QTL）定位方法是文献中备受关注的主题。实验群体中的二元性状基因座（BTL）定位受到的关注则少得多。二元性状按定义只有两个可能的值，且外显率参数被限制在零到一之间的值。由于这种限制，即使只涉及少数基因座，无穷小模型似乎也会起作用，这使得在BTL定位中选择合适的遗传模型具有挑战性。我们提出了一个针对任意数量BTL的概率模型，并证明在样本量足够的情况下，在包括大多数上位性模型在内的广泛遗传模型下，检测基因座的功效很高。基于潜在遗传图谱采用了一种新颖的模型选择策略来选择遗传模型。我们建议从每个连锁群中选择“最佳”标记，而不考虑其显著性。这减少了模型空间，从而可以在不调用逐步模型选择的情况下对上位性基因座进行有效搜索。我们的模拟以及对虹鳟实验数据的重新分析表明，该程序可以识别不连锁的上位性BTL。
