Department of Statistics, University of Wisconsin, Madison, Wisconsin 53726, USA.
Genetics. 2011 Feb;187(2):611-21. doi: 10.1534/genetics.110.122796. Epub 2010 Nov 29.
Identifying the genetic basis of complex traits remains an important and challenging problem with the potential to affect a broad range of biological endeavors. A number of statistical methods are available for mapping quantitative trait loci (QTL), but their application to high-throughput phenotypes has been limited as most require user input and interaction. Recently, methods have been developed specifically for expression QTL (eQTL) mapping, but they too are limited in that they do not allow for interactions and QTL of moderate effect. We here propose an automated model-selection-based approach that identifies multiple eQTL in experimental populations, allowing for eQTL of moderate effect and interactions. Output can be used to identify groups of transcripts that are likely coregulated, as demonstrated in a study of diabetes in mouse.
确定复杂性状的遗传基础仍然是一个重要且具有挑战性的问题，有可能影响广泛的生物研究领域。有许多统计方法可用于定位数量性状基因座 (QTL)，但由于大多数方法需要用户输入和交互，因此其在高通量表型中的应用受到限制。最近，专门为表达数量性状基因座 (eQTL) 映射开发了一些方法，但它们也受到限制，因为它们不允许存在中等效应的相互作用和 QTL。我们在此提出了一种基于自动模型选择的方法，可以在实验种群中识别多个 eQTL，允许存在中等效应和相互作用的 eQTL。输出结果可用于识别可能受到共同调控的转录本组，这在对小鼠糖尿病的研究中得到了验证。
