Li Renhua, Tsaih Shirng-Wern, Shockley Keith, Stylianou Ioannis M, Wergedal Jon, Paigen Beverly, Churchill Gary A
The Jackson Laboratory, Bar Harbor, Maine, USA.
PLoS Genet. 2006 Jul;2(7):e114. doi: 10.1371/journal.pgen.0020114. Epub 2006 Jun 7.
We introduce a method for the analysis of multilocus, multitrait genetic data that provides an intuitive and precise characterization of genetic architecture. We show that it is possible to infer the magnitude and direction of causal relationships among multiple correlated phenotypes and illustrate the technique using body composition and bone density data from mouse intercross populations. Using these techniques we are able to distinguish genetic loci that affect adiposity from those that affect overall body size and thus reveal a shortcoming of standardized measures such as body mass index that are widely used in obesity research. The identification of causal networks sheds light on the nature of genetic heterogeneity and pleiotropy in complex genetic systems.
我们介绍了一种用于分析多位点、多性状遗传数据的方法，该方法能对遗传结构进行直观且精确的表征。我们表明，有可能推断多个相关表型之间因果关系的大小和方向，并使用来自小鼠杂交群体的身体成分和骨密度数据来说明该技术。利用这些技术，我们能够区分影响肥胖的基因座和影响总体体型的基因座，从而揭示肥胖研究中广泛使用的标准化测量方法（如体重指数）的一个缺点。因果网络的识别有助于阐明复杂遗传系统中遗传异质性和多效性的本质。
