Department of Statistics, The Ohio State University, Columbus, OH 43210, USA.
BMC Bioinformatics. 2013 Jun 20;14:200. doi: 10.1186/1471-2105-14-200.
In mammalian genetics, many quantitative traits, such as blood pressure, are thought to be influenced by specific genes, but are also affected by environmental factors, making the associated genes difficult to identify and locate from genetic data alone. In particular, the application of classical statistical methods to single nucleotide polymorphism (SNP) data collected in genome-wide association studies has been especially challenging. We propose a coalescent approach to search for SNPs associated with quantitative traits in genome-wide association study (GWAS) data by taking into account the evolutionary history among SNPs.
We evaluate the performance of the new method using simulated data, and find that it performs at least as well as existing methods with an increase in performance in the case of population structure. Application of the methodology to a real data set consisting of high-density lipoprotein cholesterol measurements in mice shows the method performs well for empirical data, as well.
By combining methods from stochastic processes and phylogenetics, this work provides an innovative avenue for the development of new statistical methodology in the analysis of GWAS data.
在哺乳动物遗传学中，许多数量性状，如血压，被认为受到特定基因的影响，但也受到环境因素的影响，这使得相关基因难以仅从遗传数据中识别和定位。特别是，经典统计方法在全基因组关联研究中收集的单核苷酸多态性 (SNP) 数据中的应用特别具有挑战性。我们提出了一种合并方法，通过考虑 SNP 之间的进化历史，来搜索与全基因组关联研究 (GWAS) 数据中的定量性状相关的 SNP。
我们使用模拟数据评估了新方法的性能，发现它的性能至少与现有方法一样好，并且在群体结构的情况下性能有所提高。将该方法应用于由小鼠高密度脂蛋白胆固醇测量值组成的真实数据集表明，该方法对经验数据也表现良好。
通过将随机过程和系统发育学的方法相结合，这项工作为 GWAS 数据分析中开发新的统计方法提供了一条创新途径。
