Li Cong, Yang Can, Chen Mengjie, Chen Xiaowei, Hou Lin, Zhao Hongyu
Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA.
Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT 06520, USA.
BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S39. doi: 10.1186/1753-6561-8-S1-S39. eCollection 2014.
High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.
高通量测序技术使研究人员能够测试表型与全基因组中鉴定出的所有变异之间的关联,对于分析罕见变异尤其有用。然而,在典型的全基因组关联研究中,识别与表型相关的罕见变异的统计效力非常低,因为在无关个体中它们的等位基因频率很低。相比之下,基于家系的设计可能更具效力,因为罕见变异在家族中比在无关个体中更有可能富集。无论如何,对基于家系的关联研究进行分析时需要适当考虑家庭成员之间的相关性。我们使用4种检验方法分析了遗传分析研讨会18数据集中观察到的定量性状收缩压以及模拟的Q1数据:(a) 单变异检验,(b) 合并检验,(c) 考虑家族相关性的单变异检验,以及(d) 考虑家族相关性的合并检验。然后我们比较了这4种方法的结果,发现调整家族相关性可以适当控制假阳性率,同时保持检测几个强相关变异/基因的合理效力。