The Rare-Variant Generalized Disequilibrium Test for Association Analysis of Nuclear and Extended Pedigrees with Application to Alzheimer Disease WGS Data

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• 作者：Zongxiao He¹ ; Di Zhang¹ ; Alan E. Renton² ; Biao Li¹ ; Linhai Zhao¹ ; Gao T. Wang¹ ; ⁴ ; Alison M. Goate² ; Richard Mayeux³ ; Suzanne M. Leal¹ ; ^{sleal@bcm.edu}
• 关键词：family-based association analysis ; rare variants ; whole-genome sequence data ; exome sequence data ; missing data ; extended and nuclear pedigrees ; Alzheimer disease ; generalized disequilibrium test ; pedigree disequilibrium test ; TNK1
• 刊名：The American Journal of Human Genetics
• 出版年：2017
• 出版时间：2 February 2017
• 年：2017
• 卷：100
• 期：2
• 页码：193-204
• 全文大小：339 K
• 卷排序：100

文摘

Whole-genome and exome sequence data can be cost-effectively generated for the detection of rare-variant (RV) associations in families. Causal variants that aggregate in families usually have larger effect sizes than those found in sporadic cases, so family-based designs can be a more powerful approach than population-based designs. Moreover, some family-based designs are robust to confounding due to population admixture or substructure. We developed a RV extension of the generalized disequilibrium test (GDT) to analyze sequence data obtained from nuclear and extended families. The GDT utilizes genotype differences of all discordant relative pairs to assess associations within a family, and the RV extension combines the single-variant GDT statistic over a genomic region of interest. The RV-GDT has increased power by efficiently incorporating information beyond first-degree relatives and allows for the inclusion of covariates. Using simulated genetic data, we demonstrated that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populations and populations with substructure. It is more powerful than existing family-based RV association methods, particularly for the analysis of extended pedigrees and pedigrees with missing data. We analyzed whole-genome sequence data from families affected by Alzheimer disease to illustrate the application of the RV-GDT. Given the capability of the RV-GDT to adequately control for population admixture or substructure and analyze pedigrees with missing genotype data and its superior power over other family-based methods, it is an effective tool for elucidating the involvement of RVs in the etiology of complex traits.