General Framework for Meta-Analysis of Haplotype Association Tests

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• 作者：Shuai Wang ; Jing Hua Zhao ; Ping An ; Xiuqing Guo ; Richard A. Jensen ; Jonathan Marten ; Jennifer E. Huffman ; Karina Meidtner ; Heiner Boeing ; Archie Campbell ; Kenneth M. Rice ; Robert A. Scott ; Jie Yao ; Matthias B. Schulze ; Nicholas J. Wareham ; Ingrid B. Borecki ; Michael A. Province ; Jerome I. Rotter ; Caroline Hayward ; Mark O. Goodarzi ; James B. Meigs and José ; e Dupuis
• 关键词：meta-analysis ; haplotype association tests ; family samples ; linear mixed effects model
• 刊名：Genetic Epidemiology
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：40
• 期：3
• 页码：244-252
• 全文大小：488K
• ISSN：1098-2272

文摘

For complex traits, most associated single nucleotide variants (SNV) discovered to date have a small effect, and detection of association is only possible with large sample sizes. Because of patient confidentiality concerns, it is often not possible to pool genetic data from multiple cohorts, and meta-analysis has emerged as the method of choice to combine results from multiple studies. Many meta-analysis methods are available for single SNV analyses. As new approaches allow the capture of low frequency and rare genetic variation, it is of interest to jointly consider multiple variants to improve power. However, for the analysis of haplotypes formed by multiple SNVs, meta-analysis remains a challenge, because different haplotypes may be observed across studies. We propose a two-stage meta-analysis approach to combine haplotype analysis results. In the first stage, each cohort estimate haplotype effect sizes in a regression framework, accounting for relatedness among observations if appropriate. For the second stage, we use a multivariate generalized least square meta-analysis approach to combine haplotype effect estimates from multiple cohorts. Haplotype-specific association tests and a global test of independence between haplotypes and traits are obtained within our framework. We demonstrate through simulation studies that we control the type-I error rate, and our approach is more powerful than inverse variance weighted meta-analysis of single SNV analysis when haplotype effects are present. We replicate a published haplotype association between fasting glucose-associated locus (G6PC2) and fasting glucose in seven studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium and we provide more precise haplotype effect estimates.