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General Framework for Meta‐Analysis of Haplotype Association Tests
Author(s) -
Wang Shuai,
Zhao Jing Hua,
An Ping,
Guo Xiuqing,
Jensen Richard A.,
Marten Jonathan,
Huffman Jennifer E.,
Meidtner Karina,
Boeing Heiner,
Campbell Archie,
Rice Kenneth M.,
Scott Robert A.,
Yao Jie,
Schulze Matthias B.,
Wareham Nicholas J.,
Borecki Ingrid B.,
Province Michael A.,
Rotter Jerome I.,
Hayward Caroline,
Goodarzi Mark O.,
Meigs James B.,
Dupuis Josée
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21959
Subject(s) - haplotype , meta analysis , biology , haplotype estimation , genetic association , multiple comparisons problem , genome wide association study , genetics , sample size determination , single nucleotide polymorphism , type i and type ii errors , statistics , genotype , medicine , gene , mathematics
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.