Abstract
ABSTRACTFor 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.
Highlights
In recent years, genome-wide association studies (GWAS) have identified multiple common variants associated with disease and disease-related traits
Previous GWAS have identified the A allele of rs560887, one of the two common variants to be associated with lower fasting glucose level ([Bouatia-Naji et al, 2008]: β = –0.07 mmol/l, p = 6 × 10–16; [Dupuis et al, 2010]: β = 0.075 ± 0.003 mmol/l, p = 8.5 × 10–122)
A recent large-scale exome-chip analysis indicated that these 15 rare variants had a joint effect on fasting glucose [Wessel et al, 2015]
Summary
Genome-wide association studies (GWAS) have identified multiple common variants associated with disease and disease-related traits. ∗Genetic Epidemiology Published by Wiley Periodicals, Inc Source of the unexplained heritability, emerging approaches have attempted to account for multiple variants at once when evaluating association with a trait Such approaches include penalized regression methods [Li et al, 2011, Wu et al, 2009], pathway analysis [Holden et al, 2008], gene-based tests such as burden [Madsen and Browning, 2009] and SKAT [Wu et al, 2010], and haplotype analysis [Liu et al, 2008, Schaid et al, 2002, Tregouet et al, 2004]. Methods for meta-analysis of gene-based tests are well established and widely used [Hu et al, 2013, Liu et al, 2014], but there are no widely used methods for the meta-analysis of haplotype association tests
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
