Open AccessIdentifying systematic heterogeneity patterns in genetic association meta-analysis studies
Author(s) -
Lerato E Magosi,
Anuj Goel,
Jemma C. Hopewell,
Martin Farrall
Publication year - 2017
Publication title -
plos genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.587
H-Index - 233
eISSN - 1553-7404
pISSN - 1553-7390
DOI - 10.1371/journal.pgen.1006755
Subject(s) - biology , genetic heterogeneity , meta analysis , genome wide association study , genetic association , computational biology , genetics , genetic architecture , allelic heterogeneity , quantitative trait locus , study heterogeneity , summary statistics , locus (genetics) , disease , evolutionary biology , single nucleotide polymorphism , phenotype , genotype , gene , medline , statistics , medicine , mathematics , pathology , biochemistry
Progress in mapping loci associated with common complex diseases or quantitative inherited traits has been expedited by large-scale meta-analyses combining information across multiple studies, assembled through collaborative networks of researchers. Participating studies will usually have been independently designed and implemented in unique settings that are potential sources of phenotype, ancestry or other variability that could introduce between-study heterogeneity into a meta-analysis. Heterogeneity tests based on individual genetic variants (e.g. Q , I 2 ) are not suited to identifying locus-specific from more systematic multi-locus or genome-wide patterns of heterogeneity. We have developed and evaluated an aggregate heterogeneity M statistic that combines between-study heterogeneity information across multiple genetic variants, to reveal systematic patterns of heterogeneity that elude conventional single variant analysis. Application to a GWAS meta-analysis of coronary disease with 48 contributing studies uncovered substantial systematic between-study heterogeneity, which could be partly explained by age-of-disease onset, family-history of disease and ancestry. Future meta-analyses of diseases and traits with multiple known genetic associations can use this approach to identify outlier studies and thereby optimize power to detect novel genetic associations.