Research Article: Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation

Date Published: July 27, 2017

Publisher: Public Library of Science

Author(s): Thomas W. Winkler, Anne E. Justice, L. Adrienne Cupples, Florian Kronenberg, Zoltán Kutalik, Iris M. Heid, David Meyre.


Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms.

Partial Text

Genome-wide association studies (GWAS) and genome-wide association meta-analyses (GWAMAs) are one of the most successful approaches to identify genetic regions that are relevant for complex phenotypes and diseases [1]. Usually in GWAMAs, a group responsible for meta-analyses develops an analysis plan describing each of the models to be conducted by participating studies and distributes it to study analysts; the study analysts then conduct the specified study-specific GWAS and the meta-analysts collect, quality control, and meta-analyze the study-specific aggregated statistics across studies [2]. To not burden study partners too much, the study-specific analysis models are generally sparse, clearly described, and easy to conduct with available software in a standardized way.




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