PANACEA: a framework to maximise genetic diversity in genome-wide association study meta-analyses

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ABSTRACT There have been recent efforts by the human genetics research community to increase the genetic diversity of participants contributing to genome-wide association studies (GWAS) of complex human traits and diseases. The traditional multi-ancestry GWAS approach is to first assign participants to continental ancestry labels based on their genetic similarity to individuals in reference datasets. Ancestry-specific GWAS are then conducted separately for each continental label, the results of which are aggregated through multi-ancestry meta-analysis. However, with this approach, a participant may be assigned to an ancestry group that does not reflect their personal view of ethnicity/race or may be excluded because their genetic ancestry is not sufficiently similar to individuals in reference datasets to be assigned to a single group. Here, we present a novel pipeline (PANACEA) for fully inclusive multi-ancestry meta-analysis that employs a continuous and multi-dimensional representation of ancestry that maximises the genetic diversity of GWAS. Through application to multi-ancestry GWAS of type 2 diabetes susceptibility and simulations, we demonstrate that the inclusive pooled analysis provides equivalent protection against population structure to a traditional ancestry-stratified analysis but, importantly, offers increased power to detect association through increased sample size by not excluding participants with outlying ancestry. The pooled inclusive analysis also enables assessment of ancestry-correlated heterogeneity in allelic effects without the need to assign participants to continental labels that may not sufficiently reflect genetic diversity within ancestry groups.

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