Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Studies that traverse ancestrally diverse populations may increase power to detect novel loci and improve fine-mapping resolution of causal variants by leveraging linkage disequilibrium differences between ethnic groups. The inclusion of African ancestry samples may yield further improvements because of low linkage disequilibrium and high genetic heterogeneity. We investigate the fine-mapping resolution of trans-ethnic fixed-effects meta-analysis for five type II diabetes loci, under various settings of ancestral composition (European, East Asian, African), allelic heterogeneity, and causal variant minor allele frequency. In particular, three settings of ancestral composition were compared: (1) single ancestry (European), (2) moderate ancestral diversity (European and East Asian), and (3) high ancestral diversity (European, East Asian, and African). Our simulations suggest that the European/Asian and European ancestry-only meta-analyses consistently attain similar fine-mapping resolution. The inclusion of African ancestry samples in the meta-analysis leads to a marked improvement in fine-mapping resolution.

Original publication

DOI

10.1038/ejhg.2016.1

Type

Journal article

Journal

Eur J Hum Genet

Publication Date

08/2016

Volume

24

Pages

1330 - 1336

Keywords

Algorithms, Chromosome Mapping, Continental Population Groups, Diabetes Mellitus, Type 2, Genetic Heterogeneity, Genetic Loci, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Meta-Analysis as Topic, Models, Genetic, Pedigree, Polymorphism, Single Nucleotide, Research Design