title: Trans-ethnic study design approaches for fine-mapping.
abstract: 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.
authors.affiliation: Wellcome Trust Sanger Institute, Cambridge, UK.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Asimit
authors.affiliation: Wellcome Trust Sanger Institute, Cambridge, UK.
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Hatzikotoulas
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: McCarthy
authors.affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK.|Department of Biostatistics, University of Liverpool, Liverpool, UK.
authors.forename: Andrew P
authors.initials: AP
authors.lastname: Morris
authors.affiliation: Wellcome Trust Sanger Institute, Cambridge, UK.
authors.forename: Eleftheria
authors.identifier: 0000-0003-4238-659X
authors.initials: E
authors.lastname: Zeggini
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods* / Q000592:standards; D003924:Diabetes Mellitus, Type 2 / Q000208:ethnology / Q000235:genetics*; D018740:Genetic Heterogeneity; D056426:Genetic Loci; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D006801:Humans; D015810:Linkage Disequilibrium; D015201:Meta-Analysis as Topic; D008957:Models, Genetic; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D044469:Racial Groups / Q000235:genetics; D012107:Research Design
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