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Leveraging information between multiple population groups and traits improves fine-mapping resolution.

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Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared.


Acknowledgements: J.A. and F.Z. are supported by the UK Medical Research Council (MR/R021368/1 (JA), MC_UU_00002/4). T.C. is an international training fellow supported by the Wellcome Trust grant (214205/Z/18/Z). A.M. is supported by the UK Medical Research Council (MR/W029626/1). This work was funded in part by an “Expanding excellence in England” award from Research England to I.B. This study was supported by the National Institute for Health and Care Research Exeter Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. For the purpose of Open Access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission.

Funder: “Expanding excellence in England” award from Research England


Humans, Chromosome Mapping, Population Groups, Genome-Wide Association Study, Polymorphism, Single Nucleotide, Linkage Disequilibrium

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Nat Commun

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Springer Science and Business Media LLC
Medical Research Council (MR/R021368/1)
Medical Research Council (MC_UU_00002/4)