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

Feng Zhou, Opeyemi Soremekun, Tinashe Chikowore, Segun Fatumo, Inês Barroso, Andrew P. Morris and Jennifer L. Asimit ()
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Feng Zhou: University of Cambridge
Opeyemi Soremekun: MRC/UVRI and LSHTM
Tinashe Chikowore: University of the Witwatersrand
Segun Fatumo: MRC/UVRI and LSHTM
Inês Barroso: University of Exeter Medical School
Andrew P. Morris: University of Manchester
Jennifer L. Asimit: University of Cambridge

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-43159-5

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