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

Author

Listed:
  • Feng Zhou

    (University of Cambridge)

  • Opeyemi Soremekun

    (MRC/UVRI and LSHTM)

  • Tinashe Chikowore

    (University of the Witwatersrand
    University of the Witwatersrand
    Brigham and Women’s Hospital
    Harvard Medical School)

  • Segun Fatumo

    (MRC/UVRI and LSHTM
    London School of Hygiene and Tropical Medicine)

  • Inês Barroso

    (University of Exeter Medical School)

  • Andrew P. Morris

    (University of Manchester)

  • Jennifer L. Asimit

    (University of Cambridge)

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.

Suggested Citation

  • Feng Zhou & Opeyemi Soremekun & Tinashe Chikowore & Segun Fatumo & Inês Barroso & Andrew P. Morris & Jennifer L. Asimit, 2023. "Leveraging information between multiple population groups and traits improves fine-mapping resolution," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43159-5
    DOI: 10.1038/s41467-023-43159-5
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    References listed on IDEAS

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    1. Melina Claussnitzer & Judy H. Cho & Rory Collins & Nancy J. Cox & Emmanouil T. Dermitzakis & Matthew E. Hurles & Sekar Kathiresan & Eimear E. Kenny & Cecilia M. Lindgren & Daniel G. MacArthur & Kathry, 2020. "A brief history of human disease genetics," Nature, Nature, vol. 577(7789), pages 179-189, January.
    2. Sarah E. Graham & Shoa L. Clarke & Kuan-Han H. Wu & Stavroula Kanoni & Greg J. M. Zajac & Shweta Ramdas & Ida Surakka & Ioanna Ntalla & Sailaja Vedantam & Thomas W. Winkler & Adam E. Locke & Eirini Ma, 2021. "The power of genetic diversity in genome-wide association studies of lipids," Nature, Nature, vol. 600(7890), pages 675-679, December.
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