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The flashfm approach for fine-mapping multiple quantitative traits

Author

Listed:
  • N. Hernández

    (University of Cambridge)

  • J. Soenksen

    (University of Exeter Medical School
    University of Glasgow)

  • P. Newcombe

    (University of Cambridge)

  • M. Sandhu

    (School of Public Health, Imperial College London)

  • I. Barroso

    (University of Exeter Medical School)

  • C. Wallace

    (University of Cambridge
    University of Cambridge)

  • J. L. Asimit

    (University of Cambridge)

Abstract

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

Suggested Citation

  • N. Hernández & J. Soenksen & P. Newcombe & M. Sandhu & I. Barroso & C. Wallace & J. L. Asimit, 2021. "The flashfm approach for fine-mapping multiple quantitative traits," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26364-y
    DOI: 10.1038/s41467-021-26364-y
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    References listed on IDEAS

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    1. Matteo Sesia & Eugene Katsevich & Stephen Bates & Emmanuel Candès & Chiara Sabatti, 2020. "Multi-resolution localization of causal variants across the genome," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    2. Christopher N. Foley & James R. Staley & Philip G. Breen & Benjamin B. Sun & Paul D. W. Kirk & Stephen Burgess & Joanna M. M. Howson, 2021. "A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    3. Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
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