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Multi-resolution localization of causal variants across the genome

Author

Listed:
  • Matteo Sesia

    (Stanford University)

  • Eugene Katsevich

    (Carnegie Mellon University)

  • Stephen Bates

    (Stanford University)

  • Emmanuel Candès

    (Stanford University)

  • Chiara Sabatti

    (Stanford University)

Abstract

In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14791-2
    DOI: 10.1038/s41467-020-14791-2
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    Cited by:

    1. Zihuai He & Linxi Liu & Michael E. Belloy & Yann Guen & Aaron Sossin & Xiaoxia Liu & Xinran Qi & Shiyang Ma & Prashnna K. Gyawali & Tony Wyss-Coray & Hua Tang & Chiara Sabatti & Emmanuel Candès & Mich, 2022. "GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    2. Winn-Nuñez, Emily T. & Griffin, Maryclare & Crawford, Lorin, 2024. "A simple approach for local and global variable importance in nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    3. 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.
    4. Emmanuel Candès & Chiara Sabatti, 2020. "Discussion of the Paper “Prediction, Estimation, and Attribution” by B. Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 60-63, December.
    5. 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.
    6. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.

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