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Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes

Author

Listed:
  • Suganthi Balasubramanian

    (Yale University
    Yale University
    Regeneron Genetics Center)

  • Yao Fu

    (Yale University
    Bina Technologies, Part of Roche Sequencing)

  • Mayur Pawashe

    (Yale University)

  • Patrick McGillivray

    (Yale University)

  • Mike Jin

    (Yale University)

  • Jeremy Liu

    (Yale University)

  • Konrad J. Karczewski

    (Massachusetts General Hospital
    Broad Institute of MIT and Harvard)

  • Daniel G. MacArthur

    (Massachusetts General Hospital
    Broad Institute of MIT and Harvard)

  • Mark Gerstein

    (Yale University
    Yale University
    Yale University)

Abstract

Variants predicted to result in the loss of function of human genes have attracted interest because of their clinical impact and surprising prevalence in healthy individuals. Here, we present ALoFT (annotation of loss-of-function transcripts), a method to annotate and predict the disease-causing potential of loss-of-function variants. Using data from Mendelian disease-gene discovery projects, we show that ALoFT can distinguish between loss-of-function variants that are deleterious as heterozygotes and those causing disease only in the homozygous state. Investigation of variants discovered in healthy populations suggests that each individual carries at least two heterozygous premature stop alleles that could potentially lead to disease if present as homozygotes. When applied to de novo putative loss-of-function variants in autism-affected families, ALoFT distinguishes between deleterious variants in patients and benign variants in unaffected siblings. Finally, analysis of somatic variants in >6500 cancer exomes shows that putative loss-of-function variants predicted to be deleterious by ALoFT are enriched in known driver genes.

Suggested Citation

  • Suganthi Balasubramanian & Yao Fu & Mayur Pawashe & Patrick McGillivray & Mike Jin & Jeremy Liu & Konrad J. Karczewski & Daniel G. MacArthur & Mark Gerstein, 2017. "Using ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00443-5
    DOI: 10.1038/s41467-017-00443-5
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    Cited by:

    1. Matt C. Danzi & Maike F. Dohrn & Sarah Fazal & Danique Beijer & Adriana P. Rebelo & Vivian Cintra & Stephan Züchner, 2023. "Deep structured learning for variant prioritization in Mendelian diseases," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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