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

Suganthi Balasubramanian (), Yao Fu, Mayur Pawashe, Patrick McGillivray, Mike Jin, Jeremy Liu, Konrad J. Karczewski, Daniel G. MacArthur and Mark Gerstein ()
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Suganthi Balasubramanian: Yale University
Yao Fu: Yale University
Mayur Pawashe: Yale University
Patrick McGillivray: Yale University
Mike Jin: Yale University
Jeremy Liu: Yale University
Konrad J. Karczewski: Massachusetts General Hospital
Daniel G. MacArthur: Massachusetts General Hospital
Mark Gerstein: Yale University

Nature Communications, 2017, vol. 8, issue 1, 1-11

Abstract: 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.

Date: 2017
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DOI: 10.1038/s41467-017-00443-5

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