Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Robert Fragoza, Jishnu Das, Shayne D. Wierbowski, Jin Liang, Tina N. Tran, Siqi Liang, Juan F. Beltran, Christen A. Rivera-Erick, Kaixiong Ye, Ting-Yi Wang, Li Yao, Matthew Mort, Peter D. Stenson, David N. Cooper, Xiaomu Wei, Alon Keinan, John C. Schimenti, Andrew G. Clark and Haiyuan Yu ()
Additional contact information
Robert Fragoza: Cornell University
Shayne D. Wierbowski: Cornell University
Jin Liang: Cornell University
Tina N. Tran: Cornell University
Siqi Liang: Cornell University
Juan F. Beltran: Cornell University
Christen A. Rivera-Erick: Cornell University
Kaixiong Ye: Cornell University
Ting-Yi Wang: Cornell University
Li Yao: Cornell University
Matthew Mort: Cardiff University, Heath Park
Peter D. Stenson: Cardiff University, Heath Park
David N. Cooper: Cardiff University, Heath Park
Xiaomu Wei: Cornell University
Alon Keinan: Cornell University
John C. Schimenti: Cornell University
Andrew G. Clark: Cornell University
Haiyuan Yu: Cornell University

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: Abstract Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported; this indicates that each individual’s genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Date: 2019
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DOI: 10.1038/s41467-019-11959-3

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