Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

James Zou (), Gregory Valiant, Paul Valiant, Konrad Karczewski, Siu On Chan, Kaitlin Samocha, Monkol Lek, Shamil Sunyaev, Mark Daly and Daniel G. MacArthur ()
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James Zou: Stanford University
Gregory Valiant: Stanford University
Paul Valiant: Brown University
Konrad Karczewski: Analytic and Translational Genetics Unit, Massachusetts General Hospital
Siu On Chan: Computer Science and Engineering, Chinese University of Hong Kong
Kaitlin Samocha: Analytic and Translational Genetics Unit, Massachusetts General Hospital
Monkol Lek: Analytic and Translational Genetics Unit, Massachusetts General Hospital
Shamil Sunyaev: Broad Institute or MIT and Harvard
Mark Daly: Analytic and Translational Genetics Unit, Massachusetts General Hospital
Daniel G. MacArthur: Analytic and Translational Genetics Unit, Massachusetts General Hospital

Nature Communications, 2016, vol. 7, issue 1, 1-5

Abstract: Abstract As new proposals aim to sequence ever larger collection of humans, it is critical to have a quantitative framework to evaluate the statistical power of these projects. We developed a new algorithm, UnseenEst, and applied it to the exomes of 60,706 individuals to estimate the frequency distribution of all protein-coding variants, including rare variants that have not been observed yet in the current cohorts. Our results quantified the number of new variants that we expect to identify as sequencing cohorts reach hundreds of thousands of individuals. With 500K individuals, we find that we expect to capture 7.5% of all possible loss-of-function variants and 12% of all possible missense variants. We also estimate that 2,900 genes have loss-of-function frequency of

Date: 2016
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DOI: 10.1038/ncomms13293

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