title: Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects.
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 <0.00001 in healthy humans, consistent with very strong intolerance to gene inactivation.
authors: Department of Biomedical Data Science, Stanford University, Palo Alto, California 94305, USA.
authors: James
authors: J
authors: Zou
authors: Computer Science Department, Stanford University, Palo Alto, California 94305, USA.
authors: Gregory
authors: G
authors: Valiant
authors: Computer Science Department, Brown University, Providence, Rhode Island 02912, USA.
authors: Paul
authors: P
authors: Valiant
authors: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.|Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.
authors: Konrad
authors: K
authors: Karczewski
authors: Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
authors: Siu On
authors: SO
authors: Chan
authors: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.|Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.
authors: Kaitlin
authors: K
authors: Samocha
authors: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.|Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.
authors: Monkol
authors: M
authors: Lek
authors: Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.|Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors: Shamil
authors: S
authors: Sunyaev
authors: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.|Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.|Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors: Mark
authors: 0000-0002-0949-8752
authors: M
authors: Daly
authors: Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.|Broad Institute or MIT and Harvard, Cambridge, Massachusetts 02142, USA.|Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA.
authors: Daniel G
authors: DG
authors: MacArthur
mesh_terms: D000465:Algorithms; D000483:Alleles; D015331:Cohort Studies; D019295:Computational Biology; D018899:CpG Islands; D059472:Exome; D005787:Gene Frequency; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D016014:Linear Models; D020125:Mutation, Missense; D020641:Polymorphism, Single Nucleotide; D011506:Proteins / Q000737:chemistry*; D017422:Sequence Analysis, DNA
chemical_list: D011506:Proteins