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

Nat Commun. 2016 Oct 31;7:13293. doi: 10.1038/ncomms13293.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Alleles
  • Cohort Studies
  • Computational Biology*
  • CpG Islands
  • Exome
  • Gene Frequency
  • Genetic Variation
  • Genome, Human
  • Humans
  • Linear Models
  • Mutation, Missense*
  • Polymorphism, Single Nucleotide
  • Proteins / chemistry*
  • Sequence Analysis, DNA*


  • Proteins