A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes
Defective Gene Detective
Identifying genes that give rise to diseases is one of the major goals of sequencing human genomes. However, putative loss-of-function genes, which are often some of the first identified targets of genome and exome sequencing, have often turned out to be sequencing errors rather than true genetic variants. In order to identify the true scope of loss-of-function genes within the human genome, MacArthur et al. (p. 823; see the Perspective by Quintana-Murci) extensively validated the genomes from the 1000 Genomes Project, as well as an additional European individual, and found that the average person has about 100 true loss-of-function alleles of which approximately 20 have two copies within an individual. Because many known disease-causing genes were identified in “normal” individuals, the process of clinical sequencing needs to reassess how to identify likely causative alleles.
Abstract
Genome-sequencing studies indicate that all humans carry many genetic variants predicted to cause loss of function (LoF) of protein-coding genes, suggesting unexpected redundancy in the human genome. Here we apply stringent filters to 2951 putative LoF variants obtained from 185 human genomes to determine their true prevalence and properties. We estimate that human genomes typically contain ~100 genuine LoF variants with ~20 genes completely inactivated. We identify rare and likely deleterious LoF alleles, including 26 known and 21 predicted severe disease–causing variants, as well as common LoF variants in nonessential genes. We describe functional and evolutionary differences between LoF-tolerant and recessive disease genes and a method for using these differences to prioritize candidate genes found in clinical sequencing studies.
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Supplementary Material
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Published In
Science
Volume 335 | Issue 6070
17 February 2012
17 February 2012
Copyright
Copyright © 2012, American Association for the Advancement of Science.
Submission history
Received: 10 October 2011
Accepted: 11 January 2012
Published in print: 17 February 2012
Acknowledgments
T. Shah provided the Pyvoker software used for manual assignment of genotypes based on intensity clusters; S. Edkins was involved in the Sequenom validation; and the genotyping groups at Illumina, the Wellcome Trust Sanger Institute, and The Broad Institute of Harvard and MIT provided raw intensity data for the three Illumina arrays used for genotyping validation. The work performed at the Wellcome Trust Sanger Institute was supported by Wellcome Trust grant 098051; D.G.M. was supported by a fellowship from the Australian National Health and Medical Research Council; G.L. by the Wellcome Trust (090532/Z/09/Z); E.T.D. and S.B.M. by the Swiss National Science Foundation, the Louis Jeantet Foundation, and the NIH–National Institute of Mental Health GTEx fund; K.Y. by the Netherlands Organisation for Scientific Research (NWO) VENI grant 639.021.125; and H.Z., Y.L., and J.W. by a National Basic Research Program of China (973 program no. 2011CB809200), the National Natural Science Foundation of China (30725008, 30890032, 30811130531), the Chinese 863 program (2006AA02A302, 2009AA022707), the Shenzhen Municipal Government of China (grants JC200903190767A, JC200903190772A, ZYC200903240076A, CXB200903110066A, ZYC200903240077A, and ZYC200903240080A), and the Ole Rømer grant from the Danish Natural Science Research Council, as well as funding from the Shenzhen Municipal Government and the Local Government of Yantian District of Shenzhen. M.B.G. and C.T.-S. contributed equally to this work as senior authors. J.K.P. is on the scientific advisory board of 23andMe, and R.A.G. has a shared investment in Life Technologies. Raw sequence data for the 1000 Genomes pilot projects are available from www.1000genomes.org, and a curated list of the loss-of-function variants described in this manuscript is provided in the supporting online material.
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