Title |
A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes
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Published in |
Science, February 2012
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DOI | 10.1126/science.1215040 |
Pubmed ID | |
Authors |
Daniel G. MacArthur, Suganthi Balasubramanian, Adam Frankish, Ni Huang, James Morris, Klaudia Walter, Luke Jostins, Lukas Habegger, Joseph K. Pickrell, Stephen B. Montgomery, Cornelis A. Albers, Zhengdong D. Zhang, Donald F. Conrad, Gerton Lunter, Hancheng Zheng, Qasim Ayub, Mark A. DePristo, Eric Banks, Min Hu, Robert E. Handsaker, Jeffrey A. Rosenfeld, Menachem Fromer, Mike Jin, Xinmeng Jasmine Mu, Ekta Khurana, Kai Ye, Mike Kay, Gary Ian Saunders, Marie-Marthe Suner, Toby Hunt, If H. A. Barnes, Clara Amid, Denise R. Carvalho-Silva, Alexandra H. Bignell, Catherine Snow, Bryndis Yngvadottir, Suzannah Bumpstead, David N. Cooper, Yali Xue, Irene Gallego Romero, 1000 Genomes Project Consortium, Jun Wang, Yingrui Li, Richard A. Gibbs, Steven A. McCarroll, Emmanouil T. Dermitzakis, Jonathan K. Pritchard, Jeffrey C. Barrett, Jennifer Harrow, Matthew E. Hurles, Mark B. Gerstein, Chris Tyler-Smith |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 26 | 43% |
Switzerland | 2 | 3% |
Canada | 2 | 3% |
United Kingdom | 2 | 3% |
Japan | 2 | 3% |
Netherlands | 2 | 3% |
Peru | 1 | 2% |
Belgium | 1 | 2% |
Australia | 1 | 2% |
Other | 5 | 8% |
Unknown | 17 | 28% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 31 | 51% |
Scientists | 27 | 44% |
Science communicators (journalists, bloggers, editors) | 2 | 3% |
Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 52 | 3% |
United Kingdom | 14 | <1% |
Germany | 11 | <1% |
Netherlands | 7 | <1% |
France | 7 | <1% |
Brazil | 7 | <1% |
Italy | 6 | <1% |
Spain | 6 | <1% |
Denmark | 5 | <1% |
Other | 31 | 2% |
Unknown | 1393 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 414 | 27% |
Student > Ph. D. Student | 393 | 26% |
Student > Master | 121 | 8% |
Student > Bachelor | 115 | 7% |
Professor > Associate Professor | 99 | 6% |
Other | 270 | 18% |
Unknown | 127 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 730 | 47% |
Biochemistry, Genetics and Molecular Biology | 336 | 22% |
Medicine and Dentistry | 145 | 9% |
Computer Science | 51 | 3% |
Neuroscience | 19 | 1% |
Other | 105 | 7% |
Unknown | 153 | 10% |