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A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes

Overview of attention for article published in Science, February 2012
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Citations

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1077 Dimensions

Readers on

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1539 Mendeley
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35 CiteULike
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Title
A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes
Published in
Science, February 2012
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

X Demographics

The data shown below were collected from the profiles of 61 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,539 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 187. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 February 2024.
All research outputs
#213,790
of 25,446,666 outputs
Outputs from Science
#6,137
of 82,985 outputs
Outputs of similar age
#888
of 169,215 outputs
Outputs of similar age from Science
#23
of 796 outputs
Altmetric has tracked 25,446,666 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 82,985 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 65.7. This one has done particularly well, scoring higher than 92% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 169,215 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 796 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.