Evidence map›Paper›PMID 23217326›Full record

ArticleAmerican journal of human genetics2012

Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing.

Yali Xue, Yuan Chen, Qasim Ayub, Ni Huang, Edward V Ball, Matthew Mort, Andrew D Phillips, Katy Shaw, Peter D Stenson, David N Cooper and 2 more

Abstract read
In one paragraph

Article in American journal of human genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 161 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
161citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

161 citing papers in PubMed, 1 synthesis or guideline pooled it.

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101 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yali XueThe Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK.
Yuan Chen
Qasim Ayub
Ni Huang
Edward V Ball
Matthew Mort
Andrew D Phillips
Katy Shaw
Peter D Stenson
David N Cooper
Chris Tyler-Smith
1000 Genomes Project Consortium

Funding

Wellcome Trust 085532Wellcome Trust WT098051
6 · The paper itself

Abstract

We have assessed the numbers of potentially deleterious variants in the genomes of apparently healthy humans by using (1) low-coverage whole-genome sequence data from 179 individuals in the 1000 Genomes Pilot Project and (2) current predictions and databases of deleterious variants. Each individual carried 281-515 missense substitutions, 40-85 of which were homozygous, predicted to be highly damaging. They also carried 40-110 variants classified by the Human Gene Mutation Database (HGMD) as disease-causing mutations (DMs), 3-24 variants in the homozygous state, and many polymorphisms putatively associated with disease. Whereas many of these DMs are likely to represent disease-allele-annotation errors, between 0 and 8 DMs (0-1 homozygous) per individual are predicted to be highly damaging, and some of them provide information of medical relevance. These analyses emphasize the need for improved annotation of disease alleles both in mutation databases and in the primary literature; some HGMD mutation data have been recategorized on the basis of the present findings, an iterative process that is both necessary and ongoing. Our estimates of deleterious-allele numbers are likely to be subject to both overcounting and undercounting. However, our current best mean estimates of ~400 damaging variants and ~2 bona fide disease mutations per individual are likely to increase rather than decrease as sequencing studies ascertain rare variants more effectively and as additional disease alleles are discovered.

Indexed as

AllelesMutation RateDatabases, Nucleic AcidGenome, HumanGenome-Wide Association StudyHumansMutation, MissensePrevalence

Identifiers

PMID23217326
PMCPMC3516590

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.