Evidence map›Paper›PMID 26464126›Full record

ArticleMolecular biology and evolution2016

A Molecular Evolutionary Reference for the Human Variome.

Li Liu, Koichiro Tamura, Maxwell Sanderford, Vanessa E Gray, Sudhir Kumar

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Adaptive Landscape of Protein Variation in Human Exomes.Molecular biology and evolution · 2018
    Article
  15. Neutral Theory, Disease Mutations, and Personal Exomes.Molecular biology and evolution · 2018
    Article
  16. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Li LiuDepartment of Biomedical Informatics, Arizona State University, Scottsdale Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphila.
Koichiro TamuraDepartment of Biological Sciences, Tokyo Metropolitan University, Hachioji, Tokyo, Japan.
Maxwell SanderfordInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphila.
Vanessa E GrayDepartment of Genome Sciences, University of Washington, Seattle.
Sudhir KumarInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphila Department of Biology, Temple University, Philadelphila Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia s.kumar@temple.edu.

Funding

COMPARATIVE MOLECULAR SEQUENCE ANALYSISR01HG002096 · NHGRI · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI KUMAR, SUDHIR · 2000 to 2013
$3.0M
Methods for Evolutionary Informed Network Analysis to Discover Disease VariationR01DK098242 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI DUDLEY, JOEL THOMAS · 2013 to 2016
$1.7M
NHGRI NIH HHS HG-002096-12NHGRI NIH HHS R01 HG002096NIDDK NIH HHS DK098242-03NIDDK NIH HHS R01 DK098242
6 · The paper itself

Abstract

Widespread sequencing efforts are revealing unprecedented amount of genomic variation in populations. Such information is routinely used to derive consensus reference sequences and to infer positions subject to natural selection. Here, we present a new molecular evolutionary method for estimating neutral evolutionary probabilities (EPs) of each amino acid, or nucleotide state at a genomic position without using intraspecific polymorphism data. Because EPs are derived independently of population-level information, they serve as null expectations that can be used to evaluate selective forces on alleles at both polymorphic and monomorphic positions in populations. We applied this method to coding sequences in the human genome and produced a comprehensive evolutionary variome reference for all human proteins. We found that EPs accurately predict neutral and disease-associated alleles. Through an analysis of discordance between allelic EPs and their observed population frequencies, we discovered thousands of novel candidate sites for nonneutral evolution in human proteins. Many of these were validated in a joint analysis of disease-associated variants and population data. The EP method is also directly applicable to the analysis of noncoding sequences and genomic analyses of nonmodel species.

Indexed as

Evolution, MolecularAdaptation, BiologicalDiseaseGenetic VariationGenomeGenomicsHumansMutationPhylogenyadaptationdiseaseevolutionneutralityphylomedicine

Identifiers

PMID26464126
PMCPMC5009994

What OpenQuestion holds

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Registered trials

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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.