Evidence map›Paper›PMID 31238981›Full record

ArticleBMC evolutionary biology2019

On estimating evolutionary probabilities of population variants.

Ravi Patel, Sudhir Kumar

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Article in BMC evolutionary biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Ravi PatelInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.
Sudhir KumarInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. s.kumar@temple.edu.ORCID 0000-0002-9918-8212

Funding

eQTL mega-analysis for functional assessment of multi-enhancer gene regulationR01HG008146 · NHGRI · GEORGIA INSTITUTE OF TECHNOLOGY · PI GIBSON, GREGORY C · 2016 to 2018
$2.3M
EVOLUTIONARY BIOINFORMATICS OF TUMOR PROFILESR01LM012487 · NLM · TEMPLE UNIV OF THE COMMONWEALTH · PI KUMAR, SUDHIR · 2016 to 2018
$1.0M
NHGRI NIH HHS R01 HG008146NLM NIH HHS R01 LM012487
6 · The paper itself

Abstract

backgroundThe evolutionary probability (EP) of an allele in a DNA or protein sequence predicts evolutionarily permissible (ePerm; EP ≥ 0.05) and forbidden (eForb; EP < 0.05) variants. EP of an allele represents an independent evolutionary expectation of observing an allele in a population based solely on the long-term substitution patterns captured in a multiple sequence alignment. In the neutral theory, EP and population frequencies can be compared to identify neutral and non-neutral alleles. This approach has been used to discover candidate adaptive polymorphisms in humans, which are eForbs segregating with high frequencies. The original method to compute EP requires the evolutionary relationships and divergence times of species in the sequence alignment (a timetree), which are not known with certainty for most datasets. This requirement impedes a general use of the original EP formulation. Here, we present an approach in which the phylogeny and times are inferred from the sequence alignment itself prior to the EP calculation. We evaluate if the modified EP approach produces results that are similar to those from the original method.

resultsWe compared EP estimates from the original and the modified approaches by using more than 18,000 protein sequence alignments containing orthologous sequences from 46 vertebrate species. For the original EP calculations, we used species relationships from UCSC and divergence times from TimeTree web resource, and the resulting EP estimates were considered to be the ground truth. We found that the modified approaches produced reasonable EP estimates for HGMD disease missense variant and 1000 Genomes Project missense variant datasets. Our results showed that reliable estimates of EP can be obtained without a priori knowledge of the sequence phylogeny and divergence times. We also found that, in order to obtain robust EP estimates, it is important to assemble a dataset with many sequences, sampling from a diversity of species groups.

conclusionWe conclude that the modified EP approach will be generally applicable for alignments and enable the detection of potentially neutral, deleterious, and adaptive alleles in populations.

Indexed as

Evolution, MolecularGenetic VariationAllelesHumansPhylogenyProbabilitySequence AlignmentEvolutionary probabilityForbidden allelesGeneralized methodPotential adaptation

Identifiers

PMID31238981
PMCPMC6593550

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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.