Evidence map›Paper›PMID 27172192›Full record

ArticleG3 (Bethesda, Md.)2016

New Software for the Fast Estimation of Population Recombination Rates (FastEPRR) in the Genomic Era.

Feng Gao, Chen Ming, Wangjie Hu, Haipeng Li

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

89 citing papers in PubMed.

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  7. Ultrarapid MC1R protein and associated plumage color evolution in the domestic chicken.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  16. Genomic Insights into the Origin, High Fecundity and Environmental Adaptation of Hu Sheep.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  17. Article
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  19. Article
  20. Article

29 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

4 authors.

Feng GaoCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China University of Chinese Academy of Sciences, Beijing 100049, China.
Chen MingCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China University of Chinese Academy of Sciences, Beijing 100049, China.
Wangjie HuCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China University of Chinese Academy of Sciences, Beijing 100049, China.
Haipeng LiCAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China lihaipeng@picb.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genetic recombination is a very important evolutionary mechanism that mixes parental haplotypes and produces new raw material for organismal evolution. As a result, information on recombination rates is critical for biological research. In this paper, we introduce a new extremely fast open-source software package (FastEPRR) that uses machine learning to estimate recombination rate [Formula: see text] (=[Formula: see text]) from intraspecific DNA polymorphism data. When [Formula: see text] and the number of sampled diploid individuals is large enough ([Formula: see text]), the variance of [Formula: see text] remains slightly smaller than that of [Formula: see text] The new estimate [Formula: see text] (calculated by averaging [Formula: see text] and [Formula: see text]) has the smallest variance of all cases. When estimating [Formula: see text], the finite-site model was employed to analyze cases with a high rate of recurrent mutations, and an additional method is proposed to consider the effect of variable recombination rates within windows. Simulations encompassing a wide range of parameters demonstrate that different evolutionary factors, such as demography and selection, may not increase the false positive rate of recombination hotspots. Overall, accuracy of FastEPRR is similar to the well-known method, LDhat, but requires far less computation time. Genetic maps for each human population (YRI, CEU, and CHB) extracted from the 1000 Genomes OMNI data set were obtained in less than 3 d using just a single CPU core. The Pearson Pairwise correlation coefficient between the [Formula: see text] and [Formula: see text] maps is very high, ranging between 0.929 and 0.987 at a 5-Mb scale. Considering that sample sizes for these kinds of data are increasing dramatically with advances in next-generation sequencing technologies, FastEPRR (freely available at http://www.picb.ac.cn/evolgen/) is expected to become a widely used tool for establishing genetic maps and studying recombination hotspots in the population genomic era.

Indexed as

Genetics, PopulationGenomicsRecombination, GeneticSoftwareAlgorithmsGenomeHaplotypesHumansPolymorphism, GeneticReproducibility of Resultsboostingfast estimationgenetic mapgenomic erapopulation recombination rates

Identifiers

PMID27172192
PMCPMC4889653

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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.