Cargando…

SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data

Effective population size (N(e)) is a key population genetic parameter that describes the amount of genetic drift in a population. Estimating N(e) has been subject to much research over the last 80 years. Methods to estimate N(e) from linkage disequilibrium (LD) were developed ~40 years ago but depe...

Descripción completa

Detalles Bibliográficos
Autores principales: Barbato, Mario, Orozco-terWengel, Pablo, Tapio, Miika, Bruford, Michael W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367434/
https://www.ncbi.nlm.nih.gov/pubmed/25852748
http://dx.doi.org/10.3389/fgene.2015.00109
_version_ 1782362540107366400
author Barbato, Mario
Orozco-terWengel, Pablo
Tapio, Miika
Bruford, Michael W.
author_facet Barbato, Mario
Orozco-terWengel, Pablo
Tapio, Miika
Bruford, Michael W.
author_sort Barbato, Mario
collection PubMed
description Effective population size (N(e)) is a key population genetic parameter that describes the amount of genetic drift in a population. Estimating N(e) has been subject to much research over the last 80 years. Methods to estimate N(e) from linkage disequilibrium (LD) were developed ~40 years ago but depend on the availability of large amounts of genetic marker data that only the most recent advances in DNA technology have made available. Here we introduce SNeP, a multithreaded tool to perform the estimate of N(e) using LD using the standard PLINK input file format (.ped and.map files) or by using LD values calculated using other software. Through SNeP the user can apply several corrections to take account of sample size, mutation, phasing, and recombination rate. Each variable involved in the computation such as the binning parameters or the chromosomes to include in the analysis can be modified. When applied to published datasets, SNeP produced results closely comparable with those obtained in the original studies. The use of SNeP to estimate N(e) trends can improve understanding of population demography in the recent past, provided a sufficient number of SNPs and their physical position in the genome are available. Binaries for the most common operating systems are available at https://sourceforge.net/projects/snepnetrends/.
format Online
Article
Text
id pubmed-4367434
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-43674342015-04-07 SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data Barbato, Mario Orozco-terWengel, Pablo Tapio, Miika Bruford, Michael W. Front Genet Genetics Effective population size (N(e)) is a key population genetic parameter that describes the amount of genetic drift in a population. Estimating N(e) has been subject to much research over the last 80 years. Methods to estimate N(e) from linkage disequilibrium (LD) were developed ~40 years ago but depend on the availability of large amounts of genetic marker data that only the most recent advances in DNA technology have made available. Here we introduce SNeP, a multithreaded tool to perform the estimate of N(e) using LD using the standard PLINK input file format (.ped and.map files) or by using LD values calculated using other software. Through SNeP the user can apply several corrections to take account of sample size, mutation, phasing, and recombination rate. Each variable involved in the computation such as the binning parameters or the chromosomes to include in the analysis can be modified. When applied to published datasets, SNeP produced results closely comparable with those obtained in the original studies. The use of SNeP to estimate N(e) trends can improve understanding of population demography in the recent past, provided a sufficient number of SNPs and their physical position in the genome are available. Binaries for the most common operating systems are available at https://sourceforge.net/projects/snepnetrends/. Frontiers Media S.A. 2015-03-20 /pmc/articles/PMC4367434/ /pubmed/25852748 http://dx.doi.org/10.3389/fgene.2015.00109 Text en Copyright © 2015 Barbato, Orozco-terWengel, Tapio and Bruford. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Barbato, Mario
Orozco-terWengel, Pablo
Tapio, Miika
Bruford, Michael W.
SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title_full SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title_fullStr SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title_full_unstemmed SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title_short SNeP: a tool to estimate trends in recent effective population size trajectories using genome-wide SNP data
title_sort snep: a tool to estimate trends in recent effective population size trajectories using genome-wide snp data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4367434/
https://www.ncbi.nlm.nih.gov/pubmed/25852748
http://dx.doi.org/10.3389/fgene.2015.00109
work_keys_str_mv AT barbatomario snepatooltoestimatetrendsinrecenteffectivepopulationsizetrajectoriesusinggenomewidesnpdata
AT orozcoterwengelpablo snepatooltoestimatetrendsinrecenteffectivepopulationsizetrajectoriesusinggenomewidesnpdata
AT tapiomiika snepatooltoestimatetrendsinrecenteffectivepopulationsizetrajectoriesusinggenomewidesnpdata
AT brufordmichaelw snepatooltoestimatetrendsinrecenteffectivepopulationsizetrajectoriesusinggenomewidesnpdata