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Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information

An increasing interest is being placed in the detection of genes, or genomic regions, that have been targeted by selection because identifying signatures of selection can lead to a better understanding of genotype-phenotype relationships. A common strategy for the detection of selection signatures i...

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Autores principales: Gholami, Mahmood, Reimer, Christian, Erbe, Malena, Preisinger, Rudolf, Weigend, Annett, Weigend, Steffen, Servin, Bertrand, Simianer, Henner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494984/
https://www.ncbi.nlm.nih.gov/pubmed/26151449
http://dx.doi.org/10.1371/journal.pone.0130497
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author Gholami, Mahmood
Reimer, Christian
Erbe, Malena
Preisinger, Rudolf
Weigend, Annett
Weigend, Steffen
Servin, Bertrand
Simianer, Henner
author_facet Gholami, Mahmood
Reimer, Christian
Erbe, Malena
Preisinger, Rudolf
Weigend, Annett
Weigend, Steffen
Servin, Bertrand
Simianer, Henner
author_sort Gholami, Mahmood
collection PubMed
description An increasing interest is being placed in the detection of genes, or genomic regions, that have been targeted by selection because identifying signatures of selection can lead to a better understanding of genotype-phenotype relationships. A common strategy for the detection of selection signatures is to compare samples from distinct populations and to search for genomic regions with outstanding genetic differentiation. The aim of this study was to detect selective signatures in layer chicken populations using a recently proposed approach, hapFLK, which exploits linkage disequilibrium information while accounting appropriately for the hierarchical structure of populations. We performed the analysis on 70 individuals from three commercial layer breeds (White Leghorn, White Rock and Rhode Island Red), genotyped for approximately 1 million SNPs. We found a total of 41 and 107 regions with outstanding differentiation or similarity using hapFLK and its single SNP counterpart FLK respectively. Annotation of selection signature regions revealed various genes and QTL corresponding to productions traits, for which layer breeds were selected. A number of the detected genes were associated with growth and carcass traits, including IGF-1R, AGRP and STAT5B. We also annotated an interesting gene associated with the dark brown feather color mutational phenotype in chickens (SOX10). We compared FST, FLK and hapFLK and demonstrated that exploiting linkage disequilibrium information and accounting for hierarchical population structure decreased the false detection rate.
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spelling pubmed-44949842015-07-15 Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information Gholami, Mahmood Reimer, Christian Erbe, Malena Preisinger, Rudolf Weigend, Annett Weigend, Steffen Servin, Bertrand Simianer, Henner PLoS One Research Article An increasing interest is being placed in the detection of genes, or genomic regions, that have been targeted by selection because identifying signatures of selection can lead to a better understanding of genotype-phenotype relationships. A common strategy for the detection of selection signatures is to compare samples from distinct populations and to search for genomic regions with outstanding genetic differentiation. The aim of this study was to detect selective signatures in layer chicken populations using a recently proposed approach, hapFLK, which exploits linkage disequilibrium information while accounting appropriately for the hierarchical structure of populations. We performed the analysis on 70 individuals from three commercial layer breeds (White Leghorn, White Rock and Rhode Island Red), genotyped for approximately 1 million SNPs. We found a total of 41 and 107 regions with outstanding differentiation or similarity using hapFLK and its single SNP counterpart FLK respectively. Annotation of selection signature regions revealed various genes and QTL corresponding to productions traits, for which layer breeds were selected. A number of the detected genes were associated with growth and carcass traits, including IGF-1R, AGRP and STAT5B. We also annotated an interesting gene associated with the dark brown feather color mutational phenotype in chickens (SOX10). We compared FST, FLK and hapFLK and demonstrated that exploiting linkage disequilibrium information and accounting for hierarchical population structure decreased the false detection rate. Public Library of Science 2015-07-07 /pmc/articles/PMC4494984/ /pubmed/26151449 http://dx.doi.org/10.1371/journal.pone.0130497 Text en © 2015 Gholami et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Gholami, Mahmood
Reimer, Christian
Erbe, Malena
Preisinger, Rudolf
Weigend, Annett
Weigend, Steffen
Servin, Bertrand
Simianer, Henner
Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title_full Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title_fullStr Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title_full_unstemmed Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title_short Genome Scan for Selection in Structured Layer Chicken Populations Exploiting Linkage Disequilibrium Information
title_sort genome scan for selection in structured layer chicken populations exploiting linkage disequilibrium information
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494984/
https://www.ncbi.nlm.nih.gov/pubmed/26151449
http://dx.doi.org/10.1371/journal.pone.0130497
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