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Use of canonical discriminant analysis to study signatures of selection in cattle

BACKGROUND: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of “signatures of selection” is a useful approach to stud...

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Autores principales: Sorbolini, Silvia, Gaspa, Giustino, Steri, Roberto, Dimauro, Corrado, Cellesi, Massimo, Stella, Alessandra, Marras, Gabriele, Marsan, Paolo Ajmone, Valentini, Alessio, Macciotta, Nicolò Pietro Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983034/
https://www.ncbi.nlm.nih.gov/pubmed/27521154
http://dx.doi.org/10.1186/s12711-016-0236-7
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author Sorbolini, Silvia
Gaspa, Giustino
Steri, Roberto
Dimauro, Corrado
Cellesi, Massimo
Stella, Alessandra
Marras, Gabriele
Marsan, Paolo Ajmone
Valentini, Alessio
Macciotta, Nicolò Pietro Paolo
author_facet Sorbolini, Silvia
Gaspa, Giustino
Steri, Roberto
Dimauro, Corrado
Cellesi, Massimo
Stella, Alessandra
Marras, Gabriele
Marsan, Paolo Ajmone
Valentini, Alessio
Macciotta, Nicolò Pietro Paolo
author_sort Sorbolini, Silvia
collection PubMed
description BACKGROUND: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of “signatures of selection” is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. METHODS: A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an F(ST)-based approach. RESULTS: Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the F(ST)-based approach. CONCLUSIONS: The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the F(ST) index. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0236-7) contains supplementary material, which is available to authorized users.
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spelling pubmed-49830342016-08-14 Use of canonical discriminant analysis to study signatures of selection in cattle Sorbolini, Silvia Gaspa, Giustino Steri, Roberto Dimauro, Corrado Cellesi, Massimo Stella, Alessandra Marras, Gabriele Marsan, Paolo Ajmone Valentini, Alessio Macciotta, Nicolò Pietro Paolo Genet Sel Evol Research Article BACKGROUND: Cattle include a large number of breeds that are characterized by marked phenotypic differences and thus constitute a valuable model to study genome evolution in response to processes such as selection and domestication. Detection of “signatures of selection” is a useful approach to study the evolutionary pressures experienced throughout history. In the present study, signatures of selection were investigated in five cattle breeds farmed in Italy using a multivariate approach. METHODS: A total of 4094 bulls from five breeds with different production aptitudes (two dairy breeds: Italian Holstein and Italian Brown Swiss; two beef breeds: Piemontese and Marchigiana; and one dual purpose breed: Italian Simmental) were genotyped using the Illumina BovineSNP50 v.1 beadchip. Canonical discriminant analysis was carried out on the matrix of single nucleotide polymorphisms (SNP) genotyping data, separately for each chromosome. Scores for each canonical variable were calculated and then plotted in the canonical space to quantify the distance between breeds. SNPs for which the correlation with the canonical variable was in the 99th percentile for a specific chromosome were considered to be significantly associated with that variable. Results were compared with those obtained using an F(ST)-based approach. RESULTS: Based on the results of the canonical discriminant analysis, a large number of signatures of selection were detected, among which several had strong signals in genomic regions that harbour genes known to have an impact on production and morphological bovine traits, including MSTN, LCT, GHR, SCD, NCAPG, KIT, and ASIP. Moreover, new putative candidate genes were identified, such as GCK, B3GALNT1, MGAT1, GALNTL1, PRNP, and PRND. Similar results were obtained with the F(ST)-based approach. CONCLUSIONS: The use of canonical discriminant analysis on 50 K SNP genotypes allowed the extraction of new variables that maximize the separation between breeds. This approach is quite straightforward, it can compare more than two groups simultaneously, and relative distances between breeds can be visualized. The genes that were highlighted in the canonical discriminant analysis were in concordance with those obtained using the F(ST) index. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12711-016-0236-7) contains supplementary material, which is available to authorized users. BioMed Central 2016-08-12 /pmc/articles/PMC4983034/ /pubmed/27521154 http://dx.doi.org/10.1186/s12711-016-0236-7 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Sorbolini, Silvia
Gaspa, Giustino
Steri, Roberto
Dimauro, Corrado
Cellesi, Massimo
Stella, Alessandra
Marras, Gabriele
Marsan, Paolo Ajmone
Valentini, Alessio
Macciotta, Nicolò Pietro Paolo
Use of canonical discriminant analysis to study signatures of selection in cattle
title Use of canonical discriminant analysis to study signatures of selection in cattle
title_full Use of canonical discriminant analysis to study signatures of selection in cattle
title_fullStr Use of canonical discriminant analysis to study signatures of selection in cattle
title_full_unstemmed Use of canonical discriminant analysis to study signatures of selection in cattle
title_short Use of canonical discriminant analysis to study signatures of selection in cattle
title_sort use of canonical discriminant analysis to study signatures of selection in cattle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983034/
https://www.ncbi.nlm.nih.gov/pubmed/27521154
http://dx.doi.org/10.1186/s12711-016-0236-7
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