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Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle

BACKGROUND: 'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and...

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Autores principales: Qanbari, Saber, Gianola, Daniel, Hayes, Ben, Schenkel, Flavio, Miller, Steve, Moore, Stephen, Thaller, Georg, Simianer, Henner
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146955/
https://www.ncbi.nlm.nih.gov/pubmed/21679429
http://dx.doi.org/10.1186/1471-2164-12-318
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author Qanbari, Saber
Gianola, Daniel
Hayes, Ben
Schenkel, Flavio
Miller, Steve
Moore, Stephen
Thaller, Georg
Simianer, Henner
author_facet Qanbari, Saber
Gianola, Daniel
Hayes, Ben
Schenkel, Flavio
Miller, Steve
Moore, Stephen
Thaller, Georg
Simianer, Henner
author_sort Qanbari, Saber
collection PubMed
description BACKGROUND: 'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and population differentiation index (F(ST))--were applied to identify traces of decades of intensive artificial selection for traits of economic importance in modern cattle. RESULTS: We scanned the genome of a diverse set of dairy and beef breeds from Germany, Canada and Australia genotyped with a 50 K SNP panel. Across breeds, a total of 109 extreme |iHS| values exceeded the empirical threshold level of 5% with 19, 27, 9, 10 and 17 outliers in Holstein, Brown Swiss, Australian Angus, Hereford and Simmental, respectively. Annotating the regions harboring clustered |iHS| signals revealed a panel of interesting candidate genes like SPATA17, MGAT1, PGRMC2 and ACTC1, COL23A1, MATN2, respectively, in the context of reproduction and muscle formation. In a further step, a new Bayesian F(ST)-based approach was applied with a set of geographically separated populations including Holstein, Brown Swiss, Simmental, North American Angus and Piedmontese for detecting differentiated loci. In total, 127 regions exceeding the 2.5 per cent threshold of the empirical posterior distribution were identified as extremely differentiated. In a substantial number (56 out of 127 cases) the extreme F(ST )values were found to be positioned in poor gene content regions which deviated significantly (p < 0.05) from the expectation assuming a random distribution. However, significant F(ST )values were found in regions of some relevant genes such as SMCP and FGF1. CONCLUSIONS: Overall, 236 regions putatively subject to recent positive selection in the cattle genome were detected. Both |iHS| and F(ST )suggested selection in the vicinity of the Sialic acid binding Ig-like lectin 5 gene on BTA18. This region was recently reported to be a major QTL with strong effects on productive life and fertility traits in Holstein cattle. We conclude that high-resolution genome scans of selection signatures can be used to identify genomic regions contributing to within- and inter-breed phenotypic variation.
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spelling pubmed-31469552011-07-31 Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle Qanbari, Saber Gianola, Daniel Hayes, Ben Schenkel, Flavio Miller, Steve Moore, Stephen Thaller, Georg Simianer, Henner BMC Genomics Research Article BACKGROUND: 'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and population differentiation index (F(ST))--were applied to identify traces of decades of intensive artificial selection for traits of economic importance in modern cattle. RESULTS: We scanned the genome of a diverse set of dairy and beef breeds from Germany, Canada and Australia genotyped with a 50 K SNP panel. Across breeds, a total of 109 extreme |iHS| values exceeded the empirical threshold level of 5% with 19, 27, 9, 10 and 17 outliers in Holstein, Brown Swiss, Australian Angus, Hereford and Simmental, respectively. Annotating the regions harboring clustered |iHS| signals revealed a panel of interesting candidate genes like SPATA17, MGAT1, PGRMC2 and ACTC1, COL23A1, MATN2, respectively, in the context of reproduction and muscle formation. In a further step, a new Bayesian F(ST)-based approach was applied with a set of geographically separated populations including Holstein, Brown Swiss, Simmental, North American Angus and Piedmontese for detecting differentiated loci. In total, 127 regions exceeding the 2.5 per cent threshold of the empirical posterior distribution were identified as extremely differentiated. In a substantial number (56 out of 127 cases) the extreme F(ST )values were found to be positioned in poor gene content regions which deviated significantly (p < 0.05) from the expectation assuming a random distribution. However, significant F(ST )values were found in regions of some relevant genes such as SMCP and FGF1. CONCLUSIONS: Overall, 236 regions putatively subject to recent positive selection in the cattle genome were detected. Both |iHS| and F(ST )suggested selection in the vicinity of the Sialic acid binding Ig-like lectin 5 gene on BTA18. This region was recently reported to be a major QTL with strong effects on productive life and fertility traits in Holstein cattle. We conclude that high-resolution genome scans of selection signatures can be used to identify genomic regions contributing to within- and inter-breed phenotypic variation. BioMed Central 2011-06-16 /pmc/articles/PMC3146955/ /pubmed/21679429 http://dx.doi.org/10.1186/1471-2164-12-318 Text en Copyright ©2011 Qanbari et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Qanbari, Saber
Gianola, Daniel
Hayes, Ben
Schenkel, Flavio
Miller, Steve
Moore, Stephen
Thaller, Georg
Simianer, Henner
Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title_full Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title_fullStr Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title_full_unstemmed Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title_short Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
title_sort application of site and haplotype-frequency based approaches for detecting selection signatures in cattle
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3146955/
https://www.ncbi.nlm.nih.gov/pubmed/21679429
http://dx.doi.org/10.1186/1471-2164-12-318
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