Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds
The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2018-01-01
|
Series: | Animal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S175173111700266X |
id |
doaj-60c0bd367816435d80d670f902be9820 |
---|---|
record_format |
Article |
spelling |
doaj-60c0bd367816435d80d670f902be98202021-06-06T04:54:06ZengElsevierAnimal1751-73112018-01-0112611181125Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breedsG. Sottile0M.T. Sardina1S. Mastrangelo2R. Di Gerlando3M. Tolone4M. Chiodi5B. Portolano6Dipartimento Scienze Economiche, Aziendali e Statistiche, University of Palermo, Palermo, ItalyDipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, ItalyDipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, ItalyDipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, ItalyDipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, ItalyDipartimento Scienze Economiche, Aziendali e Statistiche, University of Palermo, Palermo, ItalyDipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, ItalyThe identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, Fst, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products.http://www.sciencedirect.com/science/article/pii/S175173111700266Xpenalized multinomial regressionstability selectionsheep breedslivestock genetic resourcessingle nucleotide polymorphism markers |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
G. Sottile M.T. Sardina S. Mastrangelo R. Di Gerlando M. Tolone M. Chiodi B. Portolano |
spellingShingle |
G. Sottile M.T. Sardina S. Mastrangelo R. Di Gerlando M. Tolone M. Chiodi B. Portolano Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds Animal penalized multinomial regression stability selection sheep breeds livestock genetic resources single nucleotide polymorphism markers |
author_facet |
G. Sottile M.T. Sardina S. Mastrangelo R. Di Gerlando M. Tolone M. Chiodi B. Portolano |
author_sort |
G. Sottile |
title |
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
title_short |
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
title_full |
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
title_fullStr |
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
title_full_unstemmed |
Penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
title_sort |
penalized classification for optimal statistical selection of markers from high-throughput genotyping: application in sheep breeds |
publisher |
Elsevier |
series |
Animal |
issn |
1751-7311 |
publishDate |
2018-01-01 |
description |
The identification of individuals’ breed of origin has several practical applications in livestock and is useful in different biological contexts such as conservation genetics, breeding and authentication of animal products. In this paper, penalized multinomial regression was applied to identify the minimum number of single nucleotide polymorphisms (SNPs) from high-throughput genotyping data for individual assignment to dairy sheep breeds reared in Sicily. The combined use of penalized multinomial regression and stability selection reduced the number of SNPs required to 48. A final validation step on an independent population was carried out obtaining 100% correctly classified individuals. The results using independent analysis, such as admixture, Fst, principal component analysis and random forest, confirmed the ability of these methods in selecting distinctive markers. The identified SNPs may constitute a starting point for the development of a SNP based identification test as a tool for breed assignment and traceability of animal products. |
topic |
penalized multinomial regression stability selection sheep breeds livestock genetic resources single nucleotide polymorphism markers |
url |
http://www.sciencedirect.com/science/article/pii/S175173111700266X |
work_keys_str_mv |
AT gsottile penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT mtsardina penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT smastrangelo penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT rdigerlando penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT mtolone penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT mchiodi penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds AT bportolano penalizedclassificationforoptimalstatisticalselectionofmarkersfromhighthroughputgenotypingapplicationinsheepbreeds |
_version_ |
1721394853717213184 |