Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds
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John Wiley & Sons
2024-01-08
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| Subjects: | Sus scrofa, SNP, Genome, Population genomics, Random forest, Signatures of selection, |
| Online Access: | http://hdl.handle.net/10261/356715 http://dx.doi.org/10.13039/501100005969 http://dx.doi.org/10.13039/501100000780 https://api.elsevier.com/content/abstract/scopus_id/85181719006 |
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Sus scrofa SNP Genome Population genomics Random forest Signatures of selection Sus scrofa SNP Genome Population genomics Random forest Signatures of selection |
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Sus scrofa SNP Genome Population genomics Random forest Signatures of selection Sus scrofa SNP Genome Population genomics Random forest Signatures of selection Schiavo, Giuseppina Bertolini, F. Bovo, Samuele Galimberti, Giuliano Muñoz, María Bozzi, Riccardo Čandek-Potokar, M. Óvilo Martín, Cristina Fontanesi, Luca Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
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13 Pág. |
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Università di Bologna |
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Università di Bologna Schiavo, Giuseppina Bertolini, F. Bovo, Samuele Galimberti, Giuliano Muñoz, María Bozzi, Riccardo Čandek-Potokar, M. Óvilo Martín, Cristina Fontanesi, Luca |
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artículo |
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Sus scrofa SNP Genome Population genomics Random forest Signatures of selection |
| author |
Schiavo, Giuseppina Bertolini, F. Bovo, Samuele Galimberti, Giuliano Muñoz, María Bozzi, Riccardo Čandek-Potokar, M. Óvilo Martín, Cristina Fontanesi, Luca |
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Schiavo, Giuseppina |
| title |
Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
| title_short |
Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
| title_full |
Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
| title_fullStr |
Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
| title_full_unstemmed |
Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds |
| title_sort |
identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: application to european autochthonous and cosmopolitan pig breeds |
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John Wiley & Sons |
| publishDate |
2024-01-08 |
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http://hdl.handle.net/10261/356715 http://dx.doi.org/10.13039/501100005969 http://dx.doi.org/10.13039/501100000780 https://api.elsevier.com/content/abstract/scopus_id/85181719006 |
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dig-inia-es-10261-3567152024-10-28T21:36:40Z Identification of population-informative markers from high-density genotyping data through combined feature selection and machine learning algorithms: Application to European autochthonous and cosmopolitan pig breeds Schiavo, Giuseppina Bertolini, F. Bovo, Samuele Galimberti, Giuliano Muñoz, María Bozzi, Riccardo Čandek-Potokar, M. Óvilo Martín, Cristina Fontanesi, Luca Università di Bologna European Commission CSIC - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Schiavo, Giuseppina [0000-0002-3497-1337] Bertolini, F. [0000-0003-4181-3895] Bovo, Samuele [0000-0002-5712-8211] Galimberti, Giuliano [0000-0002-9161-9671] Bozzi, Riccardo [0000-0001-8854-0834] Čandek-Potokar, M. [0000-0003-0231-126X] Óvilo Martín, Cristina [0000-0002-5738-8435] Fontanesi, Luca [0000-0001-7050-3760] Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72] Sus scrofa SNP Genome Population genomics Random forest Signatures of selection 13 Pág. Large genotyping datasets, obtained from high-density single nucleotide polymorphism (SNP) arrays, developed for different livestock species, can be used to describe and differentiate breeds or populations. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this study, we applied the Boruta algorithm, a wrapper of the machine learning random forest algorithm, on a database of 23 European pig breeds (20 autochthonous and three cosmopolitan breeds) genotyped with a 70k SNP chip, to pre-select informative SNPs. To identify different sets of SNPs, these pre-selected markers were then ranked with random forest based on their mean decrease accuracy and mean decrease gene indexes. We evaluated the efficiency of these subsets for breed classification and the usefulness of this approach to detect candidate genes affecting breed-specific phenotypes and relevant production traits that might differ among breeds. The lowest overall classification error (2.3%) was reached with a subpanel including only 398 SNPs (ranked based on their mean decrease accuracy), with no classification error in seven breeds using up to 49 SNPs. Several SNPs of these selected subpanels were in genomic regions in which previous studies had identified signatures of selection or genes associated with morphological or production traits that distinguish the analysed breeds. Therefore, even if these approaches have not been originally designed to identify signatures of selection, the obtained results showed that they could potentially be useful for this purpose. This work received funding from the University of Bologna RFO 2016–2019 programme and from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 634476 for the project with the acronym TREASURE. The content of this article reflects only the authors’ view, and the European Union Agency is not responsible for any use that may be made of the information it contains. The authors thank the members of the TREASURE consortium for providing samples: Estefania Alves, Yolanda Núñez, Ana I. Fernandez, Fabián García, Juan M. García-Casco (Departamento Mejora Genética Animal, INIA-CSIC, Spain), José P. Araújo (Centro de Investigação de Montanha (CIMO), Portugal), Rui Charneca, José Manuel Martins (MED – Instituto Mediterrâneo para Agricultura, Ambiente e Desenvolvimento, Portugal), Maurizio Gallo (Associazione Nazionale Allevatori Suini, ANAS, Italy), Danijel Karolyi (Department of Animal Science, Faculty of Agriculture, University of Zagreb, Croatia), Goran Kušec (Faculty of Agrobiotechnical Sciences, University of Osijek, Croatia), Marie-José. Mercat (IFIP Institut du Porc, France), Raquel Quintanilla (Programa de Genética y Mejora Animal, IRTA, Spain), Čedomir Radović (Department of Pig Breeding and Genetics, Institute for Animal Husbandry, Serbia), Violeta Razmaite (Animal Science Institute, Lithuanian University of Health Sciences, Lithuania) Juliette Riquet (GenPhySE, Université de Toulouse, INRA, France), Radomir Savić (Faculty of Agriculture, University of Belgrade, Serbia), Graziano Usai (AGRIS SARDEGNA, Italy) and Christoph Zimmer (Bäuerliche Erzeugergemeinschaft Schwäbisch Hall, Germany). The support of the Slovenian Research Agency for MČP is acknowledged (grants P4-0133 and J4-3094). Peer reviewed 2024-05-13T08:27:22Z 2024-05-13T08:27:22Z 2024-01-08 artículo http://purl.org/coar/resource_type/c_6501 Animal Genetics 55(2): 193-205 (2024) 0268-9146 http://hdl.handle.net/10261/356715 10.1111/age.13396 1365-2052 http://dx.doi.org/10.13039/501100005969 http://dx.doi.org/10.13039/501100000780 38191264 2-s2.0-85181719006 https://api.elsevier.com/content/abstract/scopus_id/85181719006 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/634476 Departamento de Mejora Genética Animal Publisher's version https://doi.org/10.1111/age.13396 Sí open application/pdf John Wiley & Sons |