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Preselection statistics and Random Forest classification identify population informative single nucleotide polymorphisms in cosmopolitan and autochthonous cattle breeds

Published online by Cambridge University Press:  23 June 2017

F. Bertolini ,
G. Galimberti ,
G. Schiavo ,
S. Mastrangelo ,
R. Di Gerlando ,
M. G. Strillacci ,
A. Bagnato ,
B. Portolano  and
L. Fontanesi
F. Bertolini
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale Fanin 46, 40127 Bologna, Italy
G. Galimberti
Affiliation:
Department of Statistical Sciences “Paolo Fortunati”, University of Bologna, Via delle Belle Arti 41, 40126 Bologna, Italy
G. Schiavo
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale Fanin 46, 40127 Bologna, Italy
S. Mastrangelo
Affiliation:
Department of Agricultural and Forestry Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
R. Di Gerlando
Affiliation:
Department of Agricultural and Forestry Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
M. G. Strillacci
Affiliation:
Department of Veterinary Medicine, Università degli Studi di Milano, Via Celoria 10, 20133 Milano, Italy
A. Bagnato
Affiliation:
Department of Veterinary Medicine, Università degli Studi di Milano, Via Celoria 10, 20133 Milano, Italy
B. Portolano
Affiliation:
Department of Agricultural and Forestry Sciences, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy
L. Fontanesi*
Affiliation:
Department of Agricultural and Food Sciences, Division of Animal Sciences, University of Bologna, Viale Fanin 46, 40127 Bologna, Italy
*
†E-mail: [email protected]
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Abstract

Commercial single nucleotide polymorphism (SNP) arrays have been recently developed for several species and can be used to identify informative markers to differentiate breeds or populations for several downstream applications. To identify the most discriminating genetic markers among thousands of genotyped SNPs, a few statistical approaches have been proposed. In this work, we compared several methods of SNPs preselection (Delta, Fst and principal component analyses (PCA)) in addition to Random Forest classifications to analyse SNP data from six dairy cattle breeds, including cosmopolitan (Holstein, Brown and Simmental) and autochthonous Italian breeds raised in two different regions and subjected to limited or no breeding programmes (Cinisara, Modicana, raised only in Sicily and Reggiana, raised only in Emilia Romagna). From these classifications, two panels of 96 and 48 SNPs that contain the most discriminant SNPs were created for each preselection method. These panels were evaluated in terms of the ability to discriminate as a whole and breed-by-breed, as well as linkage disequilibrium within each panel. The obtained results showed that for the 48-SNP panel, the error rate increased mainly for autochthonous breeds, probably as a consequence of their admixed origin lower selection pressure and by ascertaining bias in the construction of the SNP chip. The 96-SNP panels were generally more able to discriminate all breeds. The panel derived by PCA-chrom (obtained by a preselection chromosome by chromosome) could identify informative SNPs that were particularly useful for the assignment of minor breeds that reached the lowest value of Out Of Bag error even in the Cinisara, whose value was quite high in all other panels. Moreover, this panel contained also the lowest number of SNPs in linkage disequilibrium. Several selected SNPs are located nearby genes affecting breed-specific phenotypic traits (coat colour and stature) or associated with production traits. In general, our results demonstrated the usefulness of Random Forest in combination to other reduction techniques to identify population informative SNPs.

Keywords

SNPbreed assignmentRandom ForestBos taurus
Type
Research Article
Information
animal , Volume 12 , Issue 1 , January 2018 , pp. 12 - 19
Copyright
© The Animal Consortium 2017 

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