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Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel

Dimauro, Corrado and Steri, Roberto and Pintus, Maria Annunziata and Gaspa, Giustino and Macciotta, Nicolò Pietro Paolo (2011) Use of partial least squares regression to predict single nucleotide polymorphism marker genotypes when some animals are genotyped with a low-density panel. Animal, Vol. 5 (6), p. 833-837. eISSN 1751-732X. Article.

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DOI: 10.1017/S1751731110002600

Abstract

High-density single nucleotide polymorphism (SNP) platforms are currently used in genomic selection (GS) programs to enhance the selection response. However, the genotyping of a large number of animals with high-throughput platforms is rather expensive and may represent a constraint for a large-scale implementation of GS. The use of low-density marker (LDM) platforms could overcome this problem, but different SNP chips may be required for each trait and/or breed. In this study, a strategy of imputation independent from trait and breed is proposed. A simulated population of 5865 individuals with a genome of 6000 SNP equally distributed on six chromosomes was considered. First, reference and prediction populations were generated by mimicking high- and low-density SNP platforms, respectively. Then, the partial least squares regression (PLSR) technique was applied to reconstruct the missing SNP in the low-density chip. The proportion of SNP correctly reconstructed by the PLSR method ranged from 0.78 to 0.97 when 90% and 50%, respectively, of genotypes were predicted. Moreover, data sets consisting of a mixture of actual and PLSR-predicted SNP or only actual SNP were used to predict genomic breeding values (GEBVs). Correlations between GEBV and true breeding values varied from 0.74 to 0.76, respectively. The results of the study indicate that the PLSR technique can be considered a reliable computational strategy for predicting SNP genotypes in an LDM platform with reasonable accuracy.

Item Type:Article
ID Code:5981
Status:Published
Refereed:Yes
Uncontrolled Keywords:Genomic selection, SNP prediction, genotype imputation
Subjects:Area 07 - Scienze agrarie e veterinarie > AGR/17 Zootecnica generale e miglioramento genetico
Area 07 - Scienze agrarie e veterinarie > AGR/19 Zootecnica speciale
Divisions:001 Università di Sassari > 01 Dipartimenti > Scienze zootecniche
Publisher:Cambridge University Press
eISSN:1751-732X
Copyright Holders:© The Animal Consortium 2011
Deposited On:20 Apr 2011 13:57

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