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Supervised independent component analysis as an alternative method for genomic selection in pigs
Author(s) -
Azevedo C.F.,
Silva F.F.,
Resende M.D.V.,
Lopes M.S.,
Duijvesteijn N.,
Guimarães S.E.F.,
Lopes P.S.,
Kelly M.J.,
Viana J.M.S.,
Knol E.F.
Publication year - 2014
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12104
Subject(s) - best linear unbiased prediction , genomic selection , selection (genetic algorithm) , biology , statistics , cross validation , computational biology , mathematics , genetics , computer science , artificial intelligence , genotype , single nucleotide polymorphism , gene
Summary The objective of this work was to evaluate the efficiency of the supervised independent component regression ( SICR ) method for the estimation of genomic values and the SNP marker effects for boar taint and carcass traits in pigs. The methods were evaluated via the agreement between the predicted genetic values and the corrected phenotypes observed by cross‐validation. These values were also compared with other methods generally used for the same purposes, such as RR ‐ BLUP , SPCR , SPLS , ICR , PCR and PLS . The SICR method was found to have the most accurate prediction values.