Pre-selection of most significant SNPS for the estimation of genomic breeding values

The availability of a large amount of SNP markers throughout the genome of different livestock
species offers the opportunity to estimate genomic breeding values (GEBVs). However, the
estimation of many effects in a data set of limited size represent a severe statistical problem. A preselection
of SNPS based on single regression may provide a reasonable compromise between
accuracy of results, number of independent variables to be considered and computing
requirements.
A total of 595 and 618 SNPS were pre-selected using a simple linear regression for each SNP, based
on phenotypes or polygenic EBVs, respectively, with an average distance of 9–10 cM between them.
Chromosome four had the largest frequency of selected SNPS. Average correlations between
GEBVs and TBVs were about 0.82 and 0.73 for the TRAINING generations when phenotypes or
polygenic EBVs were considered as dependent variable, whereas they tend to decrease to 0.66 and
0.54 for the PREDICTION generations. The pre-selection of SNPs using the phenotypes as
dependent variable together with a BLUP estimation of marker genotype effects using a variance
contribution of each marker equal to σ2a/nsnpsresulted in a remarkable accuracy of GEBV
estimation (0.77) in the PREDICTION generations.