Accuracy of genomic prediction using RR-BLUP and Bayesian LASSO

We compared the accuracies of two genomic-selection prediction methods as affected by marker density and quantitative trait locus (QTL) number. Methods used to derive genomic estimated breeding values (GEBV) were random regression best linear unbiased prediction (RR–BLUP) and a Bayesian LASSO (Least Absolute Shrinkage and Selection Operator). In this study the genome comprised four chromosomes of 250 cM each. Also considering the number of markers 1000, 2000 and 5000 and the number of QTLs 4, 10, 20 and 40 and heritability of 5, 10 and 25 percent were compared.. In all scenarios Bayesian LASSO was more accurate than RR-BLUP, also increasing the number of QTLs, the evaluation accuracy decreases slightly which this reduction is greater in the lower heritability. The correlation between true breeding value and the genomic estimated breeding value in target generations applying RR-BLUP and Bayesian LASSO decreased from 0.918 to 0.807 and 0.933 to 0.847 respectively