Evaluation of Boosting, SS-GBLUP, SS-BayesA Methods: Consideration of Genomic Data Imputation

The objective of current study was to investigate the role of genetic relationships between the training and validation populations and different genomic architecture with simulated genomic data imputation on performance of Boosting, single-step genomic best linear unbiased prediction (SS-GBLUP) and single-step BayesA (SS-BayesA) methods. For this purpose, genomic populations were simulated to reflect variations in number of QTL (۱۰, ۱۰۰ and ۱۰۰۰) for ۲۹ chromosomes. To simulate a real condition, we randomly masked markers with ۷۰% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software, and imputation accuracy was estimated. To estimate genomic breeding values, Boosting, SS-GBLUP and SS-BayesA methods were applied for original and imputed genotypes during G۱ and G۳ generations. According to results, GEBV accuracy was influenced by the relationships between the training and validation populations for ungenotyped animals higher than genotyped ones in both original and imputed genotypes. In both original and imputed genotypes, Boosting model showed the lowest accuracy for genotyped animals. SS-GBLUP method showed an obvious advantage over SS-BayesA and Boosting methods with the scenarios of high QTL.

Keywords:

Genomic Relationship , Genomic Selection , Machine Learning , Single- Step Method