Application of Response Surface Methods To Determine Conditions for Optimal Genomic Prediction.

ABSTRACT: An epistatic genetic architecture can have a significant impact on prediction accuracies of genomic prediction (GP) methods. Machine learning methods predict traits comprised of epistatic genetic architectures more accurately than statistical methods based on additive mixed linear models. The differences between these types of GP methods suggest a diagnostic for revealing genetic architectures underlying traits of interest. In addition to genetic architecture, the performance of GP methods may be influenced by the sample size of the training population, the number of QTL, and the proportion of phenotypic variability due to genotypic variability (heritability). Possible values for these factors and the number of combinations of the factor levels that influence the performance of GP methods

SUBMITTER: Howard R 

PROVIDER: S-EPMC5592935 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature