Genomic prediction through machine learning and neural networks for traits with epistasis.

ABSTRACT: Genomic wide selection (GWS) is one contributions of molecular genetics to breeding. Machine learning (ML) and artificial neural networks (ANN) methods are non-parameterized and can develop more accurate and parsimonious models for GWS analysis. Multivariate Adaptive Regression Splines (MARS) is considered one of the most flexible ML methods, automatically modeling nonlinearities and interactions of the predictor variables. This study aimed to evaluate and compare methods based on ANN, ML, including MARS, and G-BLUP through GWS. An F2 population formed by 1000 individuals and genotyped for 4010 SNP markers and twelve traits from a model considering epistatic effect, with QTL numbers ranging from eight to 480 and heritability ( h2 ) of 0.3, 0.5 or 0.8 were simulated. Variation in heritabili

SUBMITTER: Costa WGD 

PROVIDER: S-EPMC9547190 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature