Multivariate Adaptive Regression Splines Enhance Genomic Prediction of Non-Additive Traits
Maurício de Oliveira Celeri,
Weverton Gomes da Costa,
Ana Carolina Campana Nascimento,
Camila Ferreira Azevedo,
Cosme Damião Cruz,
Vitor Seiti Sagae,
Moysés Nascimento
Affiliations
Maurício de Oliveira Celeri
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Weverton Gomes da Costa
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Ana Carolina Campana Nascimento
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Camila Ferreira Azevedo
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Cosme Damião Cruz
Department of General Biology, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Vitor Seiti Sagae
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
Moysés Nascimento
Laboratory of Computational Intelligence and Statistical Learning (LICAE), Department of Statistics, Federal University of Viçosa, Av. Peter Henry Rolfs, Viçosa 36570-900, MG, Brazil
The present work used Multivariate Adaptive Regression Splines (MARS) for genomic prediction and to study the non-additive fraction present in a trait. To this end, 12 scenarios for an F2 population were simulated by combining three levels of broad-sense heritability (h2 = 0.3, 0.5, and 0.8) and four amounts of QTLs controlling the trait (8, 40, 80, and 120). All scenarios included non-additive effects due to dominance and additive–additive epistasis. The individuals’ genomic estimated breeding values (GEBV) were predicted via MARS and compared against the GBLUP method, whose models were additive, additive–dominant, and additive–epistatic. In addition, a linkage disequilibrium study between markers and QTL was performed. Linkage maps highlighted the QTL and molecular markers identified by the methodologies under study. MARS showed superior results to the GBLUP models regarding predictive ability for traits controlled by 8 loci, and results were similar for traits controlled by more than 40 loci. Moreover, the use of MARS, together with a linkage disequilibrium study of the trait, can help to elucidate the traits’ genetic architecture. Therefore, MARS showed potential to improve genomic prediction, especially for oligogenic traits or traits controlled by approximately 40 QTLs, while enabling the elucidation of the genetic architecture of traits.
