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Genomic prediction through machine learning and neural networks for traits with epistasis.
Costa, Weverton Gomes da; Celeri, Maurício de Oliveira; Barbosa, Ivan de Paiva; Silva, Gabi Nunes; Azevedo, Camila Ferreira; Borem, Aluizio; Nascimento, Moysés; Cruz, Cosme Damião.
Afiliação
  • Costa WGD; Department of General Biology, Bioinformatics Laboratory, Federal University of Viçosa, Viçosa, MG, Brazil.
  • Celeri MO; Department of Statistics, Laboratory of Computational Intelligence and Statistical Learning, Federal University of Viçosa - UFV, Viçosa, MG, Brazil.
  • Barbosa IP; Department of Agronomy, Federal University of Viçosa, Viçosa, MG, Brazil.
  • Silva GN; Department of Mathematics and Statistics, Federal University of Rondônia, Ji-Paraná Campus, RO, Brazil.
  • Azevedo CF; Department of Agronomy, Federal University of Viçosa, Viçosa, MG, Brazil.
  • Borem A; Department of Agronomy, Federal University of Viçosa, Viçosa, MG, Brazil.
  • Nascimento M; Department of Statistics, Laboratory of Computational Intelligence and Statistical Learning, Federal University of Viçosa - UFV, Viçosa, MG, Brazil.
  • Cruz CD; Department of General Biology, Bioinformatics Laboratory, Federal University of Viçosa, Viçosa, MG, Brazil.
Comput Struct Biotechnol J ; 20: 5490-5499, 2022.
Article em En | MEDLINE | ID: mdl-36249559
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 ( h 2 ) of 0.3, 0.5 or 0.8 were simulated. Variation in heritability and number of QTL impacts the performance of methods. About quantitative traits (40, 80, 120, 240, and 480 QTLs) was observed highest R2 to Radial Base Network (RBF) and G-BLUP, followed by Random Forest (RF), Bagging (BA), and Boosting (BO). RF and BA also showed better results for traits to h 2 of 0.3 with R 2 values 16.51% and 16.30%, respectively, while MARS methods showed better results for oligogenic traits with R 2 values ranging from 39,12 % to 43,20 % in h 2 of 0.5 and from 59.92% to 78,56% in h 2 of 0.8. Non-additive MARS methods also showed high R2 for traits with high heritability and 240 QTLs or more. ANN and ML methods are powerful tools to predict genetic values in traits with epistatic effect, for different degrees of heritability and QTL numbers.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Struct Biotechnol J Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Holanda