rediction of the importance of auxiliary traits using computational intelligence and machine learning: A simulation stud

Autor(es)

da Silva Junior, Antonio Carlos ^[1] ; da Silva, Michele Jorge ^[1] ; Cruz, Cosme Damiao ^[1] ; Sant'Anna, Isabela de Castro ^[2] ; Silva, Gabi Nunes ^[3] ; Nascimento, Moyses ^[4] ; Azevedo, Camila Ferreira ^[4]

Afiliação do(s) autor(es)

^[1] Univ Fed Vicosa, Dept Gen Biol, Vicosa, MG - Brazil

^[2] Ctr Seringueira & Sistemas Agroflorestais, Inst Agron IAC, Sao Paulo - Brazil

^[3] Dept Math & Stat, R Rio Amazonas, Ji Parana, RO - Brazil

^[4] Univ Fed Vicosa, Dept Stat, Vicosa, MG - Brazil

• Tipo de documento: Artigo Científico
• Fonte: PLoS One; v. 16, n. 11 NOV 29 2021.
• Citações Web of Science: 0

Resumo

The present study evaluated the importance of auxiliary traits of a principal trait based on phenotypic information and previously known genetic structure using computational intelligence and machine learning to develop predictive tools for plant breeding. Data of an F-2 population represented by 500 individuals, obtained from a cross between contrasting homozygous parents, were simulated. Phenotypic traits were simulated based on previously established means and heritability estimates (30%, 50%, and 80%); traits were distributed in a genome with 10 linkage groups, considering two alleles per marker. Four different scenarios were considered. For the principal trait, heritability was 50%, and 40 control loci were distributed in five linkage groups. Another phenotypic control trait with the same complexity as the principal trait but without any genetic relationship with it and without pleiotropy or a factorial link between the control loci for both traits was simulated. These traits shared a large number of control loci with the principal trait, but could be distinguished by the differential action of the environment on them, as reflected in heritability estimates (30%, 50%, and 80%). The coefficient of determination were considered to evaluate the proposed methodologies. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the tested traits. Computational intelligence and machine learning were superior in extracting nonlinear information from model inputs and quantifying the relative contributions of phenotypic traits. The R-2 values ranged from 44.0% - 83.0% and 79.0% - 94.0%, for computational intelligence and machine learning, respectively. In conclusion, the relative contributions of auxiliary traits in different scenarios in plant breeding programs can be efficiently predicted using computational intelligence and machine learning. (AU)

• Processo FAPESP: 18/26408-0 - Diversidade genética e caracterização de descritores de seringueira
• Beneficiário: Isabela de Castro Sant'Anna
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado