Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models

Autor(es)

Ventorim Ferrao, Luis Felipe ^[1] ; Ferrao, Romario Gava ^[2] ; Gava Ferrao, Maria Amelia ^{[2, 3]} ; Fonseca, Aymbire ^{[2, 3]} ; Carbonetto, Peter ^{[4, 5]} ; Stephens, Matthew ^{[4, 6]} ; Franco Garcia, Antonio Augusto ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Dept Genet, Escola Super Agr Luiz de Queiroz, BR-13400970 Piracicaba, SP - Brazil

^[2] Assistencia Tecn & Extensa Rural Incaper, Inst Capixaba Pesquisa, BR-29052010 Vitoria, ES - Brazil

^[3] Empresa Brasileira Pesquisa Agr Embrapa Cafe, Brasilia, DF - Brazil

^[4] Univ Chicago, Dept Human Genet, Chicago, IL 60637 - USA

^[5] Univ Chicago, Res Comp Ctr, Chicago, IL 60637 - USA

^[6] Univ Chicago, Dept Stat, Chicago, IL 60637 - USA

• Tipo de documento: Artigo Científico
• Fonte: HEREDITY; v. 122, n. 3, p. 261-275, MAR 2019.
• Citações Web of Science: 0

Resumo

Genomic selection has been proposed as the standard method to predict breeding values in animal and plant breeding. Although some crops have benefited from this methodology, studies in Coffea are still emerging. To date, there have been no studies describing how well genomic prediction models work across populations and environments for different complex traits in coffee. Considering that predictive models are based on biological and statistical assumptions, it is expected that their performance vary depending on how well these assumptions align with the true genetic architecture of the phenotype. To investigate this, we used data from two recurrent selection populations of Coffea canephora, evaluated in two locations, and single nucleotide polymorphisms identified by Genotyping-by-Sequencing. In particular, we evaluated the performance of 13 statistical approaches to predict three important traits in the coffee-production of coffee beans, leaf rust incidence and yield of green beans. Analyses were performed for predictions within-environment, across locations and across populations to assess the reliability of genomic selection. Overall, differences in the prediction accuracy of the competing models were small, although the Bayesian methods showed a modest improvement over other methods, at the cost of more computation time. As expected, predictive accuracy for within-environment analysis, on average, were higher than predictions across locations and across populations. Our results support the potential of genomic selection to reshape traditional plant breeding schemes. In practice, we expect to increase the genetic gain per unit of time by reducing the length cycle of recurrent selection in coffee. (AU)

• Processo FAPESP: 14/20389-2 - Desenvolvimento de modelos genético-estatísticos para seleção genômica em Coffea canephora e outras espécies vegetais
• Beneficiário: Luís Felipe Ventorim Ferrão
• Modalidade de apoio: Bolsas no Brasil - Doutorado
• Processo FAPESP: 16/05127-7 - Desenvolvimento de modelos genético-estatísticos para seleção genômica em Coffea canephora e outras espécies vegetais
• Beneficiário: Luís Felipe Ventorim Ferrão
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Doutorado