Genomic prediction in Coffea canephora using polygenic modeling

• Grant number: 16/05127-7
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Effective date (Start): May 09, 2016
• Effective date (End): December 19, 2016
• Field of knowledge: Agronomical Sciences - Agronomy
• Principal Investigator: Antonio Augusto Franco Garcia
• Grantee: Luís Felipe Ventorim Ferrão
• Supervisor: Matthew Stephens
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Research place: University of Chicago, United States
• Associated to the scholarship: 14/20389-2 - Development of statistic genetic models for genomic selection in Coffea canephora and other species, BP.DR

Abstract

Genomic selection (GS) is defined as the simultaneous selection of hundreds or thousands of molecular markers (SNPs), so that the majority of quantitative trait locus (QTL) are in linkage disequilibrium with the markers. Thus, markers associated with QTLs, regardless of the effects magnitude, are used to explain the genetic variation of quantitative loci and guide the selection. Simulation and empirical results, have been shown that this approach presents sufficient accuracy to guarantee success in breeding programs. Nevertheless, the effective implementation depends of the ability to connect phenotypic and molecular informations into trustworthy predictive models. Bayesian framework has been used for this end, given the flexibility which the the genetic architecture may be modelled and examined. Hence, this project aims the investigation of predictive models, under a Bayesian perspective, that can be applied in plant breeding programs. More specifically in coffee, where these ideas are still in their infancy. Real data from experimental populations of \textit{Coffea canephora} and SNPs identified by GBS (Genotyping-by-sequencing) will be used to elucidate the genotype-phenotype relationship. As practical consequences it is expected that these results can be useful on the selection of premature genotypes, maximizing the gain selection and accelerating the development of new varieties. Under a theoretical view, the interaction elucidation and the identification of markers of common and specific effects across the environments or populations, constitute the main objective. Further, the way in which these predictive models will be formulated has potential to be expanded for crops with a similar experimental design, reinforcing the generalist character of our proposal. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

VENTORIM FERRAO, LUIS FELIPE; FERRAO, ROMARIO GAVA; GAVA FERRAO, MARIA AMELIA; FONSECA, AYMBIRE; CARBONETTO, PETER; STEPHENS, MATTHEW; FRANCO GARCIA, ANTONIO AUGUSTO. Accurate genomic prediction of Coffea canephora in multiple environments using whole-genome statistical models. HEREDITY, v. 122, n. 3, p. 261-275, MAR 2019. (14/20389-2, 16/05127-7)