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Development of statistic genetic models for genomic selection in Coffea canephora and other species

Grant number: 14/20389-2
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): January 01, 2015
Effective date (End): April 30, 2017
Field of knowledge:Agronomical Sciences - Agronomy
Cooperation agreement: Coordination of Improvement of Higher Education Personnel (CAPES)
Principal Investigator:Antonio Augusto Franco Garcia
Grantee:Luís Felipe Ventorim Ferrão
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Associated scholarship(s):16/05127-7 - Genomic prediction in Coffea canephora using polygenic modeling, BE.EP.DR


Genomic selection (GWS) may be defined as the simultaneous selection of hundreds or thousands of molecular markers (SNPs), which cover the genome of dense form, so that all the genes of a quantitative locus (QTL) are in linkage disequilibrium with one of these markers. Thus, the markers associated with QTLs, regardless of the effects magnitude, are used to explain the genetic variation of a quantitative trait. Simulations and empirical studies, show that this approach presents sufficient accuracy to guarantee success in breeding programs, when compared with traditional methods of phenotypic selection. To this end, one important step is the use of predictive models that contemplate the reliable estimation of the SNPs effects for quantitative traits of interest. Despite the relevance, studies in plants involving real data are still rare in the literature. Thus, this project aims to develop predictive models that can be applied in plant breeding programs. Real data from experimental populations of coffee (Coffea canephora) and SNPs identified by GBS (Genotyping-by-sequencing) will be used in developing these models using frequentist (random regression) and Bayesian approaches. The practical result of this study will be the development of statistic genetics models applied to studies of association between molecular and phenotypic data under different genetic assumptions. It is expected that these results will be useful in the selection of premature genotypes, maximizing the gain selection and accelerating the development of new varieties. In addition, there is potential for the conclusions of this study to be expanded to the scenario of other species of plants. (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. Web of Science Citations: 0.
GERARD, DAVID; FERRAO, LUIS FELIPE VENTORIM; FRANCO GARCIA, ANTONIO AUGUSTO; STEPHENS, MATTHEW. Genotyping Polyploids from Messy Sequencing Data. Genetics, v. 210, n. 3, p. 789-807, NOV 2018. Web of Science Citations: 12.
VENTORIM FERRAO, LUIS FELIPE; FERRAO, ROMARIO GAVA; GAVA FERRAO, MARIA AMELIA; FRANCISCO, AYMBIRE; FRANCO GARCIA, ANTONIO AUGUSTO. A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora. Tree Genetics & Genomes, v. 13, n. 5 OCT 2017. Web of Science Citations: 9.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
FERRÃO, Luís Felipe Ventorim. Development and application of statistical genetic methods to genomic prediction in Coffea canephora. 2017. Doctoral Thesis - Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz Piracicaba.

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