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TCGA: an tropical corn germplasm assembly for genomic prediction and high-throughput phenotyping studies

Grant number: 17/24327-0
Support type:Regular Research Grants
Duration: March 01, 2018 - August 31, 2020
Field of knowledge:Agronomical Sciences - Agronomy
Principal researcher:Roberto Fritsche Neto
Grantee:Roberto Fritsche Neto
Home Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil

Abstract

Genomic prediction (GS) studies using diversity panels are essential to identify genetic variations associated with traits of interest in maize. Unfortunately, most of these studies have been conducted on temperate germplasm and on a global germplasm collection in which tropical genotypes are under-represented. Nonetheless, a continuous effort has been directed to improving the accuracy of GS. While genotyping is currently a precise and efficient mechanized process, phenotyping is still laborious, low-throughput, and highly sensitive to environmental variations. Also, extreme shifts in the weather pattern due to climate change complicates the selection of superior genotypes with broad adaptability. In this context, adopting GS models that account for genotypes x environments reaction norms, crop growth models, and environmental covariates should increase the accuracy of genomic predictions. Thus, the objective of this project is to develop a diversity panel of tropical maize for genomic prediction studies that incorporate high-throughput phenotyping, plant growth models, and environmental covariables. For that, 361 tropical maize lines from ESALQ-USP, USP, IAC, and CIMMYT will be genotyped and phenotyped using traditional methods and multispectral imaging in eight environments (two locations, two years, and two seasons). With this data, several GS models will be tested and compared for prediction accuracy and selection coincidence. Besides the development of novel GS models and high-throughput phenotyping protocols in tropical maize, we will also organize, characterize, and publicize a panel of tropical maize lines (data and genetic material) to the scientific community that will serve as the benchmark for new studies of this nature in tropical maize. (AU)

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Scientific publications (5)
(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)
FRITSCHE-NETO, ROBERTO; GALLI, GIOVANNI; BORGES, KARINA LIMA REIS; COSTA-NETO, GERMANO; ALVES, FILIPE COUTO; SABADIN, FELIPE; LYRA, DANILO HOTTIS; MORAIS, PEDRO PATRIC PINHO; BRAATZ DE ANDRADE, LUCIANO ROGERIO; GRANATO, ITALO; et al. Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review. FRONTIERS IN PLANT SCIENCE, v. 12, . (17/24327-0, 13/24135-2)
YASSUE, RAFAEL MASSAHIRO; CARVALHO, HUMBERTO FANELLI; GEVARTOSKY, RAYSA; SABADIN, FELIPE; SOUZA, PEDRO HENRIQUE; BONATELLI, MARIA LETICIA; AZEVEDO, JOAO LUCIO; QUECINE, MARIA CAROLINA; FRITSCHE-NETO, ROBERTO. On the genetic architecture in a public tropical maize panel of the symbiosis between corn and plant growth-promoting bacteria aiming to improve plant resilience. MOLECULAR BREEDING, v. 41, n. 10, . (17/24327-0, 19/04697-2)
GALLI, GIOVANNI; ALVES, FILIPE COUTO; MOROSINI, JULIA SILVA; FRITSCHE-NETO, ROBERTO. On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids. PLoS One, v. 15, n. 2, . (13/24135-2, 17/24327-0, 17/25549-6)
COSTA-NETO, GERMANO; FRITSCHE-NETO, ROBERTO; CROSSA, JOSE. Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. HEREDITY, v. 126, n. 1, . (17/24327-0)
SABADIN, FELIPE; CARVALHO, HUMBERTO FANELLI; GALLI, GIOVANNI; FRITSCHE-NETO, ROBERTO. opulation-tailored mock genome enables genomic studies in species without a reference genom. Molecular Genetics and Genomics, v. 297, n. 1, . (17/24327-0)

Please report errors in scientific publications list by writing to: cdi@fapesp.br.