Banca de DEFESA: MARIA ANGÉLICA MARÇOLA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE: MARIA ANGÉLICA MARÇOLA
DATA: 29/07/2025
HORA: 13:30
LOCAL: DBI 01
TÍTULO:

APPLICATION OF GENOMIC AND PHENOMIC SELECTION FOR THE IDENTIFICATION OF SUPERIOR SOYBEAN PROGENIES

PALAVRAS-CHAVES:

BLUP; Genomic Selection; Phenomic Selection; NIR.

PÁGINAS: 69
GRANDE ÁREA: Ciências Agrárias
ÁREA: Agronomia
SUBÁREA: Fitotecnia
ESPECIALIDADE: Melhoramento Vegetal
RESUMO:

Genomic selection has been supporting breeding programs to increase genetic gains. However, innovative strategies such as phenomic selection—which is based on near-infrared (NIR) spectroscopy and records reflectance values through the chemical composition of samples to predict complex traits—have shown advantages when integrated into genomic prediction models.

With the aim of studying prediction models that consider molecular markers and NIR spectra, this study proposed to evaluate and compare different prediction models—genomic, phenomic, and the combination of both tools—to predict the genetic potential of soybean progenies, aiming at advancing inbred generations.

The data were obtained from the evaluation of progenies belonging to an F2:3 soybean population from the Federal University of Lavras (UFLA). The experiment was conducted at two locations in the state of Minas Gerais—Lavras and Ijaci—using a square lattice design.

The traits evaluated were: days to maturity (DTM), total number of pods (TNP), total number of seeds (TNG), and grain yield. Phenotypic data were analyzed using mixed models. All plots were genotyped to obtain molecular markers for estimating genomic estimated breeding values (GEBVs), and NIR spectra were collected from seed bulks obtained from both locations. After quality control, a total of 3,149 SNPs and 1,500 wavelengths were retained.

Predictive ability and coincidence index were calculated based on the predicted genetic values derived from the different strategies. Both individual trait analysis and multi-trait selection were performed using the Mulamba and Mock index.

The results highlight the potential of these strategies for both selection and discarding of progenies. Among the different prediction models studied, it can be inferred that the best predictive abilities were obtained with the genomic model and the models that integrated marker and spectral data into the relationship matrix, indicating that the incorporation of phenomic data obtained via spectroscopy enhances the predictive ability of the model.

MEMBROS DA BANCA:
Externo à Instituição - RAFAEL RAVANELI CHAGAS - GDM (Membro)
Externo ao Programa - JOSE MARIA VILLELA PADUA - DAG/ESAL (Membro)
Externo ao Programa - HELOISA OLIVEIRA DOS SANTOS - DAG/ESAL (Membro)
Interno - FLAVIA BARBOSA SILVA BOTELHO (Membro)
Externo ao Programa - FERNANDA APARECIDA CASTRO PEREIRA - DAG/ESAL (Suplente)
Externo à Instituição - ANDERSON DALLASTRA - UFT (Suplente)
Presidente - ADRIANO TEODORO BRUZI (Membro)