Banca de DEFESA: INDALÉCIO CUNHA VIEIRA JÚNIOR

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE: INDALÉCIO CUNHA VIEIRA JÚNIOR
DATA: 28/02/2020
HORA: 08:30
LOCAL: Departamento de biologia - Anfiteatro DBI02
TÍTULO:

GUASSIAN MXITURE MODELS FOR GENOTYPE SELECTION IN COFFEE BEAN

PALAVRAS-CHAVES:

Mixture Model, Mixed Model, breeding, Genomic Selection

PÁGINAS: 160
GRANDE ÁREA: Ciências Agrárias
ÁREA: Agronomia
SUBÁREA: Fitotecnia
ESPECIALIDADE: Melhoramento Vegetal
RESUMO:

Coffee is one of the most important traded commodities in the world. It is well known that coffee bean yield is subjected to strong variation through the years in a phenomenon called biennial growth. This behavior has imposed great challenges on statistical analysis of coffee bean yield data. In these species genotypes show a differential biennial behavior due to its physiological response on environmental condition which suggests a mixture of subpopulations. Previous studies have tried to solve the problem, however they assume the presence of only one stochastic process generating the phenotypes. In the first paper it is proposed a finite mixture model to deal with the biennial pattern as hidden variable. Individual (per harvest) and repeated measures analyses were performed using conventional mixed models and Gaussian mixture mixed models. The results showed a great increase on parameter efficiency estimation and lead to greater genetic gain suggesting that that for analysis of C. arabica progenies exhibiting different biennial patterns, mixture mixed models are superior to traditional mixed models and to models that structure biennial effects using covariance matrices. On the second paper the gaussian mixed mixture model is extended for genomic prediction (GMGBLUP) and compared with a traditional genomic prediction model (GBLUP). The aim was to verify the prediction accuracy when the markers effects are corrected for bias of the biennial growth. For the real data set the GBLUP performed better in all harvests, however the simulated data results showed that the GMGBLUP is superior when the subpopulations means are contrasting and the mixture parameter are close to 0.5. The results suggest that GMGBLUP should be considered as an alternative for genomic prediction in coffea genus, especially for species with strong biennial growth behavior.

MEMBROS DA BANCA:
Presidente - FLAVIA MARIA AVELAR GONCALVES (Membro)
Interno - VINICIUS QUINTAO CARNEIRO (Suplente)
Interno - EVANDRO NOVAES (Membro)
Interno - JOAO CANDIDO DE SOUZA (Suplente)
Externo ao Programa - MARCIO BALESTRE - DES (Membro)
Externo à Instituição - ANDRÉ DOMINGHETTI FERREIRA - EMBRAPA (Membro)
Externo à Instituição - ALAN CARVALHO ANDRADE - WAGENINGEN (Membro)
Externo à Instituição - CESAR ELIAS BOTELHO - UFLA (Suplente)