Período e Horário

13/10/2022 e 14/10/2022 das 8:00 às 11:30 - 14:00 às 18:00

Local

ESALQ / USP –  “Anfiteatro do Departamento de Genética”

Avenida Pádua Dias, 11 – São Dimas – Piracicaba/SP – CEP: 13418-900

 

Carga Horária

16 horas

Coordenação

Prof. Dr. José Baldin Pinheiro

E-mail: jbaldin@usp.br

Acessar CV online: Clique aqui

ESALQ/USP - Ciências Exatas

Fernanda Smaniotto Campion

E-mail: fernanda.campion@usp.br

Comissão Organizadora

Givanildo Rodrigues da Silva

E-mail: g.rodrigues@usp.br

Comissão Organizadora

Objetivo

The goal of this course is to introduce students to conventional and new prediction methods/paradigms than can be applied towards cultivar development considering the integration of multi-omics/layers of information (genomic, high-throughput phenotypic/remote sensing, weather, soil, etc.).Additionally, students will have experience in novel topics such as the utilization of sparse testing design for optimizing resources (seed availability, land, weather, reduce testing costs); and the integration of multiple omics for classifying cultivars with potential applications to study tolerance and resistance to biotic and abiotic factors.

Programa

Multi-Omics-Data-Assisted Genomic Prediction Incorporating Genotype-by-Environment Interaction

 

Bayesian Alphabet
— Challenges: The Curse of Dimensionality
— Brief review of Bayesian Inference
— Genome-Enabled Prediction (GBLUP and the alphabet)

 

Classical and Bayesian AMMI model for studying GxE via biplots
— Preliminary concepts. Stability, local adaptation, significant Genotype by environment interaction
— Two way tables
— Singular Value Decomposition
— Classical Model: Main and interaction effects
— Bayesian Model: Prior and posterior distributions
— Gibbs Sampler

 

GxE models using Environmental Covariates
— Preliminary concepts: GS in a nutshell
— Multi-environment trials
— challenges dealing with presence of significant GxE
— GxE model with and without environmental Covariates

 

Bayesian Factor Analytic Model and other Co-variances structures
— Preliminary concepts
— Usefulness of modeling co-variance structures
— Covariance between traits, covariance between environments
— Unstructured and Structured co-variances (Identity, Heterogeneity of variances, Factor analytic, etc.)
— Genomic Prediction models using structured co-variances

 

Multi trait Multi Environment Methods for Genomic Prediction
— Within environment prediction for multiple traits
— Prediction of single traits across environments
— Strategies for performing multi trait – multi environment predictions

 

Prediction of time-related traits

–Rice

–Soybean

 

Sparse testing designs

— Allocation of resources

— Genotypes in environments, training set composition, training set size, and training set size composition

 

Multi-omic Integration (genomic, soil type, high-throughput phenotypic, weather)

— Continuous response

— Categorical response

 

Número de vagas

120

Taxa de Inscrição

Inscrições disponiveis até 11/10/2022 – 15:00.

Taxa de Inscrição:

Estudantes: R$100,00

Profissionais: R$150,00