| Grant number: | 18/13964-1 |
| Support Opportunities: | Regular Research Grants |
| Start date: | November 01, 2018 |
| End date: | October 31, 2020 |
| Field of knowledge: | Physical Sciences and Mathematics - Probability and Statistics - Statistics |
| Principal Investigator: | Daiane Aparecida Zuanetti |
| Grantee: | Daiane Aparecida Zuanetti |
| Host Institution: | Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil |
Abstract
QTL mapping is an important tool for identifying regions in chromosomes which are relevant to explain a quantitative trait. It is a special case of regression model where an unknown number of missing (non-observable) covariates, usually large, are involved leading to a complex variable selection modeling. It is also a special case of mixture models where one of the major challenge is to estimate the best number of components. While several methods have been proposed for identifying main QTLs and estimating their main effects in the model, few efficient methodologies for estimating models with genetic interaction effect (epistasis) have been proposed and minimal focus has been given to the goodness-of-fit of the estimated model to the data. This research project is composed by two simultaneous sub-projects. One of them is focused on proposing a Bayesian method that selects and estimates a QTL mapping model with genetic interaction effects and the other is focused on presenting a Bayesian diagnostic analysis for models with QTL mapping's specificities. The motivation of this study is to identify QTLs associated with the blood pressure of F2 rats and check the adequacy of the fitted model. (AU)
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