Bayesian approach for QTLs mapping models, a special case of mixture and regression models

• Grant number: 18/13964-1
• Support type: Regular Research Grants
• Duration: November 01, 2018 - October 31, 2020
• Field of knowledge: Physical Sciences and Mathematics - Probability and Statistics
• Principal Investigator: Daiane Aparecida Zuanetti
• Grantee: Daiane Aparecida Zuanetti
• Home 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)

Scientific publications

(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)

ZUANETTI, DAIANE A.; PAVAN SOLER, JULIA M.; KRIEGER, JOSE E.; MILAN, LUIS A. Bayesian diagnostic analysis for quantitative trait loci mapping. STATISTICAL METHODS IN MEDICAL RESEARCH, NOV 2019. Web of Science Citations: 0.