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Mixed Models in the Genetic Mapping of the Cardiovascular Risk Factors in Brazilian Families Using SNPs Data

Grant number: 09/04111-6
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): September 01, 2009
Effective date (End): December 31, 2010
Field of knowledge:Biological Sciences - Genetics - Quantitative Genetics
Principal Investigator:Júlia Maria Pavan Soler
Grantee:Mirian de Souza
Host Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

The study of complex diseases such as hypertention, diabetes and obesity is of great importance in medicine since these diseases affect many persons in our country and the world. It is believed that the pattern of variation of these diseases involves environmental and genetics components, and their possible interactions. For mapping of genes in human complex diseases, the biotechnology resources have increased very fast allowing that more dense markers maps are available. However, the development and use of analytical methodologies to analyze genetic data set have not followed on the same velocity. In this context, SNPs (Single Nucleotide Polimorphisms) platforms consisting of a million of loci represent a big analytic challenge. In this work, for mapping of genes associated with cardiovascular risk factors will be considered different mixed models formulations in the analysis of family data and SNPs platforms. To implement the proposed methodologies will be investigated computational facilities. The motivation for this project comes from the Baependi Heart Study (Fapesp Process 2007/58150-7) that aims to map genetic determinants related to cardiovascular diseases in the Brazilian population.

News published in Agência FAPESP Newsletter about the scholarship:
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Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
SOUZA, Mirian de. Mixed models in genetic mapping of the cardiovascular risk factors in brazilian families using SNPs data. 2012. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Matemática e Estatística (IME/SBI) São Paulo.

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