Difference between revisions of "User:PedroMarquesVidal"
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Chef de Clinique Adjoint | Chef de Clinique Adjoint | ||
| − | Institut Universitaire de Médecine Sociale et Préventive | + | Institut Universitaire de Médecine Sociale et Préventive, bureau 125 |
17, rue du Bugnon | 17, rue du Bugnon | ||
Revision as of 16:55, 25 February 2010
Contact
Pedro Marques-Vidal
Chef de Clinique Adjoint
Institut Universitaire de Médecine Sociale et Préventive, bureau 125
17, rue du Bugnon
1005 Lausanne
Phone: +41 21 314 72 65
Fax: +41 21 314 72 44
Email: Pedro-Manuel.Marques-Vidal@chuv.ch
Topic
TOPIC 1: The goal of the project is to perform a genome-wide association study that will potentially identify SNPs that are related to cardiovascular risk (derived from Framingham or SCORE risk equations) and compare the results between genders. Mathematical tools: The tool of choice for this project is logistic regression analysis. The student will learn the basics of regressing a given phenotype to a genotype and how this analysis is implemented on a computer to handle a large number of SNPs.
TOPIC 2: The goal of the project is to perform a genome-wide association study that will potentially identify SNPs that are related to risk of developping diabetes (derived from Framingham, ARIC and DESIR equations) and compare the results between genders. Mathematical tools: The tool of choice for this project is logistic regression analysis. The student will learn the basics of regressing a given phenotype to a genotype and how this analysis is implemented on a computer to handle a large number of SNPs.
TOPIC 3: The goal is to perform a genome-wide association study that will potentially identify SNPs that are related to liver abnormalities in obese subjects (NAFL or NASH) by a) performing a GWAS on liver abnormalities; b) search for interactions with body mass index or obesity of the potential candidate SNPs identified; c) perform a GWAS comparing obese subjects with and without hepatic abnormalities. The tool of choice for this project is logistic regression analysis and linear regression analysis. The student will learn the basics of regressing a given phenotype to a genotype and how this analysis is implemented on a computer to handle a large number of SNPs.
References
TOPIC 1: Silander K, Alanne M, Kristiansson K, Saarela O, Ripatti S, et al. (2008) Gender Differences in Genetic Risk Profiles for Cardiovascular Disease. PLoS ONE 3(10): e3615. doi:10.1371/journal.pone.0003615
TOPIC 2: Qi L, Rifai N, Hu FB. (2009) Interleukin-6 receptor gene, plasma C-reactive protein, and diabetes risk in women. Diabetes 58(1):275-8. Epub 2008 Oct 13.
TOPIC 3: Kotronen et al. (2009) A common variant in PNPLA3, which encodes adiponutrin, is associated with liver fat content in humans. Diabetologia 52:1056–1060
