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Propensity score analysis in the Genetic Analysis Workshop 17 simulated data set on independent individuals by Chen Min Lin; Fah J Sathirapongsasuti; Berit Kerner is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

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Genetic Analysis Workshop 17 provided simulated phenotypes and exome sequence data for 697 independent individuals (209 case subjects and 488 control subjects). The disease liability in these data was influenced by multiple quantitative traits. We addressed the lack of statistical power in this small data set by limiting the genomic variants included in the study to those with potential disease-causing effect, thereby reducing the problem of multiple testing. After this adjustment, we could readily detect two common variants that were strongly associated with the quantitative trait Q1 (C13S523 and C13S522). However, we found no significant associations with the affected status or with any of the other quantitative traits, and the relationship between disease status and genomic variants remained obscure. To address the challenge of the multivariate phenotype, we used propensity scores to combine covariates with genetic risk factors into a single risk factor and created a new phenotype variable, the probability of being affected given the covariates. Using the propensity score as a quantitative trait in the case-control analysis, we again could identify the two common single-nucleoti

Who reads Propensity score analysis in the Genetic Analysis Workshop 17 simulated data set on independent individuals?

Author

Chen Min Lin; Fah J Sathirapongsasuti; Berit Kerner

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2007-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1753-6561)

Published

2011

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)