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Quantitative trait prediction based on genetic marker-array data, a simulation study.

ABSTRACT:

Motivation

Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs that achieve genome-wide significance. Using mean square prediction error as the model metric, our simulation results suggest that by using the coefficient of determination (R(2)) value as a guideline to increase or reduce the number of SNPs included in the regression model, we can achieve better prediction quality than the standard approach. However, important parameters such as trait heritability, the approximate number of QTLs, etc. have to be determined from previous studies or have to be estimated accurately.

SUBMITTER: Yip WK

PROVIDER: S-EPMC3105484 | biostudies-literature | 2011 Mar

REPOSITORIES: biostudies-literature

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Quantitative trait prediction based on genetic marker-array data, a simulation study.

Yip Wai-Ki WK Lange Christoph C

Bioinformatics (Oxford, England) 20110131 6

<h4>Motivation</h4>Using simulation studies for quantitative trait loci (QTL), we evaluate the prediction quality of regression models that include as covariates single-nucleotide polymorphism (SNP) genetic markers which did not achieve genome-wide significance in the original genome-wide association study, but were among the SNPs with the smallest P-value for the selected association test. We compare the results of such regression models to the standard approach which is to include only SNPs th ...[more]

PMID: 21285022

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