Abstract
Using the simulated data of Problem 2 for Genetic Analysis Workshop 14 (GAW14), we investigated the ability of three bootstrap-based resampling estimators (a shrinkage, an out-of-sample, and a weighted estimator) to reduce the selection bias for genetic effect estimation in genome-wide linkage scans. For the given marker density in the preliminary genome scans (7 cM for microsatellite and 3 cM for SNP), we found that the two sets of markers produce comparable results in terms of power to detect linkage, localization accuracy, and magnitude of test statistic at the peak location. At the locations detected in the scan, application of the three bootstrap-based estimators substantially reduced the upward selection bias in genetic effect estimation for both true and false positives. The relative effectiveness of the estimators depended on the true genetic effect size and the inherent power to detect it. The shrinkage estimator is recommended when the power to detect the disease locus is low. Otherwise, the weighted estimator is recommended.
Highlights
After a genetic marker or candidate gene has been identified from a genome-wide scan as a putative disease susceptibility locus, it is of interest to estimate the associated genetic effect on the related phenotype
Locusspecific effect estimates are subject to upward selection bias because of stringent test criteria adopted in genomewide scans
Sun and Bull [2] proposed three resampling-based estimators that can be applied to the original sample at the location where the maximum test statistic exceeds a genome-wide significance criterion
Summary
After a genetic marker or candidate gene has been identified from a genome-wide scan as a putative disease susceptibility locus, it is of interest to estimate the associated genetic effect on the related phenotype. Sun and Bull [2] proposed three resampling-based estimators that can be applied to the original sample at the location where the maximum test statistic exceeds a genome-wide significance criterion. They demonstrated effective bias reduction in analytic and simulation studies of a homogenous population with a single disease gene. In their simulation studies, they compared a catalog of resampling methods, including cross-validation and bootstrapping, and their results suggested that bootstrap methods perform best in terms of smaller mean squared error. We focused on the bootstrap method in the current study
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