This article is part of the supplement: Genetic Analysis Workshop 14: Microsatellite and single-nucleotide polymorphism

Proceedings

Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans

Long Y Wu¹, Sophia SF Lee^1,2, Haijiang S Shi¹, Lei Sun^3,2 and Shelley B Bull^1,2*

• * Corresponding author: Shelley B Bull bull@mshri.on.ca

Author Affiliations

¹ Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario, Canada M5G 1X5

² Department of Public Health Sciences, University of Toronto, 12 Queen's Park Crescent West, Toronto, Ontario, Canada M5S 1A8

³ Hospital for Sick Children, 555 University Avenue, Toronto, Ontario, Canada M5G 1X8

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BMC Genetics 2005, 6(Suppl 1):S24 doi:10.1186/1471-2156-6-S1-S24

Published: 30 December 2005

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.