Resampling methods to reduce the selection bias in genetic effect estimation in genome-wide scans.
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.
Wu
Long Yang
LY
Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, Ontario, Canada. lwu@mshri.on.ca
Lee
Sophia S F
SS
Shi
Haijiang Steven
HS
Sun
Lei
L
Bull
Shelley B
SB
D002874:Chromosome Mapping; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D018895:Microsatellite Repeats / Q000235:genetics; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D015983:Selection Bias