Locus-specific heritability estimation via the bootstrap in linkage scans for quantitative trait loci.
OBJECTIVE
In genome-wide linkage analysis of quantitative trait loci (QTL), locus-specific heritability estimates are biased when the original data are used to both localize linkage and estimate effects, due to maximization of the LOD score over the genome. Positive bias is increased by adoption of stringent significance levels to control genome-wide type I error. We propose multi-locus bootstrap resampling estimators for bias reduction in the situation in which linkage peaks at more than one QTL are of interest.


METHODS
Bootstrap estimates were based on repeated sample splitting in the original dataset. We conducted simulation studies in nuclear families with 0 to 5 QTLs and applied the methods in a genome-wide analysis of a blood pressure phenotype in extended pedigrees from the Framingham Heart Study (FHS).


RESULTS
Compared to naïve estimates in the original simulation samples, bootstrap estimates had reduced bias and smaller mean squared error. In the FHS pedigrees, the bootstrap yielded heritability estimates as much as 70% smaller than in the original sample.


CONCLUSIONS
Because effect estimates obtained in an initial study are typically inflated relative to those expected in an independent replication study, successful replication will be more likely when sample size requirements are based on bias-reduced estimates.
Wu
Long Yang
LY
Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, Canada.
Sun
Lei
L
Bull
Shelley B
SB
D002318:Cardiovascular Diseases / Q000235:genetics; D003198:Computer Simulation / Q000706:statistics & numerical data; D008040:Genetic Linkage; D005819:Genetic Markers; D006801:Humans; D008957:Models, Genetic; D010375:Pedigree; D040641:Quantitative Trait Loci / Q000235:genetics*; D019655:Quantitative Trait, Heritable
D005819:Genetic Markers