BACKGROUND/AIMS: 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.
BACKGROUND/AIMS: 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.
Authors: Lei Sun; Apostolos Dimitromanolakis; Laura L Faye; Andrew D Paterson; Daryl Waggott; Shelley B Bull Journal: Hum Genet Date: 2011-01-19 Impact factor: 4.132
Authors: Hussam Al-Kateb; Andrew P Boright; Lucia Mirea; Xinlei Xie; Rinku Sutradhar; Alireza Mowjoodi; Bhupinder Bharaj; Michelle Liu; Jean M Bucksa; Valerie L Arends; Michael W Steffes; Patricia A Cleary; Wanjie Sun; John M Lachin; Paul S Thorner; Michael Ho; Amy Jayne McKnight; A Peter Maxwell; David A Savage; Kenneth K Kidd; Judith R Kidd; William C Speed; Trevor J Orchard; Rachel G Miller; Lei Sun; Shelley B Bull; Andrew D Paterson Journal: Diabetes Date: 2007-10-03 Impact factor: 9.461
Authors: Julia G Poirier; Laura L Faye; Apostolos Dimitromanolakis; Andrew D Paterson; Lei Sun; Shelley B Bull Journal: Genet Epidemiol Date: 2015-09-28 Impact factor: 2.135