Wu Long Yang, Sun Lei, Bull Shelley B
Samuel Lunenfeld Research Institute of Mount Sinai Hospital, Toronto, Canada.
Hum Hered. 2006;62(2):84-96. doi: 10.1159/000096096. Epub 2006 Oct 12.
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.
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).
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.
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.
背景/目的：在数量性状基因座（QTL）的全基因组连锁分析中，由于在基因组上对LOD分数进行最大化处理，当使用原始数据进行连锁定位和效应估计时，基因座特异性遗传力估计会产生偏差。采用严格的显著性水平来控制全基因组I型错误会增加正偏差。我们提出了多位点自抽样重采样估计器，用于在感兴趣的多个QTL处出现连锁峰的情况下减少偏差。
自抽样估计基于对原始数据集进行重复样本拆分。我们在具有0至5个QTL的核心家庭中进行了模拟研究，并将这些方法应用于弗雷明汉心脏研究（FHS）扩展家系中血压表型的全基因组分析。
与原始模拟样本中的简单估计相比，自抽样估计的偏差更小，均方误差也更小。在FHS家系中，自抽样得出的遗传力估计比原始样本小70%。
由于在初始研究中获得的效应估计相对于独立重复研究中预期的效应估计通常会被夸大，因此当样本量要求基于减少偏差的估计时，成功复制的可能性更大。
