Manichaikul Ani, Dupuis Josée, Sen Saunak, Broman Karl W
Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205-2179, USA.
Genetics. 2006 Sep;174(1):481-9. doi: 10.1534/genetics.106.061549. Epub 2006 Jun 18.
The aim of many genetic studies is to locate the genomic regions (called quantitative trait loci, QTL) that contribute to variation in a quantitative trait (such as body weight). Confidence intervals for the locations of QTL are particularly important for the design of further experiments to identify the gene or genes responsible for the effect. Likelihood support intervals are the most widely used method to obtain confidence intervals for QTL location, but the nonparametric bootstrap has also been recommended. Through extensive computer simulation, we show that bootstrap confidence intervals behave poorly and so should not be used in this context. The profile likelihood (or LOD curve) for QTL location has a tendency to peak at genetic markers, and so the distribution of the maximum-likelihood estimate (MLE) of QTL location has the unusual feature of point masses at genetic markers; this contributes to the poor behavior of the bootstrap. Likelihood support intervals and approximate Bayes credible intervals, on the other hand, are shown to behave appropriately.
许多基因研究的目的是定位基因组区域（称为数量性状位点，QTL），这些区域导致数量性状（如体重）产生变异。QTL位置的置信区间对于设计进一步实验以鉴定造成该效应的一个或多个基因尤为重要。似然支持区间是获得QTL位置置信区间最广泛使用的方法，但也有人推荐使用非参数自助法。通过广泛的计算机模拟，我们表明自助置信区间表现不佳，因此不应在此背景下使用。QTL位置的轮廓似然（或LOD曲线）倾向于在遗传标记处达到峰值，因此QTL位置的最大似然估计（MLE）的分布在遗传标记处具有点质量这一不寻常特征；这导致了自助法的不良表现。另一方面，似然支持区间和近似贝叶斯可信区间表现得当。
