Wang T, Elston R C
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH 44106, USA.
Ann Hum Genet. 2007 Jan;71(Pt 1):96-106. doi: 10.1111/j.1469-1809.2006.00303.x.
Multivariate linkage analysis has been suggested for the analysis of correlated traits, such as blood pressure (BP) and body mass index (BMI), because it may offer greater power and provide clearer results than univariate analyses. Currently, the most commonly used multivariate linkage methods are extensions of the univariate variance component model. One concern about those methods is their inherent sensitivity to the assumption of multivariate normality which cannot be easily guaranteed in practice. Another problem possibly related to all multivariate linkage analysis methods is the difficulty in interpreting nominal p-values, because the asymptotic distribution of the test statistic has not been well characterized. Here we propose a regression-based multivariate linkage method in which a robust score statistic is used to detect linkage. The p-value of the statistic is evaluated by a simple and rapid simulation procedure. Theoretically, this method can be used for any number and type of traits and for general pedigree data. We apply this approach to a genome linkage analysis of blood pressure and body mass index data from the Beaver Dam Eye Study.
多变量连锁分析已被建议用于分析相关性状，如血压（BP）和体重指数（BMI），因为它可能比单变量分析提供更大的功效并给出更清晰的结果。目前，最常用的多变量连锁方法是单变量方差成分模型的扩展。对这些方法的一个担忧是它们对多变量正态性假设的固有敏感性，而这在实际中不易保证。另一个可能与所有多变量连锁分析方法相关的问题是难以解释名义p值，因为检验统计量的渐近分布尚未得到很好的刻画。在此，我们提出一种基于回归的多变量连锁方法，其中使用稳健得分统计量来检测连锁。该统计量的p值通过简单快速的模拟程序进行评估。从理论上讲，此方法可用于任何数量和类型的性状以及一般的家系数据。我们将这种方法应用于来自比弗迪姆眼研究的血压和体重指数数据的基因组连锁分析。
