Sung Heejong, Kim Yoonhee, Cai Juanliang, Cropp Cheryl D, Simpson Claire L, Li Qing, Perry Brian C, Sorant Alexa Jm, Bailey-Wilson Joan E, Wilson Alexander F
Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 333 Cassell Drive, Baltimore, MD 21224, USA.
BMC Proc. 2011 Nov 29;5 Suppl 9(Suppl 9):S15. doi: 10.1186/1753-6561-5-S9-S15.
Tiled regression is an approach designed to determine the set of independent genetic variants that contribute to the variation of a quantitative trait in the presence of many highly correlated variants. In this study, we evaluate the statistical properties of the tiled regression method using the Genetic Analysis Workshop 17 data in unrelated individuals for traits Q1, Q2, and Q4. To increase the power to detect rare variants, we use two methods to collapse rare variants and compare the results with those from the uncollapsed data. In addition, we compare the tiled regression method to traditional tests of association with and without collapsed rare variants. The results show that collapsing rare variants generally improves the power to detect associations regardless of method, although only variants with the largest allelic effects could be detected. However, for traditional simple linear regression, the average estimated type I error is dependent on the trait and varies by about three orders of magnitude. The estimated type I error rate is stable for tiled regression across traits.
分块回归是一种旨在确定在存在许多高度相关变异的情况下，导致数量性状变异的独立遗传变异集的方法。在本研究中，我们使用遗传分析研讨会17中无关个体的数据，针对性状Q1、Q2和Q4评估分块回归方法的统计特性。为了提高检测罕见变异的能力，我们使用两种方法对罕见变异进行合并，并将结果与未合并数据的结果进行比较。此外，我们将分块回归方法与传统的关联检验进行比较，包括有无合并罕见变异的情况。结果表明，无论采用何种方法，合并罕见变异通常都会提高检测关联的能力，尽管只能检测到具有最大等位基因效应的变异。然而，对于传统的简单线性回归，平均估计的I型错误率取决于性状，并且变化幅度约为三个数量级。分块回归的估计I型错误率在各性状间是稳定的。
