Division of Cancer Epidemiology and Genetics, National Cancer Institute, Executive Plaza South, Bethesda, Maryland, USA.
Genet Epidemiol. 2010 Jul;34(5):427-33. doi: 10.1002/gepi.20495.
Case-control genome-wide association studies provide a vast amount of genetic information that may be used to investigate secondary phenotypes. We study the situation in which the primary disease is rare and the secondary phenotype and genetic markers are dichotomous. An analysis of the association between a genetic marker and the secondary phenotype based on controls only (CO) is valid, whereas standard methods that also use cases result in biased estimates and highly inflated type I error if there is an interaction between the secondary phenotype and the genetic marker on the risk of the primary disease. Here we present an adaptively weighted (AW) method that combines the case and control data to study the association, while reducing to the CO analysis if there is strong evidence of an interaction. The possibility of such an interaction and the misleading results for standard methods, but not for the AW or CO approaches, are illustrated by data from a case-control study of colorectal adenoma. Simulations and asymptotic theory indicate that the AW method can reduce the mean square error for estimation with a prespecified SNP and increase the power to discover a new association in a genome-wide study, compared to CO analysis. Further experience with genome-wide studies is needed to determine when methods that assume no interaction gain precision and power, thereby can be recommended, and when methods such as the AW or CO approaches are needed to guard against the possibility of nonzero interactions.
病例对照全基因组关联研究提供了大量的遗传信息，可用于研究次要表型。我们研究的情况是主要疾病罕见，次要表型和遗传标记是二分的。仅基于对照对遗传标记与次要表型之间的关联进行分析（CO）是有效的，而如果次要表型和遗传标记与主要疾病的风险之间存在相互作用，则同时使用病例的标准方法会导致有偏估计和高度膨胀的 I 型错误。在这里，我们提出了一种自适应加权（AW）方法，该方法结合了病例和对照数据来研究关联，而如果存在强烈的相互作用证据，则将其简化为 CO 分析。通过结直肠腺瘤病例对照研究的数据说明了这种相互作用的可能性以及标准方法的误导性结果，但 AW 或 CO 方法则不然。模拟和渐近理论表明，与 CO 分析相比，AW 方法可以减少用于 SNP 估计的均方误差，并增加在全基因组研究中发现新关联的功效。需要进一步进行全基因组研究，以确定在什么情况下可以使用不假设存在相互作用的方法来提高精度和功效，从而可以推荐这些方法，以及在什么情况下需要使用 AW 或 CO 方法来防范非零相互作用的可能性。
