Whittemore Alice S, Halpern Jerry
Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California 94305, USA.
Genet Epidemiol. 2003 Jul;25(1):80-91. doi: 10.1002/gepi.10247.
We consider three tests for genetic association in data from nuclear families (the Family-Based Association Test (FBAT) test proposed by Rabinowitz and Laird ([2000] Hum. Hered. 50:211-223), a second test proposed by Rabinowitz ([2002] J. Am. Stat. Assoc. 97:742-758), and the Family Genotype Analysis Program (FGAP) nonfounder or partial score test proposed by Clayton ([1999] Am. J. Hum. Genet. 65:1170-1177) and Whittemore and Tu ([2000] Am. J. Hum. Genet. 66:1329-1340)). We show that each test statistic arises from the efficient score of the family data as the solution to a set of constraints on its null expectation. Moreover, the FBAT and Rabinowitz tests (but not the FGAP test) are locally the most powerful among all tests satisfying their constraints. We used simulations to examine how the three tests perform in situations when their assumptions are violated and the number of families is not huge. We found that the FBAT test tended to have less power than the other two tests, particularly when applied to families in whom all offspring were affected. The Rabinowitz and FGAP tests performed similarly, although the latter tended to extract more information from families containing one typed parent. While none of the tests showed good power to detect rare, recessively acting genes, the Rabinowitz test with a sample variance estimate performed particularly poorly in this case. However, the Rabinowitz test with a model-based variance had power comparable to that of the FGAP test, and more accurate type I error rates. We conclude that for the situations we considered, the Rabinowitz test with model-based variance has good power without forfeiting robustness against misspecification of parental genotype probabilities. However, its utility is limited by the lack of a simple algorithm to apply it to families with varying structures and phenotypes.
我们考虑了三种用于核心家庭数据中基因关联的检验方法（Rabinowitz和Laird [2000年，《人类遗传学》50:211 - 223]提出的基于家系的关联检验（FBAT），Rabinowitz [2002年，《美国统计协会杂志》97:742 - 758]提出的第二种检验方法，以及Clayton [1999年，《美国人类遗传学杂志》65:1170 - 1177]和Whittemore与Tu [2000年，《美国人类遗传学杂志》66:1329 - 1340]提出的家庭基因型分析程序（FGAP）非奠基者或部分得分检验）。我们表明，每个检验统计量都源自家庭数据的有效得分，它是一组关于其零期望的约束条件的解。此外，FBAT和Rabinowitz检验（但不包括FGAP检验）在满足其约束条件的所有检验中局部最具功效。我们通过模拟来研究这三种检验方法在其假设被违反且家庭数量不多的情况下的表现。我们发现，FBAT检验的功效往往低于其他两种检验，特别是当应用于所有后代都受影响的家庭时。Rabinowitz检验和FGAP检验的表现相似，尽管后者倾向于从包含一个分型亲本的家庭中提取更多信息。虽然没有一种检验方法在检测罕见的隐性作用基因方面表现出良好的功效，但采用样本方差估计的Rabinowitz检验在这种情况下表现特别差。然而，采用基于模型的方差的Rabinowitz检验具有与FGAP检验相当的功效，且I型错误率更准确。我们得出结论，对于我们所考虑的情况，采用基于模型的方差的Rabinowitz检验具有良好的功效，同时不会丧失对亲本基因型概率错误设定的稳健性。然而，它的实用性受到缺乏一种简单算法将其应用于具有不同结构和表型的家庭的限制。
