Wallenstein S, Hodge S E, Weston A
Department of Biomathematical Sciences, Mount Sinai School of Medicine, New York, New York 10029, USA.
Genet Epidemiol. 1998;15(2):173-81. doi: 10.1002/(SICI)1098-2272(1998)15:2<173::AID-GEPI5>3.0.CO;2-7.
Recently, there has been increased interest in evaluating extended haplotypes in p53 as risk factors for cancer. An allele-specific polymerase chain reaction (PCR) method, confirmed by restriction analysis, has been used to determine absolute extended haplotypes in diploid genomes. We describe statistical analyses for comparing cases and controls, or comparing different ethnic groups with respect to haplotypes composed of several biallelic loci, especially in the presence of other covariates. Tests based on cross-tabulating all possible genotypes by disease state can have limited power due to the large number of possible genotypes. Tests based simply on cross-tabulating all possible haplotypes by disease state cannot be extended to account for other variables measured on the individual. We propose imposing an assumption of additivity upon the haplotype-based analysis. This yields a logistic regression in which the outcome is case or control, and the predictor variables include the number of copies (0, 1, or 2) of each haplotype, as well as other explanatory variables. In a case-control study, the model can be constructed so that each coefficient gives the log odds ratio for disease for an individual with a single copy of the suspect haplotype and another copy of the most common haplotype, relative to an individual with two copies of the most common haplotype. We illustrate the method with published data on p53 and breast cancer. The method can also be applied to any polymorphic system, whether multiple alleles at a single locus or multiple haplotypes over several loci.
最近，人们对评估p53基因的扩展单倍型作为癌症风险因素的兴趣日益浓厚。一种通过限制性分析确认的等位基因特异性聚合酶链反应（PCR）方法已被用于确定二倍体基因组中的绝对扩展单倍型。我们描述了用于比较病例组和对照组，或比较由多个双等位基因位点组成的单倍型在不同种族群体中的统计分析方法，特别是在存在其他协变量的情况下。基于按疾病状态交叉列出所有可能基因型的检验，由于可能的基因型数量众多，其检验效能可能有限。仅基于按疾病状态交叉列出所有可能单倍型的检验无法扩展以考虑个体上测量的其他变量。我们建议在基于单倍型的分析中引入可加性假设。这会产生一个逻辑回归模型，其中结果是病例或对照，预测变量包括每个单倍型的拷贝数（0、1或2）以及其他解释变量。在病例对照研究中，可以构建模型，使得每个系数给出具有一个拷贝的可疑单倍型和另一个拷贝的最常见单倍型的个体相对于具有两个拷贝的最常见单倍型的个体患疾病的对数优势比。我们用关于p53基因和乳腺癌的已发表数据说明了该方法。该方法也可应用于任何多态系统，无论是单个位点的多个等位基因还是多个位点的多个单倍型。
