Morris Richard W, Kaplan Norman L
Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA.
Genet Epidemiol. 2004 Feb;26(2):142-54. doi: 10.1002/gepi.10297.
Genotyping errors can create a problem for the analysis of case-parents data because some families will exhibit genotypes that are inconsistent with Mendelian inheritance. The problem with correcting Mendelian inconsistent genotype errors by regenotyping or removing families in which they occur is that the remaining unidentified genotype errors can produce excess type I (false positive) error for some family-based tests for association. We address this problem by developing a likelihood ratio test (LRT) for association in a case-parents design that incorporates nuisance parameters for a general genotype error model. We extend the likelihood approach for a single SNP to include short haplotypes consisting of 2 or 3 SNPs. The extension to haplotypes is based on assumptions of random mating, multiplicative penetrances, and at most a single genotype error per family. For a single SNP, we found, using Monte Carlo simulation, that type I error rate can be controlled for a number of genotype error models at different error rates. Simulation results suggest the same is true for 2 and 3 SNPs. In all cases, power declined with increasing genotyping error rates. In the absence of genotyping errors, power was similar whether nuisance parameters for genotype error were included in the LRT or not. The LRT developed here does not require prior specification of a particular model for genotype errors and it can be readily computed using the EM algorithm. Consequently, this test may be generally useful as a test of association with case-parents data in which Mendelian inconsistent families are observed.
基因分型错误会给病例-双亲数据的分析带来问题,因为一些家庭会出现与孟德尔遗传不一致的基因型。通过重新基因分型或去除出现错误的家庭来纠正孟德尔不一致基因型错误的问题在于,剩余未识别的基因型错误会在一些基于家庭的关联检验中产生过多的I型(假阳性)错误。我们通过开发一种病例-双亲设计中的关联似然比检验(LRT)来解决这个问题,该检验纳入了一般基因型错误模型的干扰参数。我们将单个单核苷酸多态性(SNP)的似然方法扩展到包括由2个或3个SNP组成的短单倍型。对单倍型的扩展基于随机交配、相乘外显率以及每个家庭最多一个基因型错误的假设。对于单个SNP,我们通过蒙特卡洛模拟发现,对于不同错误率的多种基因型错误模型,可以控制I型错误率。模拟结果表明,对于2个和3个SNP也是如此。在所有情况下,检验效能随着基因分型错误率的增加而下降。在没有基因分型错误的情况下,无论LRT中是否包含基因型错误的干扰参数,检验效能都相似。这里开发的LRT不需要事先指定特定的基因型错误模型,并且可以使用期望最大化(EM)算法轻松计算。因此,该检验对于观察到孟德尔不一致家庭的病例-双亲数据的关联检验可能普遍有用。