Ehm M G, Kimmel M, Cottingham R W
Department of Statistics, Rice University, Houston, USA.
Am J Hum Genet. 1996 Jan;58(1):225-34.
As genetic maps become denser, the effect of laboratory typing errors becomes more serious. We review a general method for detecting errors in pedigree genotyping data that is a variant of the likelihood-ratio test statistic. It pinpoints individuals and loci with relatively unlikely genotypes. Power and significance studies using Monte Carlo methods are shown by using simulated data with pedigree structures similar to the CEPH pedigrees and a larger experimental pedigree used in the study of idiopathic dilated cardiomyopathy (DCM). The studies show the index detects errors for small values of theta with high power and an acceptable false positive rate. The method was also used to check for errors in DCM laboratory pedigree data and to estimate the error rate in CEPH-chromosome 6 data. The errors flagged by our method in the DCM pedigree were confirmed by the laboratory. The results are consistent with estimated false-positive and false-negative rates obtained using simulation.
随着遗传图谱变得更加密集，实验室分型错误的影响变得更加严重。我们回顾了一种检测系谱基因分型数据中错误的通用方法，该方法是似然比检验统计量的一种变体。它能找出具有相对不太可能基因型的个体和基因座。通过使用与CEPH系谱结构相似的模拟数据以及用于特发性扩张型心肌病（DCM）研究的一个更大的实验系谱，展示了使用蒙特卡罗方法进行的功效和显著性研究。研究表明，该指标对于小的θ值能以高功效检测错误，且误报率可接受。该方法还用于检查DCM实验室系谱数据中的错误，并估计CEPH 6号染色体数据中的错误率。我们的方法在DCM系谱中标记出的错误得到了实验室的证实。结果与使用模拟获得的估计假阳性和假阴性率一致。
