Sterrett Andrew, Wright Fred A
Colorado Prevention Center, 789 Sherman Street, Suite 200, Denver, Colorado 80203, USA.
Biometrics. 2007 Mar;63(1):33-40. doi: 10.1111/j.1541-0420.2006.00636.x.
Allelic loss is often part of a multistep process leading to tumorigenesis. Analysis of genomic markers highlights regions of elevated allelic loss, which in turn suggests a nearby tumor suppressor. Furthermore, pooling published analyses to combine evidence can increase the power to detect a tumor suppressor gene. If the pattern of loss for each tumor, or allelotype, is known, a stochastic model proposed by Newton et al. (1998, Statistics in Medicine 17, 1425-1445) can be used to analyze the correlated binary data. Many studies report only incomplete allelotypes, augmented with frequencies of allelic loss (FAL) at each marker, in which the number of informative tumors showing allelic loss is provided along with the number of informative tumors. We describe an extension of the allelotype model to handle FAL data, using a hidden Markov model or a normal approximation to compute the likelihood. The FAL model is illustrated using data from a study of colorectal cancer.
等位基因缺失通常是导致肿瘤发生的多步骤过程的一部分。对基因组标记的分析突出了等位基因缺失增加的区域，这反过来提示附近存在肿瘤抑制基因。此外，汇总已发表的分析以整合证据可以提高检测肿瘤抑制基因的能力。如果已知每个肿瘤的缺失模式，即等位基因型，那么牛顿等人（1998年，《医学统计学》17卷，1425 - 1445页）提出的一种随机模型可用于分析相关的二元数据。许多研究仅报告不完整的等位基因型，并补充每个标记处的等位基因缺失频率（FAL），其中给出了显示等位基因缺失的信息性肿瘤数量以及信息性肿瘤总数。我们描述了等位基因型模型的一种扩展，以处理FAL数据，使用隐马尔可夫模型或正态近似来计算似然性。使用来自一项结直肠癌研究的数据说明了FAL模型。
