Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S143. doi: 10.1186/1471-2156-6-S1-S143.
Linkage analysis methods that incorporate etiological heterogeneity of complex diseases are likely to demonstrate greater power than traditional linkage analysis methods. Several such methods use covariates to discriminate between linked and unlinked pedigrees with respect to a certain disease locus. Here we apply several such methods including two mixture models, ordered subset analysis, and a conditional logistic model to genome scan data on the DSM-IV alcohol dependence phenotype on the Collaborative Studies on Genetics of Alcoholism families, and compare the results to traditional nonparametric linkage analysis. In general, there was little agreement among the various covariate-based linkage statistics. Linkage signals with empirical p-values less than 0.001 were detected on chromosomes 3, 4, 7, 10, and 12, with the highest peak occurring at the GABRB1 gene using the ecb21 covariate.
连锁分析方法结合复杂疾病的病因异质性，可能比传统的连锁分析方法具有更大的功效。有几种这样的方法使用协变量来区分与特定疾病位点相关联和不相关联的家系。在这里，我们应用了几种这样的方法，包括两种混合模型、有序子集分析和条件逻辑回归模型，对合作酒精成瘾遗传研究（COGA）家族的 DSM-IV 酒精依赖表型的基因组扫描数据进行分析，并将结果与传统的非参数连锁分析进行比较。总的来说，各种基于协变量的连锁统计量之间的一致性很小。使用 ecb21 协变量检测到染色体 3、4、7、10 和 12 上的连锁信号，其经验 p 值小于 0.001，使用 ecb21 协变量时，最高峰值出现在 GABRB1 基因上。
