Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S33. doi: 10.1186/1471-2156-6-S1-S33.
Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated.
Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12.
These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.
基于协变量的连锁分析使用条件逻辑模型作为 LODPAL 中的实现可以通过最小化疾病异质性来提高检测连锁的能力。然而，分析的每个额外协变量都会增加连锁检验的自由度，因此也会增加 I 型错误率。使用倾向评分（PS）已被证明可以在模拟研究中一致提高检测连锁的统计能力。定义为给定观察到的协变量数据的情况下受到影响的条件概率，PS 将多个协变量压缩成一个单一变量。本研究评估了 PS 在对来自酒精遗传合作研究的微卫星标记数据进行全基因组连锁分析中检测连锁证据的性能。分析方法包括无协变量的非参数连锁分析、一次一个协变量，包括多个 PS 定义，以及同时使用多个与 PS 定义对应的协变量。计算了几个 PS 定义，每个定义都有越来越多的协变量，最多可达 5 个。为了考虑潜在的 I 型错误率膨胀，计算了基于置换的 p 值。
结果表明，使用单个协变量不一定会增加检测连锁的能力。然而，与同时使用所有协变量相比，使用 PS 可以导致增加。具体来说，PS3 结合了访谈时的年龄、性别和吸烟状况，导致了最多的显著标记被识别。所有方法都一致地确定了几个染色体区域为显著区域，包括染色体 2、6、7 和 12 上的基因座。
这些结果表明，在复杂疾病（如酒精中毒）中使用倾向评分可以增加检测连锁的能力，特别是当可以使用多个重要的协变量来预测风险并从而最小化连锁异质性时。然而，由于 PS 是作为受到影响的条件概率来计算的，因此它需要在受影响和未受影响的个体上都存在观察到的协变量数据，这在实际数据集上可能并不总是可用的。
