Abreu P C, Greenberg D A, Hodge S E
Division of Biostatistics, School of Public Health, Columbia University, N.Y., USA.
Am J Hum Genet. 1999 Sep;65(3):847-57. doi: 10.1086/302536.
Several methods have been proposed for linkage analysis of complex traits with unknown mode of inheritance. These methods include the LOD score maximized over disease models (MMLS) and the "nonparametric" linkage (NPL) statistic. In previous work, we evaluated the increase of type I error when maximizing over two or more genetic models, and we compared the power of MMLS to detect linkage, in a number of complex modes of inheritance, with analysis assuming the true model. In the present study, we compare MMLS and NPL directly. We simulated 100 data sets with 20 families each, using 26 generating models: (1) 4 intermediate models (penetrance of heterozygote between that of the two homozygotes); (2) 6 two-locus additive models; and (3) 16 two-locus heterogeneity models (admixture alpha = 1.0,.7,.5, and.3; alpha = 1.0 replicates simple Mendelian models). For LOD scores, we assumed dominant and recessive inheritance with 50% penetrance. We took the higher of the two maximum LOD scores and subtracted 0.3 to correct for multiple tests (MMLS-C). We compared expected maximum LOD scores and power, using MMLS-C and NPL as well as the true model. Since NPL uses only the affected family members, we also performed an affecteds-only analysis using MMLS-C. The MMLS-C was both uniformly more powerful than NPL for most cases we examined, except when linkage information was low, and close to the results for the true model under locus heterogeneity. We still found better power for the MMLS-C compared with NPL in affecteds-only analysis. The results show that use of two simple modes of inheritance at a fixed penetrance can have more power than NPL when the trait mode of inheritance is complex and when there is heterogeneity in the data set.
已经提出了几种用于对遗传模式未知的复杂性状进行连锁分析的方法。这些方法包括在疾病模型上最大化的LOD得分（MMLS）和“非参数”连锁（NPL）统计量。在之前的工作中，我们评估了在两个或更多遗传模型上最大化时I型错误的增加情况，并且我们在一些复杂的遗传模式下，将MMLS检测连锁的效能与假设真实模型的分析进行了比较。在本研究中，我们直接比较MMLS和NPL。我们使用26种生成模型模拟了100个数据集，每个数据集有20个家系：（1）4种中间模型（杂合子的外显率介于两种纯合子之间）；（2）6种双位点加性模型；以及（3）16种双位点异质性模型（混合系数α = 1.0、0.7、0.5和0.3；α = 1.0重复简单孟德尔模型）。对于LOD得分，我们假设显性和隐性遗传，外显率为50%。我们取两个最大LOD得分中的较高值并减去0.3以校正多重检验（MMLS-C）。我们使用MMLS-C和NPL以及真实模型比较了预期的最大LOD得分和效能。由于NPL仅使用受影响的家庭成员，我们还使用MMLS-C进行了仅受影响者的分析。在我们研究的大多数情况下，MMLS-C的效能均始终高于NPL，除了连锁信息较低时，并且在基因座异质性情况下接近真实模型的结果。在仅受影响者的分析中，我们仍然发现MMLS-C的效能比NPL更好。结果表明，当性状的遗传模式复杂且数据集中存在异质性时，在固定外显率下使用两种简单的遗传模式可能比NPL具有更高的效能。
