Greenberg D A
Am J Hum Genet. 1984 Jan;36(1):167-76.
We tested the power of a segregation analysis method (first proposed by Elandt-Johnson) to distinguish between single-locus and two-locus models, with and without environmentally caused reduced penetrance. We also looked at the effect of ascertainment probability on the analysis and at the proband-conditioned ascertainment correction proposed by Cannings and Thompson. We found that: (1) the segregation analysis has sufficient power to distinguish between the fully-penetrant double-recessive (RR) model and the fully-penetrant single-locus dominant and recessive models; (2) the method can also distinguish fairly well between the dominant-recessive (DR) and RR models, even when one does not take into account the population prevalence; (3) the method has much less power to distinguish between the fully-penetrant RR model and the single-locus models with reduced penetrance; (4) when environmental penetrance is taken account of in the analysis, the power of the method to distinguish between the one- and two-locus models improved substantially; (5) the estimates of ascertainment probability, pi, were robust, regardless of the model under which the data were generated; and (6) the Cannings-Thompson approach to ascertainment correction worked well only when the pi used to generate the data was less than .1.
我们测试了一种分离分析方法(最初由埃兰特 - 约翰逊提出)区分单基因座和双基因座模型的能力,这些模型存在或不存在由环境导致的降低的外显率。我们还研究了确定概率对分析的影响以及坎宁斯和汤普森提出的先证者条件确定校正。我们发现:(1)分离分析有足够的能力区分完全外显的双隐性(RR)模型与完全外显的单基因座显性和隐性模型;(2)即使不考虑群体患病率,该方法也能较好地区分显性 - 隐性(DR)模型和RR模型;(3)该方法区分完全外显的RR模型和外显率降低的单基因座模型的能力要弱得多;(4)在分析中考虑环境外显率时,该方法区分单基因座和双基因座模型的能力显著提高;(5)确定概率π的估计值是稳健的,无论生成数据所依据的模型如何;(6)坎宁斯 - 汤普森的确定校正方法仅在用于生成数据的π小于0.1时效果良好。