Chiano M N, Clayton D G
MRC Biostatistics Unit, Institute of Public Health, University of Cambridge, United Kingdom.
Genet Epidemiol. 1998;15(2):135-46. doi: 10.1002/(SICI)1098-2272(1998)15:2<135::AID-GEPI3>3.0.CO;2-5.
In mapping diseases of complex aetiology, conventional linkage approaches narrow the location of the disease susceptibility locus to quite a large region so that candidate gene association studies are then necessary to further isolate these genes. However, even in the simplest scenario where the candidate locus is bi-allelic, two statistical tests with various correcting factors have been proposed: a chi-square 1 df test (counting chromosomes) which may be slightly conservative and a 2 df chi-square test (counting genotypes) which may lack power because of the extra degree of freedom. This paper introduces a better and more powerful alternative which turns out to be a compromise between the two existing statistical tests. The asymptotic distribution of this test statistic is determined and the efficacy of the 3 tests are compared under different genetic models by simulation.
在绘制复杂病因疾病的图谱时,传统的连锁分析方法会将疾病易感基因座的位置缩小到相当大的区域,因此需要进行候选基因关联研究来进一步分离这些基因。然而,即使在最简单的情况下,即候选基因座是双等位基因的,也已经提出了两种带有各种校正因子的统计检验方法:一种是自由度为1的卡方检验(计算染色体),这种方法可能略显保守;另一种是自由度为2的卡方检验(计算基因型),由于额外的自由度,这种方法可能缺乏检验效能。本文介绍了一种更好、更有效的替代方法,它实际上是两种现有统计检验方法之间的一种折衷。确定了该检验统计量的渐近分布,并通过模拟比较了这三种检验方法在不同遗传模型下的效能。