Feng Rui, Zhang Heping
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Biostatistics. 2008 Jan;9(1):114-27. doi: 10.1093/biostatistics/kxm016. Epub 2007 May 22.
Statistical methods for linkage analysis are well established for both binary and quantitative traits. However, numerous diseases including cancer and psychiatric disorders are rated on discrete ordinal scales. To analyze pedigree data with ordinal traits, we recently proposed a latent variable model which has higher power to detect linkage using ordinal traits than methods using the dichotomized traits. The challenge with the latent variable model is that the likelihood is usually very complicated, and as a result, the computation of the likelihood ratio statistic is too intensive for large pedigrees. In this paper, we derive a computationally efficient score statistic based on the identity-by-decent sharing information between relatives. Using simulation studies, we examined the asymptotic distribution of the test statistic and the power of our proposed test under various levels of heritability. We compared the computing time as well as power of the score test with the likelihood ratio test. We then applied our method for the Collaborative Study on the Genetics of Alcoholism and performed a genome scan to map susceptibility genes for alcohol dependence. We found a strong linkage signal on chromosome 4.
用于连锁分析的统计方法在二元性状和数量性状方面都已成熟。然而,包括癌症和精神疾病在内的许多疾病是通过离散的有序量表进行评级的。为了分析具有有序性状的家系数据,我们最近提出了一种潜在变量模型,该模型在使用有序性状检测连锁方面比使用二分性状的方法具有更高的效能。潜在变量模型面临的挑战是,似然通常非常复杂,因此,对于大型家系而言,似然比统计量的计算过于繁重。在本文中,我们基于亲属之间通过同祖共享的信息推导出一种计算效率高的得分统计量。通过模拟研究,我们检验了检验统计量的渐近分布以及我们提出的检验在各种遗传度水平下的效能。我们将得分检验的计算时间和效能与似然比检验进行了比较。然后,我们将我们的方法应用于酒精中毒遗传学合作研究,并进行了全基因组扫描以绘制酒精依赖的易感基因图谱。我们在4号染色体上发现了一个强烈的连锁信号。