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连锁的全局检验。

Global tests for linkage.

作者信息

el Galta Rachid, van Houwelingen Hans C, Houwing-Duistermaat Jeanine J

机构信息

Biometrics department, GCI, Organon, BH Oss, The Netherlands.

出版信息

Biom J. 2009 Feb;51(1):70-83. doi: 10.1002/bimj.200810492.

Abstract

To test for global linkage along a genome or in a chromosomal region, the maximum over the marker locations of mean alleles shared identical by descent of affected relative pairs, Z(max), can be used. Feingold et al. (1993) derived a Gaussian approximation to the distribution of the Z(max). As an alternative we propose to sum over the observed marker locations along the chromosomal region of interest. Two test statistics can be derived. (1) The likelihood ratio statistic (LR) and (2) the corresponding score statistic. The score statistic appears to be the average mean IBD over all available marker locations. The null distribution of the LR and score tests are asymptotically a 50: 50 mixture of chi-square distributions of null and one degree of freedom and a normal distribution, respectively.We compared empirically the type I error and power of these two new test statistics and Z(max) along a chromosome and in a candidate region. Two models were considered, namely (1) one disease locus and (2) two disease loci. The new test statistics appeared to have reasonable type I error. Along the chromosome, for both models we concluded that for very small effect sizes, the score test has slightly more power than the other test statistics. For large effect sizes, the likelihood ratio statistic was comparable to and sometimes performed better than Z(max) and both test statistics performed much better than the score test. For candidate regions of about 30 cM, all test statistics were comparable when only one disease-locus existed and the score and likelihood ratio statistics had somewhat better power than Z(max) when two disease loci existed.

摘要

为了检测全基因组或染色体区域的整体连锁情况,可以使用受影响亲属对中通过系谱共享的平均等位基因在标记位置上的最大值Z(max)。Feingold等人(1993年)推导了Z(max)分布的高斯近似值。作为一种替代方法,我们建议对感兴趣的染色体区域内观察到的标记位置进行求和。可以推导出两个检验统计量。(1)似然比统计量(LR)和(2)相应的得分统计量。得分统计量似乎是所有可用标记位置上平均同源等位基因的平均值。LR检验和得分检验的零分布分别渐近于自由度为零和一的卡方分布与正态分布的50:50混合。我们通过实证比较了这两个新检验统计量以及Z(max)在染色体和候选区域上的I型错误和检验效能。考虑了两个模型,即(1)一个疾病位点和(2)两个疾病位点。新的检验统计量似乎具有合理的I型错误。在染色体上,对于这两个模型,我们得出结论,对于非常小的效应大小,得分检验的检验效能略高于其他检验统计量。对于大的效应大小,似然比统计量与Z(max)相当,有时表现优于Z(max),并且两个检验统计量的表现都比得分检验好得多。对于约30 cM的候选区域,当只存在一个疾病位点时,所有检验统计量相当,而当存在两个疾病位点时,得分和似然比统计量的检验效能比Z(max)略好。

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