Meta-analysis of genome searches
Meta-analysis of genome searches
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DOI：
10.1046/j.1469-1809.1999.6330263.x
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发表时间：
1999-05-01
影响因子：
1.9
通讯作者：
Lewis, CN
中科院分区：
文献类型：
--
作者：
Wise, LH;Lanchbury, JS;Lewis, CN
我们已经开发了一种对基因组扫描进行荟萃分析的方法，它允许从已发表的结果中系统地整合数据。基因组搜索荟萃分析方法(GSMA)使用非参数排序方法来识别显示持续增加的共享统计数据或LOD分数的遗传区域。GSMA。根据每次扫描获得的Lod分数或p值对遗传区域进行排序。假设排名是随机分配的，则将各研究的总和排名与其概率分布进行比较。GSMA可以确认原始基因组扫描中突出显示的区域的证据，并识别在任何扫描中都没有达到显著意义的新区域。在这篇文章中，GSMA被应用于多发性硬化症的四个基因组筛查和11个自身免疫性疾病的筛查。GSMA适合于具有不同家系确定、标记的研究。统计分析方法。该方法增加了在临床同质数据集中检测个体联系的能力，并具有检测临床不同疾病的易感基因的潜力，这些疾病显示了共同致病途径的参与。
We hare developed a method for meta-analysis of genome scans which allows systematic integration of data from published results. The Genome Search Meta-analysis method (GSMA) uses a non-parametric ranking method to identify genetic regions that show consistently increased sharing statistics or lod scores. The GSMA. ranks genetic regions according to the lod score or p-value achieved in each scan. The summed rank across studies is compared to its probability distribution assuming ranks are randomly assigned. The GSMA can confirm evidence for regions highlighted in the original genome scans, and identify novel regions, which did not reach significance in any scan. In this paper, the GSMA was applied to four genome screens in multiple sclerosis and across 11 screens from autoimmune disorders. The GSMA is appropriate for studies with different family ascertainment, markers. and statistical analysis methods. The method increases the power to detect individual linkages in a clinically homogeneous dataset and has the potential to detect susceptibility loci in clinically distinct diseases which show involvement of common pathogenetic pathways.