Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA.
BMC Genet. 2005 Dec 30;6 Suppl 1(Suppl 1):S67. doi: 10.1186/1471-2156-6-S1-S67.
We present a new method for fine-mapping a disease susceptibility locus using a case-control design. The new method, termed the weighted average (WA) statistic, averages the Cochran-Armitage (CA) trend test statistic and the difference between the Hardy-Weinberg disequilibrium test statistic for cases and controls (the HWD trend). The main characteristics of the WA statistic are that it improves on the weaknesses, and maintains the strengths, of both the CA trend test and the HWD trend test. Data from three different populations in the Genetic Analysis Workshop 14 (GAW14) simulated dataset (Aipotu, Karangar, and Danacaa) were first subjected to model-free linkage analysis to find regions exhibiting linkage. Then, for fine-scale mapping, 140 SNPs within the significant linkage regions were analyzed with the WA test statistic on replicates of the three populations, both separately and combined. The regions that were significant in the multipoint linkage analysis were also significant in this fine-scale mapping. The most significant regions that were obtained using the WA statistic were regions in chromosome 3 (B03T3056-B03T3058, p-value < 1 x 10(-10)) and chromosome 9 (B09T8332-B09T8334, p-value 1 x 10(-6)). Based on the results of the simulated GAW14 data, the WA test statistic showed good performance and could narrow down the region containing the susceptibility locus. However, the strength of the signal depends on both the strength of the linkage disequilibrium and the heterozygosity of the linked marker.
我们提出了一种使用病例对照设计精细定位疾病易感性位点的新方法。这种新方法称为加权平均(WA)统计量,它平均了 Cochran-Armitage(CA)趋势检验统计量和病例与对照之间 Hardy-Weinberg 不平衡检验统计量(HWD 趋势)之间的差异。WA 统计量的主要特点是,它改进了 CA 趋势检验和 HWD 趋势检验的弱点,同时保留了它们的优势。遗传分析工作坊 14(GAW14)模拟数据集的三个不同人群(Aipotu、Karangar 和 Danacaa)的数据首先进行无模型连锁分析,以找到显示连锁的区域。然后,为了进行精细映射,在三个人群的重复样本中,使用 WA 检验统计量分析显著连锁区域内的 140 个 SNP,分别和组合分析。多点点连锁分析中显著的区域在这种精细映射中也是显著的。使用 WA 统计量获得的最显著区域是染色体 3(B03T3056-B03T3058,p 值<1x10(-10))和染色体 9(B09T8332-B09T8334,p 值 1x10(-6))上的区域。基于 GAW14 模拟数据的结果,WA 检验统计量表现出良好的性能,可以缩小包含易感基因座的区域。然而,信号的强度取决于连锁不平衡的强度和连锁标记的杂合性。