Scan statistics to scan markers for susceptibility genes
Scan statistics to scan markers for susceptibility genes
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DOI：
10.1073/pnas.170179197
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发表时间：
2000-08-15
影响因子：
11.1
通讯作者：
Ott, J
中科院分区：
文献类型：
--
作者：
Hoh, J;Ott, J
应用扫描统计学来联合收割机组合在易感性基因座的基因组筛选中使用的多个连续遗传标记上的信息。该信息可以是例如同胞对的等位基因共享比例或一般小家族中的优势对数（lod）得分。我们专注于一个二分法的结果变量，例如，病例和对照个体或受影响的受影响与受影响的未受影响的兄弟姐妹，和合适的单标记统计。基于单标记统计的显著扫描统计代表易感基因存在的证据。对于一个给定的长度的扫描统计量，我们评估其显着性的Monte Carlo排列检验。比较不同长度的扫描统计量的P值，我们将观察到的最小P值作为我们感兴趣的统计量，并确定其总体显著性水平。我们将这种方法应用于自闭症家庭的基因组筛查。结果是信息丰富和令人惊讶的：发现了一个易感区域（全基因组显著性水平，P = 0.038），这是传统方法遗漏的。
Scan statistics are applied to combine information on multiple contiguous genetic markers used in a genome screen for susceptibility loci. This information may be, for example, allele sharing proportions for sib pairs or logarithm of odds (lod) scores in general small families. We focus on a dichotomous outcome variable, for example, case and control individuals or affected-affected versus affected-unaffected siblings, and suitable single-marker statistics. A significant scan statistic based on the single-marker statistics represents evidence of the presence of a susceptibility gene. For a given length of the scan statistic, we assess its significance by Monte Carlo permutation tests. Comparing P values for varying lengths of scan statistics, we treat the smallest observed P value as our statistic of interest and determine its overall significance level. We applied this method to a genome screen with autism families. The result was informative and surprising: A susceptibility region was found (genome-wide significance level, P = 0.038), which is missed with conventional approaches.