Sengul H, Weeks D E, Feingold E
Department of Human Genetics, University of Pittsburgh, 130 DeSoto Street, Pittsburgh, PA 15261, USA.
Am J Hum Genet. 2001 Jul;69(1):179-90. doi: 10.1086/321264. Epub 2001 Jun 11.
We have compared the power of a large number of allele-sharing statistics for "nonparametric" linkage analysis with affected sibships. Our rationale was that there is an extensive literature comparing statistics for sibling pairs but that there has not been much guidance on how to choose statistics for studies that include sibships of various sizes. We concentrated on statistics that can be described as assigning scores to each identity-by-descent-sharing configuration that a pedigree might take on (Whittemore and Halpern 1994). We considered sibships of sizes two through five, 27 different genetic models, and varying recombination fractions between the marker and the trait locus. We tried to identify statistics whose power was robust over a wide variety of models. We found that the statistic that is probably used most often in such studies-S(all)-performs quite well, although it is not necessarily the best. We also found several other statistics (such as the R criterion, S(robdom), and the Sobel-and-Lange statistic C) that perform well in most situations, a few (such as S(-#geno) and the Feingold-and-Siegmund version of S(pairs)) that have high power only in very special situations, and a few (such as S(-#geno), the N criterion, and the Sobel-and-Lange statistic B) that seem to have low power for the majority of the trait models. For the most part, the same statistics performed well for all sibship sizes. We also used our results to give some suggestions regarding how to weight sibships of different sizes, in forming an overall statistic.
我们比较了大量用于同胞对“非参数”连锁分析的等位基因共享统计量的效能。我们的基本理由是,已有大量文献比较同胞对的统计量,但对于如何为包含不同大小同胞组的研究选择统计量却没有太多指导。我们专注于那些可以描述为对家系可能呈现的每种同源性共享构型进行评分的统计量(惠特莫尔和哈尔彭,1994年)。我们考虑了大小从二到五的同胞组、27种不同的遗传模型,以及标记与性状位点之间不同的重组率。我们试图找出在各种模型中效能都很稳健的统计量。我们发现,在此类研究中可能最常使用的统计量——S(全部)——表现相当不错,尽管它不一定是最好的。我们还发现了其他几个在大多数情况下表现良好的统计量(如R标准、S(随机)以及索贝尔 - 兰格统计量C),一些仅在非常特殊情况下具有高效能的统计量(如S( - #基因型)以及费因戈尔德 - 西格蒙德版本的S(对)),还有一些对于大多数性状模型似乎效能较低的统计量(如S( - #基因型)、N标准以及索贝尔 - 兰格统计量B)。在很大程度上,相同的统计量对所有大小的同胞组都表现良好。我们还利用我们的结果就如何在形成总体统计量时对不同大小的同胞组进行加权给出了一些建议。