Windelinckx An, Vlietinck Robert, Aerssens Jeroen, Beunen Gaston, Thomis Martine A I
Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium.
Twin Res Hum Genet. 2007 Dec;10(6):871-85. doi: 10.1375/twin.10.6.871.
Fine mapping of linkage peaks is one of the great challenges facing researchers who try to identify genes and genetic variants responsible for the variation in a certain trait or complex disease. Once the trait is linked to a certain chromosomal region, most studies use a candidate gene approach followed by a selection of polymorphisms within these genes, either based on their possibility to be functional, or based on the linkage disequilibrium between adjacent markers. For both candidate gene selection and SNP selection, several approaches have been described, and different software tools are available. However, mastering all these information sources and choosing between the different approaches can be difficult and time-consuming. Therefore, this article lists several of these in silico procedures, and the authors describe an empirical two-step fine mapping approach, in which candidate genes are prioritized using a bioinformatics approach (ENDEAVOUR), and the top genes are chosen for further SNP selection with a linkage disequilibrium based method (Tagger). The authors present the different actions that were applied within this approach on two previously identified linkage regions for muscle strength. This resulted in the selection of 331 polymorphisms located in 112 different candidate genes out of an initial set of 23,300 SNPs.
对连锁峰进行精细定位是试图鉴定导致某一性状或复杂疾病变异的基因及遗传变异的研究人员所面临的重大挑战之一。一旦该性状与某一染色体区域连锁，大多数研究采用候选基因法，随后基于这些基因的功能可能性或相邻标记间的连锁不平衡来选择基因内的多态性。对于候选基因选择和单核苷酸多态性（SNP）选择，已经描述了几种方法，并且有不同的软件工具可用。然而，掌握所有这些信息来源并在不同方法之间进行选择可能既困难又耗时。因此，本文列出了其中一些计算机模拟程序，作者描述了一种经验性的两步精细定位方法，其中使用生物信息学方法（ENDEAVOUR）对候选基因进行优先级排序，并选择排名靠前的基因，使用基于连锁不平衡的方法（Tagger）进行进一步的SNP选择。作者展示了在该方法中应用于之前确定的两个肌肉力量连锁区域的不同操作。这导致从最初的23300个SNP中选择了位于112个不同候选基因中的331个多态性。
