Lin Guohui, Wang Zhanyong, Wang Lusheng, Lau Yu-Lung, Yang Wanling
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.
Bioinformatics. 2008 Jan 1;24(1):86-93. doi: 10.1093/bioinformatics/btm552. Epub 2007 Nov 17.
With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary.
In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the chi(2) model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data.
随着人类基因组中大量单核苷酸多态性（SNP）信息的知晓以及高通量基因分型技术的快速发展，通过确定等位基因共享状态在连锁分析中识别连锁区域将发挥越来越重要的作用，而无需考虑重组率。
在本研究中，我们开发了一个基于规则的程序，该程序利用家庭成员间的等位基因共享信息来识别与所研究疾病相关的连锁区域。我们的程序使用高密度SNP基因型数据，并且在存在基因分型错误的情况下也能运行。它适用于有两个或更多兄弟姐妹的核心家庭结构。该程序以图形方式显示系谱中所有成员的等位基因共享状态，并根据用户指定的遗传模式和外显率识别可能与所研究疾病连锁的区域。基于卡方重组模型的广泛模拟表明，我们的程序能够以高灵敏度和准确性识别连锁区域。等位基因共享状态的图形显示有助于检测遗传模式和外显率的错误指定，以及错误标记或误诊。由于等位基因共享确定对高密度SNP基因分型数据具有更好的适应性，它可能代表连锁分析的未来方向。
