Multilocus linkage analysis of affected sib pairs.
Multilocus linkage analysis of affected sib pairs.
复制标题
受影响的同胞对的多位点连锁分析。
DOI：
10.1159/000091010
复制
发表时间：
2005
期刊：
影响因子：
1.8
通讯作者：
Lo,Shaw-Hwa
中科院分区：
文献类型：
--
作者：
Ionita,Iuliana;Lo,Shaw-Hwa
目的：传统的影响同胞对分析方法只考虑边缘信息，不能准确地估计基因座上的连锁信息。我们描述了一种多位点连锁方法，它使用的边缘信息和来自几个疾病位点之间可能的相互作用的信息，从而增加了位点的意义与moderateeffects.Methods：我们的方法是基于一个统计，量化的连锁信息包含在一组标记。通过标记选择减少过程，我们筛选了一组多态性，并选择了一些似乎与疾病有关的结果：我们测试我们的方法对炎症性肠病（InfBD）的基因组扫描数据和模拟数据。在真实的数据，我们检测到6个已知的InfBD基因座;在模拟数据，我们得到的改进功率高达40%相比，一个传统的单基因座的方法。结论：我们广泛的模拟和真实的数据的结果表明，我们的方法是在一般情况下更强大的比单基因座的方法在检测疾病基因座负责复杂的性状。我们的方法的另一个优点是，它可以扩展到利用疾病位点和附近的标记之间的连锁和连锁不平衡。
Objective:The conventional affected sib pair methods evaluate the linkage information at a locus by considering only marginal information. We describe a multilocus linkage method that uses both the marginal information and information derived from the possible interactions among several disease loci, thereby increasing the significance of loci with modest effects.Methods:Our method is based on a statistic that quantifies the linkage information contained in a set of markers. By a marker selection-reduction process, we screen a set of polymorphisms and select a few that seem linked to disease.Results:We test our approach on genome scan data for inflammatory bowel disease (InfBD) and on simulated data. On real data we detect 6 of the 8 known InfBD loci; on simulated data we obtain improvements in power of up to 40% compared to a conventional single-locus method.Conclusion:Our extensive simulations and the results on real data show that our method is in general more powerful than single-locus methods in detecting disease loci responsible for complex traits. A further advantage of our approach is that it can be extended to make use of both the linkage and the linkage disequilibrium between disease loci and nearby markers.