在GAW15数据中进行连锁和关联分析以识别潜在的因果多态性。
Joint linkage and association analysis for identification of potentially causal polymorphisms in GAW15 data.
作者信息
Biernacka Joanna M, Charoen Pimphen, Cordell Heather J
机构信息
Institute of Human Genetics, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, NE1 3BZ, UK.
出版信息
BMC Proc. 2007;1 Suppl 1(Suppl 1):S36. doi: 10.1186/1753-6561-1-s1-s36. Epub 2007 Dec 18.
In a small chromosomal region, a number of polymorphisms may be both linked to and associated with a disease. Potentially directly associated causal loci may be distinguished from indirectly associated loci by determining which associations can explain the observed linkage signal. We apply methods for testing whether association with a particular polymorphism or haplotype can explain an observed linkage signal to the Genetic Analysis Workshop 15 simulated (Problem 3) data, to try to identify potentially causal polymorphisms. We compare the power of several methods for testing the null hypothesis that association with a particular variant can explain the observed linkage signal, and discuss scenarios under which the various methods may be advantageous.
在一个小的染色体区域，一些多态性可能既与一种疾病连锁又与该疾病相关。通过确定哪些关联能够解释观察到的连锁信号，可以将潜在的直接相关因果位点与间接相关位点区分开来。我们将用于检验与特定多态性或单倍型的关联是否能够解释观察到的连锁信号的方法应用于遗传分析研讨会15的模拟（问题3）数据，以试图识别潜在的因果多态性。我们比较了几种用于检验与特定变异的关联能够解释观察到的连锁信号这一零假设的方法的效能，并讨论了各种方法可能具有优势的情形。
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