Sillanpää Mikko J, Bhattacharjee Madhuchhanda
Rolf Nevanlinna Institute, University of Helsinki, Finland.
Genetics. 2006 Nov;174(3):1597-611. doi: 10.1534/genetics.106.061275. Epub 2006 Oct 8.
A novel method for Bayesian analysis of genetic heterogeneity and multilocus association in random population samples is presented. The method is valid for quantitative and binary traits as well as for multiallelic markers. In the method, individuals are stochastically assigned into two etiological groups that can have both their own, and possibly different, subsets of trait-associated (disease-predisposing) loci or alleles. The method is favorable especially in situations when etiological models are stratified by the factors that are unknown or went unmeasured, that is, if genetic heterogeneity is due to, for example, unknown genes x environment or genes x gene interactions. Additionally, a heterogeneity structure for the phenotype does not need to follow the structure of the general population; it can have a distinct selection history. The performance of the method is illustrated with simulated example of genes x environment interaction (quantitative trait with loosely linked markers) and compared to the results of single-group analysis in the presence of missing data. Additionally, example analyses with previously analyzed cystic fibrosis and type 2 diabetes data sets (binary traits with closely linked markers) are presented. The implementation (written in WinBUGS) is freely available for research purposes from http://www.rni.helsinki.fi/ approximately mjs/.
本文提出了一种在随机人群样本中对遗传异质性和多位点关联进行贝叶斯分析的新方法。该方法适用于定量和二元性状以及多等位基因标记。在该方法中，个体被随机分配到两个病因组中，这两个病因组可能有各自不同的与性状相关（疾病易感性）的基因座或等位基因子集。该方法特别适用于病因模型按未知或未测量的因素分层的情况，即如果遗传异质性是由于例如未知的基因×环境或基因×基因相互作用。此外，表型的异质性结构不需要遵循总体人群的结构；它可以有独特的选择历史。通过基因×环境相互作用的模拟示例（具有松散连锁标记的定量性状）说明了该方法的性能，并与存在缺失数据时单组分析的结果进行了比较。此外，还给出了对先前分析的囊性纤维化和2型糖尿病数据集（具有紧密连锁标记的二元性状）的示例分析。该实现（用WinBUGS编写）可从http://www.rni.helsinki.fi/～mjs/免费获取用于研究目的。
