Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA.
Genet Epidemiol. 2010 Jul;34(5):418-26. doi: 10.1002/gepi.20494.
In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases. One promising approach to incorporating prior knowledge from linkage scans, or other information, is to up- or down-weight P-values resulting from genetic association study in either a frequentist or Bayesian manner. As an alternative to these methods, we propose a fully Bayesian mixture model to incorporate previous knowledge into on-going association analysis. In this approach, both the data and previous information collectively inform the association analysis, in contrast to modifying the association results (P-values) to conform to the prior knowledge. By using a Bayesian framework, one has flexibility in modeling, and is able to comprehensively assess the impact of model specification on posterior inferences. We illustrate the use of this method through a genome-wide linkage study of colorectal cancer, and a genome-wide association study of colorectal polyps.
在过去的十年中，已经完成了许多针对复杂疾病的全基因组连锁和关联研究。目前仍存在一个关键问题，即如何最好地利用这些有潜在价值的信息，改进当前和未来遗传关联研究的研究设计和统计分析。由于复杂疾病的遗传效应相对较小，因此必须利用所有可用信息来理清复杂疾病的遗传结构。一种很有前途的方法是，以频率论或贝叶斯方式，增加或减少遗传关联研究中 P 值，从而利用连锁扫描或其他信息中的先验知识。作为这些方法的替代方法，我们提出了一个完全贝叶斯混合模型，将先前的知识纳入正在进行的关联分析中。在这种方法中，数据和先前的信息共同为关联分析提供信息，而不是修改关联结果（P 值）以符合先验知识。通过使用贝叶斯框架，可以灵活地进行建模，并能够全面评估模型规范对后验推断的影响。我们通过对结直肠癌的全基因组连锁研究和结直肠息肉的全基因组关联研究来说明该方法的使用。
