Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.
Integrative Bayesian variable selection with gene-based informative priors for genome-wide association studies.
复制标题
综合贝叶斯变量选择与基于基因的信息先验，用于全基因组关联研究。
DOI：
10.1186/s12863-014-0130-7
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发表时间：
2014-12-10
期刊：
影响因子：
2.9
通讯作者：
Yang X
中科院分区：
文献类型：
--
作者：
Zhang X;Xue F;Liu H;Zhu D;Peng B;Wiemels JL;Yang X
全基因组关联研究（GWAS）通常设计用于使用单变量分析方法单独鉴定表型相关的单核苷酸多态性（SNP）。虽然GWAS为常见疾病的遗传风险提供了有价值的见解，但GWAS识别的遗传变异通常只占复杂疾病总遗传力的一小部分。为了解决这个“缺失的遗传性”问题，我们实施了一种称为综合贝叶斯变量选择（iBVS）的策略，该策略基于一个分层模型，该模型通过将基因相互关系视为一个网络来合并信息先验。它在这里被应用到模拟和真实的数据集。模拟研究表明，iBVS方法是有利的，在其性能与最高的AUC在变量选择和结果预测，相比逐步和LASSO为基础的策略。在一项麻风病例对照研究的分析中，iBVS选择了94个SNP作为预测因子，而LASSO选择了100个SNP。逐步回归产生了仅具有3个SNP的更简约的模型。预测结果表明，iBVS方法的性能与LASSO相当，但优于Stepwise策略。所提出的iBVS策略是全基因组关联研究的一种新颖有效的方法，其额外优势在于，与LASSO和其他惩罚回归方法不同，它为每个变量产生更多可解释的后验概率。本文的在线版本（doi：10.1186/s12863-014-0130-7）包含补充材料，可供授权用户使用。
Genome-wide Association Studies (GWAS) are typically designed to identify phenotype-associated single nucleotide polymorphisms (SNPs) individually using univariate analysis methods. Though providing valuable insights into genetic risks of common diseases, the genetic variants identified by GWAS generally account for only a small proportion of the total heritability for complex diseases. To solve this “missing heritability” problem, we implemented a strategy called integrative Bayesian Variable Selection (iBVS), which is based on a hierarchical model that incorporates an informative prior by considering the gene interrelationship as a network. It was applied here to both simulated and real data sets. Simulation studies indicated that the iBVS method was advantageous in its performance with highest AUC in both variable selection and outcome prediction, when compared to Stepwise and LASSO based strategies. In an analysis of a leprosy case–control study, iBVS selected 94 SNPs as predictors, while LASSO selected 100 SNPs. The Stepwise regression yielded a more parsimonious model with only 3 SNPs. The prediction results demonstrated that the iBVS method had comparable performance with that of LASSO, but better than Stepwise strategies. The proposed iBVS strategy is a novel and valid method for Genome-wide Association Studies, with the additional advantage in that it produces more interpretable posterior probabilities for each variable unlike LASSO and other penalized regression methods. The online version of this article (doi:10.1186/s12863-014-0130-7) contains supplementary material, which is available to authorized users.
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