...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>PLoS Genetics >Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data
【24h】

Bayesian network analysis incorporating genetic anchors complements conventional Mendelian randomization approaches for exploratory analysis of causal relationships in complex data

机译：贝叶斯网络分析纳入遗传锚点补充了传统的孟德利安随机化方法，用于复杂数据中因果关系的探索性分析

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Mendelian randomization (MR) implemented through instrumental variables analysis is an increasingly popular causal inference tool used in genetic epidemiology. But it can have limitations for evaluating simultaneous causal relationships in complex data sets that include, for example, multiple genetic predictors and multiple potential risk factors associated with the same genetic variant. Here we use real and simulated data to investigate Bayesian network analysis (BN) with the incorporation of directed arcs, representing genetic anchors, as an alternative approach. A Bayesian network describes the conditional dependencies/independencies of variables using a graphical model (a directed acyclic graph) with an accompanying joint probability. In real data, we found BN could be used to infer simultaneous causal relationships that confirmed the individual causal relationships suggested by bi-directional MR, while allowing for the existence of potential horizontal pleiotropy (that would violate MR assumptions). In simulated data, BN with two directional anchors (mimicking genetic instruments) had greater power for a fixed type 1 error than bi-directional MR, while BN with a single directional anchor performed better than or as well as bi-directional MR. Both BN and MR could be adversely affected by violations of their underlying assumptions (such as genetic confounding due to unmeasured horizontal pleiotropy). BN with no directional anchor generated inference that was no better than by chance, emphasizing the importance of directional anchors in BN (as in MR). Under highly pleiotropic simulated scenarios, BN outperformed both MR (and its recent extensions) and two recently-proposed alternative approaches: a multi-SNP mediation intersection-union test (SMUT) and a latent causal variable (LCV) test. We conclude that BN incorporating genetic anchors is a useful complementary method to conventional MR for exploring causal relationships in complex data sets such as those generated from modern “omics” technologies.
机译：通过仪器变量分析实施的孟德尔随机化（MR）是遗传流行病学中使用的越来越流行的因果推理工具。但是它可以具有评估复杂数据集中的同时因果关系的局限性，该复杂数据集中包括例如多个遗传预测器和与同一遗传变体相关的多个潜在风险因素。在这里，我们使用真实的和模拟数据来调查贝叶斯网络分析（BN），并将指导的弧形集合在一起，作为替代方法。贝叶斯网络描述了使用具有随附的关节概率的图形模型（一条定向的非循环图）的变量的条件依赖性/独立性。在实际数据中，我们发现BN可用于推断出同时因果关系，以确认双向MR的各个因果关系，同时允许存在潜在的水平胸膜功能（这将违反假设假设）。在模拟数据中，具有两个定向锚的BN（模拟遗传仪器）对于比双向MR的固定型误差具有更大的功率，而具有单个方向锚的BN比和双向MR更好地执行。 BN和MR都可能因违反其潜在的假设而受到不利影响（例如由于未测量的水平胸膜复制而导致的遗传混杂）。没有方向锚的BN产生的推理，这不比偶然更好，强调BN中定向锚的重要性（如MR）。在高度磷酸盐模拟场景下，BN优于MR（及其最近的延伸）和两个最近提出的替代方法：多SNP中介交叉口 - 联合试验（SMUT）和潜在因果变量（LCV）测试。我们得出结论，结合遗传锚的BN是传统MR的有用的互补方法，用于探索复杂数据集中的因果关系，例如由现代“OMIC”技术产生的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号