Learning genetic epistasis using Bayesian network scoring criteria.
Learning genetic epistasis using Bayesian network scoring criteria.
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DOI：
10.1186/1471-2105-12-89
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发表时间：
2011-03-31
影响因子：
3
通讯作者：
Visweswaran S
中科院分区：
文献类型：
--
作者：
Jiang X;Neapolitan RE;Barmada MM;Visweswaran S
基因-基因上位性相互作用可能在许多常见疾病的遗传基础中发挥重要作用。最近，机器学习和数据挖掘方法已经被开发用于从数据中学习上位关系。一个众所周知的组合方法，已成功地应用于检测上位性是多因素的简化（MDR）。Jiang等人创建了一种称为BNMBL的组合上位学习方法来学习贝叶斯网络（BN）上位模型。他们使用模拟数据集比较了BNMBL和MDR。这些数据集中的每一个都是从一个模型中生成的，该模型将两个SNP与疾病相关联，并包括18个不相关的SNP。对于每个数据集，BNMBL和MDR用于对所有2-SNP模型进行评分，并且BNMBL学习了显著更正确的模型。在真实的数据集中，我们通常不知道影响表型的SNP的数量。如果我们也对包含两个以上SNP的模型进行评分，BNMBL可能表现不佳。此外，还制定了许多其他BN评分标准。它们可以比BNMBL更好地检测上位相互作用。尽管BN是从数据中学习上位关系的有前途的工具，但当我们在不知道该模型中SNP数量的情况下尝试学习正确的模型时，在确定哪些评分标准最有效甚至效果最好之前，我们无法自信地在该领域使用它们。我们使用28，000个模拟数据集和一个真实的阿尔茨海默病GWAS数据集评估了22个BN评分标准的性能。我们的结果令人惊讶，因为具有大值超参数α的贝叶斯评分标准表现最好。该评分在使用模拟数据集回忆、使用模拟数据集检测最难检测的模型以及使用真实的阿尔茨海默病数据集证实先前结果方面优于其他BN评分标准和MDR。我们的结论是，代表上位性的相互作用，使用BN模型和评分，使用BN评分标准持有的希望，识别数据中的上位性遗传变异。特别是，具有大值超参数α的贝叶斯评分标准似乎比许多替代方案更有前途。
Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL. Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model. We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called α performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set. We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter α appears more promising than a number of alternatives.
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DOI：
10.1038/nrg2579
发表时间：
2009-06
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Cordell HJ
通讯作者：
Cordell HJ
影响因子：
3
作者：
Logsdon BA;Hoffman GE;Mezey JG
通讯作者：
Mezey JG
影响因子：
7.5
作者：
Friedman, N;Koller, D
通讯作者：
Koller, D
影响因子：
1.7
作者：
Friedman, N;Ninio, M;Pupko, T
通讯作者：
Pupko, T
影响因子：
5.8
作者：
Hahn, LW;Ritchie, MD;Moore, JH
通讯作者：
Moore, JH