Pirooznia Mehdi, Seifuddin Fayaz, Judy Jennifer, Mahon Pamela B, Potash James B, Zandi Peter P
School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Psychiatr Genet. 2012 Apr;22(2):55-61. doi: 10.1097/YPG.0b013e32834dc40d.
Mood disorders are highly heritable forms of major mental illness. A major breakthrough in elucidating the genetic architecture of mood disorders was anticipated with the advent of genome-wide association studies (GWAS). However, to date few susceptibility loci have been conclusively identified. The genetic etiology of mood disorders appears to be quite complex, and as a result, alternative approaches for analyzing GWAS data are needed. Recently, a polygenic scoring approach that captures the effects of alleles across multiple loci was successfully applied to the analysis of GWAS data in schizophrenia and bipolar disorder (BP). However, this method may be overly simplistic in its approach to the complexity of genetic effects. Data mining methods are available that may be applied to analyze the high dimensional data generated by GWAS of complex psychiatric disorders.
We sought to compare the performance of five data mining methods, namely, Bayesian networks, support vector machine, random forest, radial basis function network, and logistic regression, against the polygenic scoring approach in the analysis of GWAS data on BP. The different classification methods were trained on GWAS datasets from the Bipolar Genome Study (2191 cases with BP and 1434 controls) and their ability to accurately classify case/control status was tested on a GWAS dataset from the Wellcome Trust Case Control Consortium.
The performance of the classifiers in the test dataset was evaluated by comparing area under the receiver operating characteristic curves. Bayesian networks performed the best of all the data mining classifiers, but none of these did significantly better than the polygenic score approach. We further examined a subset of single-nucleotide polymorphisms (SNPs) in genes that are expressed in the brain, under the hypothesis that these might be most relevant to BP susceptibility, but all the classifiers performed worse with this reduced set of SNPs. The discriminative accuracy of all of these methods is unlikely to be of diagnostic or clinical utility at the present time. Further research is needed to develop strategies for selecting sets of SNPs likely to be relevant to disease susceptibility and to determine if other data mining classifiers that utilize other algorithms for inferring relationships among the sets of SNPs may perform better.
情绪障碍是主要精神疾病中具有高度遗传性的类型。随着全基因组关联研究（GWAS）的出现，人们预期在阐明情绪障碍的遗传结构方面会取得重大突破。然而，迄今为止，仅有少数易感性位点被明确鉴定。情绪障碍的遗传病因似乎相当复杂，因此，需要采用其他方法来分析GWAS数据。最近，一种捕获多个位点等位基因效应的多基因评分方法已成功应用于精神分裂症和双相情感障碍（BP）的GWAS数据分析。然而，该方法在处理遗传效应的复杂性时可能过于简单。现有的数据挖掘方法可用于分析复杂精神疾病GWAS产生的高维数据。
我们试图在分析BP的GWAS数据时，将五种数据挖掘方法（即贝叶斯网络、支持向量机、随机森林、径向基函数网络和逻辑回归）与多基因评分方法的性能进行比较。不同的分类方法在双相基因组研究的GWAS数据集（2191例BP患者和1434例对照）上进行训练，并在威康信托病例对照协会的GWAS数据集上测试其准确分类病例/对照状态的能力。
通过比较受试者工作特征曲线下的面积来评估测试数据集中分类器的性能。贝叶斯网络在所有数据挖掘分类器中表现最佳，但这些分类器均未显著优于多基因评分方法。我们进一步检查了在大脑中表达的基因中的单核苷酸多态性（SNP）子集，假设这些可能与BP易感性最相关，但使用这一减少的SNP集时，所有分类器的表现都更差。目前，所有这些方法的判别准确性不太可能具有诊断或临床实用性。需要进一步研究来制定选择可能与疾病易感性相关的SNP集的策略，并确定是否有其他利用其他算法推断SNP集之间关系的数据挖掘分类器可能表现得更好。
