Classifying subjects into clinically and biologically homogeneous subgroups will facilitate the understanding of disease pathophysiology and development of targeted prevention and intervention strategies. Traditionally, disease subtyping is based on clinical characteristics alone, but subtypes identified by such an approach may not conform exactly to the underlying biological mechanisms. Very few studies have integrated genomic profiles (e.g., those from GWASs) with clinical symptoms for disease subtyping. Here we proposed an analytic framework capable of finding complex diseases subgroups by leveraging both GWAS-predicted gene expression levels and clinical data by a multi-view bicluster analysis. This approach connects SNPs to genes via their effects on expression, so the analysis is more biologically relevant and interpretable than a pure SNP-based analysis. Transcriptome of different tissues can also be readily modeled. We also proposed various evaluation metrics for assessing clustering performance. Our framework was able to subtype schizophrenia subjects into diverse subgroups with different prognosis and treatment response. We also applied the framework to the Northern Finland Birth Cohort (NFBC) 1966 dataset and identified high and low cardiometabolic risk subgroups in a gender-stratified analysis. The prediction strength by cross-validation was generally greater than 80%, suggesting good stability of the clustering model. Our results suggest a more data-driven and biologically informed approach to defining metabolic syndrome and subtyping psychiatric disorders. Moreover, we found that the genes "blindly" selected by the algorithm are significantly enriched for known susceptibility genes discovered in GWASs of schizophrenia or cardiovascular diseases. The proposed framework opens up an approach to subject stratification.

中文翻译：

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整合GWAS的临床数据和估算的转录组以发现复杂的疾病亚型：在精神病学和心脏病学中的应用。

将受试者分为临床和生物学上均一的亚组将促进对疾病病理生理学的了解和有针对性的预防和干预策略的发展。传统上，疾病亚型仅基于临床特征，但是通过这种方法识别出的亚型可能并不完全符合潜在的生物学机制。很少有研究将基因组图谱（例如，来自GWAS的基因图谱）与疾病亚型的临床症状整合在一起。在这里，我们提出了一个分析框架，该框架能够通过利用GWAS预测的基因表达水平和通过多视角双聚类分析的临床数据来发现复杂的疾病亚组。这种方法通过其对表达的影响将SNP连接到基因，因此，与基于纯SNP的分析相比，该分析在生物学上更具相关性和可解释性。不同组织的转录组也可以很容易地建模。我们还提出了用于评估集群性能的各种评估指标。我们的框架能够将精神分裂症患者亚型分为具有不同预后和治疗反应的不同亚组。我们还将该框架应用于北部芬兰出生队列（NFBC）1966数据集，并在按性别分层的分析中确定了高和低的心脏代谢风险亚组。交叉验证的预测强度通常大于80％，表明聚类模型具有良好的稳定性。我们的结果表明，以数据驱动和生物学上更全面的方法来定义代谢综合征和精神病亚型。此外，我们发现这些基因