Integrating Clinical Data and Imputed Transcriptome from GWAS to Uncover Complex Disease Subtypes: Applications in Psychiatry and Cardiology

Abstract

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.

Leveraging eQTLs to identify individual-level tissue of interest for a complex trait

“…Also, not all of a complex trait's heritability may mediate through gene expression. As mentioned in the introduction, a recent study [31] has proposed to integrate clinical features related to a disease and imputed gene expression profiles to identify subtypes of the disease. We note that the objective of our study is distinct, and the two approaches are not comparable.…”

“…A method addressing this objective would also allow us to explore the possibility of prioritizing a relevant tissue for the phenotype of an individual. A recent study [31] has proposed to identify disease subtypes by integrating clinical features related to the disease and gene expression profiles across patients. Using a multi-view clustering algorithm they classified patients into subgroups where each subgroup has a distinct pattern of gene expression predicted based on cis-SNPs' genotypes and various clinical features related to the disease combined together.…”

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

“…Moreover, SVM outperformed the other ML methods in predicting the repositioning drugs for schizophrenia when trained on drug expression profiles 367 . On the contrary, for schizophrenia subtyping, an unsupervised learning approach, multi‐view clustering, was employed by combining transcriptomic data with clinical phenotypes 368 . Setting a good example of the beneficiary of AI/ML in clinical drug trials, a novel AI platform AiCure 366 on mobile devices was used to assess the dosing compliance in Phase 2 clinical trial in schizophrenia patients.…”

Präzisionspsychiatrie und der Beitrag von Brain Imaging und anderen Biomarkern

“…Darüber hinaus gibt es ebenfalls Bemühungen im Bereich des nichtsupervidierten Lernens, die bisherigen diagnostischen Trennlinien zugunsten naturalistischer Einteilungen aufzulösen. Diese neuen Trennlinien werden basierend auf maschinellen Algorithmen unter anderem anhand klinischer [39,40], kognitiver [41,42], genetischer [43][44][45], hirnfunktioneller [46][47][48] oder hirnstruktureller Muster [49,50] entworfen. Des Weiteren wurden bereits auch Kindheitstraumata [51], Psychopathie [52], Suizidalität [53] oder Empathie [54] mithilfe nicht-supervidierter Algorithmen in verschiedene neurobiologische oder genetische Merkmalsräume dekonstruiert.…”