title: SEPARATING THE CAUSES AND CONSEQUENCES IN DISEASE TRANSCRIPTOME.
abstract: The causes of complex diseases are multifactorial and the phenotypes of complex diseases are typically heterogeneous, posting significant challenges for both the experiment design and statistical inference in the study of such diseases. Transcriptome profiling can potentially provide key insights on the pathogenesis of diseases, but the signals from the disease causes and consequences are intertwined, leaving it to speculations what are likely causal. Genome-wide association study on the other hand provides direct evidences on the potential genetic causes of diseases, but it does not provide a comprehensive view of disease pathogenesis, and it has difficulties in detecting the weak signals from individual genes. Here we propose an approach diseaseExPatho that combines transcriptome data, regulome knowledge, and GWAS results if available, for separating the causes and consequences in the disease transcriptome. DiseaseExPatho computationally deconvolutes the expression data into gene expression modules, hierarchically ranks the modules based on regulome using a novel algorithm, and given GWAS data, it directly labels the potential causal gene modules based on their correlations with genome-wide gene-disease associations. Strikingly, we observed that the putative causal modules are not necessarily differentially expressed in disease, while the other modules can show strong differential expression without enrichment of top GWAS variations. On the other hand, we showed that the regulatory network based module ranking prioritized the putative causal modules consistently in 6 diseases. We suggest that the approach is applicable to other common and rare complex diseases to prioritize causal pathways with or without genome-wide association studies.
authors.affiliation: Department of Bioengineering, Stanford University, USA2Stanford Genome Technology Center, Stanford University, USA.
authors.forename: Yong Fuga
authors.initials: YF
authors.lastname: Li
authors.forename: Fuxiao
authors.initials: F
authors.lastname: Xin
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003920:Diabetes Mellitus / Q000235:genetics; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D001523:Mental Disorders / Q000235:genetics; D008957:Models, Genetic; D020412:Multifactorial Inheritance; D059467:Transcriptome; D000069558:Unsupervised Machine Learning