Integrating Personalized Gene Expression Profiles into Predictive Disease-Associated Gene Pools

J. Menche, E. Guney, A. Sharma, P.J. Branigan, M.J. Loza, F. Baribaud, R. Dobrin, A.-L. Barabasi
Systems Biology and Applications
3:10 (2017)
March 13, 2017


Gene expression  data are routinely used to identify genes that on average exhibit different  expression levels between a case and a control group. Yet, very few of such  differentially expressed genes are detectably perturbed in individual  patients. Here, we develop a framework to construct personalized perturbation  profiles for individual subjects, identifying the set of genes that are  significantly perturbed in each individual. This allows us to characterize  the heterogeneity of the molecular manifestations of complex diseases by  quantifying the expression-level similarities and differences among patients  with the same phenotype. We show that despite the high heterogeneity of the  individual perturbation profiles, patients with asthma, Parkinson and Huntington’s  disease share a broadpool of sporadically disease-associated genes, and that  individuals with statistically significant overlap with this pool have a  80–100% chance of being diagnosed with the disease. The developed framework  opens up the possibility to apply gene expression data in the context of  precision medicine, with important implications for biomarker identification,  drug development, diagnosis and treatment.

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