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Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter ).

The phenomenologic diagnosis of depression is successful in increasing diagnostic reliability, but it is a classification scheme without biologic bases. One subtype of depression for which evidence suggests a unique biologic basis is late-life depression (LLD), with first onset of symptoms after the age of 65. LLD is common and poses a significant burden on affected individuals, caretakers, and society. The pathophysiology of LLD includes disruptions of the neural network underlying mood, which can be conceptualized as the result of dysfunction in multiple underlying biologic processes. Here, we briefly review current LLD hypotheses and then describe the characteristics of molecular brain aging and their overlap with disease processes. Furthermore, we propose a new hypothesis for LLD, the age-by-disease interaction hypothesis, which posits that the clinical presentation of LLD is the integrated output of specific biologic processes that are pushed in LLD-promoting directions by changes in gene expression naturally occurring in the brain during aging. Hence, the brain is led to a physiological state that is more susceptible to LLD, because additional pushes by genetic, environmental, and biochemical factors may now be sufficient to generate dysfunctional states that produce depressive symptoms. We put our propositions together into a decanalization model to aid in illustrating how age-related biologic changes of the brain can shift the repertoire of available functional states in a prodepression direction, and how additional factors can readily lead the system into distinct and stable maladaptive phenotypes, including LLD. This model brings together basic research on neuropsychiatric and neurodegenerative diseases more closely with the investigation of normal aging. Specifically, identifying biologic processes affected during normal aging may inform the development of new interventions for the prevention and treatment of LLD.

In order to fully understand the disorder, and develop more effective and targeted treatments, more accurate and sophisticated animal models are required, which incorporate genetic and environmental variables and their associated gene-environment interactions. The exploration of disease-associated polymorphisms and mutations under housing conditions in which environmental factors of clinical relevance are systematically manipulated will facilitate the testing of specific hypotheses associated with pathogenic gene-environment interactions and decanalized development. [...]genes that exhibit G × E interactions may confer evolutionary advantage in that they facilitate phenotypic plasticity and provide an organism with the flexibility to adjust its phenotype with respect to the specific environmental conditions experienced (Via et al., 1995; Lande, 2009). Several risk factors have been identified during pregnancy including season of birth, vitamin D deficiency, urbanicity or population density, and maternal viral infections (Cannon and Clarke, 2005; Brown, 2006; Patterson, 2007; McGrath et al., 2010).