Heritable and nonheritable factors play a role in multiple sclerosis, but their effect size appears too small, explaining relatively little about disease etiology. Assuming that the factors that trigger the onset of the disease are, to some extent, also those that generate its remissions and relapses, we attempted to model the erratic behaviour of the disease course as observed on a dataset containing the time series of relapses and remissions of 70 patients free of disease-modifying therapies. We show that relapses and remissions follow exponential decaying distributions, excluding periodic recurrences and confirming that relapses manifest randomly in time. It is found that a mechanistic model with a random forcing describes in a satisfactory manner the occurrence of relapses and remissions, and the differences in the length of time spent in each one of the two states. This model may describe how interactions between “soft” etiologic factors occasionally reach the disease threshold thanks to comparably small external random perturbations. The model offers a new context to rethink key problems such as “missing heritability” and “hidden environmental structure” in the etiology of complex traits. 1. Introduction Multiple sclerosis (MS) is an immune-mediated disease of the central nervous system with a relapsing-remitting course in the majority of the early stages of the disease [1]. As for other multifactorial diseases, there is no comprehensive overview of the events that lead to the disease. This limits the opportunities provided by the advancements in genetics, immunology, and neurobiology since it is difficult to contextualize each single discovery. The uncertainties in the interpretation of genome-wide association studies (GWAS) reflect, to some extent, this problem. These studies carried the expectation to define the heritable component in multifactorial diseases and, through this, also sketch the nonheritable (environmental) contribution to the phenotype. As largely witnessed by the debate about “missing heritability” in multifactorial diseases, also this powerful approach appears to be in need of interpretative keys as neither genes nor the environment seem to harbour factors that, alone or jointly, are strong enough to explain the disease etiology [2, 3]. Likewise situations are rather common in the physics of nonlinear systems; here the observed large variations are explained through the effects induced by small random perturbations [4–6]. An example is the theory of the Earth’s climate: the cooperative effect of a small stochastic perturbation
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