The origin of complex human diversity: Stochastic epistatic modules and the intrinsic compatibility between...

The continuing prevalence of a highly heritable and hypo-reproductive extreme tail of a human neurobehavioral quantitative diversity suggests the reproductive majority retains the genetic mechanisms for extremes. From the perspective of stochastic epistasis, the effect of an epistatic modifier variant can randomly vary in both phenotypic value and effect direction among carriers depending on the genetic identity and the modifier carriers are ubiquitous in the population. The neutrality of the mean genetic effect in carriers ensures the survival of the variant under selection pressures. Functionally or metabolically related modifier variants make an epistatic network module and dozens of modules may be involved in the phenotype. To assess the significance of stochastic epistasis, a simplified module-based model was simulated. The individual repertoire of the modifier variants in a module also contributes in genetic identity, which determines the genetic contribution of each carrier modifier. Because the entire contribution of a module to phenotypic outcome is unpredictable in the model, the module effect represents the total contribution of related modifiers as a stochastic unit in simulations. As a result, the intrinsic compatibility between distributional robustness and quantitative changeability could mathematically be simulated using the model. The artificial normal distribution shape in large-sized simulations was preserved in each generation even if the lowest fitness tail was non-reproductive. The robustness of normality across generations is analogous to the real situation of complex human diversity, including neurodevelopmental conditions. The repeated regeneration of a non-reproductive extreme tail may be essential for survival and change of the reproductive majority, implying extremes for others. Further simulation to illustrate how the fitness of extreme individuals can be low across generations may be necessary to increase the plausibility of this stochastic epistasis model.