Environmental seasonality is a potent evolutionary force, capable to maintain polymorphism, promote phenotypic plasticity, and cause bet-hedging. In Drosophila, it has been reported to affect life-history traits, tolerance to abiotic stressors, and immunity. Oscillations in frequencies of alleles underlying fitness-related traits were also documented alongside SNP alleles across genome. Here, we test for seasonal changes in recombination in a natural D. melanogaster population from India using morphological markers of the three major chromosomes. We show that winter flies (collected after the dry season) have significantly higher desiccation tolerance than their autumn counterparts. This difference proved to hold also for hybrids with three independent marker stocks, suggesting its genetic rather than plastic nature. Significant segment-specific changes are documented for recombination rate (in five of 13 intervals) and crossover interference (in five of 16 studied pairs of intervals); both singleand double-crossover rates tended to increase in the winter cohort. The winter flies also display weaker plasticity of recombination characteristics to desiccation. We ascribe the observed differences to indirect selection on recombination caused by directional selection on desiccation tolerance. Our findings suggest that changes in recombination can arise even after a short period of seasonal adaptation (~8-10 generations).
Crossover interference (COI) is a widespread feature of homologous meiotic recombination. It can be quantified by the classical coefficient of coincidence (CoC) `but this characteristic is highly variable and specific to the pair of chromosomal intervals considered. Several models were proposed to characterize COI on a chromosome-wise level. In the gamma model, the strength of interference is characterized by a shape parameter ν, while the gamma-sprinkled two-pathway model (GS) accounts for both interference-dependent and independent crossover (CO) events by fitting a mixture of gamma distributions with v>1 and v=1, correspondingly, and mixture proportions 1-p and p. In reality, COI can vary along chromosomes resulting in low compliance of the fitted model to real data. Additional inconsistency can be caused by common neglecting of possible negative COI in the model, earlier reported for several organisms. In this work, we propose an extension of the GS-model to take possible negative COI into account. We propose a way for data simulation and parameter estimation for such situations.
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