Modeling recombination rate as a quantitative trait reveals new insight into selection in humans.

Abstract

Meiotic recombination is both a fundamental biological process required for proper chromosomal segregation during meiosis and an important genomic parameter that shapes major features of the genomic landscape. However, despite the central importance of this phenotype, we lack a clear understanding of the selective pressures that shape its variation in natural populations, including humans. While there is strong evidence of fitness costs of low rates of recombination, the possible fitness costs of high rates of recombination are less defined. To determine whether a single lower fitness bound can explain the variation in recombination rate observed in human populations, we simulated the evolution of recombination rate as a sexually dimorphic quantitative trait. Under each scenario, we statistically compared the resulting trait distribution to the observed distribution of recombination rates from a published study of the Icelandic population. To capture the genetic architecture of recombination rate in humans, we modeled it as a moderately complex trait with modest heritability. For our fitness function, we implemented a hyperbolic tangent curve with several flexible parameters to capture a wide range of existing hypotheses. We found that costs of low rates of recombination alone are likely insufficient to explain current variation in recombination rate in both males and females, supporting the existence of fitness costs of high rates of recombination in humans. With simulations using both upper and lower fitness boundaries, we describe a parameter space for the costs of high recombination rate that produces results consistent with empirical observations.