BMC GenomicsGenomicsPeer-reviewed journal article
Genetic differentiation and intrinsic genomic features explain variation in recombination hotspots among cocoa tree populations
Enrique J. SchwarzkopfiD, Juan Carlos MotamayoriD, Omar E. CornejoiD
Abstract
Our study investigates the possible drivers of recombination hotspots in Theobroma cacao using ten genetically differentiated populations. By comparing recombination patterns between multiple populations, we obtain a novel view of recombination at the population-divergence timescale. For each population, a fine-scale recombination map was generated using the coalescent with a standard method based on linkage disequilibrium (LD). These maps revealed higher recombination rates in a domesticated population and a population that has undergone a recent bottleneck. We inferred hotspots of recombination for each population and find that the genomic locations of hotspots correlate with genetic differentiation between populations (FST). We used randomization approaches to generate appropriate null models to understand the association between hotspots of recombination and both DNA sequence motifs and genomic features. We found that hotspot regions contained fewer known retroelement sequences than expected and were overrepresented near transcription start and termination sites. Our findings indicate that recombination hotspots are evolving in a way that is consistent with genetic differentiation but are also preferentially driven to near coding regions. We illustrate that, consistent with predictions in plant domestication, the recombination rate of the domesticated population is orders of magnitude higher than that of other populations. More importantly, we find two fixed mutations in the domesticated populations FIGL1 protein. FIGL1 has been shown to increase recombination rates in Arabidopsis by several orders of magnitude, suggesting a possible mechanism for the observed increased recombination rate in the domesticated population.
- organism: plant (other)
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 22
- FWCI
- 2.74
- Field percentile
- top 11%
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