Fine mapping of meiotic crossovers in Brassica oleracea reveals patterns and variations depending on direction and combination of crosses.
Chengcheng CaiAlexandre PeléJohan BucherRichard FinkersGuusje BonnemaPublished in: The Plant journal : for cell and molecular biology (2023)
Meiotic recombination is crucial for assuring proper segregation of parental chromosomes and generation of novel allelic combinations. As this process is tightly regulated, identifying factors influencing rate and distribution of meiotic crossovers is of major importance, notably for plant breeding programs. However, high-resolution recombination maps are sparse in most crops including the Brassica genus and knowledge about intraspecific variation and sex differences is lacking. Here, we report fine-scale resolution recombination landscapes for ten female and ten male crosses in B. oleracea, by analyzing progenies of five large Four-Way-Cross populations from two reciprocally crossed F1s per population. Parents are highly diverse inbred lines representing major crops, including broccoli, cauliflower, cabbage, kohlrabi and kale. We produced ~4.56T Illumina data from 1,248 progenies and identified 15,353 crossovers across the ten reciprocal crosses, 51.13% of which being mapped to less than 10 Kb. We revealed fairly similar megabase-scale recombination landscapes among all cross combinations and between the sexes, and provided evidence that these landscapes are largely independent of sequence divergence. We evidenced strong influence of gene density and large structural variations on crossover formation in B. oleracea. Moreover, we found extensive variations in crossover number depending on the direction and combination of the initial parents crossed with, for the first time, a striking interdependency between these factors. These data improve our current knowledge on meiotic recombination and are important for Brassica breeders.
Keyphrases
- dna repair
- dna damage
- high resolution
- genome wide identification
- healthcare
- air pollution
- genome wide analysis
- open label
- public health
- arabidopsis thaliana
- big data
- transcription factor
- genome wide
- machine learning
- single molecule
- oxidative stress
- data analysis
- placebo controlled
- tandem mass spectrometry
- artificial intelligence