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 (COs) 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 10 female and 10 male crosses in Brassica 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 approximately 4.56T Illumina data from 1248 progenies and identified 15 353 CO across the 10 reciprocal crosses, 51.13% of which being mapped to <10 kb. We revealed fairly similar Mb-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 CO formation in B. oleracea. Moreover, we found extensive variations in CO 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
- genome wide identification
- high resolution
- genome wide analysis
- healthcare
- arabidopsis thaliana
- air pollution
- electronic health record
- big data
- public health
- mass spectrometry
- machine learning
- oxidative stress
- genome wide
- gene expression
- deep learning
- data analysis
- artificial intelligence
- neural network
- liquid chromatography
- plant growth