Back

The role of methylation and structural variants in shaping the recombination landscape of barley

Casale, F. A.; Arlt, C.; Kuehl, M.; Li, J.; Engelhorn, J.; Hartwig, T.; Stich, B.

2024-07-23 genomics
10.1101/2024.07.22.604552 bioRxiv
Show abstract

Meiotic recombination is not only a key mechanism for sexual adaptation in eukaryotes but crucial for the accumulation of beneficial alleles in breeding populations. The effective manipulation of recombination requires, however, a better understanding of the mechanisms regulating the rate and distribution of recombination events in genomes. Here, we identified the genomic features that best explain the recombination variation among a diverse set of segregating populations of barley at a resolution of 1 Mbp and investigated how methylation and structural variants determine recombination hotspots and coldspots at a high-resolution of 10 kb. Hotspots were found to be in proximity to genes and the genetic effects not assigned to methylation were found to be the most important factor explaining differences in recombination rates among populations along with the methylation and the parental sequence divergence. Interestingly, the inheritance of a highly-methylated genomic fragment from one parent only was enough to generate a coldspot, but both parents must be equally low methylated at a genomic segment to allow a hotspot. The parental sequence divergence was shown to have a sigmoidal correlation with recombination indicating an upper limit of mismatch among homologous chromosomes for CO formation. Structural variants (SVs) were shown to suppress COs, and their type and size were not found to influence that effect. Methylation and SVs act jointly determining the location of coldspots in barley and the weight of their relative effect depends on the genomic region. Our findings suggest that recombination in barley is highly predictable, occurring mostly in multiple short sections located in the proximity to genes and being modulated by local levels of methylation and SV load.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
PLOS Genetics
756 papers in training set
Top 0.1%
44.4%
2
Genetics
225 papers in training set
Top 0.6%
7.3%
50% of probability mass above
3
Nature Communications
4913 papers in training set
Top 36%
4.2%
4
Nucleic Acids Research
1128 papers in training set
Top 5%
3.8%
5
The Plant Genome
53 papers in training set
Top 0.2%
2.9%
6
Molecular Biology and Evolution
488 papers in training set
Top 2%
2.5%
7
eLife
5422 papers in training set
Top 34%
2.2%
8
G3 Genes|Genomes|Genetics
351 papers in training set
Top 1%
2.0%
9
The Plant Cell
141 papers in training set
Top 1%
1.8%
10
New Phytologist
309 papers in training set
Top 3%
1.6%
11
Genome Biology
555 papers in training set
Top 5%
1.6%
12
Molecular Ecology
304 papers in training set
Top 3%
1.3%
13
GENETICS
189 papers in training set
Top 0.8%
1.3%
14
Computational and Structural Biotechnology Journal
216 papers in training set
Top 6%
1.3%
15
BMC Biology
248 papers in training set
Top 2%
1.2%
16
International Journal of Molecular Sciences
453 papers in training set
Top 11%
1.0%
17
Cell Reports
1338 papers in training set
Top 29%
1.0%
18
Genome Biology and Evolution
280 papers in training set
Top 2%
1.0%
19
Genome Research
409 papers in training set
Top 3%
1.0%
20
Genomics
60 papers in training set
Top 2%
1.0%
21
Scientific Reports
3102 papers in training set
Top 69%
1.0%
22
Science Advances
1098 papers in training set
Top 25%
1.0%
23
Frontiers in Plant Science
240 papers in training set
Top 5%
0.8%
24
G3
33 papers in training set
Top 0.4%
0.8%
25
Frontiers in Genetics
197 papers in training set
Top 9%
0.8%
26
PLOS Computational Biology
1633 papers in training set
Top 23%
0.8%
27
iScience
1063 papers in training set
Top 30%
0.8%
28
Nature Genetics
240 papers in training set
Top 7%
0.8%
29
PLOS ONE
4510 papers in training set
Top 72%
0.5%
30
Genes
126 papers in training set
Top 4%
0.5%