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Pangenomics in crop improvement-from coding structural variations to finding regulatory variants with pangenome
Silvia F Zanini1, Philipp E Bayer2, Rachel Wells3
1Dep. of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig Univ. Giessen, Giessen, 35392, Germany.
The Plant Genome
|December 14, 2021
Summary
Crop pangenomes offer a comprehensive view of genetic diversity, improving the detection of structural variations (SVs) linked to important traits. This review explores their role in discovering noncoding regulatory sequences and future data structures for plant genomics.
Area of Science:
- Genomics
- Plant Science
- Bioinformatics
Background:
- Crop pangenomics has advanced significantly since 2014, driven by improved DNA sequencing technologies.
- Pangenomes offer a more complete representation of species' genetic variation than single-reference genomes.
- Studies have been published for major crops like rice, wheat, soybean, oilseed rape, and barley.
Purpose of the Study:
- To review the current literature on crop pangenomics.
- To focus on the application of pangenomes in identifying structural variations (SVs) associated with agronomic traits.
- To highlight the potential of pangenomes in discovering and characterizing noncoding regulatory sequences and their variations.
Main Methods:
- Review of existing scientific literature on crop pangenomics.
- Analysis of pangenome applications for detecting structural variations (SVs).
- Exploration of pangenome utility in identifying noncoding regulatory elements.
Main Results:
- Pangenomes enable more accurate detection and annotation of complex DNA polymorphisms, including SVs.
- SVs are major determinants of genetic diversity within species.
- Pangenomes show promise for discovering and functionally characterizing noncoding regulatory sequences.
Conclusions:
- Crop pangenomes are crucial for understanding genetic variation and identifying agronomically important traits.
- Future research should focus on innovative data structures to represent complete plant pangenomes, including coding and noncoding elements, and transcriptomic/epigenomic data.
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