Nearly 2,000 genebanks around the world hold the genetic resources of plants, from seed to stem and more, taken from all crops, even those that have never been cultivated or have not grown. for a century. These time capsules have the potential to improve future crops with unknown or previously unwanted genes as plant diseases evolve and the climate changes. However, practical implementation has been insufficient, largely due to a disconnect between genebank data management and candidate species identification, according to an international research team. The result is usually a less diverse breed without the ability to incorporate untapped resistance genes.
To address this, the researchers developed a genomics-based screening strategy that predicts genomic information within and across genebanks, identifying the best candidate parents for desired plant traits in wheat. The team published their approach, which resulted in plants that outperformed current wheat varieties in multiple field trials, Oct. 4 in Nature Genetics.
“Genetic diversity is essential for crop improvement, and significant yield gains have been achieved through the sharing of genetic information between species from novel germplasm into elite genotypes, but this has largely been fortuitous and represents only a tiny fraction of the diversity available in genebanks,” said co-first author Liu Fang, currently at the Key Laboratory for Plant Germplasm Improvement and Specialized Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences. At the time of the research, Liu worked at the Leibniz Institute for Plant Genetics and Crop Research in Gatersleben, Germany.
Genetic material, or the seeds and other samples of a plant used for research and breeding, generally represent a plant in time. Plant genetic resources, on the other hand, include all the variations of a plant over millennia, including the results of human and natural selection. Germplasm that has adapted, either through natural or selective selection, has the physical characteristics and genetic robustness to better resist contemporary diseases or environmental challenges. Unadapted germplasm can be taller, move more easily and be more susceptible to disease – all undesirable traits for wheat. So, Liu said, breeders end up relying on suitable germplasm and avoid planting unsuitable germplasm, which can be an expensive gamble to uncover untapped genetic potential that could produce better harvests.
“The diversity of plant genetic resources offers plant breeders the opportunity to develop new and improved cultivars with desirable traits,” Liu said. “Take stripe rust – a fungal infection that develops in winter and can reduce crop growth by up to 100% in severe cases. stripe rust for breeding, about 68% of the genes deployed are only partially effective or completely ineffective against virulent races in Germany.
One possible solution is to combine genome-wide marker profiles for whole genebank collections with prediction of physical traits from identified genetics, called genomic prediction, in an algorithm that can sequence the data rapidly. by association of known information. The missing link, according to Liu, is precise information about how unknown physical traits of unsuited genetic material affect yield.
“We proposed a hybrid strategy, in which performance is scored against an ‘ElitexPGR’ context to correct for lack of agronomic suitability in genebank materials,” Liu said. The “elite” part refers to genomes that have a genetic advantage over the general population for the current environment. PGR are plant genetic resources — the potential variations of a species over time.
Liu explained that by compiling a personalized base collection of various potential parents with, for example, 150 elite stripe rust-resistant genotypes and 50 plant genetic resources that were closely related to those 150 but still rust-susceptible yellow, they could find a balance in the possible genetic crosses. The 50 stripe rust susceptible individuals may have other genetic contributions to improve offspring, but they are close enough that the resistant genes from the 150 specimens are likely to be passed on.
By scanning genomes for relevant associations and applying the same diverse selection to wheat hybrids, researchers can quickly adapt the method to predict selection values from a small set of ElitexPGR cross estimates. This helps understand physical traits in wild-type wheat that may have useful genes but have been overlooked due to undesirable traits, Liu said. In one analysis, the approach found working genetic pipelines from 23 stripe rust-resistant sources — 16 of which are likely novel, Liu said.
“Our kin selection approach promises to improve pre-selection entry-exit ratios,” Liu said. “We are using the new hybrid scheme to overcome the challenges of cultivating and phenotyping wild relatives of wheat. Although it can be expensive to produce hybrids, this quick adaptation strategy can be useful for many crops. Overall, this approach effectively links plant genetic resources and elite cultivars and has the potential to be applied to many other crops.
Other contributors include co-first author Albert W. Schulthess, co-first author Sandip M. Kale, Yusheng Zhao, Norman Philipp, Maximilian Rembe, Yong Jiang, Axel Himmelbach, Jörg Fuchs, Markus Oppermann, Stephan Weise, Matthias Lange, Uwe Scholz, Nils Stein and corresponding co-authors Martin Mascher and Jochen C. Reif, Leibniz Institute for Plant Genetics and Crop Research Gatersleben; Ulrike Beukert and Albrecht Serfling, Julius Kühn Institute (Federal Center for Crop Research); Philipp HG Boeven and Johannes Schact, Limagrain GmbH; C. Friedrich H. Longin, National Institute for Plant Breeding, University of Hohenheim; Sonja Kollers and Viktor Korzun, KWS SAAT SE & Co.; and Nina Pfeiffer, KWS LOCHOW GmbH.
Kale is currently affiliated with the Carlsberg Research Laboratory in Copenhagen, Denmark. Stein is also affiliated with the Center for Integrated Livestock Research (CiBreed) and Mascher is also affiliated with the German Center for Integrative Biodiversity Research (iDiv).
The German Federal Ministry of Education and Research, through the GeneBank 2.0 project, and the German Federal Ministry of Food and Agriculture, through the GenDiv project, funded this research.
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