As climate change becomes increasingly difficult to ignore, scientists are working to diversify and improve alternatives to fossil fuel-based energy. Renewable bioenergy crops, such as Miscanthus perennials, show promise for cellulosic ethanol production and other uses, but current hybrids are limited by environmental conditions and susceptibility to pests and diseases.
Breeders have been working to develop new hybrids of Miscanthus for many years, but the sterility of the clonal crops, the complex genome and the long time to maturity make conventional breeding difficult. In a new study, researchers at the University of Illinois mine the vast genomic potential of crops in an effort to speed up the breeding process and maximize its most desirable characteristics.
“The method we are using, genomic selection, could shorten the time it takes to breed a new hybrid by at least half,” says Marcus Olatoye, lead author on the study and postdoctoral researcher in the Department of Taxation. -Illustration Sciences of Illinois. “That̵7;s the overall goal.”
In conventional breeding, one typical method is for researchers to grow individuals from a different set of populations and select those with the best breeding characteristics. But, for Miscanthus, those features do not appear until the plants are 2-3 years old. Even after the plants of this first generation are matched, it takes the offspring another 2-3 years to reveal whether the desired characteristics have been faithfully passed on.
In genomic selection, scientists take genetic samples from seeds or seedlings in a target population. This is the group of plants that should normally be grown to maturity before becoming experimental saves. Meanwhile, researchers collect both genetic and phenotypic data from related populations, known as reference sets or training, in a statistical model. Referencing genetic data from the target population with model data allows researchers to predict the phenotypic outcome of hypothetical crosses in the target population.
This allows breeders to cut to the chase, following only the most promising crosses with more on-site testing.
“Ideally, this process could allow breeders to make selections based on predicted phenotypic values before plants are planted,” says Alex Lipka, an associate professor of biometrics in the Department of Plant Sciences and co- author on the study. “Specifically, we want to make selections to optimize winter hardiness, biomass, disease tolerance, and flowering time in Multanthus, all of which limit crop performance in several regions. of North America. “
Although not a simple process at the best of times, genomic selection in Multanthus is orders of magnitude more challenging than in other crops. The hybrid of interest, Miscanthus × giganteus, is the product of two separate species, Miscanthus sinensis and Miscanthus sacchariflorus, each with a different number of chromosomes and containing a large amount of variation within and between natural populations.
“As far as we know, no one has tried to train genomic selection models from two separate species before. We decided to go nuts totally here,” says Lipka. “Unfortunately, we found that both parent species do a very good job of predicting biofuel characteristics in Multanthus × giganteus.”
The problem was twofold. First, the statistical model simply revealed too much genetic variation among parental subpopulations to capture the impact of genes that control biofuel characteristics. This means that the parental populations selected for the reference set were too diverse to reliably predict characteristics in the hybrid Miscanthus × giganteus. And second, the genes that control a particular trait – such as those related to biofuel potential – appeared to be different in the two parent species.
In other words, the genomes contributing to Miscanthus × giganteus are very complex, explaining why the statistical approach had a difficult time predicting traits in offspring from both parents.
Still, the research team kept trying. In a simulation study, Olatoye created 50 families Miscanthus × giganteus, each derived from parents randomly selected from both species. He selectively dialed each parent’s genetic contributions up and down, and these contributions formed the genetic basis of simulated phenotypes. The intention of the study was to provide a better view of what individuals and populations might be valuable to real-life crusaders.
“The results suggest that the best strategy for using parental diversity is to fit genomic selection models within each parental species separately, and then add the predicted Miscanthus × giganteus trait values from the two models separately, “Olatoye says.
Although researchers have more work to do, the simulation study showed that genomic selection can work for Miscanthus × giganteus. The next step is to further refine which populations are used to train the statistical model and evaluate the crosses in the field.
The article, “Population optimization training for genomic selection in Multanthus,” is published in G3: Genes, Genomes, Genetics.
When temperatures drop, Siberian Miscanthus plants exceed the main bioenergy variety
Marcus O. Olatoye et al, Population Optimization Training for Genomic Selection in Multanthus, G3: Genes Genomes Genetics (2020). DOI: 10.1534 / g3.120.401402
Provided by the University of Illinois at Urbana-Champaign
Citation: Research could save breeding years for new Miscanthus hybrids (2020, 28 July) obtained on 30 July 2020 from https://phys.org/news/2020-07-years-miscanthus- hybrids.html
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