A Brazilian study published in scientific reports shows that artificial intelligence (AI) can be used to create efficient models for the genomic selection of sugarcane and forage grass varieties and predict their performance in the field based on their DNA.
In terms of accuracy compared to traditional breeding techniques, the methodology developed with support from FAPESP improved predictive power by more than 50%. This is the first time that a highly efficient machine learning-based genomic selection method has been proposed for polyploid plants (in which cells have more than two complete sets of chromosomes), including the grasses studied.
Machine learning is a branch of AI and computer science that involves statistics and optimization, with countless applications. Its main goal is to create algorithms that automatically extract patterns from data sets. It can be used to predict the performance of a plant, including whether it will be resistant or tolerant to biotic stresses such as pests and diseases caused by insects, nematodes, fungi or bacteria, and abiotic stresses such as cold, drought, salinity. or insufficient soil nutrients.
Crossing is the most widely used technique in traditional breeding programs. “You establish populations by crossing plants that are interesting. In the case of sugar cane, you cross a variety that produces a lot of sugar with another that is more resistant, for example. You cross them and then evaluate the performance of the resulting genotypes in the field,” said the computer scientist. Alexander Hild Aono, first author of the article about the study published in Scientific Reports. Aono is a researcher at the Center for Molecular Biology and Genetic Engineering of the State University of Campinas (CBMEG-UNICAMP). He graduated from the Federal University of São Paulo (UNIFESP).
“But this evaluation process is time consuming and very expensive. The method we propose can predict the performance of these plants even before they grow. We were able to predict yield on the basis of genetic material. This is significant because it saves many years of evaluation,” explained Aono.
In the case of sugar cane, the challenge is very complex. Traditional breeding techniques take between nine and 12 years and incur high costs, according to Anete Pereira de Souza professor of plant genetics at the UNICAMP Institute of Biology and supervisor of Aono’s doctorate at the CBMEG.
“When breeders identify an interesting plant, they multiply it by cloning so that the genotype is not lost, but this takes time and costs a lot. An extreme example is the cultivation of rubber trees, which can take up to 30 years,” Souza said. One way to overcome these difficulties is what she called “plant breeding 4.0,” which makes heavy use of data analysis and highly efficient computational and statistical tools. Each sequencing genotyping process may involve a billion sequences.
The main obstacle scientists face in trying to breed better varieties of polyploid plants, such as sugar cane and forage grass, is the complexity of their genomes. “In this case, we didn’t even know if genomic selection would be possible, given the scarce resources and the difficulty of working with this complexity,” Aono said.
Methods
The researchers started the genomic selection process with diploid plants [ containing cells with two sets of chromosomes ], since they have simpler genomes. “The problem is that high-value tropical plants like sugarcane are not diploid but polyploid, which is a complication,” Souza said.
While humans and almost all animals are diploid, sugarcane can have up to 12 copies of each chromosome. Any individual of the Homo sapiens species can have up to two variants of each gene, one inherited from the father and one from the mother. Sugar cane is more complex because theoretically any gene can have many variants in the same individual. There are regions of your genome with six sets of chromosomes, others with eight, ten, and even 12 sets. “The genetics are so complex that breeders work with sugarcane as if it were diploid,” Souza said.
In 2001, Theodorus Meuwissen, a Dutch scientist who is currently a professor of animal genetics and reproduction at the Norwegian University of Life Sciences (NMBU), proposed genomic selection to predict complex traits in animals and plants in association with their phenotypes. observables that result from the interaction of their genotypes with the environment). The advantage of this approach to breeding is the link between the phenotypic traits of interest, such as yield, sugar level or earliness, and single nucleotide polymorphisms (SNPs). A “snip” (pronounced SNP) is a genomic variant at a single base position in DNA, Souza explained.
“It is the difference in the genomes of any two individuals. For example, one can have an A [ corresponding to the nucleotide adenine ] that produces a little more than another with a G [ guanine ] at the same place in the genome. That changes everything,” she said. “When you find an association with what you’re looking for, such as a high level of sugar production and specific SNPs at different places in the genome, you can sequence just the population your breeding work is focused on.”
The advances proposed by Aono and colleagues dispense with the need to plant and phenotype throughout the breeding cycle. “We do field experiments in the early stages of the program to obtain the phenotype of interest for each clone,” Souza said. “At the same time, we sequenced all the clones of the breeding population in a fairly simple way, without the need to have the complete genome for each clone. This is what is called sequencing genotyping, partial sequencing looking for the differences and similarities in the base pairs of the clones and their association with the production of each clone. The association between phenotype and genome shows which one produces more and which SNPs are associated with higher production. In this way, we can identify clones with a large proportion of the SNPs that contribute to the highest production observed in the initial experiments and obtain the most productive variety more quickly and cheaply.”
The project was successful thanks to years of collaboration with scientists from several research institutions and universities, such as the Luiz de Queiroz Higher School of Agriculture of the University of São Paulo (ESALQ-USP), the Institute of Science and Technology of UNIFESP , the Campinas Agronomic Institute (IAC) and its Sugar Cane Center in Ribeirão Preto, the Cattle Unit of the Brazilian Agricultural Research Company (EMBRAPA) in Campo Grande, state of Mato Grosso do Sul, the Institute of Aeronautical Technology (ITA) in São José dos Campos, state of São Paulo, and Edinburgh Roslin Institute of the University of the United Kingdom.
About the São Paulo Research Foundation (FAPESP)
The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by granting scholarships, grants and aid to researchers linked to state higher education and research institutions. from São Paulo, Brazil. FAPESP is aware that the best research can only be done by working with the best researchers at an international level. Therefore, it has established partnerships with funding agencies, higher education, private companies and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can get more information about FAPESP at www.FAPESP.br/en and visit FAPESP’s news agency at www.agencia.FAPESP.br/en to stay up-to-date with the latest scientific advances that FAPESP helps achieve through its many programs, awards and research centers. You can also subscribe to the FAPESP news agency at http://agencia.FAPESP.br/subscribe
Disclaimer: AAAS and Eurek Alert! are not responsible for the accuracy of the press releases published on EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.
See also The global EMI Shielding market size is anticipated to grow from USD 6.6 billion in 2022 to USD 8.6 billion in 2027, at a CAGR of 5.4% from 2022 to 2027