Phyllis Bongard, Extension content development and communications specialist, Candy Hirsch, Professor, Department of Agronomy and Plant Genetics , and Tom Hoverstad, Researcher, Southern Research and Outreach Center
What does the use of big data in corn genomics mean for today’s producers? Dr. Candy Hirsch, a corn geneticist and genomics professor, and Tom Hoverstad, Researcher at the Southern Research and Outreach Center, described how sequencing DNA can lead to improvements in corn production in the January 24 Strategic Farming: Let’s talk crops session.
Generating big data in corn genomics
How can big data help maximize yield increases in corn? As corn genomic data expands, it will ultimately allow plant breeders to predict performance for a very large number of progeny without having to grow every individual in every field environment. Dr. Hirsch’s Maize Translational Genomics lab at the University of Minnesota generates big data to study these relationships between corn genetics, the environment and phenotypic variation under different growing conditions. If they can understand what makes a particular hybrid look the way it does in a particular environment, different hybrids could be placed strategically where they would perform the best.
Getting that data, however, is labor intensive. After plants are sampled from the field, DNA is isolated out of all the cells and sent out for sequencing. The result is terabytes worth of sequencing data to try and make sense of. Each plant tested has 2.3 billion genomic bases and these are compared against all other samples. Eventually these comparisons will help in predicting their performance in different environments based on how similar or different they are.
There is a surprising amount of genetic diversity among corn hybrids. In fact, there can be as much variation between 2 genotypes as there is between a human and a chimpanzee. When comparing genetic differences of varieties, Hirsch’s lab describes the SNP variation (pronounced “snip” variation) and several structural variations:
- SNP variation – describes a single gene change in the same DNA location. For example, Genotype A may have a CG pair, while Genotype B has a TA pair in the same location.
- Presence/Absence variation – occurs when part of the genome is completely present or absent between two individuals.
- Copy number variation – occurs one genotype may have one copy of part of the genome while another individual may have two or other variations.
To illustrate variability, the genomes of twenty-six corn varieties were compared. Each corn plant has about 40,000 genes. However, across all 26 varieties, there were 100,000 unique genes identified. That translates into 60,000 extra genes that are sometimes present or sometimes absent within each genotype, which has major consequences for how its phenotype is expressed.
One such variation impacts a favorite food. The super sweet flavor of sweet corn is the result of a gene deletion. Other structural variations are important for things like aluminum toxicity tolerance and other adaptive and agronomic properties.
Genetic variation has significantly contributed to yield increases over time. As varieties were selected, genetic content changed. This has driven dramatic and continuous yield increases along with adaptations to the environment.
Impact of environment
While we might wish for perfect conditions each year, the reality is they are much more inconsistent. Low yielding years are the result of floods, droughts, and other climate events. Climate modeling has shown that these types of events will increase with frequency and severity. Big data can help us understand how different genotypes interact with the environment.
Just as people react differently to the environment, plants do, too. Minnesotans set a great example. Some will run around in shorts during the winter while others – like me – most decidedly will not. When different genotypes respond differently to the same environment, there is a genotype by environment interaction. For example, if an 85-day corn adapted for Staples or Crookston is grown in Waseca, it will look entirely different than in the environment it was developed for.
Recent studies have shown that different genes control a plant’s performance potential versus its ability to react to its environment – or plasticity. Other studies suggest that corn breeding may have reduced this genomic plasticity. Selection is a very effective tool for getting desired traits. If the breeding focus is on one trait – yield, for example – and there is little environmental variability in the testing area, plasticity suffers. Understanding how plants interact with their environment is increasingly important.
How do they study that?
To measure plasticity, traits need to be measured several times during the growing season in different environments. Phenology matters. Dr. Hirsch’s group used drones to measure high resolution phenotyping of 500 different varieties in four different environments. Drone images were reconstructed into field views where individual plant heights for every plant in the field could be analyzed. By capturing plant heights several times over the growing season, plant growth curves of the different varieties could be compared.
What did they find? Drone imagery captured a lot of genetic diversity in varieties’ growth curves, even when the varieties ended the season at the same height. Data like this helps to quantify the genetic elements that contribute to stable growth patterns across environments.
Canopy cover could also be measured from the drone images. As it turns out, canopy cover is more affected by environmental changes than plant height is.
Introducing management
Since drones were used successfully to measure growth curves and canopy cover under different environments, Hirsch’s group wondered if they could use drones to study how plants interact with different management practices and environments. They set up a study that included 12 varieties at two planting dates and three plant densities. Unfortunately, a massive wind event occurred, and their plans shifted into studying lodging and recovery.
As they discovered, plant height before and after the wind event could be measured. They were also able to monitor plant recovery for each variety using the drone. A repeated study the following year also had a massive lodging event, so they used the opportunity to look at the underlying variation affecting lodging and recovery. The most important factors contributing to lodging were high plant densities, pre-lodging height, and early to mid-season growth. The later planted corn recovered more completely than the earlier planted corn.
What about climate change?
Hoverstad observes that as the climate gets warmer and wetter, corn genetics also change. Since corn hybrids are bred, selected, and tested where they’re going to be grown, new hybrids are adapted to that changing environment. However, if the climate starts to change more rapidly, then it may be a challenge for corn genetics to keep up since it takes eight to ten years to develop a new hybrid.
Will the warming climate translate into growing longer relative maturity hybrids? Not necessarily, according to Hoverstad. Warmer conditions are more dramatic during the winter rather than the summer. Even if we gained a few more growing degree days during the spring, soils still need to dry out before planting. More importantly, the risk of frost hasn’t changed. First fall frost dates have not shifted dramatically. As a result, maturity ratings haven’t moved.
Using big data going forward
Hirsch’s lab has documented an incredible amount of genetic diversity in corn plants through using big data. The ability to compare genetics and phenotypes of individuals will help predict performance in different environments, ultimately leading to an improved corn crop.
Join the webinar series
Join us Wednesday, January 31 when we welcome Dr. Seth Naeve, Extension soybean agronomist, to discuss "Pushing soybean maturities to the max."
University of Minnesota’s Strategic Farming: Let’s talk crops! webinar series, offered Wednesdays through March, features discussions with specialists to provide up-to-date, research-based information to help farmers and ag professionals optimize crop management strategies for 2023.
Source : umn.edu