Spring, 2024
Zoë Migicovsky has the secret sauce to help farmers get more fruitful crops. The assistant professor of biology at Acadia University and Canada Research Chair (Tier II) in Agri-Food and Sustainable Agriculture works her magic by combining genetic data from fruit crops and agriculturally important trait information about the plants and then analyzing the data so plant breeders can make predictions about what their plants will produce in terms of fruit.
“Agriculturally important traits would be things such as when an apple might ripen or what its aroma might be,” Migicovsky says. “With an apple seedling, you're going to be waiting four to seven years for there to be enough fruit for you to have a meaningful evaluation of what that fruit is like.”
There’s an enormous resource investment in that seedling in terms of breeding the plants, fertilizing, watering, managing for pests and disease and pruning, to name a few. “Then at the end, most of the plants won’t have desirable traits,” Migicovsky says. “That will be true regardless, but if we can make predictions about some of those traits early on, we can reduce the number of plants that need to be culled at a later date and narrow down which are more likely to be desirable plants later on.
She sums up the problem she’s addressing by quoting from an October 23, 2023 Financial Times article in which a breeder started with 90,000 trees. Of those, “Only 357 varieties made it to a second round of trials, 18 went to a third and 13 reached the final round.” The article states that apples are like diamonds in that way.
In order to do her work, Migicovsky needs computational resources that will handle large genomic trait datasets and link them together. “The plant breeder isn't going to screen for 200,000 genetic markers. They would like to only screen for a couple, so we need to know which ones are the best for them to do that. If you have a quarter of a million columns to compute, you're not going to do that on your laptop,” Migicovsky says. “We need computational resources like those available through ACENET and the Digital Research Alliance of Canada.”
The more genomic data she has, the more likely she is to find good predictors, but the more genetic data she has, the more computational resources she also needs.
Using ACENET resources saves her money in her research budget and enables continuity in her work. It’s a shared system, so her students are able to access her lab’s files and software. “It’s helpful because students are only there for a relatively short time so this allows for one student to pick up where another left off.”
