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Drew Lyon: Hello, and welcome to the WSU Wheat Beat Podcast. I’m your host, Drew Lyon, and I want to thank you for joining me as we explore the world of small grains production and research at Washington State University. We have weekly discussions with researchers from WSU and the USDA-ARS to provide you with insights into the latest research on wheat and barley production. If you enjoy the WSU Wheat Beat Podcast, do us a favor and subscribe on iTunes or your favorite podcasting app. And leave us a review while you’re there so others can find the show too.
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Drew Lyon: My guest today is Jayfred Godoy. Dr. Godoy is a postdoctoral research associate with the WSU winter wheat breeding program. Jayfred is originally from the Philippines and first joined WSU in 2011 as a Ph.D. student in the Department of Crop and Soil Sciences. His current research includes the application of high-throughput phenotyping tools and genomic selection in wheat breeding. Hello, Jayfred.
Jayfred Godoy: Hi, Drew. Thanks for having me.
Drew Lyon: You’re welcome. Interested in the work you’re doing in Dr. Carter’s lab, I believe?
Jayfred Godoy: Yes.
Drew Lyon: Dr. Aaron Carter, our winter wheat breeder. And this high-throughput phenotyping is you, I’m reading about it all over the place. I’m sure some of our listeners are too. Can you tell us a little bit about genomic selection and how it differs from marker-assisted selection? The latter, marker-assisted selection, being fairly commonly used these days in plant breeding programs.
Jayfred Godoy: Yeah, okay. So first, genomic selection I would say is a relatively new strategy in plant breeding. And I would say relatively new in terms of its application routinely in plant breeding programs. And in genomic selection, it would allow you to predict breeding value of individual lines. And using molecular markers. So in contrast, and you’ve mentioned marker-assisted selection, which is I think most common to most of our listeners or to all of our listeners. In MAS, you’re also using molecular markers. However, in MAS you only use a limited number of molecular markers. And these are molecular markers previously identified that are linked to major QTL or genes controlling the specific traits of interest to the plant breeders. But in genomic selection, you’re using a higher density of molecular markers. Usually, genome-wide molecular markers, in thousands or hundreds of thousands simultaneously to estimate breeding performance of individuals.
Drew Lyon: Okay, so our knowledge base is going from just working with a few to just working with many. It must be a computational nightmare or maybe it was at one time but today it’s more easily handled?
Jayfred Godoy: Exactly. That’s part of the challenges for genomic selection is how could we analyze all this huge dataset.
Drew Lyon: Okay. So having all this — a lot more information, one of the advantages of genomic selection?
Jayfred Godoy: Yeah. So genomic selection is very effective in improving polygenic traits, or traits that are controlled by many genetic regions with small effects. Now, MAS, and this is where MAS actually has gained little success through the years. Because in MAS, remember, you’re only using a very limited number of markers. And usually these markers are linked to, or these markers have larger effects. But you’re throwing out a greater percentage also of the genetic variation of the trait. Because you are not accounting for those regions which have small effects, but cumulatively would add to a bigger proportion of the genetic variation of the trait. Now I’m not saying this is bad, but MAS is very effective for traits that are governed by major genes. So just, this is resistance. But not really for complex traits such as grain yield. Now, in genomic selection, because we are using all available markers, you’re now selecting each individual markers, then you’re pretty much sure you’ve covered the whole genome. And you’ve accounted, and that way you are accounting for the regions with small effects. And so you have covered a higher proportion of the genetic variation of the trait. And in doing genomic selection, the initial steps would be associating, again, the marker to the trait. And assigning individual effects on the markers. And once you’ve established this relationship, then you can actually have another set of lines where you only have marker data of, and then predict the performance of that line without even phenotyping them in the field, without field evaluation. And that would really significantly reduce your costs and time. And that would improve ultimately your genetic gain in the breeding program.
Drew Lyon: Okay. And time seems to be becoming more and more important, especially has always been in things like corn, or at least in the last several decades. But it is becoming very important in wheat now, too.
Jayfred Godoy: Yeah exactly. And based on published research, really significantly using genomic selection could reduce breeding time in at least three years or five years.
Drew Lyon: Okay very good. So how is genomic selection implemented in a wheat breeding program?
Jayfred Godoy: Okay, so the major component of, components of a genomic selection approach is there’s two major populations. So the first is a training population, which is both genotype and phenotype. And you have a candidate population, which is untested or unphenotyped, but only genotype. So the training population is where you build your genomic selection model, or you train your model. And that’s where you establish the association between the marker and the trade. And your candidate population, which only has your genotype, will be the ones that you’re going to use the model. So once you’ve established your model, you just plug in the genotypic data of the candidate population, and there you can predict the performance of the candidate population without phenotypic evaluation. In relating that to a breeding program, so your training population will be a group of varieties or cultivators, that the breeder really likes. So they may have good ergonomic performance, good disease resistance, excellent end-use culture traits. So these lines, this population with phenotype in different environments across years, and then they will be genotype. And from there, the genomic selection models will be created. And then your candidate population will be the offsprings maybe of this elite genotype and training population, or offsprings from different process of cultivators from the breeding program. And so once you’ve established the genomic selection model from your training population of the elite lines, you can now predict the performance of the lines in the candidate population again without field evaluation.
Drew Lyon: Okay, so models usually are changing. Are you tweaking these models as you go along and you learn a little bit more about how well different traits relate to traits?
Jayfred Godoy: Exactly. So that’s really the crucial part of any genomic selection program is you should have the best model to the have the highest accuracy in predicting the performance of lines. So you can see it every cycle, you probably have to update or retrain your model in order to catch up with the new phenotype information, the new traits that you are working on. So it’s a constant retrain and test your model so you can have better model every time you predict new candidate lines.
Drew Lyon: Okay. We’ve tossed out the word “phenotyping” several times, I wonder if you could explain to our listeners what phenotyping is.
Jayfred Godoy: Okay, yeah. So basically in plant breeding, phenotyping is just similar to evaluation of the specific traits. So measuring traits. Like for example, measuring disease resistance. Scoring how a plant is resistant or susceptible in the field wherein you score them in a scale of maybe one to five or one to ten. So that’s basically phenotyping. It’s measuring their — you could say performance in the field.
Drew Lyon: Okay. So that’s a phenotype.
Jayfred Godoy: Yeah.
Drew Lyon: So which types of traits will genomic selection be more effective for improving breeding efficiency on and genetic gain?
Jayfred Godoy: So yeah, and as I mentioned earlier, GS will be very good for more complex traits. And I would say an example for this are those traits also that require more number of field trials or evaluation in order to come up with more liable estimates. So traits like grain yield or yield component traits. Because you can’t trust a one-year measurement of grain yield because you don’t know next year genetic environment comes in, or just don’t know what’s coming in so you can’t really rely on just one estimate of grain yield. Another would be traits that are really expensive to measure. Or traits that require more seeds to measure. So I could think of those end-use quality traits. For example, baking qualities. So you need a certain number, amount of seeds in order to bake a cake and test if this line is good for baking. So the, what you call this, the struggle for that in a breeding program, so you have to wait a certain generation that you can have this number of seeds in order to test it. Okay, so if you could do genomic selection earlier, without the need for phenotyping, you could do it earlier and that would improve again your genetic gain. Because you could do that a couple times in a year. And then the last thing would be even some with these disease resistant traits where it’s not governed by major resistance genes but are really difficult to screen year to year because of inconsistencies with disease pressure. So for example like snow mold. You’re not sure that every year you’ll have a good snow cover. So your phenotype won’t be that reliable. So those kinds of traits.
Drew Lyon: Okay. So I can see a number of things that it’d be very useful for in a breeding program. And I assume WSU’s breeding program is gearing up, are we using, is the breeding program using these tools now or are they just trying to get geared up so they can use them in the near future?
Jayfred Godoy: Yeah, exactly. So now we’re at an early stage of the genomic selection strategy. So now we are developing models and building models on different traits from [inaudible] quality traits to ergonomic performance to disease rates. And hopefully, this would become a routine part of the breeding program. And because of the several benefits that I’ve talked with earlier. So.
Drew Lyon: Okay, so you’re doing a post-doctoral research position right now. What are your plans for the future?
Jayfred Godoy: Yes, I’m hoping with this additional training in genomic selection, high-throughput typing, that would make me more, would improve or add to my skill set so I could be more competitive in applying for a breeding position in the industry or in a seed company.
Drew Lyon: Okay. Very good. Well, we’re sure happy to have you here at WSU right now helping us figure out this new genomic selection approach. And hopefully, that will pay big dividends down the road. Thank you very much, Jayfred.
Jayfred Godoy: Okay, thanks, Drew.
Drew Lyon: Thanks for joining us and listening to the WSU Wheat Beat Podcast. If you like what you hear, you can subscribe on iTunes or your favorite podcasting app so you never miss an episode. And leave us a review while you’re there. If you have questions for us that you’d like to hear addressed on future episodes, please email me at firstname.lastname@example.org. You can find us online at smallgrains.wsu.edu. You can also reach out on Facebook and Twitter @WSUSmallGrains. The WSU Wheat Beat Podcast is a production of CAHNRS communications in the College of Agricultural, Human, and Natural Resource Sciences at Washington State University. I’m Drew Lyon. We’ll see you next week.