What is a podcast?
For those of you who are newer to the medium, a podcast is like a pre-recorded radio show. In the same way that you turn on a talk radio show, you have to turn on a podcast. The major difference is that while our cars are equipped to find radio frequencies, they are not built to accommodate direct access to podcasts. On your smartphone or computer with internet access (since the files tend to be on the larger side), you can discover podcast shows of any kind, in any field, on any topic.
Listed above are some of the most used podcast hosts. iTunes and the iTunes Podcast app are preinstalled on your iPhone and are the simplest tools to use. You simply search for “WSU Wheat Beat Podcast” in the search bar, hit “subscribe” and the download arrow, and listen whenever it’s convenient for you.
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If you have further questions about what a podcast is, which app is best for you or need more assistance with getting started with podcasts, don’t hesitate to contact us.
Drew Lyon: Hello. 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. In each episode, I speak 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 Lance Merrick. Lance is a first-year Ph.D. student, studying under Dr. Arron Carter, the WSU winter wheat breeder. He is from South Dakota where he grew up on a family farm. While at South Dakota State University, he completed his undergraduate degree in agronomy and his master’s degree under Dr. Karl Glover, the spring wheat breeder. The goal of his research is to create genomic selection models in the winter wheat breeding program. Hello, Lance.
Lance Merrick: Hi, Drew.
Drew Lyon: So can you tell us a little bit about genomic selection, what is genomic selection?
Lance Merrick: Genomic selection allows us to create a statistical model in which you use past trait data to predict the performance of breeding lines using just genomic data.
Drew Lyon: Okay, so you’re able to take data you already have collected on something and connect it to the genetic, what you know genetically about the type, so you connect the genetic information to the phenotypic information, is that correct?
Lance Merrick: Yes, yes that is correct.
Drew Lyon: Okay. And by phenotype we mean some characteristic of the plant, plant height or leaf width or something like that, correct?
Lance Merrick: Yes, anything like with yield disease resistance, literally anything we can measure from a plant, we use to predict…
Drew Lyon: Is what we call a phenotypic trait.
Lance Merrick: Yes.
Drew Lyon: Alright, so how do you create a genomic selection model? How do you take this information and model it?
Lance Merrick: So, the first thing we need to do is we need a lot of breeding lines that have both phenotypic data, such as yield, their disease resistance, and genetic data and we use a statistical model, it can be various things, there’s a lot of different models out there, and what we do is we use that to predict what we call a genomic estimated breeding value or the performance of a line based solely on its genetic markers. So in order to create a genomic selection model, we need to use a lot of breeding lines set up with both phenotypic and genomic data. So we use these group breeding lines in what we call a training population. This training population is used to train the statistical model in order to predict the genomic estimated breeding values or the performance of lines based solely in the genetic markers. And we usually use this to predict into a population that we call our test population, and that is usually what we use to actually select out of, right? So we use our genomic selection model to select lines that we want for whatever trait in question we’re looking at.
Drew Lyon: So how do you use this model then, you’ve selected, how do you use that in a breeding program? Normally the breeder goes out and just looks at a lot of material, right, for phenotype, does this help him some way in looking at more material or less material, what’s the purpose of it?
Lance Merrick: So really, the purpose we can do quite a few things with it since it is a prediction, right? One of the big things is we can use it to predict completion traits, such as yield, so by complex I mean a trait that is controlled by a lot of genes, a lot of different markers. And we can predict these traits in early breeding programs, so let’s, for example, if we have really early breeding program lines that don’t have enough seed, we can try and predict the yield even though we can’t actually grow it on the field or actually put it in a field trial to really determine the yield performance of it. So, we can actually use that to predict the yield performance and either one, use it back as a parent and reduce our generation time in which we call our cycle time which is a big thing to promote efficiency in a breeding program and speed up the development of new cultivars. So, we can use it in another, and since for difficult to measure traits, so this could be disease assistance where we don’t have enough disease pressure to actually see differences between lines, and then also we can use it to reduce replications in our field trials and things like that.
Drew Lyon: So it’s a model, we’re recording this during the COVID-19 pandemic, and we, I think everybody’s read about these models about where the pandemic can go, but nobody really knows, they give you a guide. How accurate are these models? Do they work pretty well or do they still have quite a bit of guesswork involved in them I guess?
Lance Merrick: So, it is a prediction, right? So there is, there’s a lot of guesswork. It depends on a lot of factors such as how much genetic effect or environmental effect really goes in the trait. So for instance, in yield, there’s a lot of environmental influence, so truly our prediction on yield would be a little less than things that, such as diseases that don’t always play into environmental effect.
Drew Lyon: So you’re a graduate student under Dr. Arron Carter, are there specific traits you’re looking at or working on?
Lance Merrick: Yeah, so what I’m looking at is seedling emergence and also stripe rust resistance. So these traits both rely pretty heavily on environment to really create a lot of variation for selection purposes.
Drew Lyon: Okay. And so the modeling will help you select when you don’t have quite as many environments as you’d like to have I guess, if you have a series of low stripe rust years while you’re here, you can still make progress on the traits.
Lance Merrick: Yes, exactly, for stripe rust you know, you need the right environment influences such as mixture, you need a certain temperature to really permit that infection in the plant. And so that’s we’re our genomic selection models can really come in and we can try and select for, well what I’m actually working on is quantitative resistance so this actually is a more durable form of stripe rust resistance and by durable I mean it still exhibits resistance even as pathogen races change from year to year. And hopefully is, shows more resistance from year to year.
Drew Lyon: Okay, and what about from the seedling emergence work, what are you looking for there?
Lance Merrick: So seedling emergence to really show variation we need crusting of the soil in areas like Lind where we have deep furrow planted winter wheat, we need proper moisture to actually promote crusting to actually show differences in seedling emergence. For years where we don’t have the right environmental conditions, we might not see the poorer emerging lines because they’re just not those favorable conditions to promote that variation.
Drew Lyon: Okay, so, I think you’re maybe getting at it, but how will this genomic selection or these genomic selection models help you help farmers?
Lance Merrick: Good question, so, I mean that’s what hopefully all our research can do, right? So, for, and since for seedling emergence, hopefully, we can select lines that have better seedling emergence. So for years where there is a lot of crusting, instead of having a quarter of your field not emerge, and you lose all that yield, hopefully, we can develop and identify lines with better seedling emergence in which we don’t have a lot of poor stand-establishment. And the same kind of goes for stripe rust, hopefully, we can identify lines that exhibit more resistance from year to year and reduce the amount of fungicide application and other inputs.
Drew Lyon: Okay, well those are two important factors that many growers are very concerned about stripe rust is almost an annual thing, and the crusting when it happens, is pretty nasty, you have to go back out and reseed the whole field, and that’s lost time and money. So, good luck with your program and how long will you be here yet?
Lance Merrick: I should be here for another 2 years if everything goes well.
Drew Lyon: Okay, very good, so we can keep an eye out on your research and see what you find for us.
Lance Merrick: Exactly.
Drew Lyon: Thank you very much, Lance, appreciate your being my guest today.
Lance Merrick: Thank you.
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Drew Lyon: Thanks for joining us and listening to the WSU Wheat Beat podcast. If you like what you hear don’t forget to subscribe and leave a review on iTunes or your favorite podcasting app. If you have questions or topics, you’d like to hear on future episodes please email me at drew.lyon — that’s email@example.com –(firstname.lastname@example.org). You can find us online at smallgrains.wsu.edu and on Facebook and Twitter @WSUSmallGrains. The WSU Wheat Beat podcast is a production of CAHNRS Communications and the College of Agricultural, Human and Natural Resource Sciences at Washington State University. I’m Drew Lyon, we’ll see you next time.