The impact of DNA sequencing technology on agriculture

Published on July 1, 2014   36 min

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Other Talks in the Series: Agricultural Genetics

0:00
Hi, I'm Stephanie Pearl, and I'm a researcher at the University of Georgia. Today, I will be giving you an overview about some of the ways recent advances in DNA sequencing technology has impacted agriculture. You may notice that some of the topics that I touch on today are covered in much more detail in some of the other lectures within the agricultural genetics section of the Henry Stewart talks.
0:26
So I will begin with an overview. And I'll begin by giving an introduction in which I first talk about some of the world's most important crops, and introduce them in the context of breeding and genomics. And then I'll delve into a little bit more information on how one goes about conducting a sequencing project. And then, how does one actually apply the sequencing data to advancing breeding populations? And then, I'll conclude with some thoughts looking forward. So to begin.
0:55
Pictured here is a list of 12 the world's most important crops. And this is based on total area harvested. And I've listed their genome sizes. So for example, if you look at ground nut or peanut, you could see that has a genome 3 billion base pairs. And I've also listed the ploidy level, or the total number of copies of chromosomes in each of these species. If you look at potato, you can actually see that different individuals have variable ploidy levels, starting from diploid all the way up to hexaploid. And I've also listed the top production areas. So there's quite a bit of information listed on this slide. So you may wish to pause for a moment to take it all in. Really, there are just a few points that I wish to make here. First of all, these are the 12 most important crops as of the year 2012. And this ordering has remain relatively unchanged in the past 50 years. Therefore, because of the importance of these crops, they have been the focus of genomics analyses. And so here, with the red stars next to each one of the crops, I've indicated which species have genome sequence data. So what about wheat, sugar cane, sunflower, and peanut? Why don't they have whole genome sequence data yet? Well, this is currently a work in progress. And as you look at the genome size and ploidy level of these species, you can see that compared to the other ones, they're a bit more unwieldy to deal with. So how is genomics data used to advance and improve these crops? Well, before I answer that question, let's first consider how traditional breeding proceed.
2:28
Pictured here is the breeding cycle. It begins with crossing among desirable individuals and then planting the offspring and growing them to maturity and assessing them for how they look-- or phenotyping-- those individuals. And then, selecting desired extremes of that population. And then planning the offspring and phenotyping and selecting again and again. Well, this is a long and laborious process, as one could imagine. And so, there are a variety of limitations to this. For one, when you select a single trait, it's often difficult to predict how selecting that one trait will influence other traits that are being selected upon. It's also difficult to tell the impact on the environment on the version of the trait that you're seeing. Also, this traditional breeding process typically works best for single gene traits. And as you may know, most agronomically important traits are continuously varying-- meaning that they're complex and included by many genes. For these complex traits, it's necessary to screen a large population of individuals to achieve the desired selective response. So how long does it take to achieve the desired selective response? Well this depends on the strength and efficiency of selection, the size of your population, the generation time of your individual species, and the amount of standing genetic variation available within the population. Let's consider this last point a bit more.
3:51
Consider the wild progenitor of your crop species. And let's compare a selected gene-- or the gene that's responsible in coding the trait of interest. Let's also look at a neutral gene-- so this is a gene that doesn't really have an impact on the trait that you're interested in. And each one of these balls pictured here represents the standing genetic diversity within the population of the wild progenitor for these two genes. During the process of domestication, you're selecting the individual variants of the selected gene-- those variants that are important for your trait of interest. And in the process, because you're sub-sampling the entire wild population, you're also reducing the amount of neutral genetic diversity available. As a result, your domesticate has few or no variation at the selected gene and a reduced amount of neutral genetic diversity. This total reduction in diversity makes it difficult to achieve the desired selected gains after multiple subsequent rounds of breeding. And this can be problematic for breeders. This situation is described by Fisher's fundamental theorem, which describes that the rate of selective response equals the genetic variance at that time.
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The impact of DNA sequencing technology on agriculture

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