Hi in this video we're gonna be talking about Q. Tl mapping. So quantitative trait Los I I feel like that term scares a lot of people. I mean it scared me when I was learning about it too. But let's just break it down first. What is a quantitative trait? Quantitative trait is a complex trait. What's a complex trait that's gonna be a trait that is controlled through multiple genes. Los I just means location. So quantitative trait Los I all you're doing or Q. Tl mapping right mapping is determining that location. So all this topic is about is finding the locations of the multiple genes contributing to a trait controlled by multiple genes. So you have this trait it could be height for instance heights controlled by let's say seven genes. It's not let's just say it's controlled by seven gene Q. Tl mapping is actually determining the locations of all seven of those genes. So it's really cool. Um So this is traits um So quantitative traits like I said here are traits that can be measured. They're usually continuous traits, their traits that are controlled by multiple genes. Detail mapping is the method for determining where they are in the genome. So the method of mapping is as follows. So the first thing you do is you make two inbred lines. What I mean by that I mean these lines have been mated to their brothers and sisters and their parents so many times that they're essentially genetic identical, genetically identical. So what you do is you take two of them. So you take one potentially large ones. So if we're looking at tomato weight will take a huge tomato and then we'll take a small tomato and they're inbred. So they're they're clones essentially they're genetically identical. And we make them together. Now if we made a very large tomato and a very small tomato, you're going to produce an F. One generation. But that F one generation is not going to be large or small, it's going to be intermediate. We'll just say 1 15 g. Right. Somewhere around there, we don't particularly know, but somewhere around there. Um And then what you do is you take these 115 g tomatoes, this F- one generation. And you perform a back cross back cross means that you made it with its mom or dad, its parent. So this is the cross that you do. In this case we're gonna look at The large tomato. So we'll take the f. One and we'll cross it to the 230 gram tomato. And this is the back cross and the offspring of this are called the back cross one generation or B. C. One. Now the BC one is really where it gets going. So once you've done those crosses, um you have this BC 1 um All these tomatoes. Right? So you do two things with them. The first thing that you do is you take DNA samples from them and you take DNA samples from the parents of the original parents, those 2 30 10. Right? So you take DNA samples from these and then all the Bc one offspring. And you sequenced the genome. But you don't only sequence the genome. What you're sequencing for is single nucleotide polymorphisms. So where are changes in the genome between all of these? And what you do is you actually take the entire genome and you divide it based on snip markers. So you say, okay well there's one snip snip one and it's here then a little bit further you have snip two and it's here and then you have snip three and it's here and you do that for the entire genome. You divide the genome by snips At the same time. The same tomatoes that you just took the genome from, you sequence it. And you divided it by snips you calculate the weight for each of those BC- one tomatoes. And you know the reason that you do this is because you need to calculate means these averages. So you calculate the means for all of the Bc one tomatoes. So the whole Bc one offspring. You calculate the mean of that. But you also calculate beans for all Bcs with the same markers. So this goes back to the snips right? So every tomato that has snip one gets a mean, every tomato that has snipped too, that gets a separate mean, everyone that has snip three gets another mean And you do this for hundreds and thousands of snips present in the genomes And so you do this and you end up with this huge data set that I can't we can't even really grasp how big it is and statisticians have to be able to do it. I'm gonna show you a very simple example here. Um But what you do with these snips is you compare each of the snips back to the snips present in the parents. And I'll show you that in a second. So we um say we can determine if there's a Q. Tl at snip one for instance if it's affecting fruit weight then um so if it's affecting fruit weight then the overall means the mean of all the Bcu tomatoes that we calculated will not equal the mean at snip one. Whereas if there's no Q. T. L. Then the overall mean will equal the marker me. And then you can use that stats called lod scores to confirm that this has actually happened. So what you do is remember you have the B. C. One and you have all these markers. So we'll say this is snip one snip to snip three snip four. Right? And so we say that our overall mean of all the BC one tomatoes is 176.3. That's their white. Now we start looking at all the tomatoes with SNIP one. What is the mean? Well this is where you start breaking it into what alleles they have. So if the you start breaking down the markers by the way. So if the if the bc one, tomatoes have the same markers as the large tomato. The 2 30. Right? So if it got both of its alleles from the large tomato, then we take its main And in this case it's 1 76.5 at SNIP one. Then we take all the tomatoes that snip one and say, okay, well what if they didn't have both of its illegals from the large tomato? What if they had won a leo from the large tomato and one allele from the small tomato? And we calculate the mean of that. And that's 1 74.5. When we say that this is uh 1 76.51 74.5 Is how close is that? Is to the overall weight. If it's equal, that means there's no gene here. So there's no QTL here affecting weight at SNIP one. Then we do the same thing for SNIP two. Right? We say, okay, let's take all those sequences. Look at their leal's, do they have the large tomato appeal to the large tomato? Well, let's take the mean 1 78.6. Let's do the same for the large and small. 1 73.4, compare it 1 76.3 to this and this fairly equal no Q. T. L. So snip two is not affecting wayne disappear. So now we're on marker three. So again we're on now at Snip three and um we're looking at you know what does this have? Does it have the largest of the small? So if it has two of the largest it's 1 82.1. And if it has one large one small 1 68.4. So although it's only a little bit different it's quite I mean if you're comparing how close these two were this is actually fairly different than these two. Right? And like I said you usually do statistics to confirm that they're actually different. But for now I'm gonna just tell you they're different. And so if it is different from the mean will not equal the marker mean. But is different than A. Q. T. L. Is here. What does that mean? Again that means that in this area between snip two and snip four. So here to here in the genome there is a gene in there that's affecting weight. If we do the same thing for step four we'll say no Q. Tl right Because this so somewhere between Snip two and Snip four in this huge genomic region here, somewhere in here there's a gene that's affecting weight but this could be a huge region. Right? This is what we get onto next. We've identified the Q. Tl we say okay it's between snip two and step four but we actually have to identify the gene and there could be hundreds of genes um in between two and sent four. Right? And so we have to actually do another step here. And this step is called fine mapping. And fine mapping is the method used to determine where the gene is in the Q. T. Oh and so how you do this is in you know tomatoes or anything that you can continually make. Like we have been um you use these special stocks called carnet stocks are nearly Aisa genic. And what they are is that these are genetically identical stocks but they have a slight difference in them. And the difference is that in this region near snip three there's been crossovers meaning that sometimes that crossover of the S or the small alil has jumped into a region with the L. A. Well in it. And so those crossovers can actually be um examined to determine which gene is important. So we have this Q. Tl here. Right so this here is the region between snip two and snip four. To snap four snip three. So here's our Q. T. L. And come to find out do some more examining into what's already known about the tomato genome. There's actually five genes here in this region. So now we have to do some more crosses and figure out or look at these lines and figure out which gene is actually causing it. So if we look at which tomato has a smaller wheel which has a larger lille for each of these genes, then we can figure it out. So a tomato with a large alil for these and a small, a little for this weighs 1 80.6. If we um keep going here. Small, small, 1 81.4, let me disappear. So I don't keep walking in front of this. If we keep going 1 65.9 and this starts to be different than what we originally looked at. Right? Large, small, small, small, small. Same here. And we then start doing the reverse. Right? Large, large. This is supposed to be read as well, right? And we just look for which tomatoes way different than the mean that they should write. And so in this case we'll say the mean is 180. Right? And so these are all weighing 1 80. Whereas these four are not close to 1 80 they're different enough. And therefore, what's the what's the soul similarity between these four weights and these four lines. And that is the fact that gene number three here has a cross over into the small alil each time. None of the other. So, these four here, two of them. Um It doesn't work. So these what am I trying to do? Yeah, here we go. These four here. I think my my numbers are a little bit off, which is why I got confusing. So this one here goes here. Um this one is here, this one is here and this one is here. So these four don't have that in common. So this can't work. These four don't have similar weights in common. That can't work saying for all of these the only one that works is this because these four have similar weights in common and therefore gene three is that that is the gene responsible for tomato. Wait. So with that hopefully that's clear. Um And how that works for fine mapping. Um So we start with the Q. Tl. We have no idea where these genes are. We do that for every gene right? Q. Tl. So it's multiple genes. So those multiple markers will identify multiple Q. TLS for every single gene. And then you do fine mapping to determine where those genes are in the genome. So remember the purpose Q. Tl mapping. The purpose is to identify you know which where those genes are for these apologetic traits. So with that let's not move on.

Okay so now let's talk about Q. Tl mapping in random mating population. So this is these are populations that are not just sort of their mating is not controlled in a laboratory. They're random mating. This includes things like humans which you can actually do Q. Tl mapping in. And this is actually given a special type of mapping called association mapping. I'll give you the tough definition and then I'll walk you through how it's actually done. So it it identifies the location of these quantitative genes these genes responsible for quantitative traits. U. G. L. S in genomes based on language disequilibrium. And so what is language dis equilibrium? We talked about it before but just want to remind you this is the non random association of illegal. So this is if leo's that would otherwise be sort of assorted independently into gametes aren't otherwise they're found in combination with each other more often than would be expected by chance. So they're trying to travel together throughout this like genetic history. They tend to be found together. And so um this method is super important because it can actually be done in humans and this is how it is done to identify the locations of genes that are responsible for these complex traits. And so it can test many alleles at once. It doesn't have to focus one and it doesn't need crosses. And so because of all these reasons we can test it in humans or other organisms in which it's not feasible to do all these different weird crosses with them. And so it also does not require by mapping because the process of association mapping identifies the gene at the Q. T. L. At the time it doesn't. So the main method of mapping using association method is called a genome wide association study. And so how you do this? Say you're interested in looking at the genes potentially multiple genes involved in a disease. So what you do is you stick with The genome you take 2000 individuals with the disease and 2000 without a disease. So 2000 cases, 2000 controls. And you identify all the snips in the genome, right? And you do this right? You map them out like you did before with the tomatoes. And so um what statisticians do? It's a huge amount of data, right? I mean the human genome is fairly large and there's a fairly ton of snips. So it's it takes a lot of computer strength to be able to do this. But statisticians come in and they say they look at all the snips but what they're looking for is they're looking for is one snip found more frequently associated with the disease than another. And if it is and they're likely to say okay this is likely associated with that disease. And therefore we know the snip we say okay what gene is in this location and therefore we can identify if that that um that gene is responsible for this complex or quantitative trait. So um don't worry about reading this tiny text down here, but this is the human genome right here. The human chromosomes and each one of these dots represents a sniff that has been associated with the disease of some kind. The diseases are listed down here. If you're interested in looking at this, obviously this is a lot. This was done in 2009 and there's a lot of more data since then. And so we can see that this type of data is super important in identifying genes that are responsible for causing diseases. So these stereotypic traits that we see in form of disease. Um So this has done a lot done often and it's super important. Um So it's good you know about it now. So with that let's now move on.