QTL Mapping - Online Tutor, Practice Problems & Exam Prep

Quantitative Trait Loci (QTL) mapping identifies the locations of multiple genes influencing complex traits, such as height or weight. The process involves creating inbred lines, performing backcrosses, and analyzing single nucleotide polymorphisms (SNPs) to determine gene associations. In random mating populations, association mapping utilizes linkage disequilibrium to identify genes related to traits or diseases through genome-wide association studies. This method allows for the identification of genes without the need for fine mapping, making it applicable in humans and other organisms.

QTL Mapping

So, quantitative trait loci. 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? A quantitative trait is a complex trait. What's a complex trait? That's going to be a trait that is controlled by multiple genes. Loci just means location. So quantitative trait loci, or QTL mapping, is determining that location. This topic is about finding the locations of the multiple genes contributing to a trait controlled by multiple genes. For instance, take height, which could be controlled by, let's say, seven genes. It's not, but for simplicity's sake, let's imagine it's controlled by seven genes. QTL mapping is actually determining the locations of all seven of those genes. So, this is about traits. Quantitative traits, like I mentioned, are traits that can be measured. They're usually continuous traits, controlled by multiple genes. QTL mapping is the method for determining where they are in the genome.

The method of QTL mapping is as follows. The first thing you do is you make two inbred lines. These lines have been mated to their siblings and parents so many times that they're essentially genetically identical. You take two of them, one potentially large one and one small one. For example, if we're looking at tomato weight, we'll take a huge tomato and a small tomato. These are inbred, so they're clones. You mate them together to produce an F1 generation. However, the F1 generation will not be large or small; it will be intermediate. Let's just say it weighs around 115 grams. We don't particularly know, but somewhere around there. Then, you take these 115 gram tomatoes, this F1 generation, and you perform a backcross. A backcross means that you mate it with one of its parents. So, we'll take the F1 and cross it with the 230 gram tomato. The offspring of this cross are called the backcross one generation or BC1.

Once you've done those crosses, you have this BC1 generation. You do two things with them: you take DNA samples from them and from the original parents, so the 230 and the 10 gram tomatoes. You sequence the genome for single nucleotide polymorphisms or SNPs. You divide the entire genome based on SNP markers. You then calculate the weight for each of the BC1 tomatoes and determine the means for all BC1 offspring as well as groups with the same SNPs. With this data, you compare each SNP to see if it affects the trait, like fruit weight. For instance, if the overall mean weight of the BC1 tomatoes is 176.3 grams, and the mean at SNP 1 for tomatoes inheriting different alleles varies around this mean, it suggests the presence or absence of a QTL affecting weight at that SNP. If they differ significantly, then a QTL is present.

After identifying a potential QTL, the next step is fine mapping to narrow down which specific gene within a large genomic region (like between SNP2 and SNP4) is responsible for the trait variance. This involves using nearly isogenic lines where small genetic differences exist due to crossover events within the QTL region. By analyzing the resulting phenotypes, researchers can determine which specific gene within the region affects the trait.

So, we start with QTL mapping to find the general areas where genes influencing polygenic traits are located and then use fine mapping to identify the specific genes. This method is crucial for understanding how complex traits are inherited and can be applied to various organisms and traits. Now, let's move on.

QTL Mapping in Humans

Okay. So now let's talk about QTL mapping and random mating populations. These are populations where mating is not controlled in a laboratory; they are randomly mating. This includes groups like humans, in which you can perform QTL mapping. This method is given a special name called association mapping, and I'll provide the tough definition and then explain how it's practically conducted. It identifies the location of these quantitative genes, those genes responsible for quantitative traits, QTLs, in genomes, based on linkage disequilibrium. So, what is linkage disequilibrium? We've discussed it before, but just to remind you, this is the non-random association of alleles. This means if alleles that would otherwise be assorted independently into gametes aren't. They are found in combination with each other more often than would be expected by chance. They tend to travel together throughout genetic history and are often found together. This method is crucial because it can be conducted in humans, locating genes responsible for complex traits. It can test many alleles simultaneously, does not require focusing on one, and does not necessitate crosses. Due to these factors, we can test it in humans or other organisms where different processes are not feasible. It also does not require fine mapping because association mapping directly identifies the gene associated with the QTL. The primary method of mapping using the association method is called a genome-wide association study (GWAS).

Here’s how it works: say you're interested in examining the genes, potentially multiple genes involved in a disease. You sequence the genome of 2,000 individuals with the disease and 2,000 without (2,000 cases and 2,000 controls). You identify all the SNPs in the genome. Right? You map them out as previously done with tomatoes. Statisticians handle a vast amount of data; considering the size and complexity of the human genome, this requires significant computational power. They look at all the SNPs to determine if one SNP is found more frequently associated with the disease than others. If it is, they infer that this SNP is likely associated with that disease. Therefore, they specify the SNP's location, identifying if the gene present there is responsible for this complex or quantitative trait. So, don’t worry about reading the tiny text below, but here is the human genome, the human chromosomes, and each dot represents a SNP associated with a disease of some kind. The diseases are listed below if you're interested in exploring further. Obviously, this is extensive, it was done in 2009, and there is much more data since then. This type of data is vital in identifying genes responsible for causing diseases, these phenotypic traits that we observe in the form of disease. This process is frequently conducted and is critically important. It’s good that you know about it now. So with that, let's now move on.

Both QTL mapping and association (GWA) mapping are used to locate genes responsible for a phenotype. Which of the two techniques does NOT require crosses to produce a mapping population

• A. QTL mapping
• B. Association mapping

Both QTL mapping and association mapping are used to locate genes responsible for a phenotype. Which of the following typically tests two differing alleles between the parents of a mapping population?

• A. QTL mapping
• B. Association mapping

True or False:Association (GWA) mapping definitively proves that the gene identified is responsible for the trait variation or phenotype?

• A. True
• B. False

Here’s what students ask on this topic:

What is QTL mapping and how is it used in genetics?

QTL mapping, or Quantitative Trait Loci mapping, is a method used in genetics to identify the locations of multiple genes that influence complex traits, such as height or weight. The process involves creating inbred lines, performing backcrosses, and analyzing single nucleotide polymorphisms (SNPs) to determine gene associations. By comparing the genetic markers (SNPs) with the phenotypic traits, researchers can pinpoint regions in the genome that contribute to the trait. This method is crucial for understanding the genetic basis of complex traits and can be applied to various organisms, including plants, animals, and humans.

What are the steps involved in QTL mapping?

The steps involved in QTL mapping are as follows: First, create two inbred lines that are genetically identical. Second, cross these lines to produce an F1 generation, which is then backcrossed with one of the parent lines to produce a BC1 generation. Third, take DNA samples from the BC1 generation and the parent lines, and sequence the genome to identify SNPs. Fourth, calculate the means of the phenotypic traits for the BC1 generation and compare them with the SNP markers. Finally, use statistical methods to determine if there is a QTL at a specific SNP, indicating a gene influencing the trait in that genomic region.

What is the difference between QTL mapping and association mapping?

QTL mapping and association mapping are both used to identify genes influencing complex traits, but they differ in methodology and application. QTL mapping involves controlled crosses and is typically used in laboratory settings with inbred lines. It requires fine mapping to pinpoint the exact gene within a QTL region. Association mapping, on the other hand, is used in random mating populations, such as humans. It relies on linkage disequilibrium and does not require controlled crosses or fine mapping. Instead, it uses genome-wide association studies (GWAS) to identify SNPs associated with traits or diseases directly.

How does linkage disequilibrium play a role in association mapping?

Linkage disequilibrium (LD) is the non-random association of alleles at different loci. In association mapping, LD is crucial because it allows researchers to identify regions of the genome where alleles are inherited together more frequently than expected by chance. By analyzing these regions, researchers can pinpoint SNPs that are associated with specific traits or diseases. This method is particularly useful in random mating populations, such as humans, where controlled crosses are not feasible. LD helps in identifying the genetic basis of complex traits without the need for fine mapping, making the process more efficient and applicable to a wider range of organisms.

What is a genome-wide association study (GWAS) and how is it conducted?

A genome-wide association study (GWAS) is a method used to identify genetic variants associated with traits or diseases across the entire genome. To conduct a GWAS, researchers first collect DNA samples from a large number of individuals, typically thousands, with and without the trait or disease of interest. They then sequence the genomes to identify SNPs. Statistical analysis is performed to determine if any SNPs are significantly associated with the trait or disease. If a SNP is found more frequently in individuals with the trait or disease, it suggests that the SNP, or a nearby gene, may be involved in the trait or disease.