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.