linkage mapping/recombination mapping/positional cloning - rely on known markers (typically SNPs) that are close to the gene responsible for a disease or trait to segregate with that marker within a family. Works great for high-penetrance, single gene traits and diseases.
QTL mapping/interval mapping - for quantitative traits like height that are polygenic. Same as linkage mapping except the phenotype is continuous and the markers are put into a scoring scheme to measure their contribution - i.e. "marker effects" or "allelic contribution". Big in agriculture.
GWAS/linkage disequilibrium mapping - score thousands of SNPs at once from a population of unrelated individuals. Measure association with a disease or trait with the presumption that some markers are in LD with, or actually are, causative SNPs.
So linkage mapping and QTL mapping are similar in that they rely on Mendelian inheritance to isolate loci. QTL mapping and GWAS are similar in that they typically measure association in terms of log-odds along a genetic or physical map and do not assume one gene or locus is responsible. And finally, linkage mapping and GWAS are both concerned with categorical traits and diseases.
Hi! A very good answer (GWAS vs linkage analysis) by David and Jarretinha is here. This is one of the answers-
Most of your definitions are correct. Linkage studies are performed when you have pedigrees of related individals and a phenotype (such as breast cancer) that is present in some but not all of the family members. These individuals could be humans or animals; linkage in humans is studied using existing families, so no breeding is involved. For each locus, you tabulate cases where parents and children who do or don't show the phenotype also have the same allele. Linkage studies are the most powerful approach when studying highly penetrant phenotypes, which means that if you have the allele you have a strong probability of exhibiting the phenotype. They can identiy rare alleles that are present in small numbers of families, usualy due to a founder mutation. Linkage is how you find an allele such as the mutations in BRCA1 associated with breast cancer.
Association studies are used when you don't have pedigrees; here the statistical test is a logistic regression or a related test for trends. They work when the phenotype has much lower penetrance; they are in fact more powerful than linkage analysis in those cases, provided you have enough informative cases and matched controls. Association studies are how you find common, low penetrance alleles such as the variations in FGFR2 that confer small increases in breast cancer susceptibility.
In The Old Days, neither association tests nor linkage tests were "genome-wide"; there wasn't a technically feasable or affordable way to test the whole genome at once. Studies were often performed at various levels of resolution as the locus associated with the phenotype was refined. Studies were often performed with a small number of loci chosen because of prior knowledge or hunches. Now the most common way to perform these studies in humans is to use SNP chips that measure hundreds of thousands of loci spread across the whole genome, thus the name GWAS. The reason you're testing "the whole genome" without sequencing the whole genome of each case and control is an important point that is a separate topic; if you don't yet know how this works, start with the concept of Linkage Disequilibrium. I haven't encountered the term GWLS myself, but I think it's safe to say that this is just a way to indicate that the whole genome was queried for linkage to a phenotype.
For organisms whose genomes are known, one might now try to exclude genes in the identified region whose function is known with some certainty not to be connected with the trait in question. If the genome is not available, it may be an option to sequence the identified region and determine the putative functions of genes by their similarity to genes with known function, usually in other genomes. This can be done using BLAST, an online tool that allows users to enter a primary sequence and search for similar sequences within the BLAST database of genes from various organisms. It is often not the actual gene underlying the phenotypic trait, but rather a region of DNA that is closely linked with the gene.
Another interest of statistical geneticists using QTL mapping is to determine the complexity of the genetic architecture underlying a phenotypic trait. For example, they may be interested in knowing whether a phenotype is shaped by many independent loci, or by a few loci, and do those loci interact. This can provide information on how the phenotype may be evolving.
Very good article: Identification of loci governing eight agronomic traits using a GBS-GWAS approach and validation by QTL mapping in soya bean describing GWAS and QTL mapping and combining both for precision mapping. I feel this is very good teaching material. Authors have analysed same traits with GWAS and also with QTL mapping under same environment, so that mapping resolution can be compared. GWAS is having advantage over QTL mapping but it is not always true specifically when trait distribution is not even. In other word, if your trait of interest present in very small proportion (<5%), then it is not possible to map loci by GWAS. But, QTL mapping can map such trait using bi-parental population.