eQTL tries to regress each gene expression against each SNP, in order to find those regulatory elements. And eQTL uses “normal” samples, right? (by normal I mean “no disease” like those in 1000genome project)

GWAS compares SNPs between normal(control) and disease(test) samples, trying to find out those higher-frequency variants enriched for diseases.

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.

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.

Genomic Convergence of Genome-wide Investigations for Complex Traits

###############################################################################

The following comes from Khader Shameer:

The following articles were really useful for me to understand the concepts around GWAS.

I would recommend the following reviews to understand the concept and methods. Most of these reviews refers the major studies and specific details can be obtained from individual papers. But you can get an overall idea about the concept, statistical methods and expected results from a GWAS studies from these review articles.

How to Interpret a Genome-wide Association Study

An easy to ready review article that start with basic concepts and discuss future prospects of GWAS Genome-wide association studies and beyond.

A detailed introduction to basic concepts of GWAS from the perspective of vascular disease : Genome-wide Association Studies for Atherosclerotic Vascular Disease and Its Risk Factors

Great overview of the current state of GWAS studies: Genomewide Association Studies and Assessment of the Risk of Disease

Detailed overview of statistical methods : Prioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application

For a bioinformatics perspective Jason Moore et.al’s review will be a good start : Bioinformatics Challenges for Genome-Wide Association Studies

Soumya Raychaudhuri’s review provides overview of various approaches for interpretations of variants from GWAS Mapping rare and common causal alleles for complex human diseases.

A tutorial on statistical methods for population association studies

Online Resources: I would recommend to start from GWAS page at Wikipedia followed by NIH FAQ on GWAS, NHGRI Catalog of GWAS, dbGAP, GWAS integrator and related question at BioStar.

##############################################################################

For introductory material, the new blog Genomes Unzipped has a couple of great posts: (From Neilfws)

Advertisements