Genetic association is when one or more genotypes within a population co-occur with a phenotypic trait, more often than it would be expected by chance occurrence.
Studies of genetic association aim to test whether single-locus alleles or genotype frequencies (or more generally, multilocus haplotype frequencies) differ between two groups of individuals (usually diseased subjects and healthy controls). Genetic association studies are based on the principle that genotypes can be compared "directly", i.e. with the sequences of the actual genomes.
What is genetic association?
Genetic association can be between phenotypes, such as visible characteristics such as flower colour or height, between a phenotype and a genetic polymorphism, such as a single nucleotide polymorphism (SNP), or between two genetic polymorphisms. Association between genetic polymorphisms occurs when there is non-random association of their alleles as a result of their proximity on the same chromosome; this is known as genetic linkage.
Much of genetic variation in the human genome is in the form of SNPs which is the result of point mutations that produce single base-pair differences (substitutions or deletions) among chromosome sequences. GWA studies have become an important tool for discovering susceptibility genes for complex diseases. Information, including genotype frequencies, linkage disequilibrium (LD), and recombination rates, across populations help researchers to conduct GWA analysis using millions of SNP markers.
Linkage disequilibrium (LD) is a term used in the study of population genetics for the non-random association of alleles at two or more loci, not necessarily on the same chromosome. It is not the same as linkage, which is the phenomenon whereby two or more loci on a chromosome have reduced recombination between them because of their physical proximity to each other. LD describes a situation in which some combinations of alleles or genetic markers occur more or less frequently in a population than would be expected from a random formation of haplotypes from alleles based on their frequencies.
Genetic association studies are performed to determine whether a genetic variant is associated with a disease or trait: if association is present, a particular allele, genotype or haplotype of a polymorphism or polymorphisms will be seen more often than expected by chance in an individual carrying the trait. Thus, a person carrying one or two copies of a high-risk variant is at increased risk of developing the associated disease or having the associated trait.
Case control studies are a classical epidemiological tool. Case-control studies use subjects who already have a disease, trait or other condition and determine if there are characteristics of these patients that differ from those who do not have the disease or trait. In genetic case-control studies, the frequency of alleles or genotypes is compared between the cases and controls. The cases will have been diagnosed with the disease under study, or have the trait under test; the controls, who are either known to be unaffected, or who have been randomly selected from the population. A difference in the frequency of an allele or genotype of the polymorphism under test between the two groups indicates that the genetic marker may increase risk of the disease or likelihood of the trait, or be in linkage disequilibrium with a polymorphism which does. Haplotypes can also show association with a disease or trait.
One problem with the case-control design is that genotype and haplotype frequencies vary between ethnic or geographic populations. If the case and control populations are not well matched for ethnicity or geographic origin then false positive association can occur because of the confounding effects of population stratification.
Family based designs
Family based association designs aim to avoid the potential confounding effects of population stratification by using the parents or using unaffected siblings as controls for the case, which is their affected offspring/ siblings. The most commonly used test is the transmission disequilibrium test, or TDT. Two similar tests are used, the transmission disequilibrium test (TDT) and haploid-relative-risk (HRR). Both measure association of genetic markers in nuclear families by transmission from parent to offspring. If an allele increases the risk of having a disease then that allele is expected to be transmitted from parent to offspring more often in populations with the disease.
Quantitative trait association
A quantitative trait (see quantitative trait locus) is a measurable trait that shows continuous variation, such as height or weight. Quantitative traits often have a 'normal' distribution in the population. In addition to the case control design, quantitative trait association can also be performed using an unrelated population sample or family trios in which the quantitative trait is measured in the offspring.
Statistical programs of association analysis
There are many computer packages for analyzing genetic association, such as UNPHASED, WHAP, FBAT, Merlin, PLINK, ParseCNV and Golden Helix's SNP & Variation Suite. However simple genotypic or allelic association with a dichotomous trait can be measured using the chi-squared test for significance.
- Genetic epidemiology
- Genetic linkage
- Linkage disequilibrium
- Personality genetics
- Family based QTL mapping
- Association mapping
- Genome-wide association study
- A list of computer programs for genetic analysis including genetic association analysis
- Golden Helix SNP & Variation Suite: Software package for population and family-based genetic association analysis
- GWAS Central - a central database of summary-level genetic association findings
- Fareed, M., Afzal, M (2013) "Single nucleotide polymorphism in genome-wide association of human population: A tool for broad spectrum service". Egyptian Journal of Medical Human Genetics 14: 123–134. http://dx.doi.org/10.1016/j.ejmhg.2012.08.001.
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- Tushar R Bhangale, Mark J Rieder and Debora A. Nickerson. Estimating coverage and power for genetic association studies using near-complete variation data. NATURE GENETICS v. 40 N.7 July 2008 pp 841-843