In genetics, a genome-wide association study (GWASs), also known as whole genome association study, is an observational study of a genome-wide set of genetic variants in different individuals to see if any variant is associated with a trait. GWASs typically focus on associations between single-nucleotide polymorphisms (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.
It is an approach mostly used in genetics research to associate specific genetic variations with particular diseases. The method involves scanning the genomes from many different people and looking for genetic markers that can be used to predict the presence of a disease. Once such genetic markers are identified, they can be used to understand how genes come up with the disease and develop better prevention and treatment strategies.
The most common approach of GWA studies is the case-control setup, which compares two large groups of individuals, one healthy control group and one case group affected by a disease. All individuals in each group are genotyped for the majority of common known SNPs. The exact number of SNPs depends on the genotyping technology, but are typically one million or more. For each of these SNPs it is then investigated if the allele frequency is significantly altered between the case and the control group. In such setups, the basic unit for reporting effect sizes is the odds ratio. The odds ratio is the ratio of two odds, which in the context of GWA studies are the odds of case for individuals having a specific allele and the odds of case for individuals who do not have that same allele.
When the allele frequency in the case group is much higher than in the control group, the odds ratio is higher than 1, and vice versa for lower allele frequency. Additionally, a P-value for the significance of the odds ratio is typically calculated using a simple chi-squared test. Finding odds ratios that are significantly different from 1 is the objective of the GWA study because this shows that a SNP is associated with disease. Because so many variants are tested, it is standard practice to require the p-value to be lower than 5×10−8 to consider a variant significant. A p-value shows the significance of the difference in frequency of the allele tested between cases and controls. GWAS results are often displayed in a Manhattan plot with -log10 (p-value) plotted against the position in the genome.
There are several variations to this case-control approach. A common alternative to case-control GWA studies is the analysis of quantitative phenotypic data, e.g. height or biomarker concentrations or even gene expression. Likewise, alternative statistics designed for dominance or recessive penetrance patterns can be used. Calculations are typically done using bioinformatics software such as SNPTEST and PLINK, which also include support for many of these alternative statistics. GWAS focuses on the effect of individual SNPs.
Genome-wide association studies have helped identify SNPs associated with conditions such as type 2 diabetes, Alzheimer’s disease, Parkinson’s disease and Crohn’s disease.
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