Memorial Day Offer

Discover your mystery discount!

Genome-wide association study

Genome-wide Association Study Overview: – GWA studies identify common variants with small effect sizes. – Genetic association studies were suggested as an alternative to linkage […]

« Back to Glossary Index

Genome-wide Association Study Overview:
– GWA studies identify common variants with small effect sizes.
– Genetic association studies were suggested as an alternative to linkage studies for common diseases.
– Factors enabling GWA studies included biobanks and the International HapMap Project.
– The haploblock structure identified by the HapMap project aided in focusing on common SNPs.
– GWA studies investigate the entire genome for SNPs and genetic variants associated with diseases.
– GWA studies commonly use a case-control setup to genotype individuals at common SNPs.
– Odds ratio and P-values from chi-squared tests are fundamental in reporting effect sizes and determining significance in GWA studies.

Progress and Milestones in GWA Studies:
– The first successful GWAS was published in 2002 on myocardial infarction.
– Over 3,000 human GWA studies have examined 1,800+ diseases and traits as of 2017.
– Thousands of SNP associations have been discovered through GWA studies.
– Recent GWA studies focus on larger sample sizes and more narrowly defined phenotypes.
– Genome-wide studies now include millions of participants for better risk-SNPs detection.
– Manhattan plots and P-value thresholds are used to visualize SNP associations and correct for multiple testing issues in GWA studies.

Challenges and Considerations in GWA Studies:
– GWA studies cannot specify causal genes on their own.
– Weak individual associations provide insights into critical genes and pathways.
– Detecting statistically significant interactions in GWAS data is computationally and statistically challenging.
– Confounding variables like sex, age, and ancestry need to be accounted for in GWA studies.
– Population stratification due to genetic variations associated with geographical and historical populations must be controlled for.
– Validation of significant SNPs is typically done in an independent cohort post discovery analysis.

Clinical Applications and Examples of GWA Studies:
– GWA studies aim to accelerate drug and diagnostics development.
– Risk-SNP markers can improve prognosis accuracy but with varying success.
– Genetic variants can influence response to treatments like anti-hepatitis C virus treatment.
– GWA studies have identified SNPs that influence the efficacy of specific treatments.
– Genetic studies play a crucial role in personalized medicine, especially in infectious diseases like hepatitis C.

Applications of GWA Studies in Various Fields:
– GWA studies can identify adaptive genes for species to cope with changing environmental conditions.
– GWA studies in agriculture contribute to improving crop yield and disease resistance.
– Genetic studies of diseases like atrial fibrillation and schizophrenia provide insights into pathophysiology and potential therapies.
– Understanding eQTLs can lead to actionable drug targets for diseases like cardiovascular conditions.
– GWA studies offer valuable insights for conservation biology, biodiversity preservation, and enhancing agricultural practices.

Genome-wide association study (Wikipedia)

In genomics, a genome-wide association study (GWA study, or GWAS), 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. GWA studies 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.

Manhattan plot of a GWAS
An illustration of a Manhattan plot depicting several strongly associated risk loci. Each dot represents a SNP, with the X-axis showing genomic location and Y-axis showing association level. This example is taken from a GWA study investigating kidney stone disease, so the peaks indicate genetic variants that are found more often in individuals with kidney stones.

When applied to human data, GWA studies compare the DNA of participants having varying phenotypes for a particular trait or disease. These participants may be people with a disease (cases) and similar people without the disease (controls), or they may be people with different phenotypes for a particular trait, for example blood pressure. This approach is known as phenotype-first, in which the participants are classified first by their clinical manifestation(s), as opposed to genotype-first. Each person gives a sample of DNA, from which millions of genetic variants are read using SNP arrays. If there is significant statistical evidence that one type of the variant (one allele) is more frequent in people with the disease, the variant is said to be associated with the disease. The associated SNPs are then considered to mark a region of the human genome that may influence the risk of disease.

GWA studies investigate the entire genome, in contrast to methods that specifically test a small number of pre-specified genetic regions. Hence, GWAS is a non-candidate-driven approach, in contrast to gene-specific candidate-driven studies. GWA studies identify SNPs and other variants in DNA associated with a disease, but they cannot on their own specify which genes are causal.

The first successful GWAS published in 2002 studied myocardial infarction. This study design was then implemented in the landmark GWA 2005 study investigating patients with age-related macular degeneration, and found two SNPs with significantly altered allele frequency compared to healthy controls. As of 2017, over 3,000 human GWA studies have examined over 1,800 diseases and traits, and thousands of SNP associations have been found. Except in the case of rare genetic diseases, these associations are very weak, but while each individual association may not explain much of the risk, they provide insight into critical genes and pathways and can be important when considered in aggregate.

« Back to Glossary Index
This site uses cookies to offer you a better browsing experience. By browsing this website, you agree to our use of cookies.