Unraveling the Genetic Code: An Introduction to Genome-Wide Association Studies
Posted by kiko on April 4th, 2023
Introduction to Genome-wide Association Studies
A genome-wide association study is what the National Institutes of Health call "a study of common genetic variation across the whole human genome with the goal of finding genetic links to observable traits."Even though family linkage studies and studies with tens of thousands of gene-based SNPs measure genetic variation across the genome, the National Institutes of Health definition requires a good density and selection of genetic markers to capture a large proportion of common variants in the study population, measured in enough people to give enough power to detect variants with modest effects.
This talk is mostly about studies that try to test at least 100,000 SNPs that were chosen to stand in for as many SNPs as possible. A typical GWA study has four parts: 1) a large number of people with the disease or trait of interest and a suitable control group; 2) DNA isolation, genotyping, and data analysis to ensure high genotyping quality; 3) statistical tests for associations between SNPs that pass quality thresholds and disease/trait; and 4) replication of found associations in a separate sample population or experimental examination of functional implications.
Methods Used in Genome-wide Association Studies
The case-control design, which compares two large groups of people—one healthy control group and one diseased case group—is the most common approach used in GWA studies. The majority of common known SNPs are genotyped in each group's members. The exact number of SNPs varies depending on genotyping technology, but it is usually one million or more. The allele frequency of each of these SNPs is then compared between the case and control groups to see if there is a significant difference. The odds ratio is the fundamental unit for reporting effect sizes in such situations. The odds ratio is the ratio of two odds, which in GWA studies are the odds of a case for people who have a specific allele and the odds of a case for people who do not have that same allele.
The assessment of quantitative phenotypic data, such as height or biomarker concentrations or even gene expression, is a common alternative to case-control GWA studies. Alternative statistics for dominance and recessive penetrance patterns can also be used. Bioinformatics software such as SNPTEST and PLINK, which support many of these alternative statistics, is commonly used for calculations. The effect of individual SNPs is the focus of GWAS. Complex interactions between two or more SNPs, known as epistasis, may also play a role in complex diseases. Identifying statistically relevant interactions in GWAS data is computationally and statistically difficult due to the potentially infinite number of interactions.
Applications of Genome-wide Association Studies
High-throughput genotyping technologies are used in GWA studies to test hundreds of thousands of single-nucleotide polymorphisms (SNPs) and link them to clinical conditions and traits that can be measured. Since 2005, GWA studies have classified and replicated nearly 100 loci for as many as 40 common diseases and traits, many in genes not initially suspected of playing a role in the illness under study and some in genomic areas containing no known genes. GWA studies are a significant step forward in the discovery of disease-causing genetic variants, but they also have significant drawbacks, such as the potential for false-positive and false-negative results, as well as biases related to study participant selection and genotyping errors. Although applications of GWA findings in prevention and treatment are currently being pursued, these studies primarily serve as a valuable discovery tool for analyzing genomic function and clarifying pathophysiologic mechanisms.
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Joined: November 27th, 2018
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