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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Gene set analysis for interpreting genetic studies.

Tune H Pers1

  • 1Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic, Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark tune.pers@sund.ku.dk.

Human Molecular Genetics
|August 12, 2016
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Summary

Interpreting genome-wide association studies (GWAS) is challenging. Gene set analysis (GSA) offers a data-driven approach to uncover biological insights from genetic data, but requires improved gene set representations.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) rapidly discover genetic associations but lag in interpretation.
  • A significant gap exists between the discovery of genetic variants and understanding their biological implications for complex traits.
  • Existing gene set analysis (GSA) tools face challenges due to inconsistencies in gene set definitions.

Purpose of the Study:

  • To review current gene set analysis (GSA) methodologies for interpreting genome-wide association study (GWAS) results.
  • To highlight the impact of inconsistent gene set definitions on GSA outcomes.
  • To explore future directions for enhancing GSA in genetic research.

Main Methods:

  • Review of existing literature on gene set analysis (GSA) and genome-wide association studies (GWAS).
  • Examination of different types of gene sets and their representations.
  • Discussion of how inconsistencies in gene set definitions affect GSA results.

Main Results:

  • Gene set analysis (GSA) can identify etiological pathways and functional annotations, providing biological insights.
  • Inconsistencies in gene set definitions hinder efficient and accurate interpretation of GWAS findings.
  • Improved and standardized gene set representations are crucial for advancing GSA.

Conclusions:

  • More complete and consistent gene set representations are essential for efficient interpretation of GWAS.
  • Gene set analysis (GSA) holds significant potential for uncovering novel biological insights from genetic data.
  • Future research should focus on standardizing gene set definitions to maximize the utility of GSA.