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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
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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.
Forward genetic screens
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Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
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Related Experiment Video

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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GATES: a rapid and powerful gene-based association test using extended Simes procedure.

Miao-Xin Li1, Hong-Sheng Gui, Johnny S H Kwan

  • 1Department of Psychiatry and State Key Laboratory for Cognitive and Brain Sciences, the University of Hong Kong, Pokfulam, Hong Kong.

American Journal of Human Genetics
|March 15, 2011
PubMed
Summary

A new gene-based statistical test enhances genome-wide association studies (GWAS) by integrating functional data. This powerful method improves the detection of disease-susceptibility genes in complex diseases.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with complex diseases.
  • Existing methods for gene-level association testing in large GWAS datasets often lack power, speed, or the ability to integrate functional information.
  • A need exists for a robust statistical framework to analyze genes as units within comprehensive genomic datasets.

Purpose of the Study:

  • To develop and validate a novel, powerful, and efficient gene-based statistical test for analyzing large-scale genome-wide association study data.
  • To integrate single nucleotide polymorphism (SNP) association p-values within genes, incorporating functional information for enhanced significance testing.
  • To improve the identification of novel disease-susceptibility genes for complex genetic disorders.

Main Methods:

  • Development of an extended Simes test to combine p-values of SNPs within a gene, creating an overall gene-level p-value.
  • Integration of functional information and association evidence into the statistical testing framework.
  • Computer simulations to assess the test's power, type 1 error rate control, and comparison with existing SNP-based and gene-based methods.
  • Application of the test to real GWAS data for Crohn disease.

Main Results:

  • The proposed extended Simes test demonstrated higher statistical power compared to standard SNP-based tests.
  • The test effectively controlled the type 1 error rate across various gene sizes and linkage disequilibrium patterns without requiring permutation or simulation.
  • In simulated data, its power was comparable or superior to alternative gene-based tests.
  • Application to Crohn disease GWAS data identified more significant genes than existing methods.

Conclusions:

  • The developed extended Simes test offers a powerful and efficient approach for gene-level association analysis in large GWAS datasets.
  • This method effectively integrates functional and association data, enhancing the discovery of novel disease-susceptibility genes.
  • The open-source implementation has the potential to advance genetic research for complex diseases.