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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.
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Related Experiment Video

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

A model-based approach to capture genetic variation for future association studies.

Susana Eyheramendy1, Jonathan Marchini, Gilean McVean

  • 1Department of Statistics, University of Oxford, Oxford, OX1 3TG, United Kingdom. eyheram@stat.uni-muenchen.de

Genome Research
|November 11, 2006
PubMed
Summary

This study introduces a probabilistic model to predict untyped SNPs (non-tags) using selected tag SNPs. This approach captures more genetic variation and aids in identifying disease susceptibility genes.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with diseases.
  • The high cost of genotyping limits the scope of GWAS.
  • Selecting a representative subset of single nucleotide polymorphisms (SNPs) as 'tag SNPs' is essential for efficient genetic variation capture.

Purpose of the Study:

  • To develop a novel probabilistic model for predicting the genotypes of non-tag SNPs based on tag SNPs.
  • To improve the capture of genetic variation in independent datasets.
  • To propose new methods for selecting tag SNPs.

Main Methods:

  • Utilizing a probabilistic model to predict non-tag SNP genotypes from tag SNP data.
  • Developing new algorithms for tag SNP selection.
  • Empirical validation using HapMap data.

Main Results:

  • The proposed probabilistic model effectively predicts non-tag SNP genotypes, enabling association testing.
  • The method provides confidence estimates for genotype predictions.
  • Empirical results demonstrate superior genetic variation capture compared to methods relying solely on pairwise linkage disequilibrium (LD).

Conclusions:

  • The probabilistic approach enhances genetic variation capture in GWAS by predicting untyped SNPs.
  • This method facilitates the identification of genes associated with complex traits and diseases.
  • The proposed tag SNP selection strategies and prediction model offer a cost-effective alternative for genetic studies.