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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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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
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Published on: November 3, 2010

Bayesian method to predict individual SNP genotypes from gene expression data.

Eric E Schadt1, Sangsoon Woo, Ke Hao

  • 1Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, USA. eric.schadt@mssm.edu

Nature Genetics
|April 10, 2012
PubMed
Summary
This summary is machine-generated.

Researchers developed a Bayesian method using RNA expression data and expression quantitative trait loci (eQTLs) to predict single nucleotide polymorphism (SNP) genotypes. This approach can accurately identify individuals in large populations from their RNA profiles.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene expression patterns, particularly those linked to expression quantitative trait loci (eQTLs), offer insights into genetic variation.
  • Accurate genotype inference is crucial for genetic studies and population-level analyses.

Purpose of the Study:

  • To develop and validate a novel Bayesian method for predicting single nucleotide polymorphism (SNP) genotypes solely from RNA expression data.
  • To assess the accuracy and uniqueness of individual identification using predicted genotypes across different tissue types.

Main Methods:

  • Utilized published cis eQTLs to build a Bayesian model for genotype prediction.
  • Applied the model to RNA expression datasets from independent cohorts.
  • Evaluated the performance of genotype prediction in identifying individuals within large populations.

Main Results:

  • The developed Bayesian approach accurately and uniquely identified individuals using predicted SNP genotypes derived from RNA expression data.
  • When using eQTLs from the same tissue type, 99% of individuals were resolved with high statistical significance (P(adjusted) ≤ 1 × 10(-5)).
  • Even when eQTLs and expression data were from different tissues, 90% of individuals could be identified (P(adjusted) ≤ 1 × 10(-5)).

Conclusions:

  • RNA expression data, combined with eQTL information, provides a powerful tool for genotype inference and individual identification.
  • The method demonstrates robustness across different tissue types, highlighting its potential for broad application.
  • This research has significant implications for leveraging publicly available RNA data for genotypic information extraction.