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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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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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A variational Bayes algorithm for fast and accurate multiple locus genome-wide association analysis.

Benjamin A Logsdon1, Gabriel E Hoffman, Jason G Mezey

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA.

BMC Bioinformatics
|January 29, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces V-Bay, a variational Bayes algorithm for genome-wide association (GWA) analysis. V-Bay efficiently identifies weaker genetic associations, helping to explain missing heritability in complex diseases.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genomics

Background:

  • Genome-wide association (GWA) studies have identified candidate loci for complex diseases but fail to explain substantial heritability.
  • A significant portion of heritability remains unexplained, suggesting the contribution of weaker genetic associations.
  • Existing methods face computational challenges in analyzing multiple loci simultaneously.

Purpose of the Study:

  • To introduce V-Bay, a novel variational Bayes algorithm for multiple locus GWA analysis.
  • To address the limitations of current methods in identifying weaker genetic associations.
  • To improve the explanation of missing heritability in complex diseases.

Main Methods:

  • Developed V-Bay, a variational Bayes algorithm for simultaneous multiple locus GWA analysis.
  • Evaluated V-Bay using simulated genetic and GWA experimental data.
  • Compared V-Bay's performance against single-marker testing and a lasso-based multiple locus method.

Main Results:

  • V-Bay offers a computationally efficient solution, analyzing a million genetic markers in hours on a desktop.
  • Demonstrated high accuracy in identifying weak associations missed by single-marker approaches.
  • V-Bay outperformed a lasso-based method with similar scaling properties for large marker sets.
  • Confirmed associations with gene expression in HapMap cell lines.

Conclusions:

  • V-Bay is a versatile, fast, and accurate tool for multiple locus GWA analysis.
  • Enables practitioners to identify weaker associations with reduced false positive rates.
  • Provides a valuable approach for tackling the missing heritability problem.