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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Multi-SNP haplotype analysis methods for association analysis.

Daniel O Stram1, Venkatraman E Seshan

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. stram@usc.edu

Methods in Molecular Biology (Clifton, N.J.)
|February 7, 2012
PubMed
Summary
This summary is machine-generated.

This study explores using haplotypes for genetic association testing, offering practical methods for analyzing associations with traits. It demonstrates that simpler haplotype analysis approaches are effective and efficient for both candidate genes and genome-wide studies.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Haplotypes offer advantages over single nucleotide polymorphisms (SNPs) in association studies.
  • Haplotypes can be in closer linkage disequilibrium (LD) with causal variants or be causal variants themselves.
  • Haplotype uncertainty presents statistical challenges in genetic association analysis.

Purpose of the Study:

  • To review the rationale for using haplotypes in association-based testing.
  • To provide practical guidance for testing haplotype-based associations with phenotypes.
  • To discuss computational strategies for genome-wide haplotype analysis.

Main Methods:

  • Incorporation of SNP haplotype analysis into generalized linear regression models.
  • Three methods discussed: imputed haplotypes, simultaneous maximum likelihood (ML) estimation, and a simplified ML approximation for case-control data.
  • Comparison of approximation-based methods with full ML for candidate gene analysis.

Main Results:

  • Simpler haplotype analysis methods are practical and effective.
  • Approximation-based methods perform well in practice compared to full ML.
  • Genome-wide haplotype risk estimation can be implemented efficiently with computational shortcuts.

Conclusions:

  • Haplotype analysis enhances genetic association studies by improving coverage and identifying causal variants.
  • Practical and computationally efficient methods exist for both candidate gene and genome-wide haplotype analyses.
  • The discussed methods provide valuable tools for genetic research and understanding trait associations.