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Genome-wide Association Studies-GWAS01:11

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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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Multiple SNP Set Analysis for Genome-Wide Association Studies Through Bayesian Latent Variable Selection.

Zhao-Hua Lu1, Hongtu Zhu1,2, Rebecca C Knickmeyer3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, North Carolina, United States of America.

Genetic Epidemiology
|October 31, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian latent variable selection (BLVS) method to enhance genome-wide association studies (GWAS). BLVS improves the power of GWAS for complex traits by jointly analyzing SNP sets, outperforming traditional single-SNP and SNP-set approaches.

Keywords:
Bayesian variable selectionGWASimaging phenotypeslinkage disequilibrium blocks

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) using single-nucleotide polymorphism (SNP) analysis face limitations in mapping complex traits due to modest SNP effects, unobserved causal SNPs, linkage disequilibrium, and SNP-SNP interactions.
  • Single SNP set analysis offers improved power over single-SNP approaches but does not fully account for correlations among SNP sets.
  • Developing powerful and accurate methods for genetic association mapping of complex traits remains a critical challenge in genomics.

Purpose of the Study:

  • To propose a novel Bayesian latent variable selection (BLVS) method for joint association mapping of multiple SNP sets and complex traits.
  • To enhance the power and accuracy of GWAS by simultaneously modeling associations between numerous SNP sets and phenotypes.
  • To address limitations of existing GWAS methods, including modest SNP effects and complex genetic architectures.

Main Methods:

  • A Bayesian latent variable selection (BLVS) framework is developed to jointly model associations between a large number of SNP sets and complex traits.
  • The method utilizes a spike-and-slab prior on SNP set effects to perform simultaneous variable selection and reduce dimensionality.
  • An efficient Markov chain Monte Carlo (MCMC) algorithm is implemented for computational feasibility.

Main Results:

  • The proposed BLVS method effectively models correlations among SNP sets, improving joint association mapping for complex traits.
  • BLVS demonstrates the capability to detect causal SNP sets that may not be marginally correlated with the phenotype.
  • Simulation studies indicate that BLVS outperforms several existing variable selection methods in key scenarios.

Conclusions:

  • The BLVS method provides a powerful and computationally efficient approach for enhancing GWAS of complex traits.
  • Joint association mapping using BLVS accounts for complex genetic architectures and improves the detection of relevant genetic variants.
  • This approach offers a promising alternative for dissecting the genetic basis of complex diseases and quantitative traits.