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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.
GWAS does not require the identification of the target gene involved in...
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Single Nucleotide Polymorphisms-SNPs

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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

A novel algorithm for simultaneous SNP selection in high-dimensional genome-wide association studies.

Verena Zuber1, A Pedro Duarte Silva, Korbinian Strimmer

  • 1Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany. vzuber@uni-leipzig.de

BMC Bioinformatics
|November 2, 2012
PubMed
Summary

We developed a novel algorithm for selecting causal single nucleotide polymorphisms (SNPs) by considering their correlations. Our CAR score-based method outperforms existing approaches in identifying true causal SNPs.

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Area of Science:

  • Genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Genome-Wide Association Studies (GWAS) often analyze single nucleotide polymorphisms (SNPs) individually.
  • Ignoring correlations among SNPs can lead to inaccurate causal SNP identification.
  • Simultaneous SNP selection methods are computationally intensive but account for SNP dependencies.

Purpose of the Study:

  • To develop a novel multivariate algorithm for large-scale SNP selection using CAR score regression.
  • To propose an efficient procedure for shrinkage estimation of CAR scores from high-dimensional data.
  • To compare the performance of the CAR score algorithm against other advanced regression and univariate methods.

Main Methods:

  • Development of a CAR score regression algorithm for SNP selection.
  • Computationally efficient shrinkage estimation for CAR scores.
  • Comparative analysis involving boosting, lasso, NEG, MCP, CAR score, and marginal correlation methods.

Main Results:

  • The proposed CAR score-based algorithm demonstrates superior performance in identifying true causal SNPs.
  • The algorithm excels in ranking SNPs compared to all competing univariate and multivariate approaches.
  • The CAR score method provides a computationally efficient solution for high-dimensional SNP data.

Conclusions:

  • Simultaneous SNP selection is crucial for accurate genetic analysis.
  • The CAR score-based algorithm offers a robust and effective solution for causal SNP identification.
  • The developed algorithm and associated code are publicly available for reproducibility and application.