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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...
Single Nucleotide Polymorphisms-SNPs01:05

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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Generalized linear modeling with regularization for detecting common disease rare haplotype association.

Wei Guo1, Shili Lin

  • 1Department of Statistics, The Ohio State University, Columbus, Ohio 43210-1247, USA.

Genetic Epidemiology
|November 26, 2008
PubMed
Summary

Whole genome association studies (WGAS) can now detect rare disease variants using a novel regularized generalized linear model (rGLM). This method enhances power for common and rare variant detection, even with moderate sample sizes.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

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
  • Statistical genetics
  • Bioinformatics

Background:

  • Whole genome association studies (WGAS) are increasingly used to identify genetic associations with diseases.
  • Current WGAS methods often struggle with the common disease/rare variant (CD/RV) scenario due to limited statistical power, especially with smaller sample sizes.
  • The common disease/common variant (CD/CV) hypothesis underlies many WGAS approaches, but CD/RV detection remains challenging.

Purpose of the Study:

  • To propose a novel statistical approach, the regularized generalized linear model (rGLM), for detecting disease-haplotype associations.
  • To enhance the power of WGAS for both common disease/common variant (CD/CV) and common disease/rare variant (CD/RV) scenarios.
  • To improve the identification of rare disease-associated variants that may be missed by existing methods.

Main Methods:

  • Developed a regularized generalized linear model (rGLM) incorporating dimension-reduction techniques from data mining.
  • Applied the rGLM to unphased single nucleotide polymorphism (SNP) data for haplotype association analysis.
  • Utilized high-dimensional data analysis techniques to investigate interacting effects among haplotypes across different genomic blocks.

Main Results:

  • The rGLM approach demonstrated increased statistical power for detecting genetic associations, particularly with moderate sample sizes.
  • rGLM effectively identified associated haplotypes, including rare ones, which are often missed by conventional methods like hapassoc.
  • The method showed superior performance in uncovering associated variants compared to existing regression-based approaches, especially in the CD/RV context.

Conclusions:

  • The proposed rGLM method offers a powerful and flexible framework for disease-haplotype association studies.
  • rGLM improves the detection of both common and rare disease-associated variants, addressing limitations of current WGAS practices.
  • This approach holds significant promise for advancing genetic research in complex diseases by enabling more precise identification of causal variants.