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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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Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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Published on: July 1, 2020

Pathway-based analysis for genome-wide association studies using supervised principal components.

Xi Chen1, Lily Wang, Bo Hu

  • 1Division of Cancer Biostatistics, Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee 37232, USA.

Genetic Epidemiology
|September 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new pathway analysis method for genome-wide association studies (GWAS) called supervised principal component analysis (SPCA). SPCA enhances the detection of genetic variations influencing complex diseases by analyzing biological pathways.

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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 and Bioinformatics
  • Statistical Genomics
  • Computational Biology

Background:

  • Complex diseases often result from cumulative effects of multiple genes, each with minor impact.
  • Genome-wide association studies (GWAS) are crucial for identifying genetic risk factors.
  • Pathway analysis is gaining traction in GWAS for its ability to aggregate weak signals and reveal biological mechanisms.

Purpose of the Study:

  • To propose a novel pathway-based analysis method for GWAS, termed supervised principal component analysis (SPCA).
  • To enhance the identification of genetic variations associated with complex disease susceptibility.
  • To improve the biological interpretation of GWAS findings by focusing on pathways.

Main Methods:

  • Developed the supervised principal component analysis (SPCA) model for pathway analysis in GWAS.
  • SPCA utilizes a subset of single nucleotide polymorphisms (SNPs) most associated with disease outcome to estimate pathway-specific latent variables.
  • The model allows for borrowing strength across SNPs within a pathway and performs within-category SNP selection.

Main Results:

  • The SPCA model effectively identifies pathways associated with disease outcome.
  • It pinpoints key SNPs within gene sets, offering deeper biological insights.
  • Demonstrated the method's robustness and applicability using realistic linkage disequilibrium data and the Crohn's Disease dataset.

Conclusions:

  • The proposed SPCA method offers a powerful and statistically sound approach for pathway analysis in GWAS.
  • SPCA improves the detection of complex disease genetic architectures by integrating SNP information within biological pathways.
  • This method facilitates the identification of biologically relevant pathways and key genetic variants, advancing our understanding of disease etiology.