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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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Group sparse canonical correlation analysis for genomic data integration.

Dongdong Lin1, Jigang Zhang, Jingyao Li

  • 1Biomedical Engineering Department, Tulane University, New Orleans, LA, USA.

BMC Bioinformatics
|August 14, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel group sparse canonical correlation analysis (CCA) method to analyze complex genomic data, effectively identifying relationships between single nucleotide polymorphisms (SNPs) and gene expression. The new method improves feature selection accuracy compared to existing sparse CCA techniques.

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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

Published on: July 27, 2021

Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • High-throughput genomic data (gene expression, SNPs, CNV) offer insights into complex diseases.
  • Analyzing relationships between diverse genomic datasets is challenging.
  • Existing sparse Canonical Correlation Analysis (CCA) methods overlook crucial group effects within genomic data.

Purpose of the Study:

  • To develop a novel group sparse CCA method (CCA-sparse group) for analyzing relationships between different genomic data types, such as SNPs and gene expression.
  • To extend the CCA-sparse group model to encompass existing sparse CCA methods.
  • To evaluate the performance of the proposed method in feature selection using simulated and real-world genomic datasets.

Main Methods:

  • Proposed a new group sparse CCA method (CCA-sparse group) with an effective numerical algorithm.
  • Extended the model to a general formulation accommodating existing sparse CCA (sCCA) models.
  • Applied the method to feature/variable selection on two real datasets (human gliomas and NCI60) and compared it with existing sCCA methods (CCA-l1, CCA-group) using simulation studies.

Main Results:

  • The CCA-sparse group method effectively incorporates group effects and performs simultaneous individual feature selection.
  • Graphical representation of samples using canonical variates demonstrated the discriminating characteristics of selected features.
  • Pathway analysis was conducted for biological interpretation of the selected features.

Conclusions:

  • The CCA-sparse group method outperforms existing sCCA methods (CCA-l1, CCA-group) in identifying correlated features with higher true positives and lower discordance.
  • The proposed method shows robustness even with non-existent group effects or irrelevant features grouped with true correlated features.
  • Compared to CCA-l1 and CCA-group, CCA-sparse group provides a more accurate selection of correlated features, avoiding excessive redundant feature selection.