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Genome-wide Association Studies-GWAS

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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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Related Experiment Video

Updated: Jun 3, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

Joint Screening for Ultra-High Dimensional Multi-Omics Data.

Ulrich Kemmo Tsafack¹, Chien-Wei Lin¹, Kwang Woo Ahn¹

¹Division of Biostatistics, Medical College of Wisconsin (MCW), Milwaukee, WI 53226, USA.

Bioengineering (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces a novel gene clustering and screening method for ultra-high dimensional multi-omics data. The approach effectively identifies significant genes and omics, outperforming existing methods in simulations and real-world cancer data analysis.

Keywords:

multi-omics screening ultra-high dimensional data variable selection

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Bioinformatics
Genomics
Systems Biology

Background:

Ultra-high dimensional multi-omics data presents challenges in identifying significant genes and omics.
Genes form groups of multiple omics, and correlations between genes create a tri-level hierarchical structure.
Variable screening is crucial for reducing dimensionality before penalized regression analysis.

Purpose of the Study:

To develop a novel screening method for ultra-high dimensional multi-omics data by clustering genes.
To ensure the proposed screening method possesses the sure screening property.
To identify significant genes and omics related to breast cancer using the TCGA dataset.

Main Methods:

Propose a gene clustering and screening strategy for tri-level hierarchical multi-omics data.
Demonstrate the sure screening property of the proposed method.
Validate the method through extensive simulations and application to the TCGA breast cancer dataset.

Main Results:

The proposed gene clustering and screening method demonstrates the sure screening property.
Simulations show superior performance compared to competing screening methods.
The method successfully identified genes and omics associated with breast cancer in the TCGA dataset.

Conclusions:

The proposed gene clustering and screening method is effective for ultra-high dimensional multi-omics data analysis.
This approach enhances the identification of significant genes and omics in complex biological datasets.
The findings have implications for understanding breast cancer through multi-omics integration.