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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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.
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Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

Updated: Jun 8, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Genome wide association studies are enriched for interacting genes.

Peter T Nguyen1, Simon G Coetzee1, Irina Silacheva1

  • 1Cedars-Sinai Medical Center.

Research Square
|November 6, 2024
PubMed
Summary

Genetic algorithms applied to association studies reveal cell types contributing to disease risk. This approach models how genetic variants influence disease by analyzing multi-omics data and protein interactions.

Keywords:
GWASbreast cancercomplex diseaseetiologygene networkgenetic algorithmsmulti-omicssusceptibilityvariant prioritization

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Area of Science:

  • Genomics
  • Systems Biology
  • Computational Biology

Background:

  • Recent single-cell technologies offer insights into disease mechanisms and cell type origins.
  • Multi-omics data, including single-nucleus RNA and ATAC sequencing, are crucial for understanding genetic variant influence on disease.
  • Genome-wide association studies (GWAS) identify genetic variants associated with diseases.

Purpose of the Study:

  • To develop a method using genetic algorithms to link genetic variants from GWAS to specific cell types and genes involved in disease risk.
  • To demonstrate how multi-omics data, genome annotations, and protein-protein interaction networks can be integrated to build a cellular model of disease risk.

Main Methods:

  • Utilized genetic algorithms to evaluate gene-cell set proposals based on objective functions incorporating multi-omics data and annotations.
  • Employed protein-protein interaction data as a key objective function to identify biologically relevant gene-cell associations.
  • Compared fitness scores and subgraph sizes between sets of disease-associated variants and control variants.

Main Results:

  • Genetic algorithms identified gene-cell sets with significantly higher fitness scores and subgraph sizes for disease-associated variants compared to controls.
  • The model successfully identified known gene targets and ligand-receptor interactions, validating its predictive capability.
  • Demonstrated that susceptibility variants in diseases like breast cancer exhibit a higher degree of physical interactions than expected by chance.

Conclusions:

  • Application of genetic algorithms to GWAS provides a robust method for generating coherent cellular models of disease risk.
  • This approach effectively integrates diverse biological data to elucidate the functional roles of genetic variants in disease pathogenesis.
  • The findings highlight the importance of considering cell type-specific effects and molecular interactions in understanding genetic contributions to disease.