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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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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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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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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Updated: Jun 2, 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 Coetzee2, Irina Silacheva1

  • 1The Department of Biomedical and Translational Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.

Biodata Mining
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

Genetic algorithms applied to multi-omics data reveal how genetic variants contribute to disease risk by identifying key genes and cell types. This approach generates cellular models of disease, highlighting variant interactions in conditions like breast cancer.

Keywords:
Breast cancerComplex diseaseEtiologyGWASGene networkGenetic algorithmsMulti-omicsSusceptibilityVariant prioritization

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Area of Science:

  • Genomics
  • Computational Biology
  • Systems Biology

Background:

  • Single-cell technologies offer insights into disease mechanisms and cell type origins.
  • Genome-wide association studies (GWAS) identify genetic variants associated with diseases.
  • Integrating multi-omics data is crucial for understanding genetic influences on disease development.

Purpose of the Study:

  • To develop a method for understanding how genetic variants from GWAS influence disease development.
  • To utilize genetic algorithms with multi-omics data to model gene and cell type contributions to disease risk.
  • To explore the collective impact of genes and cell types on increased disease susceptibility.

Main Methods:

  • Employed genetic algorithms with paired single-nucleus RNA-seq and ATAC-seq data.
  • Integrated genome annotations and protein-protein interaction data.
  • Assessed gene-cell set proposals using objective functions, including protein-protein interactions.

Main Results:

  • Genetic algorithms identified gene-cell sets with significantly higher fitness scores compared to control sets.
  • The model successfully identified known gene targets and ligand-receptor interactions.
  • Analysis revealed that disease-associated variants exhibit more physical interactions than expected by chance, exemplified in breast cancer.

Conclusions:

  • Genetic algorithms can generate coherent cellular models of disease risk from susceptibility variants.
  • This computational approach enhances the interpretation of GWAS findings by linking variants to cellular mechanisms.
  • The study demonstrates the utility of multi-omics integration for dissecting complex disease etiologies.