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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.
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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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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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Genomics02:02

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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: Dec 10, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Genome-wide pathogenesis interpretation using a heat diffusion-based systems genetics method and implications for

Yuan Quan1,2, Qing-Ye Zhang2, Bo-Min Lv2

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, China.

Molecular Genetics & Genomic Medicine
|September 2, 2020
PubMed
Summary
This summary is machine-generated.

HotNet2, a systems genetics algorithm, reveals disease pathogenesis by analyzing genome-wide association study (GWAS) data. This approach enhances understanding of gene function and disease mechanisms, aiding drug development.

Keywords:
Genome-Wide Association StudiesPhenome-Wide Association Studiesdrug discoverygene function annotationpathogenesissystems genetics

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Area of Science:

  • Genetics and Systems Biology
  • Genomics and Bioinformatics

Background:

  • Genome-Wide Association Studies (GWAS) and Phenome-Wide Association Studies (PheWAS) have advanced genetic variant identification but struggle with elucidating pathogenesis.
  • Interpreting complex genotype-phenotype relationships remains a significant challenge in genetic research.

Purpose of the Study:

  • To apply the HotNet2 systems genetics algorithm for deeper insights into disease pathogenesis at a molecular level.
  • To establish genotype-phenotype links using biological networks derived from genetic association data.

Main Methods:

  • Utilized HotNet2, a heat diffusion-based algorithm, to analyze disease gene networks from GWAS and PheWAS data.
  • Integrated biomedical information with GWAS-derived networks for pathogenesis interpretation.

Main Results:

  • Identified significant disease networks for 202 GWAS and 167 PheWAS disease types.
  • Elucidated pathogenesis for 64 diseases via transcriptional regulation and 47 via protein-protein interactions.
  • Assigned new functions to 3,802 genes, including 46 previously unknown, with validation through mouse knockout experiments.

Conclusions:

  • HotNet2 efficiently links genotypes to phenotypes through biological networks, offering stronger biomedical significance than raw GWAS/PheWAS data.
  • Provides enhanced interpretations of genome-wide variant pathogenesis and novel gene function insights.
  • Findings support potential applications in drug development.