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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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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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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Evolutionary Relationships through Genome Comparisons02:54

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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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.
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Related Experiment Video

Updated: Jun 5, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph.

Kexin Huang, Tony Zeng, Soner Koc

    Medrxiv : the Preprint Server for Health Sciences
    |December 16, 2024
    PubMed
    Summary

    KGWAS, a new deep learning method, enhances variant discovery in genome-wide association studies (GWASs) for rare diseases by integrating functional knowledge graphs. This approach significantly boosts detection power, especially in small cohorts, uncovering more disease-associated variants.

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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

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    Area of Science:

    • Genomics
    • Computational Biology
    • Artificial Intelligence

    Background:

    • Genome-wide association studies (GWASs) have identified numerous disease-associated variants but struggle with rare and uncommon diseases due to limited sample sizes.
    • Discovering genetic variants for diseases with small patient populations remains a significant challenge in human genetics.

    Purpose of the Study:

    • To introduce KGWAS, a novel geometric deep learning method designed to improve variant detection power in small-cohort GWASs.
    • To leverage a functional knowledge graph to enhance the identification of disease-associated variants, particularly for rare and uncommon conditions.

    Main Methods:

    • KGWAS utilizes a massive functional knowledge graph connecting variants and genes to aggregate GWAS evidence.
    • It assesses variant-disease association strength based on molecular interactions within the knowledge graph.
    • The method was evaluated using comprehensive simulations and real-world data from UK Biobank.

    Main Results:

    • KGWAS identified up to 100% more statistically significant associations than state-of-the-art GWAS methods in small sample sizes (N=1-10K).
    • It achieved equivalent statistical power with up to 2.67x fewer samples compared to existing methods.
    • Application to 554 uncommon and 141 rare diseases in UK Biobank revealed substantial improvements in variant discovery (46.9% and 79.8% increase, respectively).
    • KGWAS discoveries were supported by functional evidence, linking variants to gene regulation in relevant cell types (e.g., ulcerative colitis, myasthenia gravis).
    • The method also improved downstream analyses, including gene and cell population identification.

    Conclusions:

    • KGWAS is a powerful AI tool that effectively integrates functional genomics data to discover novel variants, genes, and cell populations.
    • It offers significant advantages for GWASs of diseases with small patient cohorts, overcoming limitations of traditional methods.
    • KGWAS enhances the interpretation of genetic associations and aids in understanding disease mechanisms through network and cell type analysis.