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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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Genetic Screens02:46

Genetic Screens

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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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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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Protein Networks02:26

Protein Networks

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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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Epistasis Analysis

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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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Related Experiment Video

Updated: Oct 26, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Decoding disease: from genomes to networks to phenotypes.

Aaron K Wong1, Rachel S G Sealfon1, Chandra L Theesfeld2

  • 1Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.

Nature Reviews. Genetics
|August 3, 2021
PubMed
Summary

Interpreting genetic variants aids disease susceptibility and personalized medicine. Machine learning models integrate genomic data to link variants to disease pathways and phenotypes.

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Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Interpreting genetic variants is crucial for understanding disease susceptibility and developing personalized therapies.
  • Advances in experimental technologies generate vast amounts of human genome, molecular, and phenotypic data.
  • Functional genomic data, including expression and epigenomics, provide further insights.

Purpose of the Study:

  • To review recent developments in machine learning algorithms for genome interpretation.
  • To discuss integrative computational models for understanding variant impact on biological pathways and disease.
  • To highlight challenges and opportunities in linking genetic variants to disease phenotypes.

Main Methods:

  • Review of machine learning algorithms for genome interpretation.
  • Discussion of integrative computational modeling approaches.
  • Analysis of methods for linking genetic variants to molecular pathways and disease phenotypes.

Main Results:

  • Machine learning and integrative models can leverage large-scale genomic data to interpret variant effects.
  • These models elucidate the impact of dysregulated genes on biological pathways in disease contexts.
  • Computational approaches extend the understanding of disease risk beyond experimental limitations.

Conclusions:

  • Integrative computational models are essential for comprehensive genome interpretation.
  • Machine learning advances are key to linking genetic variants to disease mechanisms and outcomes.
  • Future opportunities lie in refining methods to connect genetic variants to specific disease phenotypes.