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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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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
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Human Genetics01:28

Human Genetics

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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Learning gene networks underlying clinical phenotypes using SNP perturbation.

Calvin McCarter1, Judie Howrylak2, Seyoung Kim3

  • 1Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

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Summary

PerturbNet, a new statistical framework, analyzes genetic variants as natural perturbations to uncover gene networks influencing complex diseases. This approach offers deeper insights into molecular mechanisms and improves SNP detection for complex traits.

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Area of Science:

  • Genomics
  • Systems Biology
  • Statistical Genetics

Background:

  • Large-scale genomic and phenotypic data enable dissecting complex disease genetics.
  • Previous methods analyzing SNP-trait associations were limited to single-gene effects.

Purpose of the Study:

  • Introduce PerturbNet, a framework to learn gene networks modulating genetic variant effects on phenotypes.
  • Utilize genetic variants as natural perturbations to model biological systems.

Main Methods:

  • PerturbNet employs a probabilistic graphical model to represent perturbation cascades from variants to phenotypes.
  • The model integrates genetic, gene, and phenotype networks.
  • Efficient algorithm solves a single optimization problem for genome-wide data analysis.

Main Results:

  • PerturbNet enhances statistical power for detecting disease-associated SNPs.
  • Identifies gene networks and modules mediating SNP effects on traits.
  • Demonstrates improved insights into molecular mechanisms using simulated and asthma data.

Conclusions:

  • PerturbNet provides a systems-level understanding of how gene networks mediate genetic influences on complex diseases.
  • Offers a powerful tool for dissecting genetic architecture and uncovering disease mechanisms.