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Related Concept Videos

Epistasis Analysis01:09

Epistasis Analysis

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...
Epistasis01:39

Epistasis

In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Genetic Screens02:46

Genetic Screens

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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Published on: August 24, 2013

Bayesian models for detecting epistatic interactions from genetic data.

Yu Zhang1, Bo Jiang, Jun Zhu

  • 1Department of Statistics, Penn State University, University Park, PA, USA.

Annals of Human Genetics
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

This study reviews Bayesian partition methods for identifying gene-gene and gene-environment interactions in genome-wide association studies. These methods help map complex disease risks, especially with new high-throughput sequencing data.

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) test millions of genetic markers for disease associations.
  • Identifying gene-gene and gene-environment interactions is crucial for understanding disease susceptibility.
  • The vast number of potential interactions poses significant computational and statistical challenges.

Purpose of the Study:

  • To review and discuss Bayesian partition methods for interaction mapping in GWAS.
  • To demonstrate the performance of these methods using simulations and real data.
  • To compare Bayesian methods with existing interaction mapping algorithms.

Main Methods:

  • Review of Bayesian partition methods for single-nucleotide polymorphism (SNP) mapping.
  • Extension of methods to quantitative traits and multiple traits.
  • Simulation studies and real data analysis to evaluate method performance.

Main Results:

  • Bayesian partition methods offer a robust approach to interaction mapping in GWAS.
  • These methods are effective for case-control studies, quantitative traits, and multiple traits.
  • Performance evaluation demonstrates the utility of Bayesian methods compared to existing algorithms.

Conclusions:

  • Bayesian partition methods provide powerful tools for mapping complex genetic interactions.
  • Advancements in high-throughput sequencing present new opportunities for genome-scale interaction mapping.
  • Sophisticated statistical approaches are essential to leverage new data for improved interaction detection.