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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...
Pleiotropy01:33

Pleiotropy

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,...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

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Related Experiment Video

Updated: Jun 16, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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A Bayesian partition method for detecting pleiotropic and epistatic eQTL modules.

Wei Zhang1, Jun Zhu, Eric E Schadt

  • 1UBS Equities, Stamford, Connecticut, United States of America.

Plos Computational Biology
|January 22, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian method to identify interactions between expression quantitative trait loci (eQTLs) and co-regulated genes. The novel approach enhances the detection of genetic effects on gene expression, improving biological insights.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genetics
  • Systems Biology
  • Bioinformatics

Background:

  • Expression quantitative trait loci (eQTL) mapping studies DNA variation's effect on gene expression.
  • Identifying interactions among multiple eQTLs for co-regulated genes is computationally challenging.
  • Existing methods struggle to dissect complex regulatory networks involving numerous genes and markers.

Purpose of the Study:

  • To develop a Bayesian method for simultaneously identifying interacting eQTLs and their target genes within co-expressed gene modules.
  • To improve the power and accuracy of detecting genetic associations with gene expression.
  • To analyze complex genetic architectures influencing mRNA levels.

Main Methods:

  • A Bayesian approach treating co-expressed genes as modules characterized by latent variables.
  • Utilizing a Markov chain Monte Carlo algorithm for simultaneous discovery of module genes and linked markers.
  • Application to gene expression and genotype data from S. cerevisiae segregants.

Main Results:

  • The Bayesian method demonstrated higher power in detecting true eQTLs and target genes compared to traditional QTL mapping.
  • Identified gene modules within known eQTL hotspots, suggesting distinct biological functions or regulatory responses.
  • Discovered nine modules associated with pairs of eQTLs, including novel findings like daughter-cell expressed genes regulated by AMN1 and BPH1.

Conclusions:

  • The Bayesian partition method effectively detects pleiotropic and epistatic effects by considering all traits and markers simultaneously.
  • This approach offers enhanced power for dissecting complex genetic regulation of gene expression.
  • The findings provide a more nuanced understanding of genetic architecture underlying gene expression variation.