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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...
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...
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...
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...

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Related Experiment Video

Updated: Jul 13, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

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Published on: August 24, 2013

GPDTI: a Genetic Programming Decision Tree induction method to find epistatic effects in common complex diseases.

Jesús K Estrada-Gil1, Juan C Fernández-López, Enrique Hernández-Lemus

  • 1Computer Science Department, Instituto Tecnológico y de Estudios Superiores de Monterrey Campus Estado de Mexico, Mexico. jestrada@inmegen.gob.mx

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
Summary

This study introduces a novel Genetic Programming approach to identify gene interactions associated with common diseases. The method effectively detects low heritability interactions, improving our understanding of genetic disease mechanisms.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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Area of Science:

  • Genetics
  • Computational Biology
  • Statistical Genetics

Background:

  • Identifying genetic variants linked to common diseases is difficult.
  • Standard statistical methods often miss interactions between genetic variants.
  • Detecting and interpreting these interactions presents significant statistical and computational challenges.

Purpose of the Study:

  • To develop a computational method for detecting interactions among genetic variants.
  • To improve the understanding of genetic disease mechanisms through interaction detection.
  • To address the limitations of traditional statistical approaches in identifying low heritability interactions.

Main Methods:

  • Implementation of a Genetic Programming (GP) method to induce Decision Trees.
  • Utilizing a cross-validation strategy for robust estimation of classification and prediction errors.
  • Introduction of a novel consistency measure tailored for GP environments to account for interactions.

Main Results:

  • The GP method successfully detected five distinct interaction models.
  • Interactions with heritabilities as low as 0.008 were identified.
  • The method achieved prediction errors comparable to those of the generated data.

Conclusions:

  • Genetic Programming offers a powerful approach for detecting complex genetic interactions.
  • The proposed method enhances the ability to find low heritability interactions.
  • This work contributes to a better understanding of the genetic underpinnings of common diseases.