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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...
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...
Genetic Drift03:33

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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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Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
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GLIDE: GPU-based linear regression for detection of epistasis.

Tony Kam-Thong1, Chloé-Agathe Azencott, Lawrence Cayton

  • 1Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany.

Human Heredity
|September 12, 2012
PubMed
Summary

We developed GLIDE, a new method for mapping complex traits to pairs of genetic loci. GLIDE efficiently detects epistatic interactions, uncovering missing heritability in genetic studies.

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Area of Science:

  • Statistical genetics
  • Genomics
  • Computational biology

Background:

  • Single-locus mapping is standard but explains limited phenotypic variance in complex traits.
  • Complex traits are influenced by interactions between multiple genes (epistasis).
  • Identifying these interactions is crucial for understanding missing heritability.

Purpose of the Study:

  • To present GLIDE, a novel computational method for two-locus genome-wide association studies.
  • To systematically search for epistatic interactions between pairs of genetic loci.
  • To accelerate the detection of genetic factors contributing to complex traits.

Main Methods:

  • GLIDE maps phenotypes to pairs of genetic loci.
  • It utilizes graphics processing units (GPUs) for accelerated linear regression analysis.
  • The method was applied to disease and quantitative trait data.

Main Results:

  • GLIDE successfully identified epistatic interactions.
  • The computational approach significantly reduced analysis time from over a year to 6 hours per dataset.
  • Enabled systematic two-locus mapping on large datasets.

Conclusions:

  • GLIDE offers a computationally efficient solution for detecting gene-gene interactions.
  • This approach can help uncover the genetic basis of complex traits and missing heritability.
  • Accelerated genome-wide epistasis analysis is now feasible for large-scale studies.