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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...
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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...
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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.
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Detecting High-Order Epistasis in Nonlinear Genotype-Phenotype Maps.

Zachary R Sailer1,2, Michael J Harms3,4

  • 1Institute of Molecular Biology, University of Oregon, Eugene, Oregon 97403.

Genetics
|January 20, 2017
PubMed
Summary

High-order epistasis, or complex gene interactions, is common in genotype-phenotype maps. This study developed a method to account for nonlinear scales, revealing significant epistasis in all tested maps.

Keywords:
genotype-phenotype maphigh-order epistasisnonlinearscalestatistical analysis

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Area of Science:

  • Genetics and Evolutionary Biology
  • Systems Biology
  • Bioinformatics

Background:

  • High-order epistasis (multi-way interactions between mutations) is frequently observed in genotype-phenotype maps.
  • These interactions can be crucial for understanding complex traits and evolution, but may also arise from statistical artifacts.
  • Current epistasis models often assume additive effects, potentially misinterpreting nonlinear biological processes.

Purpose of the Study:

  • To develop a method for estimating and correcting nonlinear scales in genotype-phenotype maps.
  • To re-evaluate the prevalence and significance of high-order epistasis after accounting for nonlinear scaling.
  • To provide a robust approach for analyzing complex genetic interactions.

Main Methods:

  • Developed a novel approach to estimate nonlinear scales of arbitrary genotype-phenotype maps.
  • Linearized genotype-phenotype maps using estimated nonlinear scales.
  • Analyzed seven experimental genotype-phenotype datasets previously reported to show high-order epistasis.

Main Results:

  • Five out of seven investigated genotype-phenotype maps exhibited significant nonlinear scales.
  • After accounting for nonlinearity, statistically significant high-order epistasis was detected in all seven maps.
  • High-order epistasis contributed between 2.2% and 31.0% (average 12.7%) to the total variation across maps.

Conclusions:

  • Nonlinear scaling is a common feature of genotype-phenotype maps that can obscure or mimic epistasis.
  • The developed method effectively linearizes maps, allowing for accurate detection of high-order epistasis.
  • Results strongly support the widespread existence of significant high-order epistasis in biological systems.