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Epistasis Analysis

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...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Epistasis

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...

Polygenic Traits

Polygenic Traits

When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...

Trihybrid Crosses

Trihybrid Crosses

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Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal chance to...

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Related Experiment Video

Updated: May 9, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Multi-QTL mapping for quantitative traits using epistatic distorted markers.

Shang-Qian Xie¹, Jia Wen, Yuan-Ming Zhang

¹Statistical Genomics Group, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Department of Crop Genetics and Breeding, Nanjing Agricultural University, Nanjing, Jiangsu, China.

|July 23, 2013

Summary

This study introduces a new quantitative trait locus (QTL) mapping method that accounts for epistatic segregation distortion loci (SDL). The enhanced approach improves QTL detection power by correcting genetic distances and considering linked SDL interactions.

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Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Segregation distortion loci (SDL) interactions are common in mapping populations.
The impact of epistatic SDL on quantitative trait locus (QTL) mapping remains poorly understood.

Purpose of the Study:

To develop and validate a novel multi-QTL mapping approach that incorporates epistatic SDL.
To enhance the accuracy and power of QTL detection by addressing marker distortion and epistasis.

Main Methods:

Proposed a multi-QTL mapping strategy utilizing epistatic distorted markers.
Identified epistatic SDL using corrected linkage groups.
Integrated SDL parameters to adjust conditional probabilities of QTL genotypes within the mapping approach.
Validated the method with simulated and real mouse F2 population data.

Main Results:

The new method corrects genetic distances between distorted markers and accounts for epistasis between linked SDL.
Demonstrated increased power in QTL detection compared to the conventional method.
Observed significant differences in QTL parameter estimates, particularly for additive effects in mouse weight QTL, due to identified epistatic SDL between markers C66 and T93.

Conclusions:

The proposed multi-QTL mapping approach effectively incorporates epistatic SDL, leading to improved QTL detection.
Accounting for epistatic SDL is crucial for accurate genetic parameter estimation in mapping studies.
This method offers a more robust framework for genetic analysis in the presence of segregation distortion.