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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...
Polygenic Traits01:18

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...
Polygenic Traits01:18

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...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

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Related Experiment Video

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

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Genomic value prediction for quantitative traits under the epistatic model.

Zhiqiu Hu1, Yongguang Li, Xiaohui Song

  • 1Soybean Research Institute (Chinese Education Ministry's Key Laboratory of Soybean Biology), Northeast Agricultural University, Harbin, PR China.

BMC Genetics
|January 29, 2011
PubMed
Summary

Genome selection significantly improves plant breeding by predicting genetic values. Including epistatic effects in genome selection models dramatically enhances prediction accuracy for quantitative traits in soybean.

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Area of Science:

  • Plant breeding
  • Quantitative genetics
  • Genomics

Background:

  • Quantitative traits are controlled by multiple quantitative trait loci (QTLs).
  • Genome selection (GS) uses genome-wide markers for genetic improvement, potentially outperforming traditional methods.
  • Incorporating epistatic effects alongside main effects in GS can maximize prediction efficiency.

Purpose of the Study:

  • To apply genome selection to predict the genomic value of somatic embryo number in soybean.
  • To evaluate the predictive accuracy of genome selection models with and without epistatic effects.

Main Methods:

  • Developed 126 recombinant inbred lines of soybean.
  • Genotyped 80 genome-wide markers.
  • Applied cross-validation to assess prediction accuracy of genome selection models.

Main Results:

  • Genome selection using only additive effects yielded a prediction accuracy (r²) of 0.33.
  • Including epistatic effects in the genome selection model increased prediction accuracy (r²) to 0.78.
  • This highlights the substantial contribution of epistasis to the genetic architecture of somatic embryo number.

Conclusions:

  • Genome selection is a powerful tool for plant breeding.
  • Accounting for epistatic interactions is crucial for accurate genomic prediction of complex traits.
  • This study serves as a prime example of applying advanced genomic prediction strategies in crop improvement.