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Epistasis Analysis01:09

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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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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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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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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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.
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Multi-trait multi-locus SEM model discriminates SNPs of different effects.

Anna A Igolkina1, Georgy Meshcheryakov2, Maria V Gretsova2,3

  • 1Peter the Great Saint-Petersburg Polytechnic University, Russian Federation, Polytechnicheskaya, 29, St. Petersburg, 195251, Russia. igolkinaanna11@gmail.com.

BMC Genomics
|July 30, 2020
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Summary

We developed a novel multi-trait, multi-locus model using structural equation modeling (SEM) to analyze complex genetic associations. This approach accurately identifies pleiotropic SNPs and predicts trait values in chickpea populations.

Keywords:
Bayesian inferenceChickpeaGWASMulti-trait multi-locus SEMSEM

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Area of Science:

  • Genetics
  • Bioinformatics
  • Plant Breeding

Background:

  • Genome-wide association studies (GWAS) often analyze single traits and loci.
  • Few methods integrate multiple correlated phenotypes and multiple genetic variants simultaneously.
  • Understanding complex genetic architectures requires advanced analytical approaches.

Purpose of the Study:

  • To propose a novel multi-trait, multi-locus model for analyzing complex genetic associations.
  • To develop a method capable of distinguishing between pleiotropic and single-trait SNPs with direct and indirect effects.
  • To implement a robust statistical framework that handles non-normally distributed data.

Main Methods:

  • Structural Equation Modeling (SEM) was employed to build the multi-trait multi-locus SEM (mtmlSEM) model.
  • Factor analysis and maximum likelihood methods were used for automatic model construction.
  • Bayesian inference and Gibbs sampling were utilized for parameter estimation, accommodating non-normal variables.

Main Results:

  • The mtmlSEM model successfully identified approximately 230 SNPs associated with 16 phenotypic traits in chickpea.
  • Sixty of the identified SNPs exhibited pleiotropic effects, influencing multiple traits.
  • The model demonstrated high accuracy in predicting trait values through 20-fold cross-validation.

Conclusions:

  • The developed mtmlSEM model provides a powerful tool for dissecting complex genetic architectures in plant populations.
  • The method effectively identifies pleiotropic single nucleotide polymorphisms (SNPs) and their pathways.
  • Accurate trait prediction highlights the utility of this approach in crop improvement and breeding programs.