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Related Concept Videos

Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
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When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
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Related Experiment Video

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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Published on: December 10, 2012

From phenotype to genotype: a Bayesian solution.

M J Denwood1, A E Mather, D T Haydon

  • 1Boyd Orr Centre for Population and Ecosystem Health, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G61 1QH, UK.

Proceedings. Biological Sciences
|October 29, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model to infer genetic elements from observed phenotypes, revealing fixed forms of SGI1 variants in Salmonella Typhimurium populations. This method aids in understanding population genetics.

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Area of Science:

  • Microbiology
  • Genetics
  • Computational Biology

Background:

  • Inferring hidden genetic variables from observed phenotypes is crucial in biological systems.
  • Traditional methods fail when multiple genetic mechanisms underlie a single phenotype.
  • Understanding population genetics requires robust methods for inferring genetic structures.

Purpose of the Study:

  • To develop and apply a novel latent class Bayesian model for inferring genetic element prevalence from phenotypic data.
  • To analyze antimicrobial resistance phenotypes in Salmonella Typhimurium DT104 to infer resistance gene and SGI1 variant prevalence.
  • To assess model performance using posterior predictive p-values for predicting observed phenotypes.

Main Methods:

  • Latent class Bayesian modeling approach.
  • Analysis of phenotypic antimicrobial resistance data from Salmonella Typhimurium DT104.
  • Model comparison using posterior predictive p-values.

Main Results:

  • The model successfully inferred the prevalence of individual resistance genes and SGI1 variants.
  • Evidence suggests several SGI1 variants circulate in fixed forms within the studied population.
  • Model comparison identified the best-fitting model for the observed phenotypic data.

Conclusions:

  • The novel Bayesian model is effective for inferring genetic population structure from phenotypic data.
  • The findings indicate the circulation of specific SGI1 variants in Salmonella Typhimurium DT104 populations.
  • This approach offers a versatile tool for genetic population structure inference across various biological systems.