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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Genetic Screens02:46

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Behavioral Genetics and Its Designs01:23

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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Genome-wide Association Studies-GWAS01:11

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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Types of Selection01:46

Types of Selection

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Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...
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Related Experiment Video

Updated: Jan 5, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Functional models in genome-wide selection.

Ernandes Guedes Moura1, Andrezza Kellen Alves Pamplona2, Marcio Balestre3

  • 1Federal Institute of Maranhão - Campus São João dos Patos, São João dos Patos, Maranhão, Brasil.

Plos One
|October 24, 2019
PubMed
Summary

A new Bayesian functional model improves genomic prediction by incorporating marker locations. This method enhances accuracy in identifying causal regions and predicting genotypic values across various populations and scenarios.

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Area of Science:

  • Genomics
  • Quantitative Genetics
  • Statistical Genetics

Background:

  • Advanced sequencing technologies facilitate genome-wide marker discovery.
  • Marker locations are crucial for identifying causal regions and predicting genomic values.

Purpose of the Study:

  • To introduce a Bayesian functional approach for integrating marker locations into genomic analysis.
  • To enhance the search for causal regions and the prediction of genotypic values using stochastic methods.

Main Methods:

  • Analysis of simulated F2 and F∞ populations with varying heritability and SNP markers.
  • Evaluation using Eucalyptus spp. data with simulated phenotypes from QTLs and known SNP positions.
  • Comparison of the proposed Bayesian functional model against RR-BLUP, Bayes B, and Bayesian Lasso.

Main Results:

  • The Bayesian functional model demonstrated comparable or superior predictive ability to classical regression methods.
  • The model showed higher computational efficiency, utilizing 12 SNPs per MCMC round.
  • Effective identification of causal regions and high analytical flexibility were observed.

Conclusions:

  • The proposed Bayesian functional model is efficient for genomic selection, offering improved predictive ability and computational efficiency.
  • The model's adaptability makes it suitable for various genomic selection applications.
  • Integrating marker locations via this functional approach advances genomic prediction accuracy.