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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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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
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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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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Genetic Screens02:46

Genetic Screens

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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.
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Related Experiment Video

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Processing the Loblolly Pine PtGen2 cDNA Microarray
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Genomic selection in maritime pine.

Fikret Isik1, Jérôme Bartholomé2, Alfredo Farjat3

  • 1INRA, UMR1202, BIOGECO, Cestas F-33610, France.

Plant Science : an International Journal of Experimental Plant Biology
|November 15, 2015
PubMed
Summary

Genomic selection shows promise for improving maritime pine (Pinus pinaster) growth and stem straightness. Predictive abilities were moderate, with progeny validation improving accuracy for some traits.

Keywords:
Bayesian regressionGenomic relationshipLinkage disequilibriumPinus pinasterTree breeding

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An Improved Method of RNA Isolation from Loblolly Pine P. taeda L. and Other Conifer Species
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Area of Science:

  • Forest Genetics
  • Quantitative Genetics
  • Plant Breeding

Background:

  • Maritime pine (Pinus pinaster Ait.) is a valuable timber species.
  • Genomic selection offers potential for accelerating breeding programs.
  • Understanding linkage disequilibrium (LD) is crucial for effective genomic selection.

Purpose of the Study:

  • To investigate the extent of linkage disequilibrium in a maritime pine breeding population.
  • To evaluate the utility of genomic selection for improving growth and stem straightness.
  • To compare different statistical models and validation strategies for genomic prediction.

Main Methods:

  • Genotyping of 661 maritime pine individuals using 2500 SNP markers.
  • Calculation of linkage disequilibrium (r²).
  • Application of Genomic BLUP, Bayesian Ridge Regression, and Bayesian LASSO models for genomic estimated breeding values.
  • Validation using random population sampling and progeny generation sampling.

Main Results:

  • Low overall intra-chromosomal linkage disequilibrium (r²=0.01).
  • Average predictive abilities ranged from 0.43 (tree diameter) to 0.49 (stem sweep).
  • Progeny generation validation improved predictive ability for tree diameter and stem sweep.

Conclusions:

  • Genomic selection is a viable tool for maritime pine breeding, despite low marker density and LD.
  • The choice of validation strategy can impact predictive ability.
  • Further research with higher marker density could enhance genomic prediction accuracy.