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Population Growth

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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

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What is Population Genetics?

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Frequency-dependent Selection

Frequency-dependent Selection

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 Variation

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Related Experiment Video

Updated: Apr 21, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Training set optimization under population structure in genomic selection.

Julio Isidro¹, Jean-Luc Jannink, Deniz Akdemir

¹Cornell University, Ithaca, NY, USA, ji66@cornell.edu.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|November 5, 2014

Summary

Optimizing the training set population is crucial for accurate genomic selection. Methods like CDmean and stratified sampling perform best depending on the population structure and trait architecture, maximizing prediction accuracy.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Area of Science:

Genomics
Quantitative Genetics
Plant and Animal Breeding

Background:

Genomic selection (GS) accuracy relies heavily on the training set population (TRS).
Optimizing TRS selection is critical for effective genomic prediction models in breeding programs.
Population structure within the TRS can significantly impact prediction performance.

Purpose of the Study:

To evaluate five different TRS sampling algorithms for prediction accuracy.
To assess the impact of varying population structure levels on TRS optimization methods.
To identify optimal TRS selection criteria for genomic prediction.

Main Methods:

Comparison of stratified sampling, CDmean, PEVmean, StratCDmean, and random sampling algorithms.
Evaluation of prediction accuracy using wheat and rice datasets with differing population structures.
Analysis of phenotypic variance captured and genotype relationships within TRS.

Main Results:

CDmean and StratCDmean showed highest accuracies in wheat (mild structure).
Stratified sampling performed best in rice (strong structure).
CDmean minimized TRS genotype relationships, enhancing TRS-test set correlation.

Conclusions:

Population structure evaluation is essential before TRS optimization.
Maximizing phenotypic variance in the TRS is key for optimal GS performance.
The best TRS optimization criterion depends on trait architecture and population structure.