Selection Intensity and Genetic Gain
Relate selection differential, accuracy, heritability, genetic variation, generation interval, and diversity retention to expected and realized gain.
Relate selection differential, accuracy, heritability, genetic variation, generation interval, and diversity retention to expected and realized gain.
Core science
Selection intensity describes how strongly a breeder favors the upper or lower part of a measured population. Stronger selection can increase short-term gain when measurements are accurate and heritable variation exists, but it reduces the number of parents and can accelerate inbreeding, drift, and loss of rare favorable alleles.
The breeder’s equation is often summarized as response equals narrow-sense heritability multiplied by the selection differential. More complete forms include selection intensity, accuracy, additive genetic variation, and generation interval. These are expectations for a defined population and environment, not promises. Heritability is not a permanent property of a trait; it changes with population, environment, design, and measurement error.
Selection on one trait can change others because of genetic correlations and physiological tradeoffs. Selecting only concentration may reduce yield, uniformity, fertility, or disease resistance. Index selection combines weighted traits, while independent culling requires minimum standards for each trait. Realized gain must be measured by comparing descendants with a valid base population or check across environments. Selecting the best-looking survivors without recording mortality overestimates progress.
Why this matters in cultivation
- Define how many individuals and families will be retained before evaluation. Monitor effective parent number and compare predicted gain with realized progeny performance.
Measure and record
Record 1
Record the base population, generation, trait definition, population mean and variance, relationship structure when known, selected proportion, and the accuracy or heritability estimate used for predicting response.
Record 2
Calculate and retain the selection differential, family contributions, selected-parent values, generation interval, predicted response, and any correlated traits that could change as a consequence of selection.
Record 3
After progeny evaluation, compare realized response with the prediction and record changes in diversity, inbreeding indicators, fertility, and family representation. Revise the selection strategy when short-term gain is being purchased with excessive loss of diversity.
Common misconceptions
Evidence limits and uncertainty
Predicted response depends on valid variance components, relationships, trait definitions, selection intensity, and target environments. Estimates can change when any of those change.
Realized gain should be measured against a comparable base or control population; environmental drift between generations can mimic genetic progress.
Check your reasoning
- For "Selection Intensity and Genetic Gain", explain the mechanism behind this objective: Relate selection differential, accuracy, heritability, genetic variation, generation interval, and diversity retention to expected and realized gain. Which observation or measurement would best test whether that mechanism is operating in the real crop?
- A learner claims, "Selecting only the top plant creates the fastest long-term progress." Use the lesson’s science and evidence limits to explain why that claim is unreliable, then name one observation or measurement that could separate the competing explanations.
- Applied case — Define how many individuals and families will be retained before evaluation. Monitor effective parent number and compare predicted gain with realized progeny performance. Build a verification plan using the lesson’s record set (Base population; trait mean and variance; heritability/accuracy estimate; selected proportion; selection differential; family contributions; generation interval; predicted response; realized progeny response; correlated traits; inbreeding indicators.). What would you compare before and after the action, and what result would make you revise the original interpretation?
Require lesson-specific evidence, not memorized universal targets. Open the rationales after you have written or discussed your own answer.
Answer rationale 1: Mechanism / workflow rationale
- A strong answer should connect the response to the lesson objective: Relate selection differential, accuracy, heritability, genetic variation, generation interval, and diversity retention to expected and realized gain.
- Selection intensity describes how strongly a breeder favors the upper or lower part of a measured population. Stronger selection can increase short-term gain when measurements are accurate and heritable variation exists, but it reduces the number of parents and can accelerate inbreeding, drift, and loss of rare favorable alleles.
- The breeder’s equation is often summarized as response equals narrow-sense heritability multiplied by the selection differential. More complete forms include selection intensity, accuracy, additive genetic variation, and generation interval. These are expectations for a defined population and environment, not promises. Heritability is not a permanent property of a trait; it changes with population, environment, design, and measurement error.
- The most useful verification evidence includes Record the base population, generation, trait definition, population mean and variance, relationship structure when known, selected proportion, and the accuracy or heritability estimate used for predicting response..
- Keep this limit explicit: Predicted response depends on valid variance components, relationships, trait definitions, selection intensity, and target environments. Estimates can change when any of those change.
Answer rationale 2: Misconception rationale
- The shortcut is unreliable because the lesson explicitly teaches a more conditional explanation.
- Representative misconception: Selecting only the top plant creates the fastest long-term progress. Extreme selection can increase short-term differential while sharply reducing effective population size and future response.
- Selection intensity describes how strongly a breeder favors the upper or lower part of a measured population. Stronger selection can increase short-term gain when measurements are accurate and heritable variation exists, but it reduces the number of parents and can accelerate inbreeding, drift, and loss of rare favorable alleles.
- A useful discriminator is Calculate and retain the selection differential, family contributions, selected-parent values, generation interval, predicted response, and any correlated traits that could change as a consequence of selection..
- Do not overextend the conclusion beyond this limit: Predicted response depends on valid variance components, relationships, trait definitions, selection intensity, and target environments. Estimates can change when any of those change.
Answer rationale 3: Applied verification rationale
- In practice: Define how many individuals and families will be retained before evaluation. Monitor effective parent number and compare predicted gain with realized progeny performance.
- Record before action: Record the base population, generation, trait definition, population mean and variance, relationship structure when known, selected proportion, and the accuracy or heritability estimate used for predicting response..
- Also record: Calculate and retain the selection differential, family contributions, selected-parent values, generation interval, predicted response, and any correlated traits that could change as a consequence of selection..
- After the action, repeat the same measurement or observation so the comparison is valid.
- Revise the interpretation if the result conflicts with the lesson limit or the expected response: Predicted response depends on valid variance components, relationships, trait definitions, selection intensity, and target environments. Estimates can change when any of those change.
Related lessons
Sources and evidence
- Falconer and Mackay — Introduction to Quantitative GeneticsV20-SRC-006
Inbreeding, variance, heritability, selection response, and quantitative-trait foundations; model assumptions must be stated.
- Bernardo — Breeding for Quantitative Traits in PlantsV20-SRC-007
Selection, prediction, multi-environment testing, genetic gain, and genomic selection; general crop-breeding source.
- Machine-learning multi-trait genomic prediction for cannabinoids, 2025V20-SRC-025
High-density genotyping and genomic-prediction research; prediction depends on training population and validation.
Downloads
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