Advanced Genetics Fitness Calculator
Enter allele frequencies or genotype counts. The calculator estimates relative fitness from gene frequency movement across generations.
Example Data Table
Use these sample cases to test the calculator. They show different evolutionary signals.
| Case | p0 | p1 | Generations | h | Expected signal |
|---|---|---|---|---|---|
| Directional selection | 0.42 | 0.55 | 5 | 0.50 | A gains fitness. |
| Weak decline | 0.61 | 0.58 | 8 | 0.25 | a gains fitness. |
| Near neutral | 0.50 | 0.505 | 10 | 0.50 | Small shift only. |
| Migration adjusted | 0.34 | 0.46 | 4 | 0.70 | Correction changes p*. |
Formula Used
Allele frequency from genotype counts: p = (2AA + Aa) / (2N).
Correction for migration and mutation: p* = (((p1 - mpm)/(1 - m)) - v) / (1 - u - v).
Exact allelic relative fitness: R = [(p* × q0) / (p0 × q*)]^(1/t).
Normalized allele fitness: wA = R / max(R,1) and wa = 1 / max(R,1).
Diploid model: wAA = 1 + s, wAa = 1 + hs, waa = 1. A numerical solver estimates s.
Mean diploid fitness: w̄ = p²wAA + 2pqwAa + q²waa.
How to Use This Calculator
- Enter initial and final A allele frequencies.
- Use genotype counts when frequency is unknown.
- Set elapsed generations for per generation fitness.
- Add dominance when using diploid interpretation.
- Enter mutation, migration, and migrant frequency if needed.
- Add effective population size to screen drift uncertainty.
- Press the calculate button and review the result panel.
- Export the table with the CSV or PDF buttons.
Understanding Relative Fitness From Gene Frequency
What the Measure Shows
Relative fitness compares reproductive success between genetic types. It does not need absolute offspring totals. It uses frequency change as evidence. If allele A rises faster than allele a, its relative fitness is higher. If it falls, the opposite allele has the advantage. This calculator treats gene frequency as a measurable selection record.
Why Frequency Shifts Matter
Allele frequencies carry history from real populations. A small change can mean weak selection. A rapid change can mean strong selection. Yet frequency can also move through drift, mutation, or migration. Good analysis separates these forces. That is why this calculator includes correction fields, dominance settings, and a population size screen.
Allelic and Diploid Views
The allelic model is exact for a simple fitness ratio. It compares the odds of allele A before and after selection. The diploid model adds genotype structure. It uses AA, Aa, and aa fitness values. The dominance coefficient controls heterozygote behavior. A value near zero makes A more recessive. A value near one makes A more dominant. A value of 0.5 gives an additive effect.
Using Count Data
Real genetic samples often arrive as genotype counts. The calculator converts AA, Aa, and aa counts into allele frequency. Counts override manual p values when supplied. This reduces typing work and supports field datasets. Keep sample methods consistent between generations. Unequal sampling can create false selection signals. Small samples also widen uncertainty. Replicate sampling improves trust.
Mutation and Migration
Mutation slowly changes alleles. Migration can change them quickly. The calculator reverses these expected effects before estimating selection. This gives a corrected selected frequency, called p star. Use these fields only when rates are known. Guessing can create misleading precision. Leave them at zero for a pure selection estimate. Add migrant frequency only when gene flow is measured.
Reading the Output
A ratio above one favors allele A. A ratio below one favors allele a. Normalized fitness scales the best type to one. The selection coefficient shows the proportional gap. The log selection intensity is useful for models. It behaves well across several generations. Mean fitness helps compare allele and genotype views. The drift note is a quick screen, not a formal likelihood test.
Best Practice
Use replicate samples when possible. Record population size, sampling dates, and laboratory methods. Check whether mating is random. Check whether viability differs by life stage. Compare several models before drawing conclusions. Relative fitness is powerful, but context matters. Ecology, mating structure, and survival timing can affect interpretation. Treat the result as a structured estimate, not final proof of adaptation. Use independent evidence whenever possible. Report assumptions clearly. Save raw counts. Share code inputs. Avoid overfitting one sample. Strong claims need repeated changes. Stable frequency can still hide genotype selection. A balanced polymorphism may need extra equations. These checks make results easier to review later too.
FAQs
What is relative fitness?
Relative fitness compares the success of one allele or genotype against another. The best type is often scaled to one. Lower values show reduced survival, fertility, or transmission across generations.
Can I use genotype counts instead of allele frequency?
Yes. Enter AA, Aa, and aa counts for each generation. The calculator converts them into allele frequencies. Count inputs override manual frequency boxes when a full row is supplied.
What does p0 mean?
p0 is the starting frequency of allele A. It must be between zero and one. Values near zero or one can make relative fitness estimates highly sensitive.
What does p1 mean?
p1 is the observed final frequency of allele A. The calculator can adjust it for migration and mutation. The corrected value is used for selection estimation.
What is dominance coefficient h?
The dominance coefficient controls heterozygote fitness in the diploid model. A value of zero makes the A effect recessive. A value of one makes it dominant. A value of 0.5 is additive.
What does a fitness ratio above one mean?
A ratio above one means allele A increased faster than allele a after corrections. This suggests allele A had higher relative fitness during the measured interval.
What does a fitness ratio below one mean?
A ratio below one means allele A lost ground relative to allele a. In that case, allele a has the higher estimated relative fitness.
Should I include mutation rates?
Include mutation rates only when you have reliable estimates. Small guessed rates rarely improve analysis. Unknown rates should usually remain zero for a clean selection calculation.
How does migration affect the result?
Migration can change allele frequency without selection. The calculator removes the expected migrant contribution first. This helps isolate selection from incoming gene flow.
Is the drift screen a formal test?
No. It is a simple uncertainty guide based on effective population size. Use deeper likelihood or simulation methods for publication level inference.
Why normalize fitness values?
Normalization makes results easier to compare. The highest fitness becomes one. Other values show proportional reduction relative to that best type.