Escience Lab 15 Population Genetics Answers

eScience Lab 15 Population Genetics Answers: A Complete Guide to Understanding the Experiment, Analyzing Data, and Writing Your Report

Population genetics is the study of how genetic composition of populations changes over time, and eScience Lab 15 provides a hands‑on simulation that lets students explore these dynamics without stepping into a field site. This lab walks learners through the core principles of allele and genotype frequencies, Hardy‑Weinberg equilibrium, and the forces that can shift those frequencies—mutation, migration, genetic drift, and natural selection. Below you will find a detailed walkthrough of the lab’s objectives, step‑by‑step instructions, expected results, common questions, and tips for crafting a strong lab report. Use this guide as a reference while you work through the simulation, and refer back to it when you need to check your interpretations or clarify confusing points.

1. What Is eScience Lab 15 All About?

eScience Lab 15 focuses on population genetics using a computerized model that mimics a breeding population of organisms (often fruit flies or a similar model organism). The simulation allows you to:

• Set initial allele frequencies for a single gene with two alleles (e.g., A and a).
• Choose which evolutionary forces to act on the population (none, mutation, migration, drift, selection).
• Run multiple generations and observe how allele and genotype frequencies change.
• Compare the observed outcomes to the expectations of the Hardy‑Weinberg principle.

The lab’s primary learning goals are:

1. Calculate allele (p) and genotype (p², 2pq, q²) frequencies from raw count data.
2. Test whether a population is in Hardy‑Weinberg equilibrium using a chi‑square goodness‑of‑fit test.
3. Interpret how each evolutionary force alters genotype frequencies over successive generations.
4. Communicate findings clearly in a written lab report, including tables, graphs, and a discussion of sources of error.

2. Step‑by‑Step Walkthrough of the Simulation

Below is a concise protocol that mirrors the instructions you will see inside the eScience platform. Follow each step carefully; the numbers correspond to the screens you will encounter.

2.1. Initial Setup

1. Open Lab 15 from your eScience dashboard.
2. Select the “Population Genetics” module.
3. Choose the organism (usually Drosophila melanogaster) and the trait (e.g., eye color: red (dominant, R) vs. white (recessive, r)).
4. Set the starting allele frequencies:
   • p (frequency of R) = 0.6
   • q (frequency of r) = 0.4 (You can adjust these values later to test different scenarios.)

2.2. Running the Baseline (No Evolutionary Forces)

5. Ensure all force toggles (Mutation, Migration, Drift, Selection) are set to Off.
6. Click “Run Simulation” for 10 generations.
7. The program will output a table showing the number of RR, Rr, and rr individuals each generation.
8. Record these counts in your lab notebook (or export the CSV file provided).

2.3. Calculating Frequencies

For each generation, compute:

• Allele frequencies:
  [ p = \frac{2(\text{RR}) + (\text{Rr})}{2N} ] [ q = \frac{2(\text{rr}) + (\text{Rr})}{2N} ] where N = total individuals.

• Genotype frequencies (observed):
  [ f_{RR}^{obs} = \frac{\text{RR}}{N},; f_{Rr}^{obs} = \frac{\text{Rr}}{N},; f_{rr}^{obs} = \frac{\text{rr}}{N} ]

• Expected Hardy‑Weinberg frequencies (using the p and q you just calculated):
  [ f_{RR}^{exp} = p^2,; f_{Rr}^{exp} = 2pq,; f_{rr}^{exp} = q^2 ]

2.4. Chi‑Square Test for Equilibrium

9. Use the formula:
   [ \chi^2 = \sum \frac{(O_i - E_i)^2}{E_i} ] where O = observed count, E = expected count (expected frequency × N).
10. Degrees of freedom = number of genotypes – number of alleles = 3 – 2 = 1.
11. Compare your χ² value to the critical value from a χ² table (α = 0.05, df = 1 → 3.84).
    • If χ² ≤ 3.84 → fail to reject H₀ (population is in Hardy‑Weinberg equilibrium).
    • If χ² > 3.84 → reject H₀ (significant deviation).

2.5. Introducing Evolutionary ForcesRepeat steps 5‑11 for each of the following scenarios (run at least 20 generations to see clear trends):

For each scenario, note how the allele frequencies shift over generations and whether the population deviates from Hardy‑Weinberg expectations more strongly than in the baseline.

3. Interpreting the Results

3.1. Baseline (No Forces)

• Expectation: Allele frequencies should remain constant (p ≈ 0.6, q ≈ 0.4) and genotype frequencies should match p², 2pq, q² within sampling error.
• Typical Outcome: χ² values hover around 1–2, well below the critical threshold, indicating equilibrium.

3.2. Mutation

• Effect: Very slow change; over 20 generations you might see p decrease by ~0.001–0.002 per generation if μ > ν.

2.5. Introducing Evolutionary Forces (Continued)

Genetic Drift

Effect: In small populations, random sampling of alleles during reproduction leads to unpredictable shifts in allele frequencies. Over time, this can result in the loss or fixation of alleles purely by chance.
Parameters: Population size N = 50 (small).
Outcome:

• Allele frequencies: p and

2.5. Introducing Evolutionary Forces (continued)

Genetic drift – In a population of only fifty individuals the random sampling of alleles from one generation to the next can produce noticeable swings in the allele frequencies. Because the sampling error is proportional to 1/√N, the variance in p grows quickly, and over twenty generations it is common to see p drift upward or downward by as much as ten percent of its initial value. In many simulated runs one of the two alleles eventually becomes fixed (frequency 1.0) while the other is lost, a pattern that is readily observable as a sharp decline in heterozygosity.

Migration – Introducing a steady influx of migrants carrying a different allele frequency (for example, a migrant pool with p = 0.9) immediately perturbs the local gene pool. Each generation the new migrants contribute a proportion m of the breeding individuals; even a modest migration rate of five percent can shift the resident allele frequency toward the migrant value within a handful of generations. The effect is cumulative: the resident frequency moves part‑way toward the migrant frequency each generation, producing a monotonic approach that can be tracked on a simple line graph.

Mutation – When only mutation operates, the change in allele frequency per generation is extremely small because the mutation rates (μ = 0.001, ν = 0.0005) are low. Over twenty generations the resident allele R will decline by roughly μ × average frequency of R while the alternative allele r gains a comparable amount from ν. The trajectory is gradual and almost linear, with the equilibrium point settling near the balance of the two rates (approximately p ≈ ν/(μ+ν) ≈ 0.33). Heterozygosity remains relatively high because the system does not drive any genotype to fixation.

Natural selection – Selective differences among genotypes generate a rapid, directional shift. With fitness values of 1, 0.9, and 0.8 for RR, Rr, and rr respectively, the advantageous genotype (RR) enjoys a reproductive advantage that translates into a steady increase in p. After ten generations the frequency of R can climb from its initial 0.6 to well above 0.8, and by the twentieth generation it may approach 0.95. The genotype proportions move away from the Hardy‑Weinberg expectations, producing a pronounced excess of the favored homozygote and a deficit of heterozygotes.

Combined forces – When mutation, migration, drift, and selection act simultaneously, the system exhibits a complex, often non‑linear pattern. Migration may introduce a higher frequency of the advantageous allele, while drift adds stochastic fluctuations that can either amplify or counteract the selective push. Mutation supplies a continuous source of new variation, slightly offsetting the deterministic trends. The net result is a trajectory that reflects the interplay of deterministic pressure (selection, migration) and stochastic noise (drift), with the population frequently deviating from Hardy‑Weinberg proportions in a way that depends on the relative magnitudes of the forces.

3. Interpreting the Results

The baseline simulation demonstrates that, in the absence of any evolutionary pressure, genotype frequencies remain tightly clustered around the expected Hardy‑Weinberg values, and the chi‑square statistic stays well below the critical threshold. When mutation is introduced, the shift is subtle and progresses at a sluggish pace, preserving most of the original equilibrium structure. Migration acts as a directional force that pulls

allele frequencies towards the migrant pool, but its effect is tempered by the population size and the degree of difference between the resident and migrant alleles. Drift, conversely, introduces randomness, causing frequencies to wander and occasionally leading to the loss of alleles, particularly in smaller populations. The most dramatic changes, however, are observed when natural selection is active. The clear deviation from Hardy-Weinberg expectations, coupled with a rapidly changing chi-square statistic, highlights the power of selection to reshape genetic composition.

The combined forces scenario reveals the realistic complexity of evolutionary dynamics. The interplay between selection, drift, mutation, and migration creates a dynamic equilibrium where the population is rarely, if ever, in true Hardy-Weinberg balance. The magnitude of the chi-square statistic fluctuates considerably, reflecting the ongoing tension between opposing forces. For instance, a strong selective advantage for the R allele might drive its frequency upwards, but drift could temporarily reduce its prevalence, especially if the population experiences a bottleneck. Mutation continually introduces r alleles, counteracting selection, while migration can either reinforce or oppose the selective trend depending on the migrant allele frequencies.

Furthermore, the simulations demonstrate the importance of population size. In larger populations, drift is weaker, and the effects of selection and migration are more pronounced. Conversely, in smaller populations, drift can overwhelm selection, leading to unpredictable outcomes and potentially hindering adaptation. This underscores the vulnerability of small, isolated populations to genetic erosion and loss of adaptive potential. The sensitivity analysis, varying parameters like mutation rates and selection coefficients, further illustrates that even small changes in these values can significantly alter the evolutionary trajectory.

In conclusion, these simulations provide a valuable framework for understanding the fundamental processes driving evolutionary change. They demonstrate that evolution is not a simple, unidirectional process, but rather a complex interplay of deterministic and stochastic forces. The Hardy-Weinberg equilibrium serves as a useful null hypothesis, but real populations are rarely in this state. By manipulating individual evolutionary forces and observing their combined effects, we gain a deeper appreciation for the dynamic nature of genetic variation and the intricate mechanisms that shape the diversity of life. The ability to model these processes computationally allows researchers to explore a wide range of scenarios and test hypotheses about the evolutionary history and future trajectory of populations.