Biology • Year 12 • Module 5 • Lesson 18
Large-Scale Population Genetics Data, Disease, Conservation, Human Evolution
Apply population-genetics reasoning to three data scenarios covering conservation genetics, disease inheritance studies, and human evolutionary patterns, using only lesson concepts and vocabulary.
1. Conservation, genetic diversity before and after a population bottleneck
A research team sampled allele frequencies at multiple genetic marker positions in an endangered marsupial population over 20 years. The population was hit by a severe disease outbreak in Year 6, reducing numbers by 80%. The table below shows the number of distinct allele forms detected at 10 sampled marker positions before the outbreak and at 5-year intervals after it. 8 marks
| Time point | Population size (approx.) | Mean number of allele forms per marker |
|---|---|---|
| Before outbreak (Year 0) | 2 400 | 5.8 |
| Year 5 (post-outbreak) | 480 | 3.1 |
| Year 10 | 650 | 3.4 |
| Year 15 | 820 | 3.6 |
| Year 20 | 910 | 3.9 |
1.1 Describe the trend in genetic diversity (mean allele forms per marker) across the five time points. 2 marks
1.2 Explain why the sudden reduction in population size (the bottleneck) causes a drop in genetic diversity. Use the terms allele, bottleneck and genetic diversity in your answer. 3 marks
1.3 A conservation manager concludes: "Diversity has been recovering, the population is safe now." Use the lesson's framing of the limits of inference to evaluate this claim and state one type of additional data you would want. 3 marks
2. Disease inheritance, variant frequency across population groups
A large international research collaboration combined data from six population groups to measure the allele frequency of a disease-linked variant at a single gene locus. The table summarises the results. 7 marks
| Population group | Sample size (n) | Disease-linked variant frequency | Approximate carrier rate |
|---|---|---|---|
| Group 1 | 58 000 | 0.0180 | ~1 in 28 |
| Group 2 | 5 200 | 0.0190 | ~1 in 26 |
| Group 3 | 18 000 | 0.0090 | ~1 in 55 |
| Group 4 | 21 000 | 0.0035 | ~1 in 143 |
| Group 5 | 9 500 | 0.0012 | ~1 in 417 |
| Group 6 | 1 200 | 0.0014 | ~1 in 357 |
Hypothetical data based on the general structure of large population genomics databases.
2.1 Describe the trend in disease-linked variant frequency across the six groups. State which has the highest and which has the lowest frequency. 2 marks
2.2 Why would using Group 1's variant frequency as the reference for all patients potentially produce misleading results for patients from Group 4 or Group 5? Use the lesson's concept of inference in your answer. 2 marks
2.3 A health-care worker says: "Anyone from Group 2 will get the disease." Identify two reasoning errors in that claim and correct each one. 2 marks
2.4 Group 6 has only 1,200 individuals sampled. Explain one reason why this limits how much confidence we can place in its variant frequency estimate compared with Group 1. 1 mark
3. Human evolution, shared genetic markers across population groups
Researchers compared the proportion of shared genetic marker patterns (from many sampled SNP positions) between six human population groups and a reference African group. The table below shows the proportion of markers that were shared between each group and the African reference group. 7 marks
Figure 3.1. Proportion of shared genetic marker patterns with the African reference group across six population groups, ordered by approximate geographic distance from Africa (A = nearest, F = most distant). Hypothetical data.
3.1 Describe the relationship shown in the bar chart between geographic distance from Africa and the proportion of shared genetic markers. 2 marks
3.2 Using the lesson's concept of shared ancestry, explain why groups geographically closer to Africa share more genetic markers with the African reference group than groups that are geographically more distant. 3 marks
3.3 State one limitation of inferring human evolutionary history from this type of shared-marker data alone. 2 marks
4. Apply, using large-scale data appropriately
A health authority is planning a population genetic screening program. The lead researcher argues: "We have huge data sets, we should just use the overall population-average frequency figures for every individual." 5 marks
4.1 Using the disease inheritance data from Section 2, explain why applying a single overall average variant frequency to all individuals from different population groups could produce misleading results. 2 marks
4.2 Suggest one way the team could use large-scale population genetics data more appropriately, drawing on the lesson's discussion of disease inheritance studies. 2 marks
4.3 Even with group-specific population data, one key limit of inference remains. Identify it. 1 mark
Q1.1, Conservation trend (2 marks)
Genetic diversity (mean allele forms per marker) dropped sharply after the bottleneck, from 5.8 before the outbreak to 3.1 in Year 5 [1]. After Year 5 there is a slow gradual recovery as population size increases, but diversity has not returned to the pre-outbreak level even by Year 20 (3.9 vs 5.8) [1].
Q1.2, Mechanism (3 marks)
A bottleneck is a sharp reduction in population size [1]. When only a small fraction of the original population survives to reproduce, many rare alleles that were present in the original population are lost by chance, they simply are not carried by the surviving individuals [1]. With fewer distinct allele forms remaining, genetic diversity falls and the remaining individuals share a higher proportion of their alleles with each other [1].
Q1.3, Evaluate manager's claim (3 marks)
The claim overstates what the data can show. A recovery in mean allele count is a positive trend, but it does not guarantee the population will survive long-term, diversity remains well below pre-outbreak levels, and many environmental threats (e.g. further disease outbreaks, habitat loss) could still cause decline [1]. The lesson is explicit: large data improves confidence in broad trends but does not remove uncertainty about exact future outcomes [1]. Additional data that would be useful: actual population growth rate over time, presence of ecologically important variants, or evidence of continued disease pressure [1].
Q2.1, Disease variant frequency trend (2 marks)
The disease-linked variant is most common in Group 2 (0.0190 ≈ 1 in 26) and least common in Group 5 (0.0012 ≈ 1 in 417) [1]. There is a clear difference in carrier rates across the six groups, some groups have variant frequencies approximately 15 times higher than others [1].
Q2.2, Why single reference frequency misleads (2 marks)
Group 1's frequency (0.0180) is much higher than that of Groups 4 and 5 (0.0035 and 0.0012 respectively). Applying Group 1's frequency to everyone would over-estimate disease risk for Groups 4 and 5 patients, potentially leading to unnecessary concern or interventions [1]. This is exactly the situation the lesson warns against: a population-level inference (using Group 1 as a universal standard) does not accurately describe the variation between groups, and drawing conclusions beyond the data the sample actually represents is an error of inference [1].
Q2.3, Two errors in health-care worker's claim (2 marks)
Error 1: Even in Group 2, the carrier rate is approximately 1 in 26, most individuals in Group 2 do not carry the variant at all. The claim generalises a population frequency to every individual, which is not justified [1]. Error 2: Being a carrier (one copy of the disease-linked variant) is typically different from having the disease, the worker's claim jumps from variant frequency to disease outcome, without acknowledging that individual phenotype depends on genotype, environment, and other factors [1].
Q2.4, Limitation of small sample in Group 6 (1 mark)
A sample of 1,200 is much smaller than Group 1's 58,000. With fewer individuals, the frequency estimate is less stable, a slightly different random selection of 1,200 people could give a noticeably different value. The estimate is less reliable and should be treated as a rough indication rather than a confirmed population frequency [1].
Q3.1, Relationship in bar chart (2 marks)
The proportion of shared genetic markers with the African reference group decreases as geographic distance from Africa increases [1]. Group A (nearest) shares 0.91 of markers, while Group F (most distant) shares only 0.71, a difference of 0.20 across the range of groups [1].
Q3.2, Shared ancestry explanation (3 marks)
Groups geographically closer to Africa are likely to have shared ancestry, that is, they are more closely related to the African reference population because their ancestors separated from the African ancestral population more recently [1]. Groups that separated earlier and then migrated further away have accumulated more genetic differences over more generations of independent reproduction [1]. The lesson states that populations sharing more genetic markers are likely to share more recent common ancestry, so the pattern of decreasing shared markers with distance from Africa suggests that the further away a group is geographically, the deeper (more ancient) its separation from the African ancestral population [1].
Q3.3, Limitation of shared-marker inference (2 marks)
The data describe marker patterns in currently living people, they cannot directly record what happened in the past [1]. The inferred ancestral relationships depend on assumptions about population history, and those conclusions remain inferences that may be revised as more data (from additional groups, different markers, or other lines of evidence) becomes available [1]. Conclusions about human evolutionary history from shared markers are evidence-based but not certain.
Q4.1, Why single average misleads (2 marks)
The variant frequencies in Section 2 differ markedly between groups (0.0190 in Group 2 versus 0.0012 in Group 5). Using an overall average would under-estimate disease risk for individuals from Groups 1 and 2, and over-estimate it for individuals from Groups 4, 5, and 6, leading to inappropriate clinical decisions in both cases [1 each side, max 2].
Q4.2, Better use of large-scale data (2 marks)
The team could use group-specific frequency data, drawing on large collaborative data sets that pool samples from many populations, to provide each patient with a disease-risk estimate relevant to the population groups their ancestry most closely matches [1]. This is the lesson's key argument: large collaborative data sets allow researchers to identify and compare carrier frequencies across different groups, rather than applying a single universal figure [1].
Q4.3, Remaining limit of inference (1 mark)
Even a perfectly group-matched frequency estimate is a population-level trend, it cannot predict whether any one specific individual will develop the disease. Large-scale data tightens the trend inference but does not provide individual certainty. [1]