SPEC 3.4.7 • GENETIC INFORMATION — INVESTIGATING DIVERSITY
Investigating Diversity & Statistical Analysis
How scientists measure genetic diversity within and between species using DNA, mRNA, and protein sequences — plus how to interpret means and standard deviations from random samples.
Why Measure Genetic Diversity?
We now know how to measure biodiversity at the community level (Lesson 4.07). This lesson is about measuring genetic diversity within and between species.
Why It Matters
Quantify evolutionary relationships (how closely related are two species?)
Measure variation within a species (which populations are genetically diverse?)
Inform conservation priorities (low-diversity populations are more vulnerable)
Understand selection pressure effects on allele frequencies
Synoptic Link
Links back to Topic 4.4 (genetic diversity = number of different alleles) and Topic 4.6 (phylogenetic classification). In both, measuring DNA sequence differences is the modern gold standard.
Four AQA-Specified Methods
AQA spec 3.4.7 requires you to know four ways of comparing genetic diversity within or between species:
1Frequency of measurable or observable characteristics — e.g. body size, coat colour, leaf shape
2Base sequence of DNA — direct comparison of nucleotide sequences
3Base sequence of mRNA — which genes are being expressed
4Amino acid sequence of proteins encoded by DNA and mRNA — e.g. haemoglobin, cytochrome c
Key AQA Point
⚠️ Gene technology has changed the methods. Inferring DNA differences from observable characteristics has been replaced by direct investigation of DNA sequences. Note: knowledge of gene technology itself is NOT tested here.
Method 1 — Observable Characteristics
Observable Characteristics
Compare the frequency of measurable or observable features (size, shape, colour, behaviour) across populations or species.
How It Works
Take random samples from each population/species
Measure the chosen characteristic (e.g. leaf length, body mass, wing length)
Calculate means and standard deviations
Compare: similar means and overlapping distributions suggest close relationship
Example
Measuring beak depth across 50 finches from two populations and comparing the distributions — if means differ and distributions barely overlap, the populations are behaving differently.
Limitations of Observable Characteristics
Before genetic technology, this was the only option. But it has real problems:
Problems
Convergent evolution: unrelated species can evolve similar appearance in similar environments (e.g. dolphins and sharks). Similarity of appearance does not prove close ancestry.
Environmental influences: characteristics can be affected by environment, not just genes — two plants with identical genes can look different if grown in different soils.
Subjective: "similarity" of appearance is often judged by eye and isn't easily quantified
Polygenic traits: most observable features are controlled by many genes → indirect link to DNA
Examiner Alert
⚠️ Don't confuse phenotypic similarity with genetic similarity. Two organisms can look very similar but have very different DNA sequences (convergent evolution), or look different but have very similar DNA (environmental influences or phenotypic plasticity).
Method 2 — DNA Base Sequence
DNA Base Sequence Comparison
Compare the order of bases (A, T, C, G) in the same gene across different organisms. More closely related → fewer differences.
Species X: ATG CCA TTG AAG GCC TAA
Species Y: ATG GCA TTG AAG TCC TAA
Species Z: ATG CCA TTT AAA GGC TAA
Highlighted bases differ from Species X. Species X and Y differ at 2 bases; Species X and Z differ at 3 bases → X is more closely related to Y than to Z.
Why This Is Powerful
Objective, quantitative — counts actual differences between strands
Works on any organism — all life uses DNA
Includes non-coding regions — detects variation that doesn't change the phenotype at all
Large sample of data — whole genomes contain billions of bases → huge statistical power
Method 3 — mRNA Base Sequence
mRNA Base Sequence
Compare the base sequence of mRNA molecules produced from gene transcription. This tells us which genes are being expressed.
What It Adds Beyond DNA
DNA tells us what genes an organism has; mRNA tells us which are active in a cell at a given time
Two organisms could have identical DNA but differ in which genes are expressed — useful for comparing cell types, tissues, or developmental stages
mRNA reflects only the coding regions (introns have been removed by splicing)
Synoptic Link
Links to Topic 1.8 (DNA and Protein Synthesis): mRNA is produced by transcription from DNA, with introns spliced out. Links to Topic 8.2 (Gene Expression) — different cells in the same organism express different genes, producing different mRNA.
Method 4 — Amino Acid Sequence
Amino Acid Sequence Comparison
Compare the primary structure of proteins (the order of amino acids) — especially of proteins found in many species, like haemoglobin or cytochrome c.
Species P (partial haemoglobin β): Val - His - Leu - Thr - Pro - Glu - Glu - Lys
Species Q: Val - His - Leu - Thr - Gly - Glu - Glu - Lys
Species R: Val - His - Ile - Thr - Gly - Glu - Asp - Lys
P vs Q: 1 difference. P vs R: 3 differences. P and Q are more closely related.
Why Amino Acid Sequences Are Useful
Cytochrome c is present in all aerobic organisms — used extensively for deep evolutionary comparisons
Amino acid sequence is determined by DNA sequence — so it's an indirect record of the DNA
Redundancy of genetic code: some DNA differences produce no amino acid change (silent mutations). So amino acid differences are a subset of DNA differences.
How Gene Technology Changed Things
AQA explicitly highlights that inferring DNA differences from observable characteristics has been replaced by direct investigation of DNA sequences. Before 1970s, the only option was to compare observable traits.
FeatureObservable CharacteristicsDNA Sequencing
Direct or indirect?Indirect — inferred from phenotypeDirect — measures the genetic material itself
Quantitative?Often subjectivePrecise numerical data (count of base differences)
Affected by environment?Yes — can obscure genetic differencesNo — DNA itself is fixed regardless of environment
Detects silent mutations?No — only differences that alter phenotypeYes — detects all base changes including silent ones
Distinguishes convergent evolution?No — similar appearance can misleadYes — DNA reveals true ancestry
Quantitative Investigation — Random Sampling
For quantitative investigations of variation within a species, AQA requires three steps:
1Collect data from random samples — to avoid bias and ensure the sample represents the whole population
2Calculate a mean value — the average of the measurements, summarising the typical value
3Calculate and interpret the standard deviation of the mean — showing how spread out the data are
Why Random Sampling?
Every member of the population has an equal chance of being selected. This eliminates bias — for example, you don't only measure the biggest plants, or only the ones near the path.
Methods to ensure randomness: random number generator, random coordinate tables, grid with randomly chosen coordinates.
Calculating the Mean
Arithmetic Mean
The mean is the sum of all values divided by the number of values. It summarises the typical value of a dataset.
Worked Example
A student measures the length of 10 leaves (mm) from a randomly-sampled oak tree:
42, 45, 38, 47, 44, 41, 46, 43, 45, 49
Sum = 42 + 45 + 38 + 47 + 44 + 41 + 46 + 43 + 45 + 49 = 440
Mean = 440 ÷ 10 = 44.0 mm
Exam Tip
Always include units with the mean. Use an appropriate number of significant figures — usually one more than the raw data. Report with the same units as the original measurements (mm, g, etc.).
Standard Deviation — What It Tells You
Standard Deviation (SD)
A measure of the spread of data around the mean. A large SD indicates wide variation; a small SD indicates values cluster close to the mean.
AQA Point
⚠️ You will NOT be required to CALCULATE standard deviation in written papers. But you MUST be able to INTERPRET what a given SD tells you about the spread of data.
Interpreting SD — Worked Example
PopulationMean leaf length (mm)SD (mm)Oak A (shaded)44.03.2Oak B (sunny)52.01.1
Oak B has longer leaves on average (52 vs 44 mm)
Oak B has less variation (SD = 1.1 vs 3.2) — its leaves are more uniform in length
The shaded Oak A has more variation, perhaps because some leaves grow in micro-sunny patches
Overlapping Error Bars & Statistical Tests
The 68% Rule
Roughly 68% of data points lie within ±1 SD of the mean for a normally-distributed population. So "mean ± SD" is a useful way to summarise where most values fall.
Comparing Two Means Using SDs
When plotting two means on a graph with error bars (showing ± 1 SD):
If error bars overlap substantially: the two samples may NOT be significantly different — differences might be due to chance
If error bars do not overlap: there is likely a real difference between the two populations
Going Further — Statistical Test
To formally test whether two means differ significantly, scientists use the Student's t-test. A smaller p-value (e.g. p < 0.05) indicates the difference is unlikely to be due to chance alone. Full calculation of t is beyond 3.4.7 — you only need to interpret results.
DEEP Answer — Investigating Diversity
Typical 6-mark question: "Explain why comparing DNA base sequences gives a more reliable measure of evolutionary relationship than comparing observable characteristics."
DDefine: Genetic diversity can be compared using four methods: frequency of observable characteristics, DNA base sequence, mRNA base sequence, or amino acid sequence of proteins. DNA sequencing measures differences in the genetic material directly.
EExplain: Observable characteristics can be affected by environment (phenotypic plasticity) and can appear similar in unrelated organisms due to convergent evolution — similar selection pressures producing similar traits. DNA sequences reflect the genetic material itself, unaffected by environment.
EEvidence: Dolphins and sharks have similar streamlined bodies (observable) but very different DNA — placed in different classes. Conversely, humans and chimpanzees share ~98% DNA (close relatives) despite clear appearance differences.
PPractical: DNA comparison gives quantitative data (number of base differences) that can be analysed statistically. Gene technology has caused this shift from inference to direct measurement — the modern gold standard for phylogenetic classification.
Practice Question 1
Q1. A scientist compared a 120-base section of a gene across four species. The results are shown:
ComparisonNumber of base differences
W vs X4
W vs Y12
W vs Z36
(a) Which species is most closely related to W? Explain your reasoning. [2 marks]
(b) Calculate the percentage similarity between W and Y. [1 mark]
(c) Explain one advantage of using DNA base sequence comparison over comparing observable characteristics. [2 marks]
(a)
Species X. (1) W and X have the fewest base differences (4). (2) Fewer differences means mutations have accumulated for less time since a common ancestor, so X shares a more recent common ancestor with W than Y or Z do.
(b)
(120 − 12) ÷ 120 × 100 = 108/120 × 100 = 90%.
(c)
(1) DNA comparison is direct and quantitative — it counts base differences objectively rather than making subjective judgements about appearance. (2) DNA is unaffected by the environment and is not misled by convergent evolution (similar appearance in unrelated species), giving a more reliable measure of evolutionary relationship.
Practice Question 2 — Statistical Interpretation
Q2. A student measured shell diameter (mm) in random samples of 20 snails from each of two locations:
LocationMean (mm)Standard deviation (mm)
Woodland18.42.1
Grassland15.22.8
(a) Describe what the means tell us about the two populations. [2 marks]
(b) Compare the standard deviations and explain what this tells us. [2 marks]
(c) The student wants to know whether the difference in mean shell diameter is statistically significant. Suggest how they could investigate this. [2 marks]
(a)
(1) Woodland snails have a larger mean shell diameter (18.4 mm) than grassland snails (15.2 mm) — a difference of 3.2 mm. (2) This suggests woodland snails typically grow larger shells, perhaps due to different environmental conditions or selection pressures.
(b)
(1) Grassland snails show greater variation (SD = 2.8) than woodland snails (SD = 2.1). (2) The grassland population has shell diameters more spread out around their mean — individuals are less uniform in size.
(c)
(1) Plot both means with error bars showing ±1 SD; if they don't overlap, the difference is likely real. (2) To formally test significance, use a statistical test such as Student's t-test and compare p with 0.05. (3) Alternative: increase sample size to reduce the effect of random variation.
Practice Question 3 — Synoptic
Q3. Scientists compared cytochrome c, a protein used in aerobic respiration, across three species. The amino acid sequence is 104 residues long. Differences from the human cytochrome c were: Chimpanzee = 1; Horse = 12; Tuna = 21.
(a) What is the percentage similarity between human and horse cytochrome c (to 1 dp)? [2 marks]
(b) Use the data to rank the three species from most to least closely related to humans. Justify your ranking. [2 marks]
(c) Cytochrome c is found in all aerobic organisms. Explain one reason why cytochrome c is often chosen for comparisons across distantly-related species. [2 marks]
(a)
Similarity = (104 − 12) ÷ 104 × 100 = 92/104 × 100 = 88.5%.
(b)
Most to least closely related: Chimpanzee (1 difference) → Horse (12) → Tuna (21). Fewer amino acid differences indicate a more recent common ancestor, because fewer mutations have accumulated since the lineages diverged.
(c)
(1) Cytochrome c is highly conserved — it carries out a fundamental function in all aerobic organisms, so most mutations are deleterious and are removed by natural selection. (2) This means the protein changes very slowly over evolutionary time, allowing comparison across very distantly related species (e.g. plants vs animals) that would otherwise appear too different to compare.
Summary
Four AQA Methods of Comparing Diversity
1
Frequency of observable / measurable characteristics
2
DNA base sequence
3
mRNA base sequence
4
Amino acid sequence of proteins
Key Principle
Fewer differences = more closely related (more recent common ancestor; less time for mutations to accumulate).
Why DNA Replaced Observation
Direct, quantitative, unaffected by environment, detects silent mutations, not misled by convergent evolution.
Quantitative Investigations
Random sampling — eliminates bias; every individual has equal chance
Mean — summarises typical value
Standard deviation (SD) — measures spread around the mean; larger SD = more variation
No SD calculation required — interpret only . Overlapping SDs suggest no significant difference; non-overlapping SDs suggest a real difference (confirmed by t-test).
Top Exam Tip
When interpreting a data table with means and SDs: first compare the means, then compare the SDs, then state what the overlap (or non-overlap) of SDs tells you about whether the difference in means is likely to be real.
Next: Why Measure Genetic Diversity?