Read to answer a question
A scientific paper is an argument supported by observations and analysis. Your first task is to identify that argument, then decide how strongly the evidence supports it. You do not need to understand every method or abbreviation on a first reading.
This optional activity takes about 45–60 minutes. Use a notebook or document for your own notes; there is nothing to submit. By the end, aim to explain the study’s purpose, identify one result, and describe one limitation in plain language.
A paper to practise with
The 1000 Genomes Project Consortium (2015). A global reference for human genetic variation. Nature 526, 68–74. DOI: 10.1038/nature15393. Read the freely available full paper or open the publisher’s version.
This foundational resource paper is useful for practising how to read population genomics. It is a historical study, rather than a catalogue of everything known today. Its combination of study design, figures and quality checks provides several entry points for readers with different backgrounds.
The project’s final dataset included 2,504 people from 26 sampled populations. Participants contributed data for a reference resource; the main collection did not include medical or phenotype records. The project team’s overview provides a short orientation.
Try three passes through the paper
1. Find the question: about 10 minutes
Read the title, abstract and opening paragraphs. Write one sentence beginning “The researchers wanted to…”. Identify the type of study: does it describe a resource, test an intervention, compare groups, or develop a method? A resource paper can make a valuable contribution without testing one simple biological hypothesis.
2. Follow one result: about 20 minutes
Read Figure 1 and its legend, then the relevant results paragraphs. Before interpreting a pattern, identify what each panel measures, its units, the meaning of colours, and whether a point represents a person, sample, population or summary. Distinguish the number of observations from the quantity plotted.
3. Investigate how the result was obtained: about 15–30 minutes
Follow the result back to the methods and quality checks. Ask which samples were included, what was measured directly, what was inferred computationally, and what filtering changed. Read the discussion with your own list of uncertainties beside it. You can leave specialist algorithm details for a later visit.
Four questions for this paper
Try a short response before opening each suggested approach. These prompts refer to the published study and its figures.
Why combine whole-genome, exome and microarray data?
The study used mean sequencing depths of 7.4× genome-wide and 65.7× in targeted exomes, alongside arrays. Consider how these complementary measurements support discovery and genotype inference. Higher exome depth does not imply equal coverage everywhere.
What does the size of a pie in Figure 1a represent?
It represents the number of polymorphic variants in that sampled population, rather than the number of participants. The slices describe patterns of sharing. Reading the legend prevents a plausible but incorrect interpretation.
Does the reported catalogue contain every human variant?
No. The paper reports over 88 million variants, but its sampling, detection sensitivity and accessible genomic regions limit completeness. Its strong coverage of common SNPs should not be generalised to every variant class or frequency.
Which limitation would you investigate next?
One useful starting point is the discussion of remaining difficulty with structural variation. Ask how the measurement technology affects what the catalogue can reveal.
Separate a claim from its evidence
Use three columns in your notes. Put the authors’ interpretation in the first, the observation or analysis supporting it in the second, and your question about its limits in the third. Write in your own words and record the figure or section so you can find it again.
| Claim | Evidence offered | Question to ask |
|---|---|---|
| A new method detects more variants. | It produces a longer list from the same synthetic dataset. | Are the extra calls correct, and was a truth set used? |
| The method will work for other datasets. | It performs well in one simulation. | Which assumptions were simulated, and what independent evaluation is available? |
Critical reading does not mean finding fault with every result. It means judging the size and scope of the conclusion fairly. A well-supported narrow conclusion is more informative than an impressive claim that outruns its evidence.
Finish with a five-sentence account
Explain the question, the design, one result, one limitation and one possible next step. Keep observations separate from your own suggestions. Add a small vocabulary list and one question you would bring to a discussion.
You have done enough for this activity when another student could understand what the study contributes from your account. Understanding every supplementary method is a later goal.
Updated September 2026.