Clue 1: Orient yourself
What happens when thousands of sensors each can false-alarm?
Use the labels and the picture's left-to-right, near-to-far, or before-and-after order. Name only what you can point to.
The question
What happens when thousands of sensors each can false-alarm?
Why it matters: Clinical recommendations affect real children and families. Fair comparisons, bias control, ethical limits, and honest uncertainty keep a promising result from becoming a harmful claim. Today you practice the professional reasoning behind that work: Many tests inflate false positives, so thresholds and independent replication must be planned before results are seen.
On your WebXam
Applying the genome-wide significance threshold and the Bonferroni rationale
For life
To find the cause, change one thing and watch what changes.
Principle: Same look, different cause
Five principles we return to
Having it is not using it
Same instructions, different switches
Same look, different cause
Change one thing and watch
Boss or doorman?
Decides the result or only allows it
Held down, not gone
Remove the brake and it returns
Many roads, one ending
One result can begin many ways
Try the everyday version first
Thousands of airport alarms require a stricter screen and a second check
When thousands of alarms are tested, some will ring by chance. A stricter first rule and an independent second check reduce false alerts.
Do not jump to the biology yet. Treat the picture as a small system. Track its parts, follow one change at a time, and keep more than one explanation open until the picture supplies a way to separate them.
Clue 2: Trace one change
Why set the alert rule before scanning?
Follow one object, stage, or path. Point to the first place where the situation changes instead of jumping to the ending.
Clue 3: Keep the cause open
What does the second scanner add?
List more than one explanation that still fits. Name the extra observation that would help you separate those possibilities.
Now inspect the illustration
Work from the visible evidence. A useful answer names the part of the picture that supports it and leaves unknown causes open.
- 1What happens when thousands of sensors each can false-alarm?
- 2Why set the alert rule before scanning?
- 3What does the second scanner add?
Tier 1 check
Finish with the everyday model
Use the everyday picture to answer today's question in plain words: What happens when thousands of sensors each can false-alarm?
You can complete today's required check without opening the technical details below.
Ready for the real names? Optional tier 2
Technical rules and limits
Rule 1: Count how many tests are being run.
Rule 2: Predefine a correction or threshold suited to the analysis.
Rule 3: Replicate the signal in independent data.
Where the analogy stops: Genome signals are not threats, and statistical correction does not determine biological importance.
Carry the previous idea forward
Effect size and confidence interval show magnitude and precision; a p-value measures model compatibility, not truth.
Today's technical takeaway
Many tests inflate false positives, so thresholds and independent replication must be planned before results are seen.
Now map the same rules onto biology
Control false positives in a common-variant GWAS
Many sensors
Millions of variant tests
Strict alert rule
Multiple-testing threshold
Second scanner
Independent replication
Educational illustration, not a clinical photograph or a patient-specific study plan. Use the supplied evidence cards and claim ceiling.
Mateo's case file: evidence supplied in this lesson
EXP08-E1
Testing one million variants at p below 0.05 would produce many chance hits under the null.
Why it matters: A usual single-test threshold is not adequate for a genome-wide scan.
EXP08-E2
For many common-variant GWAS analyses, p below 5 x 10^-8 is a conventional threshold.
Why it matters: The convention is not universal for every genetic analysis.
EXP08-E3
The candidate peak repeats in an independent cohort with the same direction of effect.
Why it matters: Replication lowers concern that the first signal was sample-specific chance or error.
Make the clinical decision
You are the GWAS quality-control lead.
A peak has p equal to 2 x 10^-6 in the discovery sample and no replication result.
ALabel it suggestive and require the planned threshold, quality checks, and replication.
BDeclare it significant because it is below 0.05.
CUse 5 x 10^-8 as a magic rule for every genetic test.
Choose the report language and explain how multiplicity and replication change the claim.
Evidence required
EXP08-E1 + EXP08-E2 Claim ceiling
You may apply the supplied common-variant GWAS convention. You may not treat that number as universal or proof of mechanism.Go deeper Optional tier 3
Everything required for today is above. Open these only if you want the explainer, source trail, or download files.
The everyday model and Tier 1 check are the complete required path for this lesson.
Use these checks to keep your place. They are not turned in through the portal.
Check off as you finish
- Worked through the everyday picture and answered its three questions.
- Completed the Tier 1 check in plain words.
Turn in: Experimental Design lesson 8: Why Gene Studies Use Such Tiny P-Values
Go to Schoology to turn this in.
Submit one PDF. Put your first and last name in the document header. Name the file: FirstName LastName - Assignment Title - YYYY-MM-DD.pdf.
Open Schoology PDF upload helpIf you cannot get in, see Mr. Mendoza. Do not skip the work.
Optional legacy technical materials Open only if you want the original notes, vocabulary, artifact, and CER work
Before this page, you should know
- An association means two things tend to occur together; an (or ) measures effect size, where 1.0 means no difference.
- A p-value is the probability of a result at least this extreme if there were truly no effect; by convention p < 0.05 is called statistically significant.
If not, review
Today's new idea is only
Testing many hypotheses at once breeds false positives, so a genome scan must correct its threshold down to p < 5 x 10^-8 and demand replication.
Goal: Students will explain why testing many hypotheses at once inflates false positives, and apply a Bonferroni-style correction to reach the threshold p < 5 x 10^-8.
Know by the end
- A problem appears the moment you test many hypotheses on the same data, because each test gets its own chance to throw a .
- At p < 0.05, running 1,000,000 tests with no real effect yields on average about 50,000 false alarms by chance.
- The divides the 0.05 bar by the number of tests; 0.05 / 1,000,000 equals 5 x 10^-8, the threshold a real European GWAS held its hits to.
- Replication in a second independent sample is the strongest check; the (FDR, Benjamini-Hochberg) is a less strict alternative used heavily in gene-expression work.
1
The multiple-testing trap
Model start: means each of many tests gets its own chance to throw a , so flukes pile up across a genome scan.
- When you test one hypothesis, p < 0.05 keeps your false-alarm rate at about ____ percent (5 percent).
- But a problem appears the moment you test many hypotheses on the same data, so the flukes pile up; running 1,000,000 tests yields about 50,000 false alarms by chance.
2
The Bonferroni fix
- The divides your significance bar by the number of tests: new bar = 0.05 / (number of tests).
- For about a million common-variant tests, 0.05 / 1,000,000 lands near ____ x 10^-8 (5 x 10^-8); this is exactly where the threshold comes from, and the real European GWAS held its hits to it.
3
Two more tools
- The (FDR), controlled by Benjamini-Hochberg, is a less strict alternative that controls the expected fraction of reported hits that are false, used heavily in gene-expression work.
- The strongest check of all is replication: a true signal should reappear in a second independent group; a hit that clears the bar once but never replicates is treated with suspicion. (5 x 10^-8 is the standard but debated; some argue for an even tinier 5 x 10^-9.)
Everything you need for today is on this page. These links are optional.
What to read
Read the short plain-language explanation written for this lesson. Plain-language explainer for this lesson
Why this source matters
This explanation gives you the background for today's idea without making you decode a research paper: Testing many hypotheses at once breeds false positives, so a genome scan must correct its threshold down to p < 5 x 10^-8 and demand replication.
Words to unlock first
multiple testingfalse positiveBonferroni correctiongenome-wide significancefalse discovery rate
Reading moves
- Skim the title and abstract first to get the gist.
- Circle the one sentence that states the main claim.
- Box the evidence the authors give for that claim.
- Mark one sentence that confuses you, and move on.
Stop point
Stop after the final 'Use it now' section. The research citations are available separately for advanced readers.
Your output
Write one claim-evidence sentence: state the main idea, then name the example or evidence that supports it.
Where this fits
Tested on (Ohio WebXam)
Genetics of Disease · 072130
PLTW lesson
MI · Experimental Design domain · Statistics for research, multiple testing and correction
WebXam domain
Molecular and Genetic Technology
Evidence to produce
Decide for four scanned SNPs whether each clears genome-wide significance (p < 5 x 10^-8) with one sentence per call (IRF6: p = 2 x 10^-9; chromosome 8: p = 3 x 10^-6; unknown gene: p = 0.0004; chromosome 16: p = 1 x 10^-8), then name the single study that would make a cleared hit more trustworthy.
Lab / skill
Biomedical Innovations (BI) · Medical Interventions (MI)
Everything required for today's decision is already in the case file and plain-language explainer. The links below are original papers and database records for teachers and advanced readers, not assigned student reading.
- Fadista et al. The genome-wide significance threshold, 2016.
- Sun B, et al. 2023. Ezh2-dependent methylation in oral epithelia promotes secondary palatogenesis. Birth Defects Res. [PMID:37435868]
- van Rooij IALM, et al. 2019. Nonsyndromic cleft lip/palate: GWAS in Europeans identifies risk loci. Genes. [PMID:31817908]
- Park JW, et al. 2007. Association between IRF6 and nonsyndromic cleft lip/palate in four populations. Genet Med. [PMID:17438386]
- Claim: A SNP reported at p = 0.001 in a genome-wide scan is not yet trustworthy.
- Evidence: A genome scan runs about ____ (a million) tests, and the accepted bar is p < ____ (5 x 10^-8).
- Reasoning: Because so many tests run at once, a p-value of 0.001 would be expected to occur by chance about ____ (1,000) times, so it does not clear the genome-wide bar.
How this is graded (rubric)
For: Decide for four scanned SNPs whether each clears genome-wide significance (p < 5 x 10^-8) with one sentence per call (IRF6: p = 2 x 10^-9; chromosome 8: p = 3 x 10^-6; unknown gene: p = 0.0004; chromosome 16: p = 1 x 10^-8), then name the single study that would make a cleared hit more trustworthy.
| Criterion | Proficient | Developing | Beginning |
|---|---|---|---|
| Complete | Every required part of the artifact is present and filled in. | Most parts are present, but one is missing or left blank. | Several parts are missing. |
| Accurate | The science and data are correct and match the evidence. | Mostly correct, with a small factual slip. | Key science or data is wrong. |
| Scientific reasoning (CER) | States a claim, backs it with specific evidence, and explains the reasoning. | Has a claim and evidence, but the reasoning is thin or missing. | Gives an answer with no evidence or reasoning. |
| Professional communication | Clear, organized, and labeled the way a clinician or scientist would write it. | Readable but disorganized or missing labels. | Hard to follow. |
| Submitted | Turned in through the route named under Submit here and confirmed. | Turned in, but in the wrong place or unconfirmed. | Not turned in. |
How the model answer scores against this rubric
- CompleteProficient: Nothing is left blank: the model fills every part of "Decide for four scanned SNPs whether each clears genome-wide significance (p < 5 x 10^-8) with one sentence per call (IRF6: p = 2 x 10^-9; chromosome 8: p = 3 x 10^-6; unknown gene: p = 0.0004; chromosome 16: p = 1 x 10^-8), then name the single study that would make a cleared hit more trustworthy.".
- AccurateProficient: Every number and claim matches the case evidence.
- Scientific reasoning (CER)Proficient: It names a claim, cites the specific evidence, and explains the reasoning, not just the answer.
- Professional communicationProficient: It is organized and labeled like a real chart note.
- SubmittedProficient: It would be attached to your class form or handed in, and confirmed.
What's next: Gene studies use a tiny p-value because a million tests breed a million chances to be fooled, and replication seals the deal. But even a rock-solid statistical link never proves IRF6 does anything to . How would we leave the spreadsheet, go to the bench, and test what IRF6 actually does to a developing ?