Sensory Genetics and Pedigree Reconstruction

ISEF Category: Cellular and Molecular Biology

Subcategory: Genetics  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

Your tongue and ears can act like tiny genetics detectors. A bitter taste, a cilantro reaction, and earwax type can all point to variants you cannot see. When you combine those traits with frequency data, you can test how well simple inheritance models match real people. That is a real genetics puzzle, not just a classroom chart.

What Is It?

This project asks you to connect visible or self-reported traits to inherited DNA variants. PTC tasting is linked to taste receptor genes, cilantro aversion has a genetic component, and earwax type is tied to a well-known allele in ABCC11. Think of each trait like a clue in a family mystery. One clue can mislead you, but several clues together can narrow the answer.

You also compare your survey patterns with HapMap allele frequencies. HapMap is a public dataset that reports how common many genetic variants are in different populations. You can use those frequencies to build expected genotype and phenotype patterns, then compare them with the survey data you collected. That lets you ask whether the three traits behave like independent Mendelian loci, or whether the patterns suggest linkage, population structure, or simple noise.

Why This Is a Good Topic

This is a strong science fair topic because you can collect real human data, test clear inheritance models, and use public genetics databases for comparison. The question is narrow enough to measure, but rich enough to support serious analysis. You can learn survey design, genotype frequency estimation, pedigree logic, and basic population genetics. The project also connects to how genetic traits are studied in medicine and consumer genetics.

Research Questions

• How does self-reported PTC tasting status relate to predicted genotype frequencies from HapMap data?
• What is the effect of cilantro aversion status on the accuracy of pedigree-based inheritance models?
• Does earwax type improve prediction of the other two traits when used in a combined linkage model?
• To what extent do observed school survey frequencies match Hardy-Weinberg expectations for each locus?
• Which pair of traits shows the strongest evidence of non-independence in the collected dataset?
• How does ancestry group, if voluntarily reported and anonymized, affect trait frequency estimates in the survey?

Basic Materials

• Anonymous survey form with parent consent language and school approval documentation.
• Clipboard or secure digital form for data collection.
• Computer with spreadsheet software.
• Public HapMap allele frequency tables.
• Reference sheet for known trait associations from peer-reviewed sources.
• Calculator or spreadsheet functions for frequency calculations.
• Data codebook for anonymized trait categories.
• Basic pedigree worksheet templates.

Advanced Materials

• Access to a university statistics consult or genetics lab mentor.
• R or Python environment for frequency modeling and visualization.
• Statistical genetics software or packages for Hardy-Weinberg and linkage analysis.
• Public genotype reference datasets from HapMap or 1000 Genomes for comparison.
• Secure data storage system for de-identified survey records.
• Methods for bootstrapping or permutation testing.
• Optional ancestry-informative marker summaries from public databases.
• ImageJ or similar software if you create visual pedigree maps or figure panels.

Software & Tools

• Google Sheets: Organizes survey responses, calculates frequencies, and builds simple charts.
• R: Runs Hardy-Weinberg tests, permutation tests, and linkage-style comparisons.
• Python: Cleans data, automates frequency calculations, and makes publication-style plots.
• ImageJ: Helps prepare clean figure panels if you include pedigree or genotype visuals.
• GraphPad Prism: A lower-friction option for basic statistics and graph formatting if your school has access.

Experiment Steps

1. Define the three traits you will measure and decide how you will score each one consistently.
2. Build an anonymized survey plan that protects privacy and separates consent from response data.
3. Collect a sample large enough to compare observed frequencies with expected population frequencies.
4. Organize the data into genotype or phenotype categories and decide which assumptions each category requires.
5. Choose the inheritance model you will test, then build a frequency model from public allele data.
6. Compare observed and expected patterns with a statistical test, then check whether any pair of traits behaves as if it is linked.

Common Pitfalls

• Using inconsistent trait definitions, which makes PTC tasting, cilantro aversion, or earwax type hard to classify the same way across students.
• Treating self-reported taste or smell preferences as direct genotypes, which inflates the certainty of your conclusions.
• Mixing consent forms with response data, which breaks anonymity and can stop the project from being approved.
• Ignoring ancestry structure, which can make frequency differences look like linkage when they actually reflect population differences.
• Claiming linkage from a small sample, which can produce false patterns from random variation alone.

What Makes This Competitive

A strong version of this project goes beyond a simple survey chart. You would compare several models, test whether the traits behave independently, and quantify uncertainty instead of just describing patterns. You would also explain where the model fails, such as ancestry effects, imperfect trait penetrance, or weak self-report accuracy. Careful statistics, clean privacy handling, and a clear biological interpretation would make the work stand out.

Project Variations

• Use only earwax type and PTC tasting, then test whether the simpler two-trait model fits better than the full three-trait model.
• Replace one survey trait with a public genotype dataset comparison for TAS2R38 or ABCC11 and analyze concordance across sources.
• Compare trait frequencies across grade levels or self-reported ancestry groups to study how population structure changes the expected inheritance pattern.

Learn More

• NCBI Gene: Search for TAS2R38 and ABCC11 to read gene summaries, variant links, and reference articles.
• PubMed: Search for review articles on PTC tasting, cilantro aversion, and earwax genotype associations.
• HapMap Project data browser: Find public allele frequency tables and population summaries for common human variants.
• NIH Genetics Home Reference archive: Read plain-language background on inheritance and common trait genetics topics.
• MIT OpenCourseWare Genetics lectures: Use free lecture materials to review pedigree analysis, linkage, and population genetics.