Combine genotypes, weights, and population values with confidence. See raw scores, z values, and percentiles. Interpret each variant's contribution through structured biology-focused result summaries.
Enter trait settings first. Then add up to eight variants. Genotype values may be 0, 1, 2, or imputed dosages.
| Variant | Genotype Dosage | Effect Type | Effect Size | Weight | Quality |
|---|---|---|---|---|---|
| rs100001 | 1.0 | Beta | 0.18 | 1.00 | 0.98 |
| rs100002 | 2.0 | Odds Ratio | 1.12 | 0.95 | 0.97 |
| rs100003 | 0.0 | Beta | -0.09 | 1.00 | 1.00 |
| rs100004 | 1.4 | Beta | 0.11 | 0.90 | 0.92 |
Example global settings: baseline risk 18%, population mean 0.00, population SD 0.60, odds ratio per 1 SD 1.35, reference population “Reference population”.
Raw polygenic risk score:
PRS = Σ(genotype dosage × beta × weight × quality)
If an effect is entered as an odds ratio, the calculator converts it first: beta = ln(odds ratio).
Standardized score:
Z = (Raw PRS − population mean) ÷ population SD
This places the score onto a reference distribution for easier comparison.
Percentile estimate:
Percentile = Normal cumulative probability of Z × 100
Higher percentiles indicate a larger modeled genetic burden relative to the selected reference population.
Risk adjustment:
Relative Risk = (OR per SD) ^ Z
Adjusted Absolute Risk = (Baseline Risk × Relative Risk) ÷ [(1 − Baseline Risk) + (Baseline Risk × Relative Risk)]
This approximation converts a baseline probability into a risk-adjusted estimate. It is for educational modeling only.
A polygenic risk score combines many genetic variant effects into one weighted value. It estimates relative genetic burden for a trait, not certainty.
No. It is an educational and research-style estimator. Diagnosis requires clinical evaluation, phenotype data, family history, and validated laboratory interpretation.
The z score standardizes raw scores against a reference distribution. That makes interpretation easier across studies, populations, and different score scales.
You can enter discrete allele counts like 0, 1, or 2, or imputed dosages such as 1.37. Keep your dosage format consistent across variants.
Choose odds ratio when the study reports per-allele OR values. The calculator converts them to log effects before summing contributions.
Weight lets you scale a variant’s influence manually. Quality can down-weight uncertain calls, imputation confidence, or study-specific reliability assumptions.
Absolute risk depends on baseline risk, ancestry match, calibration method, prevalence, and study design. Small changes in those assumptions can shift results noticeably.
Use as many validated variants as your model requires. This page includes eight rows for convenience, but larger published scores often use many more.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.