Polygenic Risk Score Calculator

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.

Calculator Inputs

Enter trait settings first. Then add up to eight variants. Genotype values may be 0, 1, 2, or imputed dosages.

Variant Entries

Variant 1

Variant 2

Variant 3

Variant 4

Variant 5

Variant 6

Variant 7

Variant 8

Example Data Table

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”.

Formula Used

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.

How to Use This Calculator

  1. Enter the trait or phenotype you want to evaluate.
  2. Provide the reference population name, baseline absolute risk, population mean, and population standard deviation.
  3. Enter the odds ratio per one standard deviation from your reference study.
  4. Add variant rows using genotype dosage, effect size, optional weight, and quality multiplier.
  5. Select whether each effect size is already a beta value or an odds ratio.
  6. Click Calculate Score to show the results above the form.
  7. Review raw score, standardized z score, percentile, adjusted risk, and the ranked variant contribution table.
  8. Use the export buttons to save summary results as CSV or PDF.

Frequently Asked Questions

1) What is a polygenic risk score?

A polygenic risk score combines many genetic variant effects into one weighted value. It estimates relative genetic burden for a trait, not certainty.

2) Does this calculator diagnose disease?

No. It is an educational and research-style estimator. Diagnosis requires clinical evaluation, phenotype data, family history, and validated laboratory interpretation.

3) Why is the z score important?

The z score standardizes raw scores against a reference distribution. That makes interpretation easier across studies, populations, and different score scales.

4) What genotype values can I enter?

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.

5) When should I choose odds ratio as effect type?

Choose odds ratio when the study reports per-allele OR values. The calculator converts them to log effects before summing contributions.

6) What do weight and quality fields do?

Weight lets you scale a variant’s influence manually. Quality can down-weight uncertain calls, imputation confidence, or study-specific reliability assumptions.

7) Why can adjusted absolute risk look uncertain?

Absolute risk depends on baseline risk, ancestry match, calibration method, prevalence, and study design. Small changes in those assumptions can shift results noticeably.

8) How many variants should I include?

Use as many validated variants as your model requires. This page includes eight rows for convenience, but larger published scores often use many more.

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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.