Heritability Calculator

Calculate heritability estimates with confidence intervals, statistical power, and age adjustment. Professional tool for genetic researchers.

Twin Study Method
Family Study Method
Variance Components
Advanced Methods

Falconer's Formula for Twin Studies: h² = 2 × (rMZ - rDZ)

Where: h² = Heritability, rMZ = Monozygotic twin correlation, rDZ = Dizygotic twin correlation

Correlation coefficient for identical twins (range: 0.0 to 1.0)
Number of monozygotic twin pairs in study
Correlation coefficient for fraternal twins (range: 0.0 to 1.0)
Number of dizygotic twin pairs in study
Age Adjustment (Optional)
Average age in years
Standard deviation of age in years
Name of the trait being studied

Family Study Heritability: h² = b / r

Where: h² = Heritability, b = Regression coefficient, r = Coefficient of relationship

Regression coefficient of offspring on parent (range: -1.0 to 1.0)
Standard error of the regression coefficient
Genetic relatedness between individuals
Total number of families in study
Name of the trait being studied

Variance Components Method: h² = VA / VP

Where: h² = Heritability, VA = Additive genetic variance, VP = Phenotypic variance

Variance due to additive genetic effects
Standard error of additive variance
Total phenotypic variance in the population
Standard error of phenotypic variance
Variance due to dominance genetic effects
Variance due to environmental factors
Total sample size for variance estimation
Name of the trait being studied

Advanced Heritability Methods: GREML, LD Score Regression, or Bayesian Approaches

These methods require more complex inputs and are typically used with genomic data

Select the advanced heritability estimation method
Heritability estimate from genomic analysis (0-1)
Standard error of genomic heritability estimate
Number of SNPs used in analysis
Total sample size for genomic analysis
Name of the trait being studied
Show Advanced Options
Advanced Statistical Options
Confidence level for interval estimation
Number of bootstrap iterations for CI calculation
Calculating... This may take a moment for bootstrap methods.

Advanced Heritability Analysis

This advanced calculator provides comprehensive heritability estimation with confidence intervals, statistical power analysis, and adjustment for covariates like age.

New Features in This Advanced Version:

  • Confidence Intervals: 95% CI with bootstrap or analytic methods
  • Statistical Power: Power analysis for detecting heritability
  • Age Adjustment: Adjust for age effects when data available
  • Sample Size Analysis: Evaluate impact of sample size on precision
  • Advanced Methods: Support for genomic heritability methods

Confidence Interval Estimation Methods

1

Analytic Method (Delta Method): Uses Taylor series approximation to estimate standard errors

SE(h²) ≈ √[ (∂h²/∂r_MZ)²·Var(r_MZ) + (∂h²/∂r_DZ)²·Var(r_DZ) ]
2

Bootstrap Method: Resampling approach that makes fewer assumptions

CI = [Percentile2.5%, Percentile97.5%] of bootstrap distribution
3

Profile Likelihood: More accurate but computationally intensive

CI = {h²: -2[log L(h²) - log L(ĥ²)] ≤ χ²1,1-α}

Statistical Power Analysis

Power analysis helps determine if your study has sufficient sample size to detect heritability of a given magnitude.

Sample Size (Twin Pairs) Minimum Detectable h² (Power=80%) Recommended For 100 MZ + 100 DZ h² > 0.4 Pilot studies, highly heritable traits 250 MZ + 250 DZ h² > 0.25 Standard genetic studies 500 MZ + 500 DZ h² > 0.18 Well-powered studies, gene discovery 1000 MZ + 1000 DZ h² > 0.13 Large consortium studies, polygenic traits

Age Adjustment Methodology

Age can confound heritability estimates, especially for traits that change with development or aging. Our calculator uses residualization methods to adjust for age effects:

y_adj = y - β_age·(age - mean_age)

Where y is the trait value, β_age is the age effect estimated from the data (or provided by the user), and mean_age is the average age in the sample.

Interpreting Confidence Intervals

Wide vs. Narrow CIs:

  • Wide CI (e.g., 0.2-0.8): Imprecise estimate, often due to small sample size
  • Narrow CI (e.g., 0.45-0.55): Precise estimate, adequate sample size
  • CI includes 0: Study may be underpowered to detect heritability
  • CI excludes 0: Statistically significant evidence for heritability

Sample Size Recommendations

For Twin Studies:

  • ≥500 twin pairs total: Adequate for most traits
  • 200-500 twin pairs: Moderate power for highly heritable traits
  • <200 twin pairs: Limited power, wide confidence intervals

For Genomic Studies:

  • ≥10,000 individuals: Standard for genome-wide studies
  • 1,000-10,000 individuals: Adequate for highly polygenic traits
  • <1,000 individuals: Limited for genomic heritability estimation

Important Statistical Note: Heritability estimates are always subject to uncertainty. Always report confidence intervals along with point estimates. Consider study power when interpreting non-significant results (wide CIs including 0 may indicate low power rather than absence of heritability).

Frequently Asked Questions

Confidence intervals can be calculated using several methods: (1) Delta method (analytic approximation), (2) Bootstrap resampling (computationally intensive but fewer assumptions), or (3) Profile likelihood (most accurate but most computationally intensive). This calculator uses the delta method for quick estimates and offers bootstrap for more accurate intervals.

Sample size affects the precision of all statistical estimates. Larger samples provide more information about the population, reducing sampling error. The standard error of heritability estimates is inversely proportional to √N, so doubling the sample size reduces the standard error (and thus CI width) by about 30%. Very small samples (<100 twin pairs) often produce uninformatively wide CIs.

Yes, in most cases. Age is often correlated with both genetic factors and trait values, especially for developmental traits, cognitive abilities, or age-related diseases. Failure to adjust for age can inflate or deflate heritability estimates. The calculator provides age adjustment options when age data is available. For traits known to be age-invariant (e.g., fingerprint patterns), adjustment may be unnecessary.

Analytic CIs use mathematical formulas (delta method) that assume normality and make approximations. They're fast but may be inaccurate with small samples or non-normal data. Bootstrap CIs resample your data many times to create an empirical distribution. They're computationally intensive but make fewer assumptions and are often more accurate, especially with non-normal data or complex estimators.

A confidence interval that includes 0 means the data are consistent with no heritability. However, this could mean either: (1) The trait truly has no genetic component, OR (2) The study is underpowered to detect the actual heritability. Check the power analysis results. If power is low (<80%), you cannot conclude absence of heritability. Consider increasing sample size or using more precise measurement methods.