Genetic Power for Quantitative Traits
A genetic power calculator helps estimate whether a study can detect an allele effect on a measured trait. Quantitative traits include height, blood pressure, enzyme activity, lung capacity, or any value recorded on a continuous scale. The calculator uses sample size, trait spread, allele frequency, effect size, and significance level to estimate power.
Why Power Matters
Power is the chance of finding a true effect when it exists. Low power can hide useful biology. Very low power can also waste samples, money, and laboratory time. Strong planning gives a clearer view of the number of participants needed before genotyping begins.
Key Inputs
Sample size controls the amount of information in the test. A larger sample lowers the standard error. Allele frequency also matters. Rare alleles give less genotype variation, so they often need more participants. The trait standard deviation describes natural spread. A larger spread makes small effects harder to detect. The alpha level sets the evidence threshold. Multiple testing lowers the effective alpha when Bonferroni correction is used.
Model Choice
The calculator supports additive, dominant, and recessive coding. Additive coding treats genotypes as zero, one, and two effect alleles. Dominant coding compares carriers with noncarriers. Recessive coding compares homozygous effect carriers with everyone else. Each model changes genotype variance and power.
Interpreting Results
Calculated power is reported as a percentage. A value near eighty percent is often used as a planning target, but the best target depends on study cost and risk. The Z effect shows signal strength relative to the standard error. Required sample size estimates how many participants are needed for the chosen target power. The minimum detectable effect shows the smallest effect likely to be detected with the current design.
Practical Notes
This tool gives an approximation for linear association testing. It assumes random sampling, independent participants, reliable phenotypes, and correct trait scaling. Real studies may need adjustments for ancestry, relatedness, batch effects, missing data, and phenotype transformation. Use the output as planning guidance, then confirm the final design with specialist statistical review. Good records improve repeatability. Save assumptions with every report. Compare several effect sizes and allele frequencies before selecting the design for recruitment and funding decisions and team review.