Introduction
============

One of the most useful estimates of whether a study can reliably explain the hypothesis that it set out to is by performing experiments to estimate the statistical power of a study. In the context of genome-wide association studies (GWAS), this can amount to being able to answer questions of the following nature:

* What sample size to we need to have 80% power to discover an effect at this frequency and this causal effect size?
* What is the minimum detectable effect-size at a frequency and power threshold for a study's sample size?
* How well-powered is our study to discover previously established genetic effects for a trait of interest?


This package attempts to provide modules and functions for addressing these questions across a number of different GWAS design styles (e.g. quantitative traits, rare variants). If you are interested in estimating GWAS power for a specific study design you can find a set of tutorials as `jupyter` notebooks that are executable via `mybinder` from the `repository page on github <https://github.com/aabiddanda/qtl-power>`_.
