Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (Liability Estimator As a Phenotype, https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and demonstrate that this can lead to a substantial power increase.
@article{arxiv.1409.2448,
title = {Accurate Liability Estimation Improves Power in Ascertained Case Control Studies},
author = {Omer Weissbrod and Christoph Lippert and Dan Geiger and David Heckerman},
journal= {arXiv preprint arXiv:1409.2448},
year = {2016}
}