title: Accurate liability estimation improves power in ascertained case-control studies.
abstract: Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in nonrandomly 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 we demonstrate that this can lead to a substantial power increase.
authors: Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel.
authors: Omer
authors: O
authors: Weissbrod
authors: eScience Group, Microsoft Research, Los Angeles, California, USA.
authors: Christoph
authors: 0000000163632556
authors: C
authors: Lippert
authors: Computer Science Department, Technion - Israel Institute of Technology, Haifa, Israel.
authors: Dan
authors: D
authors: Geiger
authors: eScience Group, Microsoft Research, Los Angeles, California, USA.
authors: David
authors: D
authors: Heckerman
mesh_terms: D056808:Biostatistics; D016022:Case-Control Studies; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D009103:Multiple Sclerosis / Q000235:genetics; D018401:Sample Size