NDLI: Mixed Model Association with Family-Biased Case-Control Ascertainment.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Zaitlen, Noah A. Loh, Po-Ru Price, Alkes L. Hayeck, Tristan J. Patterson, Nick Pollack, Samuela Gusev, Alexander
Description	Author Affiliation: Hayeck TJ ( Institute for Genomic Medicine, Columbia University, New York, NY 10032, USA); Loh PR ( Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA); Pollack S ( Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA); Gusev A ( Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA); Patterson N ( Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.); Zaitlen NA ( Department of Medicine, Lung Biology Center, University of California, San Francisco, CA 94158, USA.); Price AL ( Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA)
Abstract	Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples.
ISSN	00029297
e-ISSN	15376605
Journal	The American Journal of Human Genetics
Issue Number	1
Volume Number	100
Language	English
Publisher	Cell Press (on behalf of American Society of Human Genetics)
Publisher Date	2017-01-05
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Human Genetics
Content Type	Text
Resource Type	Article
Subject	Genetics Genetics (clinical)

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