Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies

Naomi C. Brownstein, Jianwen Cai, Shad Smith, Luda Diatchenko, Gary D. Slade and Eric Bair
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Naomi C. Brownstein: Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
Jianwen Cai: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA
Shad Smith: Center for Translational Pain Medicine, Department of Anesthesiology, Duke University Medical Center, Durham, NC 27710, USA
Luda Diatchenko: Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montréal, QC H3A 0G1, Canada
Gary D. Slade: School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7450, USA
Eric Bair: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA

Stats, 2022, vol. 5, issue 1, 1-12

Abstract: Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study.

Keywords: bootstrap; case–control studies; inverse-probability weighting; secondary analysis (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2022
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