IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v5y2022i1p14-214d755139.html
   My bibliography  Save this article

Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies

Author

Listed:
  • 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
    Department of Anesthesia, Faculty of Medicine, 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
    School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7450, USA)

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.

Suggested Citation

  • Naomi C. Brownstein & Jianwen Cai & Shad Smith & Luda Diatchenko & Gary D. Slade & Eric Bair, 2022. "Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies," Stats, MDPI, vol. 5(1), pages 1-12, February.
    • Handle: RePEc:gam:jstats:v:5:y:2022:i:1:p:14-214:d:755139
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/5/1/14/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/5/1/14/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan Zhou & Haibo Zhou & Tengfei Li & Hongtu Zhu, 2020. "Analysis of secondary phenotypes in multigroup association studies," Biometrics, The International Biometric Society, vol. 76(2), pages 606-618, June.
    2. Jiawei Wei & Raymond J. Carroll & Ursula U. Müller & Ingrid Van Keilegom & Nilanjan Chatterjee, 2013. "Robust estimation for homoscedastic regression in the secondary analysis of case–control data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(1), pages 185-206, January.
    3. Yanyuan Ma & Raymond J. Carroll, 2016. "Semiparametric estimation in the secondary analysis of case–control studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 127-151, January.
    4. Wei, Jiawei & Carroll, Raymond & Muller, Ursula & Van Keilegom, Ingrid & Chatterjee, Nilanjan, 2013. "Robust estimation for homoscedastic regression in the secondary analysis of case–control data," LIDAM Reprints ISBA 2013007, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yinghao Pan & Jianwen Cai & Sangmi Kim & Haibo Zhou, 2018. "Regression analysis for secondary response variable in a case‐cohort study," Biometrics, The International Biometric Society, vol. 74(3), pages 1014-1022, September.
    2. Kartsonaki, Christiana & Cox, D. R., 2023. "Regression Reconstruction from a Retrospective Sample," Econometrics and Statistics, Elsevier, vol. 25(C), pages 87-92.
    3. Jianxuan Liu & Yanyuan Ma & Lan Wang, 2018. "An alternative robust estimator of average treatment effect in causal inference," Biometrics, The International Biometric Society, vol. 74(3), pages 910-923, September.
    4. Fan Zhou & Haibo Zhou & Tengfei Li & Hongtu Zhu, 2020. "Analysis of secondary phenotypes in multigroup association studies," Biometrics, The International Biometric Society, vol. 76(2), pages 606-618, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:5:y:2022:i:1:p:14-214:d:755139. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.