IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v76y2020i3p700-710.html
   My bibliography  Save this article

Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies

Author

Listed:
  • Yanyi Song
  • Xiang Zhou
  • Min Zhang
  • Wei Zhao
  • Yongmei Liu
  • Sharon L. R. Kardia
  • Ana V. Diez Roux
  • Belinda L. Needham
  • Jennifer A. Smith
  • Bhramar Mukherjee

Abstract

Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high‐throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of ‐omics data, joint analysis of molecular‐level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high‐dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high‐dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi‐Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.

Suggested Citation

  • Yanyi Song & Xiang Zhou & Min Zhang & Wei Zhao & Yongmei Liu & Sharon L. R. Kardia & Ana V. Diez Roux & Belinda L. Needham & Jennifer A. Smith & Bhramar Mukherjee, 2020. "Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies," Biometrics, The International Biometric Society, vol. 76(3), pages 700-710, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:700-710
    DOI: 10.1111/biom.13189
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13189
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13189?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. VanderWeele Tyler J, 2011. "Principal Stratification -- Uses and Limitations," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-14, July.
    2. Tian Ge & Chia-Yen Chen & Yang Ni & Yen-Chen Anne Feng & Jordan W. Smoller, 2019. "Polygenic prediction via Bayesian regression and continuous shrinkage priors," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Andriy Derkach & Ruth M. Pfeiffer & Ting‐Huei Chen & Joshua N. Sampson, 2019. "High dimensional mediation analysis with latent variables," Biometrics, The International Biometric Society, vol. 75(3), pages 745-756, September.
    4. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    5. Xiang Zhou & Peter Carbonetto & Matthew Stephens, 2013. "Polygenic Modeling with Bayesian Sparse Linear Mixed Models," PLOS Genetics, Public Library of Science, vol. 9(2), pages 1-14, February.
    6. Yen-Tsung Huang & Wen-Chi Pan, 2016. "Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators," Biometrics, The International Biometric Society, vol. 72(2), pages 402-413, June.
    7. Chanmin Kim & Michael J. Daniels & Bess H. Marcus & Jason A. Roy, 2017. "A framework for Bayesian nonparametric inference for causal effects of mediation," Biometrics, The International Biometric Society, vol. 73(2), pages 401-409, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Meng An & Haixiang Zhang, 2023. "High-Dimensional Mediation Analysis for Time-to-Event Outcomes with Additive Hazards Model," Mathematics, MDPI, vol. 11(24), pages 1-11, December.
    2. Yu-Bo Wang & Cuilin Zhang & Zhen Chen, 2021. "Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 524-542, December.
    3. Lulu Shang & Wei Zhao & Yi Zhe Wang & Zheng Li & Jerome J. Choi & Minjung Kho & Thomas H. Mosley & Sharon L. R. Kardia & Jennifer A. Smith & Xiang Zhou, 2023. "meQTL mapping in the GENOA study reveals genetic determinants of DNA methylation in African Americans," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    4. Yanyi Song & Xiang Zhou & Jian Kang & Max T. Aung & Min Zhang & Wei Zhao & Belinda L. Needham & Sharon L. R. Kardia & Yongmei Liu & John D. Meeker & Jennifer A. Smith & Bhramar Mukherjee, 2021. "Bayesian sparse mediation analysis with targeted penalization of natural indirect effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1391-1412, November.
    5. Caubet, Miguel & Samoilenko, Mariia & Drouin, Simon & Sinnett, Daniel & Krajinovic, Maja & Laverdière, Caroline & Marcil, Valérie & Lefebvre, Geneviève, 2023. "Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator: Exploring the role of obesity in the association between cranial radiation therapy for childhood acut," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    6. Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.

    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. Cai, Xizhen & Zhu, Yeying & Huang, Yuan & Ghosh, Debashis, 2022. "High-dimensional causal mediation analysis based on partial linear structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    2. Carla Márquez-Luna & Steven Gazal & Po-Ru Loh & Samuel S. Kim & Nicholas Furlotte & Adam Auton & Alkes L. Price, 2021. "Incorporating functional priors improves polygenic prediction accuracy in UK Biobank and 23andMe data sets," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Wen Wei Loh & Beatrijs Moerkerke & Tom Loeys & Stijn Vansteelandt, 2022. "Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown," Biometrics, The International Biometric Society, vol. 78(1), pages 46-59, March.
    4. Qi Zhang, 2022. "High-Dimensional Mediation Analysis with Applications to Causal Gene Identification," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 432-451, December.
    5. Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow, 2022. "Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 973-996, July.
    6. Haoyu Wei & Hengrui Cai & Chengchun Shi & Rui Song, 2024. "On Efficient Inference of Causal Effects with Multiple Mediators," Papers 2401.05517, arXiv.org.
    7. Meng An & Haixiang Zhang, 2023. "High-Dimensional Mediation Analysis for Time-to-Event Outcomes with Additive Hazards Model," Mathematics, MDPI, vol. 11(24), pages 1-11, December.
    8. Dominic Holland & Oleksandr Frei & Rahul Desikan & Chun-Chieh Fan & Alexey A Shadrin & Olav B Smeland & V S Sundar & Paul Thompson & Ole A Andreassen & Anders M Dale, 2020. "Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model," PLOS Genetics, Public Library of Science, vol. 16(5), pages 1-30, May.
    9. Junyang Qian & Yosuke Tanigawa & Wenfei Du & Matthew Aguirre & Chris Chang & Robert Tibshirani & Manuel A Rivas & Trevor Hastie, 2020. "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-30, October.
    10. Arman Oganisian & Nandita Mitra & Jason A. Roy, 2021. "A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation," Biometrics, The International Biometric Society, vol. 77(1), pages 125-135, March.
    11. Kenichi Yamamoto & Kyuto Sonehara & Shinichi Namba & Takahiro Konuma & Hironori Masuko & Satoru Miyawaki & Yoichiro Kamatani & Nobuyuki Hizawa & Keiichi Ozono & Loic Yengo & Yukinori Okada, 2023. "Genetic footprints of assortative mating in the Japanese population," Nature Human Behaviour, Nature, vol. 7(1), pages 65-73, January.
    12. Mealli Fabrizia & Mattei Alessandra, 2012. "A Refreshing Account of Principal Stratification," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-19, April.
    13. Yanyi Song & Xiang Zhou & Jian Kang & Max T. Aung & Min Zhang & Wei Zhao & Belinda L. Needham & Sharon L. R. Kardia & Yongmei Liu & John D. Meeker & Jennifer A. Smith & Bhramar Mukherjee, 2021. "Bayesian sparse mediation analysis with targeted penalization of natural indirect effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1391-1412, November.
    14. Antonio R. Linero, 2022. "Simulation‐based estimators of analytically intractable causal effects," Biometrics, The International Biometric Society, vol. 78(3), pages 1001-1017, September.
    15. Tzu-Ting Chen & Jaeyoung Kim & Max Lam & Yi-Fang Chuang & Yen-Ling Chiu & Shu-Chin Lin & Sang-Hyuk Jung & Beomsu Kim & Soyeon Kim & Chamlee Cho & Injeong Shim & Sanghyeon Park & Yeeun Ahn & Aysu Okbay, 2024. "Shared genetic architectures of educational attainment in East Asian and European populations," Nature Human Behaviour, Nature, vol. 8(3), pages 562-575, March.
    16. Laura Forastiere & Patrizia Lattarulo & Marco Mariani & Fabrizia Mealli & Laura Razzolini, 2021. "Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(1), pages 244-258, January.
    17. Corwin M. Zigler & Thomas R. Belin, 2012. "A Bayesian Approach to Improved Estimation of Causal Effect Predictiveness for a Principal Surrogate Endpoint," Biometrics, The International Biometric Society, vol. 68(3), pages 922-932, September.
    18. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.
    19. Marco Doretti & Martina Raggi & Elena Stanghellini, 2022. "Exact parametric causal mediation analysis for a binary outcome with a binary mediator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 87-108, March.
    20. Xu Qin & Jonah Deutsch & Guanglei Hong, 2021. "Unpacking Complex Mediation Mechanisms And Their Heterogeneity Between Sites In A Job Corps Evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 158-190, January.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:76:y:2020:i:3:p:700-710. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.