IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i1p65-77.html
   My bibliography  Save this article

Asymptotic powers for matched trend tests and robust matched trend tests in case-control genetic association studies

Author

Listed:
  • Zang, Yong
  • Fung, Wing Kam
  • Zheng, Gang

Abstract

The matched trend test (MTT), developed using a conditional logistic regression, has been proposed to test for association in matched case-control studies to control the bias of known confounding effects and reduce the potential impact of population stratification. The MTT requires a known genetic model. When the genetic model is unknown, a Monte Carlo robust test, MAX, has been proposed for the analysis of matched case-control studies. The MAX statistic takes the maximum of three MTTs optimal for three common genetic models. We derive the asymptotic power for MTTs and robust tests. In particular, we derive the asymptotic p-value for MAX. Using these analytical results, we conduct simulation studies to compare the performance of MAX and the two-degree-of-freedom Chi-square test for matched case-control studies, where the latter is implemented in most computing software. Our simulation results show that MAX is always asymptotically more powerful than the two-degree-of-freedom Chi-square test under common genetic models. Our results provide guidelines for the analysis of genetic association using matched case-control data. An illustration of our results to a real matched pair case-control etiologic study of sarcoidosis is given.

Suggested Citation

  • Zang, Yong & Fung, Wing Kam & Zheng, Gang, 2010. "Asymptotic powers for matched trend tests and robust matched trend tests in case-control genetic association studies," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 65-77, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:65-77
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00257-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Robert Sladek & Ghislain Rocheleau & Johan Rung & Christian Dina & Lishuang Shen & David Serre & Philippe Boutin & Daniel Vincent & Alexandre Belisle & Samy Hadjadj & Beverley Balkau & Barbara Heude &, 2007. "A genome-wide association study identifies novel risk loci for type 2 diabetes," Nature, Nature, vol. 445(7130), pages 881-885, February.
    2. B. Devlin & Kathryn Roeder, 1999. "Genomic Control for Association Studies," Biometrics, The International Biometric Society, vol. 55(4), pages 997-1004, December.
    3. Jinbo Chen & Carmen Rodriguez, 2007. "Conditional Likelihood Methods for Haplotype-Based Association Analysis Using Matched Case–Control Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1099-1107, December.
    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. Zang, Yong & Fung, Wing Kam & Cao, Sha & Ng, Hon Keung Tony & Zhang, Chi, 2019. "Robust tests for gene–environment interaction in case-control and case-only designs," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 79-92.

    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. Peristera Paschou & Petros Drineas & Jamey Lewis & Caroline M Nievergelt & Deborah A Nickerson & Joshua D Smith & Paul M Ridker & Daniel I Chasman & Ronald M Krauss & Elad Ziv, 2008. "Tracing Sub-Structure in the European American Population with PCA-Informative Markers," PLOS Genetics, Public Library of Science, vol. 4(7), pages 1-13, July.
    2. Lei Zhang & Yu-Fang Pei & Jian Li & Christopher J Papasian & Hong-Wen Deng, 2009. "Univariate/Multivariate Genome-Wide Association Scans Using Data from Families and Unrelated Samples," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-12, August.
    3. 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.
    4. Vincent Michaud & Eulalie Lasseaux & David J. Green & Dave T. Gerrard & Claudio Plaisant & Tomas Fitzgerald & Ewan Birney & Benoît Arveiler & Graeme C. Black & Panagiotis I. Sergouniotis, 2022. "The contribution of common regulatory and protein-coding TYR variants to the genetic architecture of albinism," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    5. Natalie DeForest & Yuqi Wang & Zhiyi Zhu & Jacqueline S. Dron & Ryan Koesterer & Pradeep Natarajan & Jason Flannick & Tiffany Amariuta & Gina M. Peloso & Amit R. Majithia, 2024. "Genome-wide discovery and integrative genomic characterization of insulin resistance loci using serum triglycerides to HDL-cholesterol ratio as a proxy," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    6. Parsa Akbari & Dragana Vuckovic & Luca Stefanucci & Tao Jiang & Kousik Kundu & Roman Kreuzhuber & Erik L. Bao & Janine H. Collins & Kate Downes & Luigi Grassi & Jose A. Guerrero & Stephen Kaptoge & Ju, 2023. "A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    7. Ping Rao & Hao Wang & Honghong Fang & Qing Gao & Jie Zhang & Manshu Song & Yong Zhou & Youxin Wang & Wei Wang, 2016. "Association between IGF2BP2 Polymorphisms and Type 2 Diabetes Mellitus: A Case–Control Study and Meta-Analysis," IJERPH, MDPI, vol. 13(6), pages 1-13, June.
    8. Greve, Jane, 2008. "Obesity and labor market outcomes in Denmark," Economics & Human Biology, Elsevier, vol. 6(3), pages 350-362, December.
    9. Gang Zheng & Zhaohai Li & Mitchell H. Gail & Joseph L. Gastwirth, 2010. "Impact of Population Substructure on Trend Tests for Genetic Case–Control Association Studies," Biometrics, The International Biometric Society, vol. 66(1), pages 196-204, March.
    10. Sandosh Padmanabhan & Olle Melander & Toby Johnson & Anna Maria Di Blasio & Wai K Lee & Davide Gentilini & Claire E Hastie & Cristina Menni & Maria Cristina Monti & Christian Delles & Stewart Laing & , 2010. "Genome-Wide Association Study of Blood Pressure Extremes Identifies Variant near UMOD Associated with Hypertension," PLOS Genetics, Public Library of Science, vol. 6(10), pages 1-11, October.
    11. Jakris Eu-ahsunthornwattana & E Nancy Miller & Michaela Fakiola & Wellcome Trust Case Control Consortium 2 & Selma M B Jeronimo & Jenefer M Blackwell & Heather J Cordell, 2014. "Comparison of Methods to Account for Relatedness in Genome-Wide Association Studies with Family-Based Data," PLOS Genetics, Public Library of Science, vol. 10(7), pages 1-20, July.
    12. Jianzhong Ma & Christopher I Amos, 2010. "Theoretical Formulation of Principal Components Analysis to Detect and Correct for Population Stratification," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-14, September.
    13. Claire L Simpson & Robert Wojciechowski & Konrad Oexle & Federico Murgia & Laura Portas & Xiaohui Li & Virginie J M Verhoeven & Veronique Vitart & Maria Schache & S Mohsen Hosseini & Pirro G Hysi & Le, 2014. "Genome-Wide Meta-Analysis of Myopia and Hyperopia Provides Evidence for Replication of 11 Loci," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-19, September.
    14. Matthieu Bouaziz & Christophe Ambroise & Mickael Guedj, 2011. "Accounting for Population Stratification in Practice: A Comparison of the Main Strategies Dedicated to Genome-Wide Association Studies," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-13, December.
    15. John PA Ioannidis & Nikolaos A Patsopoulos & Evangelos Evangelou, 2007. "Heterogeneity in Meta-Analyses of Genome-Wide Association Investigations," PLOS ONE, Public Library of Science, vol. 2(9), pages 1-7, September.
    16. Aditi Shendre & Howard W Wiener & Marguerite R Irvin & Bradley E Aouizerat & Edgar T Overton & Jason Lazar & Chenglong Liu & Howard N Hodis & Nita A Limdi & Kathleen M Weber & Stephen J Gange & Degui , 2017. "Genome-wide admixture and association study of subclinical atherosclerosis in the Women’s Interagency HIV Study (WIHS)," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-23, December.
    17. Paul F O’Reilly & Clive J Hoggart & Yotsawat Pomyen & Federico C F Calboli & Paul Elliott & Marjo-Riitta Jarvelin & Lachlan J M Coin, 2012. "MultiPhen: Joint Model of Multiple Phenotypes Can Increase Discovery in GWAS," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-1, May.
    18. Li Shaoyu & Lu Qing & Fu Wenjiang & Romero Roberto & Cui Yuehua, 2009. "A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-30, October.
    19. Warrington Nicole M. & Tilling Kate & Howe Laura D. & Paternoster Lavinia & Pennell Craig E. & Wu Yan Yan & Briollais Laurent, 2014. "Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(5), pages 567-587, October.
    20. Wang, Linglu & Li, Qizhai & Li, Zhaohai & Zheng, Gang, 2011. "Bayes factors in the presence of population stratification," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 836-841, July.

    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:eee:csdana:v:54:y:2010:i:1:p:65-77. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.