IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0124967.html
   My bibliography  Save this article

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

Author

Listed:
  • Chin Lin
  • Chi-Ming Chu
  • John Lin
  • Hsin-Yi Yang
  • Sui-Lung Su

Abstract

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.

Suggested Citation

  • Chin Lin & Chi-Ming Chu & John Lin & Hsin-Yi Yang & Sui-Lung Su, 2015. "Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-13, April.
  • Handle: RePEc:plo:pone00:0124967
    DOI: 10.1371/journal.pone.0124967
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0124967
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0124967&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0124967?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. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    2. Chin Lin & Hsin-Yi Yang & Chia-Chao Wu & Herng-Sheng Lee & Yuh-Feng Lin & Kuo-Cheng Lu & Chi-Ming Chu & Fu-Huang Lin & Sen-Yeong Kao & Sui-Lung Su, 2014. "Angiotensin-Converting Enzyme Insertion/Deletion Polymorphism Contributes High Risk for Chronic Kidney Disease in Asian Male with Hypertension–A Meta-Regression Analysis of 98 Observational Studies," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-16, January.
    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. Chin Lin & Hsiang-Cheng Chen & Wen-Hui Fang & Chih-Chien Wang & Yi-Jen Peng & Herng-Sheng Lee & Hung Chang & Chi-Ming Chu & Guo-Shu Huang & Wei-Teing Chen & Yu-Jui Tsai & Hong-Ling Lin & Fu-Huang Lin , 2016. "Angiotensin-Converting Enzyme Insertion/Deletion Polymorphism and Susceptibility to Osteoarthritis of the Knee: A Case-Control Study and Meta-Analysis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-18, September.
    2. Susan E Hodge & Valerie R Hager & David A Greenberg, 2016. "Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-18, January.

    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. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    2. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    3. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    4. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    5. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
    6. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    7. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2015.
    8. Nucera, Federico & Valente, Giorgio, 2013. "Carry trades and the performance of currency hedge funds," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 407-425.
    9. Nickole Moon & Christopher P. Morgan & Ruth Marx-Rattner & Alyssa Jeng & Rachel L. Johnson & Ijeoma Chikezie & Carmen Mannella & Mary D. Sammel & C. Neill Epperson & Tracy L. Bale, 2024. "Stress increases sperm respiration and motility in mice and men," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    10. Iain Melvin & Jason Weston & William Stafford Noble & Christina Leslie, 2011. "Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-8, January.
    11. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    12. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    13. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
    14. Dvorkin Daniel & Biehs Brian & Kechris Katerina, 2013. "A graphical model method for integrating multiple sources of genome-scale data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 469-487, August.
    15. Robert A. Connolly & Richard J. Rendleman, 2012. "What It Takes to Win on the PGA TOUR (If Your Name Is “Tiger” or If It Isn't)," Interfaces, INFORMS, vol. 42(6), pages 554-576, December.
    16. Yuan Zhao, 2014. "Cross-sector fund performance comparison: the role of real estate mutual funds," ERES eres2014_213, European Real Estate Society (ERES).
    17. Wang Chamont & Gevertz Jana L., 2016. "Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(4), pages 321-347, August.
    18. David Ardia & Lukasz Gatarek & Lennart F. hoogerheide, 2014. "A New Bootstrap Test for the Validity of a Set of Marginal Models for Multiple Dependent Time Series: an Application to Risk Analysis," Cahiers de recherche 1413, CIRPEE.
    19. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2021. "Do actively managed US mutual funds produce positive alpha?," Journal of Economic Behavior & Organization, Elsevier, vol. 182(C), pages 472-492.
    20. Christopher Walters, 2024. "Empirical Bayes Methods in Labor Economics," RF Berlin - CReAM Discussion Paper Series 2422, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).

    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:plo:pone00:0124967. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.