IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i5p1661-1673.html
   My bibliography  Save this article

Combining quantitative trait loci analyses and microarray data: An empirical likelihood approach

Author

Listed:
  • Wang, Dong
  • Chen, Song Xi

Abstract

Selective transcriptional profiling is an attractive approach for alleviating the high cost of genetical genomics research as it requires only a subset of individuals in the QTL mapping study for microarray experiments. Current statistical methods for this approach are based on parametric models that might not be appropriate for all experiments. To provide a nonparametric method for analyzing data obtained in selective transcriptional profiling studies, an empirical-likelihood-based inference is derived for multi-sample comparisons when information is available on surrogate variables. The results show that when testing for the association between the transcriptional abundance of a given gene and a known QTL, using relatively inexpensive trait data on extra individuals significantly improves the power for the proposed test.

Suggested Citation

  • Wang, Dong & Chen, Song Xi, 2009. "Combining quantitative trait loci analyses and microarray data: An empirical likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1661-1673, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1661-1673
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00092-3
    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. F. Zou, 2002. "A note on a partial empirical likelihood," Biometrika, Biometrika Trust, vol. 89(4), pages 958-961, December.
    2. F. Zou, 2002. "On empirical likelihood for a semiparametric mixture model," Biometrika, Biometrika Trust, vol. 89(1), pages 61-75, March.
    3. Dong Wang & Dan Nettleton, 2006. "Identifying Genes Associated with a Quantitative Trait or Quantitative Trait Locus via Selective Transcriptional Profiling," Biometrics, The International Biometric Society, vol. 62(2), pages 504-514, June.
    4. Chen S.X. & Leung D.H.Y. & Qin J., 2003. "Information Recovery in a Study With Surrogate Endpoints," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1052-1062, January.
    5. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    6. Jin, Chunfang & Fine, Jason P. & Yandell, Brian S., 2007. "A Unified Semiparametric Framework for Quantitative Trait Loci Analyses, With Application to Spike Phenotypes," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 56-67, March.
    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. Takahashi, Makoto & Watanabe, Toshiaki & Omori, Yasuhiro, 2016. "Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution," International Journal of Forecasting, Elsevier, vol. 32(2), pages 437-457.
    2. Chen, Qihao & Huang, Zhuo & Liang, Fang, 2023. "Measuring systemic risk with high-frequency data: A realized GARCH approach," Finance Research Letters, Elsevier, vol. 54(C).
    3. Gloria González‐Rivera & C. Vladimir Rodríguez‐Caballero & Esther Ruiz, 2024. "Expecting the unexpected: Stressed scenarios for economic growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 926-942, August.
    4. Yeap, Claudia & Kwok, Simon S. & Choy, S. T. Boris, 2016. "A Flexible Generalised Hyperbolic Option Pricing Model and its Special Cases," Working Papers 2016-14, University of Sydney, School of Economics.
    5. Hu, Shuowen & Poskitt, D.S. & Zhang, Xibin, 2012. "Bayesian adaptive bandwidth kernel density estimation of irregular multivariate distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 732-740.
    6. Phella, Anthoulla & Gabriel, Vasco J. & Martins, Luis F., 2024. "Predicting tail risks and the evolution of temperatures," Energy Economics, Elsevier, vol. 131(C).
    7. repec:wrk:wrkemf:27 is not listed on IDEAS
    8. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    9. Reinaldo B. Arellano-Valle & Marc G. Genton, 2010. "Multivariate extended skew-t distributions and related families," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3), pages 201-234.
    10. Lucrezia Reichlin & Giovanni Ricco & Thomas Hasenzagl, 2020. "Financial Variables as Predictors of Real Growth Vulnerability," Documents de Travail de l'OFCE 2020-06, Observatoire Francais des Conjonctures Economiques (OFCE).
    11. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    12. Alexander, Carol & Cordeiro, Gauss M. & Ortega, Edwin M.M. & Sarabia, José María, 2012. "Generalized beta-generated distributions," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1880-1897.
    13. N. Banholzer & S. Heiden & D. Schneller, 2019. "Exploiting investor sentiment for portfolio optimization," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 671-702, December.
    14. James Mitchell & Martin Weale, 2023. "Censored density forecasts: Production and evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 714-734, August.
    15. Emmanuel Afuecheta & Idika E. Okorie & Saralees Nadarajah & Geraldine E. Nzeribe, 2024. "Forecasting Value at Risk and Expected Shortfall of Foreign Exchange Rate Volatility of Major African Currencies via GARCH and Dynamic Conditional Correlation Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 271-304, January.
    16. M. Hallin & D. La Vecchia & H. Liu, 2022. "Center-Outward R-Estimation for Semiparametric VARMA Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 117(538), pages 925-938, April.
    17. Abe, Toshihiro & Miyata, Yoichi & Shiohama, Takayuki, 2023. "Bayesian estimation for mode and anti-mode preserving circular distributions," Econometrics and Statistics, Elsevier, vol. 27(C), pages 136-160.
    18. Giovanni Compiani & Yuichi Kitamura, 2016. "Using mixtures in econometric models: a brief review and some new results," Econometrics Journal, Royal Economic Society, vol. 19(3), pages 95-127, October.
    19. Loperfido, Nicola, 2008. "A note on skew-elliptical distributions and linear functions of order statistics," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3184-3186, December.
    20. Jiménez-Gamero, M. Dolores & Kim, Hyoung-Moon, 2015. "Fast goodness-of-fit tests based on the characteristic function," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 172-191.
    21. Eling, Martin, 2014. "Fitting asset returns to skewed distributions: Are the skew-normal and skew-student good models?," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 45-56.

    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:53:y:2009:i:5:p:1661-1673. 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.