IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v114y2019i525p358-369.html
   My bibliography  Save this article

Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models

Author

Listed:
  • Zijian Guo
  • Wanjie Wang
  • T. Tony Cai
  • Hongzhe Li

Abstract

Estimating the genetic relatedness between two traits based on the genome-wide association data is an important problem in genetics research. In the framework of high-dimensional linear models, we introduce two measures of genetic relatedness and develop optimal estimators for them. One is genetic covariance, which is defined to be the inner product of the two regression vectors, and another is genetic correlation, which is a normalized inner product by their lengths. We propose functional de-biased estimators (FDEs), which consist of an initial estimation step with the plug-in scaled Lasso estimator, and a further bias correction step. We also develop estimators of the quadratic functionals of the regression vectors, which can be used to estimate the heritability of each trait. The estimators are shown to be minimax rate-optimal and can be efficiently implemented. Simulation results show that FDEs provide better estimates of the genetic relatedness than simple plug-in estimates. FDE is also applied to an analysis of a yeast segregant dataset with multiple traits to estimate the genetic relatedness among these traits. Supplementary materials for this article are available online.

Suggested Citation

  • Zijian Guo & Wanjie Wang & T. Tony Cai & Hongzhe Li, 2019. "Optimal Estimation of Genetic Relatedness in High-Dimensional Linear Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 358-369, January.
    • Handle: RePEc:taf:jnlasa:v:114:y:2019:i:525:p:358-369
    DOI: 10.1080/01621459.2017.1407774
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2017.1407774
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2017.1407774?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
    ---><---

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

    Citations

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


    Cited by:

    1. Xingyu Chen & Lin Liu & Rajarshi Mukherjee, 2024. "Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics," Papers 2408.06103, arXiv.org.
    2. Qingliang Fan & Zijian Guo & Ziwei Mei, 2022. "A Heteroskedasticity-Robust Overidentifying Restriction Test with High-Dimensional Covariates," Papers 2205.00171, arXiv.org, revised May 2024.
    3. The Tien Mai, 2023. "Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing," Mathematics, MDPI, vol. 11(9), pages 1-13, May.
    4. Adel Javanmard & Jason D. Lee, 2020. "A flexible framework for hypothesis testing in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 685-718, 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:taf:jnlasa:v:114:y:2019:i:525:p:358-369. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.