IDEAS home Printed from https://ideas.repec.org/a/plo/pgen00/1011563.html
   My bibliography  Save this article

ADELLE: A global testing method for trans-eQTL mapping

Author

Listed:
  • Takintayo Akinbiyi
  • Mary Sara McPeek
  • Mark Abney

Abstract

Understanding the genetic regulatory mechanisms of gene expression is an ongoing challenge. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%–2% of 10,000 traits, among the 8 methods we consider ADELLE is clearly the most powerful overall, with either the highest power or power not significantly different from the highest for all settings in that range. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. We also apply ADELLE to trans-eQTL mapping in the eQTLGen data, and for 1,451 previously identified trans-eQTLs, we discover trans association with additional expression traits beyond those previously identified. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.Author summary: Identification of trans-eQTLs, i.e., genetic variants that regulate expression of genes that are not proximal, has proved challenging, even though previous studies suggest that they may account for a large proportion of complex trait variance. Compared to cis-eQTLs, i.e., variants that regulate expression of proximal genes, trans-eQTLs are much harder to detect because their effect sizes tend to be smaller, and the space of possible genes whose expression they might be associated with is much bigger, leading to a higher burden of multiple comparisons. We developed ADELLE, a powerful statistical method that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%–2% of the expression traits, ADELLE is clearly the most powerful overall among the 8 methods we compare. We apply ADELLE to eQTLGen data and also to a mouse advanced intercross line data set and show its ability to detect trans-eQTL signal that was not significant under a standard analysis. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

Suggested Citation

  • Takintayo Akinbiyi & Mary Sara McPeek & Mark Abney, 2025. "ADELLE: A global testing method for trans-eQTL mapping," PLOS Genetics, Public Library of Science, vol. 21(1), pages 1-22, January.
    • Handle: RePEc:plo:pgen00:1011563
    DOI: 10.1371/journal.pgen.1011563
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1011563
    Download Restriction: no
    File URL: https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1011563&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pgen.1011563?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. Natalia M. Gonzales & Jungkyun Seo & Ana I. Hernandez Cordero & Celine L. St. Pierre & Jennifer S. Gregory & Margaret G. Distler & Mark Abney & Stefan Canzar & Arimantas Lionikas & Abraham A. Palmer, 2018. "Genome wide association analysis in a mouse advanced intercross line," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
    2. Warton, David I., 2008. "Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 340-349, 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. Avagyan, Vahe & Alonso Fernández, Andrés Modesto & Nogales, Francisco J., 2015. "D-trace Precision Matrix Estimation Using Adaptive Lasso Penalties," DES - Working Papers. Statistics and Econometrics. WS 21775, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Yeojin Chung & Sophia Rabe-Hesketh & Vincent Dorie & Andrew Gelman & Jingchen Liu, 2013. "A Nondegenerate Penalized Likelihood Estimator for Variance Parameters in Multilevel Models," Psychometrika, Springer;The Psychometric Society, vol. 78(4), pages 685-709, October.
    3. Wessel N. Wieringen & Gwenaël G. R. Leday, 2024. "Ridge-type covariance and precision matrix estimators of the multivariate normal distribution," Statistical Papers, Springer, vol. 65(9), pages 5835-5849, December.
    4. Bar, Haim & Wells, Martin T., 2023. "On graphical models and convex geometry," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    5. Ong, Victor M.-H. & Nott, David J. & Tran, Minh-Ngoc & Sisson, Scott A. & Drovandi, Christopher C., 2018. "Likelihood-free inference in high dimensions with synthetic likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 271-291.
    6. Ilya R Fischhoff & Felicia Keesing & Richard S Ostfeld, 2017. "The tick biocontrol agent Metarhizium brunneum (= M. anisopliae) (strain F52) does not reduce non-target arthropods," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-15, November.
    7. Lam, Clifford, 2020. "High-dimensional covariance matrix estimation," LSE Research Online Documents on Economics 101667, London School of Economics and Political Science, LSE Library.
    8. Shen, Yanfeng & Lin, Zhengyan, 2015. "An adaptive test for the mean vector in large-p-small-n problems," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 25-38.
    9. Chi, Eric C. & Lange, Kenneth, 2014. "Stable estimation of a covariance matrix guided by nuclear norm penalties," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 117-128.
    10. Azam Kheyri & Andriette Bekker & Mohammad Arashi, 2022. "High-Dimensional Precision Matrix Estimation through GSOS with Application in the Foreign Exchange Market," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    11. David I Warton & Loïc Thibaut & Yi Alice Wang, 2017. "The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-18, July.
    12. De Keyser, Steven & Gijbels, Irène, 2025. "High-dimensional copula-based Wasserstein dependence," Computational Statistics & Data Analysis, Elsevier, vol. 204(C).
    13. Jakub Stoklosa & Heloise Gibb & David I. Warton, 2014. "Fast forward selection for generalized estimating equations with a large number of predictor variables," Biometrics, The International Biometric Society, vol. 70(1), pages 110-120, March.
    14. Lim Johan & Kim Jayoun & Kim Sang-cheol & Yu Donghyeon & Kim Kyunga & Kim Byung Soo, 2012. "Detection of Differentially Expressed Gene Sets in a Partially Paired Microarray Data Set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-30, February.
    15. Joong-Ho Won & Johan Lim & Seung-Jean Kim & Bala Rajaratnam, 2013. "Condition-number-regularized covariance estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 427-450, June.
    16. Sven Husmann & Antoniya Shivarova & Rick Steinert, 2021. "Cross-validated covariance estimators for high-dimensional minimum-variance portfolios," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(3), pages 309-352, September.
    17. Yeojin Chung & Andrew Gelman & Sophia Rabe-Hesketh & Jingchen Liu & Vincent Dorie, 2015. "Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models," Journal of Educational and Behavioral Statistics, , vol. 40(2), pages 136-157, April.
    18. Carel F. W. Peeters & Mark A. Wiel & Wessel N. Wieringen, 2020. "The spectral condition number plot for regularization parameter evaluation," Computational Statistics, Springer, vol. 35(2), pages 629-646, June.
    19. Vahe Avagyan & Andrés M. Alonso & Francisco J. Nogales, 2018. "D-trace estimation of a precision matrix using adaptive Lasso penalties," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 425-447, June.
    20. David I. Warton, 2011. "Regularized Sandwich Estimators for Analysis of High-Dimensional Data Using Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 67(1), pages 116-123, March.

    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:pgen00:1011563. 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: plosgenetics (email available below). General contact details of provider: https://journals.plos.org/plosgenetics/ .

    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.