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Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing

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  • The Tien Mai

    (Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway)

Abstract

In this paper, we aim to investigate the problem of estimating the genetic correlation between two traits. Instead of making assumptions about the distribution of effect sizes of the genetic factors, we propose the use of a high-dimensional linear model to relate a trait to genetic factors. To estimate the genetic correlation, we develop a generic strategy that combines the use of sparse penalization methods and multiple sample splitting approaches. The final estimate is determined by taking the median of the calculations, resulting in a smoothed and reliable estimate. Through simulations, we demonstrate that our proposed approach is reliable and accurate in comparison to naive plug-in methods. To further illustrate the advantages of our method, we apply it to a real-world example of a bacterial GWAS dataset, specifically to estimate the genetic correlation between antibiotic resistant traits in Streptococus pneumoniae . This application not only validates the effectiveness of our method but also highlights its potential in real-world applications.

Suggested Citation

  • The Tien Mai, 2023. "Reliable Genetic Correlation Estimation via Multiple Sample Splitting and Smoothing," Mathematics, MDPI, vol. 11(9), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2163-:d:1139389
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    1. Malka Gorfine & Sonja I Berndt & Jenny Chang-Claude & Michael Hoffmeister & Loic Le Marchand & John Potter & Martha L Slattery & Nir Keret & Ulrike Peters & Li Hsu, 2017. "Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-19, August.
    2. 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.
    3. Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
    4. Claudia Giambartolomei & Damjan Vukcevic & Eric E Schadt & Lude Franke & Aroon D Hingorani & Chris Wallace & Vincent Plagnol, 2014. "Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics," PLOS Genetics, Public Library of Science, vol. 10(5), pages 1-15, May.
    5. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    6. Junyang Qian & Yosuke Tanigawa & Wenfei Du & Matthew Aguirre & Chris Chang & Robert Tibshirani & Manuel A Rivas & Trevor Hastie, 2020. "A fast and scalable framework for large-scale and ultrahigh-dimensional sparse regression with application to the UK Biobank," PLOS Genetics, Public Library of Science, vol. 16(10), pages 1-30, October.
    7. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    8. Ryan J. Tibshirani & Jonathan Taylor & Richard Lockhart & Robert Tibshirani, 2016. "Exact Post-Selection Inference for Sequential Regression Procedures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 600-620, April.
    9. Lucas Janson & Rina Foygel Barber & Emmanuel Candès, 2017. "EigenPrism: inference for high dimensional signal-to-noise ratios," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1037-1065, September.
    10. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.
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