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Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models

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  • Ping Zeng

    (Xuzhou Medical University
    University of Michigan)

  • Xiang Zhou

    (University of Michigan
    University of Michigan)

Abstract

Using genotype data to perform accurate genetic prediction of complex traits can facilitate genomic selection in animal and plant breeding programs, and can aid in the development of personalized medicine in humans. Because most complex traits have a polygenic architecture, accurate genetic prediction often requires modeling all genetic variants together via polygenic methods. Here, we develop such a polygenic method, which we refer to as the latent Dirichlet process regression model. Dirichlet process regression is non-parametric in nature, relies on the Dirichlet process to flexibly and adaptively model the effect size distribution, and thus enjoys robust prediction performance across a broad spectrum of genetic architectures. We compare Dirichlet process regression with several commonly used prediction methods with simulations. We further apply Dirichlet process regression to predict gene expressions, to conduct PrediXcan based gene set test, to perform genomic selection of four traits in two species, and to predict eight complex traits in a human cohort.

Suggested Citation

  • Ping Zeng & Xiang Zhou, 2017. "Non-parametric genetic prediction of complex traits with latent Dirichlet process regression models," Nature Communications, Nature, vol. 8(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-00470-2
    DOI: 10.1038/s41467-017-00470-2
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    Cited by:

    1. Guangbao Guo & Guoqi Qian & Lu Lin & Wei Shao, 2021. "Parallel inference for big data with the group Bayesian method," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 225-243, February.
    2. Song Zhai & Hong Zhang & Devan V. Mehrotra & Judong Shen, 2022. "Pharmacogenomics polygenic risk score for drug response prediction using PRS-PGx methods," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Niloy Biswas & Anirban Bhattacharya & Pierre E. Jacob & James E. Johndrow, 2022. "Coupling‐based convergence assessment of some Gibbs samplers for high‐dimensional Bayesian regression with shrinkage priors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(3), pages 973-996, July.
    4. Geyu Zhou & Hongyu Zhao, 2021. "A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics," PLOS Genetics, Public Library of Science, vol. 17(7), pages 1-17, July.
    5. Qile Dai & Geyu Zhou & Hongyu Zhao & Urmo Võsa & Lude Franke & Alexis Battle & Alexander Teumer & Terho Lehtimäki & Olli T. Raitakari & Tõnu Esko & Michael P. Epstein & Jingjing Yang, 2023. "OTTERS: a powerful TWAS framework leveraging summary-level reference data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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