Multivariate regression with missing response data for modelling regional DNA methylation QTLs Article Swipe

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Shomoita Alam , Yixiao Zeng , Sasha Bernatsky , Marie Hudson , Inés Colmegna , David A. Stephens , Celia M.T. Greenwood , Archer Y. Yang ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2507.05990

Identifying genetic regulators of DNA methylation (mQTLs) with multivariate models enhances statistical power, but is challenged by missing data from bisulfite sequencing. Standard imputation-based methods can introduce bias, limiting reliable inference. We propose \texttt{missoNet}, a novel convex estimation framework that jointly estimates regression coefficients and the precision matrix from data with missing responses. By using unbiased surrogate estimators, our three-stage procedure avoids imputation while simultaneously performing variable selection and learning the conditional dependence structure among responses. We establish theoretical error bounds, and our simulations demonstrate that \texttt{missoNet} consistently outperforms existing methods in both prediction and sparsity recovery. In a real-world mQTL analysis of the CARTaGENE cohort, \texttt{missoNet} achieved superior predictive accuracy and false-discovery control on a held-out validation set, identifying known and credible novel genetic associations. The method offers a robust, efficient, and theoretically grounded tool for genomic analyses, and is available as an R package.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2507.05990
PDF: https://arxiv.org/pdf/2507.05990
OA Status: green
OpenAlex ID: https://openalex.org/W4416061787

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OpenAlex ID: https://openalex.org/W4416061787

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DOI: https://doi.org/10.48550/arxiv.2507.05990

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Title: Multivariate regression with missing response data for modelling regional DNA methylation QTLs

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-07-08

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Authors: Shomoita Alam, Yixiao Zeng, Sasha Bernatsky, Marie Hudson, Inés Colmegna, David A. Stephens, Celia M.T. Greenwood, Archer Y. Yang

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Landing page: https://arxiv.org/abs/2507.05990

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PDF URL: https://arxiv.org/pdf/2507.05990

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Open access: Yes

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OA URL: https://arxiv.org/pdf/2507.05990

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Cited by: 0

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