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Sparse latent factor regression models for genome-wide and epigenome-wide association studies

  • Basile Jumentier , Kevin Caye , Barbara Heude , Johanna Lepeule EMAIL logo und Olivier François ORCID logo EMAIL logo
Veröffentlicht/Copyright: 7. März 2022
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Statistical Applications in Genetics and Molecular Biology
Aus der Zeitschrift Statistical Applications in Genetics and Molecular Biology Band 21 Heft 1

Abstract

Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator and a Bayesian sparse linear mixed model. In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

Keywords: confounding factors; epigenome-wide association; genome-wide association; sparse model; statistical methods
Corresponding authors: Johanna Lepeule, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Institute for Advanced Biosciences, INSERM U 1209, CNRS UMR 5309, Université Grenoble-Alpes, Grenoble, 38000, France, E-mail: ; and Olivier François, Centre National de la Recherche Scientifique, Grenoble INP, TIMC-IMAG CNRS UMR 5525, Université Grenoble-Alpes, Grenoble, 38000, France; and Inria Grenoble, Equipe Statify, Laboratoire Jean Kuntzmann, Rhône-Alpes Inovallée 655 Avenue de l’Europe - CS 90051, Montbonnot, 38334, France, E-mail:

Funding source: Agence Nationale de la Recherche

Award Identifier / Grant number: ANR-11-LABX-0025-01

Award Identifier / Grant number: ANR-15-IDEX-02

Award Identifier / Grant number: ANR-18-CE36-0005

Acknowledgments

This article was developed in the framework of the Grenoble Alpes Data Institute, supported by the French National Research Agency under the Investissements d’Avenir program (ANR-15-IDEX-02). It received support from LabEx PERSYVAL Lab, ANR-11-LABX-0025-01, and from the French National Research Agency (Agence Nationale pour la Recherche) ETAPE, ANR-18-CE36-0005. The EDEN mother-child study was supported by Foundation for medical research (FRM), National Agency for Research (ANR), National Institute for Research in Public health (IRESP), French Ministry of Health (DGS), French Ministry of Research, INSERM Bone and Joint Diseases National Research (PRO-A), and Human Nutrition National Research Programs, Nestlé, French National Institute for Population Health Surveillance (InVS), French National Institute for Health Education (INPES), the European Union FP7 programmes (FP7/2007–2013, HELIX, ESCAPE, ENRIECO, Medall projects), Diabetes National Research Program, French Agency for Environmental Health Safety (ANSES), Mutuelle Générale de l’Education Nationale (MGEN), French national agency for food security, French-speaking association for the study of diabetes and metabolism (ALFEDIAM). We thank all the participants and members of the EDEN mother-child cohort study group.

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/sagmb-2021-0035).

Received: 2021-05-04
Revised: 2022-01-20
Accepted: 2022-02-06
Published Online: 2022-03-07

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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  2. Challenges for machine learning in RNA-protein interaction prediction
  3. Research Articles
  4. Distinct characteristics of correlation analysis at the single-cell and the population level
  5. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
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https://doi.org/10.1515/sagmb-2021-0035
Schlagwörter für diesen Artikel
confounding factors; epigenome-wide association; genome-wide association; sparse model; statistical methods

Artikel in diesem Heft

  1. Review Article
  2. Challenges for machine learning in RNA-protein interaction prediction
  3. Research Articles
  4. Distinct characteristics of correlation analysis at the single-cell and the population level
  5. pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples
  6. Use of SVM-based ensemble feature selection method for gene expression data analysis
  7. A robust association test with multiple genetic variants and covariates
  8. Estimation of the covariance structure from SNP allele frequencies
  9. GMEPS: a fast and efficient likelihood approach for genome-wide mediation analysis under extreme phenotype sequencing
  10. Sparse latent factor regression models for genome-wide and epigenome-wide association studies
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