ArticleBiometrika2019
Accounting for unobserved covariates with varying degrees of estimability in high-dimensional biological data.
Article in Biometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 31 citations in OpenAlex.
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- Deconvolution of DNA methylation data reveals cell type-specific epigenomic signatures in endometriosis and eutopic endometrium.Molecular human reproduction · 2026Article
- Oral Disease and Atherosclerosis May Be Associated with Overlapping Metabolic Pathways.JDR clinical and translational research · 2025Article
- Simultaneous inference for generalized linear models with unmeasured confounders.Journal of the American Statistical Association · 2025Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- Exploring causal mechanisms and quantifying direct and indirect effects using a joint modeling approach for recurrent and terminal events.Statistics in medicine · 2023Article
- A functional genomics pipeline to identify high-value asthma and allergy CpGs in the human methylome.The Journal of allergy and clinical immunology · 2023Article
- Multi-omic association study identifies DNA methylation-mediated genotype and smoking exposure effects on lung function in children living in urban settings.PLoS genetics · 2023Article
- Maternal prenatal immunity, neonatal trained immunity, and early airway microbiota shape childhood asthma development.Allergy · 2022Article
- African-specific alleles modify risk for asthma at the 17q12-q21 locus in African Americans.Genome medicine · 2022Article
- DNA methylation signatures in airway cells from adult children of asthmatic mothers reflect subtypes of severe asthma.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Estimating and accounting for unobserved covariates in high-dimensional correlated data.Journal of the American Statistical Association · 2022Article
- Asthma-associated genetic variants induce IL33 differential expression through an enhancer-blocking regulatory region.Nature communications · 2021Article
- UNIFYING AND GENERALIZING METHODS FOR REMOVING UNWANTED VARIATION BASED ON NEGATIVE CONTROLS.Statistica Sinica · 2021Article
- Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels.Epigenetics · 2021Article
- ESTIMATION AND INFERENCE IN METABOLOMICS WITH NON-RANDOM MISSING DATA AND LATENT FACTORS.The annals of applied statistics · 2020Article
- Data-based RNA-seq simulations by binomial thinning.BMC bioinformatics · 2020Article
- Expression quantitative trait locus fine mapping of the 17q12-21 asthma locus in African American children: a genetic association and gene expression study.The Lancet. Respiratory medicine · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
An important phenomenon in high-throughput biological data is the presence of unobserved covariates that can have a significant impact on the measured response. When these covariates are also correlated with the covariate of interest, ignoring or improperly estimating them can lead to inaccurate estimates of and spurious inference on the corresponding coefficients of interest in a multivariate linear model. We first prove that existing methods to account for these unobserved covariates often inflate Type I error for the null hypothesis that a given coefficient of interest is zero. We then provide alternative estimators for the coefficients of interest that correct the inflation, and prove that our estimators are asymptotically equivalent to the ordinary least squares estimators obtained when every covariate is observed. Lastly, we use previously published DNA methylation data to show that our method can more accurately estimate the direct effect of asthma on DNA methylation levels compared to existing methods, the latter of which likely fail to recover and account for latent cell type heterogeneity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.