Molecular Group and Correlation Guided Structural Learning for Multi-Phenotype Prediction

We propose a supervised learning algorithm to perform feature selection and outcome prediction for genomic data with multi-phenotypic responses. Our algorithm particularly incorporates the genome and/or phenotype grouping structures and phenotype correlation structures in feature selection, effect estimation, and outcome prediction under a penalized multi-response linear regression model. Extensive simulations demonstrate its superior performance over its competing methods. We apply the proposed algorithm to two omics studies. In the first study, we identified novel association signals between multivariate gene expressions and high-dimensional DNA methylation profiles, providing biological insights into how CpG sites regulate gene expressions. The second study is for cell type deconvolution. Using the proposed algorithm, we were able to achieve better cell type fraction predictions using high-dimensional gene expression data. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was partially supported by National Science Foundation (award number 2225775). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: 1. Epigenetic Variation and Childhood Asthma in Puerto Ricans (EVAPR) study (methylation, gene expression, and cell fraction data). 2. Framingham Heart(FHS) Study (gene expression and cell fraction data). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data used in the present study are available upon reasonable request to the authors.