ArticleBMC genomics2017
Prediction of gene expression with cis-SNPs using mixed models and regularization methods.
Article in BMC genomics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.PLoS computational biology · 2026Article
- OmiGA for ultra-efficient molecular quantitative trait loci mapping.Nature communications · 2026Article
- Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.Journal of translational medicine · 2024Article
- Predicting the genetic component of gene expression using gene regulatory networks.Bioinformatics advances · 2024Article
- Assessment of Performance, Interpretability, and Explainability in Artificial Intelligence-Based Health Technologies: What Healthcare Stakeholders Need to Know.Mayo Clinic proceedings. Digital health · 2023Review
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- Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling.Science advances · 2022Article
- Multi-scale inference of genetic trait architecture using biologically annotated neural networks.PLoS genetics · 2021Article
- A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES.The annals of applied statistics · 2021Article
- Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach.Frontiers in genetics · 2021Article
- Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome-wide association studies.Genetic epidemiology · 2020Article
- How Can Gene-Expression Information Improve Prognostic Prediction in TCGA Cancers: An Empirical Comparison Study on Regularization and Mixed Cox Models.Frontiers in genetics · 2020Article
- Potential Genes Associated with the Survival of Lung Adenocarcinoma Were Identified by Methylation.Computational and mathematical methods in medicine · 2020Article
- Learning Cell-Type-Specific Gene Regulation Mechanisms by Multi-Attention Based Deep Learning With Regulatory Latent Space.Frontiers in genetics · 2020Article
- Article
- Jackknife Model Averaging Prediction Methods for Complex Phenotypes with Gene Expression Levels by Integrating External Pathway Information.Computational and mathematical methods in medicine · 2019Article
- Stable predictive markers for Phytophthora sojae avirulence genes that impair infection of soybean uncovered by whole genome sequencing of 31 isolates.BMC biology · 2018Article
- Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.Scientific reports · 2017Article
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3 authors.
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Abstract
backgroundIt has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases.
methodsWe compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data.
resultsThe simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R
conclusionsGene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.
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