ArticleBioinformatics (Oxford, England)2009
A multivariate regression approach to association analysis of a quantitative trait network.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways.Nature genetics · 2012Pooled it
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- A Novel Method for Identifying a Parsimonious and Accurate Predictive Model for Multiple Clinical Outcomes.Computer methods and programs in biomedicine · 2021Article
- An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.Genetic epidemiology · 2020Article
- Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities.Frontiers in genetics · 2019Review
- Robust network-based analysis of the associations between (epi)genetic measurements.Journal of multivariate analysis · 2018Article
- An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease.BMC bioinformatics · 2018Article
- Longitudinal Genotype-Phenotype Association Study through Temporal Structure Auto-Learning Predictive Model.Journal of computational biology : a journal of computational molecular cell biology · 2018Article
- Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.Frontiers in genetics · 2018Article
- Analysis of cancer gene expression data with an assisted robust marker identification approach.Genetic epidemiology · 2017Article
- Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-Learning Predictive Model.Research in computational molecular biology : ... Annual International Conference, RECOMB ... : proceedings. RECOMB (Conference : 2005- ) · 2017Article
- Generalized LASSO with under-determined regularization matrices.Signal processing · 2016Article
- A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study.Scientific reports · 2016Article
- Integrative regression network for genomic association study.BMC medical genomics · 2016Article
- Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach.Bioinformatics (Oxford, England) · 2015Article
- A Review of Emerging Technologies for the Management of Diabetes Mellitus.IEEE transactions on bio-medical engineering · 2015Review
Corrections and comments
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
motivationMany complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses.
resultsWe propose a new statistical framework called graph-guided fused lasso to address this issue in a principled way. Our approach represents the dependency structure among the quantitative traits explicitly as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently, our approach analyzes all of the traits jointly in a single statistical method to discover the genetic markers that perturb a subset of correlated traits jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal single nucleotide polymorphisms when we incorporate the correlation pattern in traits using our proposed methods. AVAILABILITY: Software for GFlasso is available at http://www.sailing.cs.cmu.edu/gflasso.html.
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