ArticleGenetic epidemiology2014
Poly-omic prediction of complex traits: OmicKriging.
Article in Genetic epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Development and application of type 1 diabetes polygenic scores across diverse populations.Diabetologia · 2026Review
- eQTL analysis: A bridge from genome to mechanism.Genes & diseases · 2026Review
- Comparing statistical learning methods for complex trait prediction from gene expression.PloS one · 2025Article
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- Comparing statistical learning methods for complex trait prediction from gene expression.bioRxiv : the preprint server for biology · 2024Article
- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- Deep Clinical Phenotyping of Schizophrenia Spectrum Disorders Using Data-Driven Methods: Marching towards Precision Psychiatry.Journal of personalized medicine · 2023Article
- A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.Briefings in bioinformatics · 2022Article
- Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction.Molecular psychiatry · 2022Review
- Incorporation of Trait-Specific Genetic Information into Genomic Prediction Models.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Ten Years of EWAS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Review
- Leveraging Multiple Layers of Data To PredictG3 (Bethesda, Md.) · 2020Article
- Article
- Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2020Article
- Systematic Multi-Omics Integration (MOI) Approach in Plant Systems Biology.Frontiers in plant science · 2020Review
- Prior Biological Knowledge Improves Genomic Prediction of Growth-Related Traits inFrontiers in genetics · 2020Article
- Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community.Metabolites · 2019Article
- A likelihood-based approach to transcriptome association analysis.Statistics in medicine · 2019Article
- Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity.The pharmacogenomics journal · 2019Article
- Germline Variation and Breast Cancer Incidence: A Gene-Based Association Study and Whole-Genome Prediction of Early-Onset Breast Cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2018Article
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Authors and funding
8 authors.
Funding
Abstract
High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).
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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.