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OmicKriging specifications


Unique identifier OMICS_18028
Name OmicKriging
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.0
Stability Stable
Maintained Yes


No version available


  • person_outline Hae Kyung Im

Additional information

Manual: Tuto:

Publication for OmicKriging

OmicKriging citations


Increased Proportion of Variance Explained and Prediction Accuracy of Survival of Breast Cancer Patients with Use of Whole Genome Multiomic Profiles

PMCID: 4937492
PMID: 27129736
DOI: 10.1534/genetics.115.185181

[…] Bayesian regularized regressions (; ), and (3) modern approaches for modeling interactions between high-dimensional inputs primarily developed for the study of genetic-by-environment interactions (). OmicKriging, a multiomic risk-assessment method (; ), can be seen as a special case of the BGAM that assumes additive action across omics and a homogeneous architecture of effects (with Gaussian assum […]


Genomic prediction of complex human traits: relatedness, trait architecture and predictive meta models

Hum Mol Genet
PMCID: 4476450
PMID: 25918167
DOI: 10.1093/hmg/ddv145

[…] he future, we will investigate how we can improve prediction accuracy in complex traits by combining genomic data with intermediate phenotypes, such as gene expression and methylation, similar to the OmicKriging method developed by Wheeler et al. (). Such intermediate phenotypes can mediate the predictive signal from the genomic data, but also capture a proportion of the phenotypic variation that […]


Jumping on the Train of Personalized Medicine: A Primer for Non Geneticist Clinicians: Part 3. Clinical Applications in the Personalized Medicine Area

PMCID: 4287884
PMID: 25598768
DOI: 10.2174/1573400510666140630170549

[…] e molecular features and biological pathways that occurred as the subject transitioned from healthy to diseased conditions. Using poly-omics dataset, Heather et al. recently developed a method called OmicKriging and showed substantially better performance in prediction of seven diseases than any single omics dataset in the study from the Wellcome Trust Case Control Consortium (WTCCC) []. With this […]


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OmicKriging institution(s)
Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, USA; Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA; Department of Health Studies, University of Chicago, Chicago, IL, USA
OmicKriging funding source(s)
Supported by the Pharmacogenetics of Anticancer Agents Research (PAAR) Group (NIH/NIGMS grant UO1GM61393), the Genotype-Tissue Expression project (GTeX) (R01 MH101820 and R01 MH090937), the University of Chicago DRTC (Diabetes Research and Training Center; P60 DK20595; P30 DK020595), the University of Chicago Cancer Center Support Grant (NCI P30 CA014599-36), the PGRN Statistical Analysis Resource (U19 HL065962), and the Conte Center grant P50MH094267; by the National Research Service Award F32CA165823 and by Award Number K12CA139160 from the National Cancer Institute.

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