Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions

Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions

We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of geno...

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Bibliographic Details
Main Authors: Felix Agakov, Paul Mckeigue, Jon Krohn, Jonathan Flint
Format: Article
Language:English
Published: International Institute of Informatics and Cybernetics 2011-12-01
Series:Journal of Systemics, Cybernetics and Informatics
Subjects:
Mendelian Randomization
Structure Learning
causality
Instrumental Variables
Sparse Linear Models
Bayesian Networks
Online Access:http://www.iiisci.org/Journal/CV$/sci/pdfs/RO162NN.pdf

Internet

http://www.iiisci.org/Journal/CV$/sci/pdfs/RO162NN.pdf

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