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Summary: Inference of Causal Relationships between Biomarkers and Outcomes in High
Dimensions
Felix AGAKOV and Paul MCKEIGUE
Center for Public Health Sciences, University of Edinburgh
Edinburgh EH8 9AG, UK
and
Jon KROHN and Jonathan FLINT
Wellcome Trust Center for Human Genetics
Oxford OX3 7BN, UK
ABSTRACT
We describe a unified computational framework for learning
causal dependencies between genotypes, biomarkers, and phe-
notypic outcomes from large-scale data. In contrast to previous
studies, our framework allows for noisy measurements, hidden
confounders, missing data, and pleiotropic effects of genotypes
on outcomes. The method exploits the use of genotypes as "in-
strumental variables" to infer causal associations between pheno-
typic biomarkers and outcomes, without requiring the assumption
that genotypic effects are mediated only through the observed
biomarkers. The framework builds on sparse linear methods
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