A formal statistical hypothesis test, resulting in a p-value, is provided to quantify uncertainty in a causal inference pertaining to a measured factor, e.g. a molecular species, which potentially mediates a known causal association between a locus and a quantitative trait. The test is applicable to data that includes genotype (discrete), possible causal mediator such as gene expression (continuous) and an outcome of interest (continuous).

A likelihood-based hypothesis testing approach is implemented for assessing causal mediation. For example, it could be used to test for mediation of a known causal association between a DNA variant, the 'instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.

person("Joshua", "Millstein", email = "joshua.millstein@usc.edu", role =
c("aut", "cre"), comment = c(ORCID="0000-0001-7961-8943"))
A likelihood-based hypothesis testing approach is implemented for
assessing causal mediation. Described in Millstein, Chen, and Breton (2016),
<DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation
of a known causal association between a DNA variant, the 'instrumental variable',
and a clinical outcome or phenotype by gene expression or DNA methylation, the
potential mediator. Another example would be testing mediation of the effect
of a drug on a clinical outcome by the molecular target. The hypothesis test
generates a p-value or permutation-based FDR value with confidence intervals
to quantify uncertainty in the causal inference. The outcome can be represented
by either a continuous or binary variable, the potential mediator is continuous,
and the instrumental variable can be continuous or binary and is not limited to
a single variable but may be a design matrix representing multiple variables.
gsl (with development libraries libgsl-dev)
Joshua Millstein [aut, cre] (<https://orcid.org/0000-0001-7961-8943>)