binary_to_liability

This function implements an approximate conversion of binary traits to liabilties as
proposed in the LT-MAPIT model (Crawford and Zhou 2018,
https://doi.org/10.1101/374983). Note that this is only good for low prevalence. 
To run LT-MAPIT (MAPIT on case-control traits), convert the binary traits to
liabilities using this function and pass the liabilities to mvmapit as trait.

Epistasis, commonly defined as the interaction between genetic
loci, is known to play an important role in the phenotypic variation of
complex traits. As a result, many statistical methods have been developed to
identify genetic variants that are involved in epistasis, and nearly all of
these approaches carry out this task by focusing on analyzing one trait at a
time. Previous studies have shown that jointly modeling multiple phenotypes
can often dramatically increase statistical power for association mapping. In
this package, we present the 'multivariate MArginal ePIstasis Test'
('mvMAPIT') – a multi-outcome generalization of a recently proposed epistatic
detection method which seeks to detect marginal epistasis or the combined
pairwise interaction effects between a given variant and all other variants.
By searching for marginal epistatic effects, one can identify genetic variants
that are involved in epistasis without the need to identify the exact
partners with which the variants interact – thus, potentially alleviating
much of the statistical and computational burden associated with conventional
explicit search based methods. Our proposed 'mvMAPIT' builds upon this
strategy by taking advantage of correlation structure between traits to
improve the identification of variants involved in epistasis.
We formulate 'mvMAPIT' as a multivariate linear mixed model and develop a
multi-trait variance component estimation algorithm for efficient parameter
inference and P-value computation. Together with reasonable model
approximations, our proposed approach is scalable to moderately sized
genome-wide association studies.
Crawford et al. (2017) <doi:10.1371/journal.pgen.1006869>.
Stamp et al. (2023) <doi:10.1093/g3journal/jkad118>.
Stamp et al. (2025) <doi:10.1016/j.ajhg.2025.07.004>.

Julian Stamp

mvMAPIT

Multivariate Genome Wide Marginal Epistasis Test

Lorin Crawford

binary_to_liability function

<dl><dt>case_control_trait</dt>
<dd>Case-control trait encoded as binary trait with 0 as control or 1 as case.</dd>
<dt>prevalence</dt>
<dd>Case prevalence between 0 and 1. Proportion of cases in the population.</dd></dl>

Arguments

Convert binary traits to liabilities for low prevalence — binary_to_liability

<dl>

<dt>case_control_trait</dt>
<dd>Case-control trait encoded as binary trait with 0 as control or 1 as case.</dd>


<dt>prevalence</dt>
<dd>Case prevalence between 0 and 1. Proportion of cases in the population.</dd>

</dl>

binary_to_liability: Convert binary traits to liabilities for low prevalence

Description

Usage

Value

Arguments