knitr::opts_chunk$set( comment = "" ) devtools::load_all() options(width = 100)
This package implements regression models for family-based genetic studies, including mixed models. While such models can be fit with other existing software, this package tries to provide a unified solution that leverages the flexibility of the R environment. It also leverages modern automatic differentiation techniques to fit likelihood-based models quickly.
You can install the latest released version of FamModel from GitHub with:
# install.packages("devtools") devtools::install_github("kinnamon-lab/FamModel", ref = "[tag]")
where [tag]
is the tag of the most recent release.
The following code fits a univariate version of the linear mixed model presented
in Cowan et al. (2018) with an
equivalent parameterization using the lmna_nonseg
example data provided with
the package. Note how the package permits flexible use of formula
constructs
and provides an appropriate likelihood ratio test for a null narrow-sense
heritability on the boundary of the parameter space.
lmna_data <- copy(lmna_nonseg)[, `:=`( female = as.integer(sex == 2), # Use N rather than N-1 for SD divisor in standardization (like Mendel 16.0) age_echo_std = (age_echo_yrs - mean(age_echo_yrs, na.rm = TRUE)) / ( sd(age_echo_yrs, na.rm = TRUE) * sqrt((sum(!is.na(age_echo_yrs)) - 1) / sum(!is.na(age_echo_yrs))) ) ) ] lmna_fd <- FamData$new( lmna_data, family_id = "family_ID", indiv_id = "individual_ID", proband = "proband", sex = "sex", maternal_id = "maternal_ID", paternal_id = "paternal_ID", mzgrp = "mzpair", dzgrp = "dzpair" ) lmna_lvef_model <- lmna_fd$lmm( lvef ~ female + age_echo_std + I(n_lmna_vars > 0) + I(n_oth_vars > 0) ) lmna_lvef_model$print()
Development of this software was supported by the National Heart, Lung, and Blood Institute and National Human Genome Research Institute of the National Institutes of Health under award numbers R01HL128857 and R01HL149423. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health.
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