Zim4rv: an R package to modeling zero-inflated count phenotype on regional-based rare variants - Xiaomin Liu; Yi-Ju Li; Qiao Fan (BMC Bioinformatics)
Abstract Background With the advance of next-generation sequencing, various gene-based rare variant association tests have been developed, particularly for binary and continuous phenotypes. In contrast, fewer methods are available for traits not following binomial or normal distributions. To address this, we previously proposed a set of burden- and kernel-based rare variant tests for count data following zero-inflated Poisson (ZIP) distributions, referred to as ZIP-b and ZIP-k tests. We sought to extend the methods to accommodate negative binomial distribution and implemented these tests in a new R package. Results We introduce ZIM4rv, an R package designed to analyze the association of rare variants with zero-inflated counts outcomes. Our package offers two novel models developed by our team: our previously proposed ZIP-b and ZIP-k tests, and the newly derived Negative Binomial Burden and Kernel Test (ZINB-b, ZINB-k). Additionally, we include an ad-hoc two-stage analysis, testing zero and non-zero as a binary outcome and non-zero as a continuous outcome, respectively. To showcase the utility of our platform, we applied this program to analyze neuritic plaque count data from the ROSMAP cohort. Conclusion The R package ZIM4rv presents an integrated workflow for conducting association tests on a set of rare variants with zero-inflated counts data.