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ABSTRACT: Summary
Densely measured SNP data is routinely analyzed but faces challenges due to its high dimensionality, especially when gene-environment (G-E) interactions are incorporated. In recent literature, a functional analysis strategy has been developed, which treats dense SNP measurements as a realization of a genetic function and can "bypass" the dimensionality challenge. However, there is a lack of portable and friendly software, which hinders practical utilization of these functional methods. We fill this knowledge gap and develop the R package FunctanSNP. This comprehensive package encompasses estimation, identification, and visualization tools and has undergone extensive testing using both simulated and real data, confirming its reliability. FunctanSNP can serve as a convenient and reliable tool for analyzing SNP and other densely measured data.Availability
The package is available at https://CRAN.R-project.org/package=FunctanSNP.Supplementary information
Supplementary materials are available at Bioinformatics online.
SUBMITTER: Ren R
PROVIDER: S-EPMC10723032 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature

Ren Rui R Fang Kuangnan K Zhang Qingzhao Q Ma Shuangge S
Bioinformatics (Oxford, England) 20231201 12
<h4>Summary</h4>Densely measured SNP data are routinely analyzed but face challenges due to its high dimensionality, especially when gene-environment interactions are incorporated. In recent literature, a functional analysis strategy has been developed, which treats dense SNP measurements as a realization of a genetic function and can 'bypass' the dimensionality challenge. However, there is a lack of portable and friendly software, which hinders practical utilization of these functional methods. ...[more]