Year: 2023
Type: article
Abstract: SUMMARY: 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. 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 AND IMPLEMENTATION: The package is available at https://CRAN.R-project.org/package=FunctanSNP.
Source: Bioinformatics
Authors: Rui Ren, Kuangnan Fang, Qingzhao Zhang, Shuangge Ma
Institutions: Yale University, Xiamen University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,618
Awards: G1354162025, Yale SPORE in Lung Cancer (YSILC): The Biology and Personalized Treatment of Lung Cancer, G3055610919, G3089755109, Novel Methods for Identifying Genetic Interactions for Cancer Prognosis, G6278299708, Integrated Cancer Modeling: A New Dimension, G6854599904, G7123194934, G734671176, G8375298767, G8490343050, G9000144101, G9000169908, G9000286398
OpenAlex ID: W4389426664