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ArticleScientific reports2025

Introduction of FixFRM package for gene-based association test and its innovative application to mixture exposure analysis.

Xin Peng, Haixin Feng, Renhuizi Wei, Wenqian Ruan, Jun Wu, Chulan Ou, Zhongxin Zhu, Linxi He, Bingsong Zhang

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Xin PengDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Haixin FengDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Renhuizi WeiOffice of Quality Management, Hospital of Huangjiang Dongguan, Dongguan, 523808, Guangdong, China.
Wenqian RuanDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Jun WuDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Chulan OuDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Zhongxin ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Linxi HeDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China.
Bingsong ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Guangdong Medical University, Dongguan, 523808, Guangdong, China. zhangbingsong@gdmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional regression-based method (FRM) is an efficient and powerful tool for identifying genetic variants associated with complex traits and diseases by jointly considering the effects of multiple variants within a gene, however, no accessible software has been developed to realize the methods in practice. In this paper, we introduce the FixFRM R package that implements FRMs for gene-based tests. It provides a unified framework for quantitative and dichotomous trait analysis with various options for genetic variant and effect estimation. We use the package to identify gene that significantly associated withasthma using data from SNPassoc R package and compare to SKAT and SKAT-O. We further showcase an innovative application of FRMs to mixture exposure analysis using National Health and Nutrition Examination Survey data, modeling the joint effects of multiple environmental exposures by treating exposure levels as "genetic variants". FRM achieves acceptable performance in the simulation study and obtains similar p-value as weighted quantile sum regression does in real application. The FixFRM package bridges the gap between the theoretical advantages and practical implementation for gene-based tests and mixture exposure analysis. Its user-friendly interface and comprehensive options facilitate powerful screening of genetic and environmental effects on complex traits.

Indexed as

AsthmaEnvironmental ExposureGenetic Association StudiesSoftwareComputer SimulationGenetic Predisposition to DiseaseHumansModels, GeneticPolymorphism, Single NucleotideFunctional regression modelGene-based association testMixture exposure analysisR packagesWeighted quantile sum regression

Identifiers

PMID41006765
PMCPMC12475290

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