ArticleHuman heredity2015
Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.
Article in Human heredity, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The sequence kernel association test for the proportional odds model.Bioinformatics (Oxford, England) · 2025Article
- A general kernel machine regression framework using principal component analysis for jointly testing main and interaction effects: Applications to human microbiome studies.NAR genomics and bioinformatics · 2024Article
- Gene-based association tests in family samples using GWAS summary statistics.Genetic epidemiology · 2024Article
- Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model.Frontiers in genetics · 2022Article
- Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.Frontiers in genetics · 2022Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- KMgene: a unified R package for gene-based association analysis for complex traits.Bioinformatics (Oxford, England) · 2018Article
- Longitudinal data analysis for rare variants detection with penalized quadratic inference function.Scientific reports · 2017Article
- Longitudinal SNP-set association analysis of quantitative phenotypes.Genetic epidemiology · 2017Article
- The impact of genotype calling errors on family-based studies.Scientific reports · 2016Article
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10 authors.
Funding
Abstract
objectiveThe kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.
methodsThis approach uses KM regression based on the linear mixed model framework and is applicable to longitudinal data from either population (L-KM) or family samples (LF-KM).
resultsIn our population-based simulation studies, L-KM has good control of Type I error rate and increased power in all the scenarios we considered compared with other competing methods. Conversely, in the family-based simulation studies, we found an inflated Type I error rate when L-KM was applied directly to the family samples, whereas LF-KM retained the desired Type I error rate and had the best power performance overall. Finally, we illustrate the utility of our proposed LF-KM approach by analyzing data from an association study between rare variants and blood pressure from the Genetic Analysis Workshop 18 (GAW18).
conclusionWe propose a method for rare-variant association testing in population and family samples using phenotypes measured at multiple time points for each subject. The proposed method has the best power performance compared to competing approaches in our simulation study.
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