ArticleGenetic epidemiology2016
Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.
Article in Genetic epidemiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 13 citations in OpenAlex.
- Modeling and application of alzheimer's disease complex trait prediction based on multi-task learning.Scientific reports · 2026Article
- Auto-branch multi-task learning for simultaneous prediction of multiple correlated traits associated with Alzheimer's disease.Frontiers in genetics · 2025Article
- Sex differences in the influence of type 2 diabetes (T2D)-related genes, parental history of T2D, and obesity on T2D development: a case-control study.Biology of sex differences · 2023Article
- Definition and Nomenclature of Chronic Obstructive Pulmonary Disease: Time for Its Revision.American journal of respiratory and critical care medicine · 2022Article
- Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits.American journal of human genetics · 2019Article
- Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.BMC bioinformatics · 2018Article
- WISARD: workbench for integrated superfast association studies for related datasets.BMC medical genomics · 2018Article
- CpG-set association assessment of lipid concentration changes and DNA methylation.BMC proceedings · 2018Article
- Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.BMC bioinformatics · 2017Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
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Abstract
Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.
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