ArticleBMC bioinformatics2015
Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.
Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multiple genotype-phenotype association study reveals intronic variant pair on SIDT2 associated with metabolic syndrome in a Korean population.Human genomics · 2018Article
- Powerful and robust cross-phenotype association test for case-parent trios.Genetic epidemiology · 2018Article
- WISARD: workbench for integrated superfast association studies for related datasets.BMC medical genomics · 2018Article
- Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.BMC bioinformatics · 2017Article
- Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.Genetic epidemiology · 2016Article
- Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Genetics · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
backgroundMany disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes.
resultsThe proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes.
conclusionsThe proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .
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