ArticleGenetics2015
Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.
Article in Genetics, 2015. 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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Who cites it
9 citing papers in PubMed.
- Gene-based association tests in family samples using GWAS summary statistics.Genetic epidemiology · 2024Article
- Associating Multivariate Traits with Genetic Variants Using Collapsing and Kernel Methods with Pedigree- or Population-Based Studies.Computational and mathematical methods in medicine · 2021Article
- A review of kernel methods for genetic association studies.Genetic epidemiology · 2019Review
- Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes.Genetic epidemiology · 2019Article
- KMgene: a unified R package for gene-based association analysis for complex traits.Bioinformatics (Oxford, England) · 2018Article
- Inference on phenotype-specific effects of genes using multivariate kernel machine regression.Genetic epidemiology · 2018Article
- CpG-set association assessment of lipid concentration changes and DNA methylation.BMC proceedings · 2018Article
- The impact of genotype calling errors on family-based studies.Scientific reports · 2016Article
- Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Human heredity · 2015Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
The recent development of sequencing technology allows identification of association between the whole spectrum of genetic variants and complex diseases. Over the past few years, a number of association tests for rare variants have been developed. Jointly testing for association between genetic variants and multiple correlated phenotypes may increase the power to detect causal genes in family-based studies, but familial correlation needs to be appropriately handled to avoid an inflated type I error rate. Here we propose a novel approach for multivariate family data using kernel machine regression (denoted as MF-KM) that is based on a linear mixed-model framework and can be applied to a large range of studies with different types of traits. In our simulation studies, the usual kernel machine test has inflated type I error rates when applied directly to familial data, while our proposed MF-KM method preserves the expected type I error rates. Moreover, the MF-KM method has increased power compared to methods that either analyze each phenotype separately while considering family structure or use only unrelated founders from the families. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
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