2018
DOI: 10.1002/gepi.22121
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Testing cross‐phenotype effects of rare variants in longitudinal studies of complex traits

Abstract: Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately … Show more

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Cited by 5 publications
(2 citation statements)
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References 43 publications
(78 reference statements)
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“…In this article, we have shown the ability of kernel machines to have high power for testing for compositional associations with clinical outcomes, which mirrors its success in other settings (e.g., Wu et al (2011);Rudra et al (2018)). Nevertheless, because the methodology operates at the level of samples and not individual features, one criticism is its interpretability.…”
Section: Discussionmentioning
confidence: 70%
“…In this article, we have shown the ability of kernel machines to have high power for testing for compositional associations with clinical outcomes, which mirrors its success in other settings (e.g., Wu et al (2011);Rudra et al (2018)). Nevertheless, because the methodology operates at the level of samples and not individual features, one criticism is its interpretability.…”
Section: Discussionmentioning
confidence: 70%
“…However, such single‐trait analyses have been found to be less powerful (Zhu et al, 2015), especially for the traits that are weakly associated with the given SNP (Luo et al, 2020). To boost statistical power, methods for jointly analyzing multiple traits in a unified framework have been proposed (Galesloot et al, 2014; Maier et al, 2015; Rudra et al, 2018), which can be summarized as multivariate methods, univariate methods, and data reduction methods (Aschard et al, 2014).…”
Section: Introductionmentioning
confidence: 99%