2015
DOI: 10.1534/genetics.115.178343
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Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models

Abstract: Meta-analysis of genetic data must account for differences among studies including study designs, markers genotyped, and covariates. The effects of genetic variants may differ from population to population, i.e., heterogeneity. Thus, meta-analysis of combining data of multiple studies is difficult. Novel statistical methods for meta-analysis are needed. In this article, functional linear models are developed for meta-analyses that connect genetic data to quantitative traits, adjusting for covariates. The model… Show more

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Cited by 25 publications
(51 citation statements)
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“…If we only have one quantitative trait, ie, J = 1, the three approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are equivalent to the F-test statistics of the standard multiple linear regression. The models proposed in this article and the related approximate F-distribution tests extend the models and the F-test statistics in Fan et al 18 In practice, we find that the results of the three approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are similar to each other. 10 In this article, we only report the results of approximate F-distribution tests based on Pillai-Bartlett trace.…”
Section: Approximate F-distributed Test Statisticssupporting
confidence: 76%
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“…If we only have one quantitative trait, ie, J = 1, the three approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are equivalent to the F-test statistics of the standard multiple linear regression. The models proposed in this article and the related approximate F-distribution tests extend the models and the F-test statistics in Fan et al 18 In practice, we find that the results of the three approximate F-distribution tests based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are similar to each other. 10 In this article, we only report the results of approximate F-distribution tests based on Pillai-Bartlett trace.…”
Section: Approximate F-distributed Test Statisticssupporting
confidence: 76%
“…To estimate the GVFs X ci ðtÞ from the genotypes G ci , we use an ordinary linear square smoother. [16][17][18][19][20]42,43 Let ϕ k (t), k = 1, ⋯, K, be a series of K basis functions, such as the B-spline basis and Fourier basis functions. Denote ϕ(t) = (ϕ 1 (t), ⋯, ϕ K (t))′.…”
Section: Expansion Of Genetic Effectmentioning
confidence: 99%
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