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Gene LevelMeta-Analysis ofQuantitative Traitsby Functional LinearModels Ruzong FanYifan WangMichael Boehnke2015 год

Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models
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Аннотация: 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 models can be used to analyze rare variants, common variants, or a combination of the two. Both likelihood-ratio test (LRT) and F-distributed statistics are introduced to test association between quantitative traits and multiple variants in one genetic region. Extensive simulations are performed to evaluate empirical type I error rates and power performance of the proposed tests. The proposed LRT and F-distributed statistics control the type I error very well and have higher power than the existing methods of the meta-analysis sequence kernel association test (MetaSKAT). We analyze four blood lipid levels in data from a meta-analysis of eight European studies. The proposed methods detect more significant associations than MetaSKAT and the P-values of the proposed LRT and F-distributed statistics are usually much smaller than those of MetaSKAT. The functional linear models and related test statistics can be useful in whole-genome and whole-exome association studies.
Год издания: 2015
Авторы: Ruzong Fan, Yifan Wang, Michael Boehnke, Wei Chen, Yun Li, Haobo Ren, Iryna Lobach, Momiao Xiong
Издательство: Oxford University Press
Источник: Genetics
Ключевые слова: Genetic Mapping and Diversity in Plants and Animals, Genetic Associations and Epidemiology, Genetic and phenotypic traits in livestock
Другие ссылки: Genetics (PDF)
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Europe PMC (PubMed Central) (PDF)
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Carolina Digital Repository (University of North Carolina at Chapel Hill) (HTML)
PubMed Central (HTML)
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