ArticleGenetics2015
Gene Level Meta-Analysis of Quantitative Traits by Functional Linear Models.
Article in Genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.European journal of human genetics : EJHG · 2017Pooled it
- Meta-analysis of Complex Diseases at Gene Level with Generalized Functional Linear Models.Genetics · 2016Pooled it
- CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms.Epigenetics · 2021Article
- Penalized partial least squares for pleiotropy.BMC bioinformatics · 2021Article
- Gene-Based Association Testing of Dichotomous Traits With Generalized Functional Linear Mixed Models Using Extended Pedigrees: Applications to Age-Related Macular Degeneration.Journal of the American Statistical Association · 2021Article
- An adaptive test for meta-analysis of rare variant association studies.Genetic epidemiology · 2020Article
- A novel gene-set association test based on variance-gamma distribution.Statistical methods in medical research · 2019Article
- An integrative U method for joint analysis of multi-level omic data.BMC genetics · 2019Article
- A generalized model for combining dependent SNP-level summary statistics and its extensions to statistics of other levels.Scientific reports · 2019Article
- Iterative hard thresholding for model selection in genome-wide association studies.Genetic epidemiology · 2017Article
- A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.Genetic epidemiology · 2017Article
- Gene- and pathway-based association tests for multiple traits with GWAS summary statistics.Bioinformatics (Oxford, England) · 2017Article
- On Robust Association Testing for Quantitative Traits and Rare Variants.G3 (Bethesda, Md.) · 2016Article
- A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.Genetic epidemiology · 2016Article
- Across-Platform Imputation of DNA Methylation Levels Incorporating Nonlocal Information Using Penalized Functional Regression.Genetic epidemiology · 2016Article
- Adaptive gene- and pathway-trait association testing with GWAS summary statistics.Bioinformatics (Oxford, England) · 2016Article
- Some pitfalls in application of functional data analysis approach to association studies.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.