ArticleEuropean journal of human genetics : EJHG2013
Smoothed functional principal component analysis for testing association of the entire allelic spectrum of genetic variation.
Article in European journal of human genetics : EJHG, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 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
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- The heterogeneity of NOTCH1 to tumor immune infiltration in pan-cancer.Scientific reports · 2024Article
- A genome-wide comprehensive analysis of nucleosome positioning in yeast.PLoS computational biology · 2024Article
- 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
- A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation.Frontiers in genetics · 2021Review
- Gene-based association analysis for bivariate time-to-event data through functional regression with copula models.Biometrics · 2020Article
- Fifteen Years of Gene Set Analysis for High-Throughput Genomic Data: A Review of Statistical Approaches and Future Challenges.Entropy (Basel, Switzerland) · 2020Review
- Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.Genetic epidemiology · 2019Article
- Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.Genetic epidemiology · 2019Article
- A quadratically regularized functional canonical correlation analysis for identifying the global structure of pleiotropy with NGS data.PLoS computational biology · 2017Article
- Sharing of Genes and Pathways Across Complex Phenotypes: A Multilevel Genome-Wide Analysis.Genetics · 2017Article
- A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.Genetic epidemiology · 2017Article
- A Comparison Study of Fixed and Mixed Effect Models for Gene Level Association Studies of Complex Traits.Genetic epidemiology · 2016Article
- Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models.Genetic epidemiology · 2016Article
- Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.Genetic epidemiology · 2016Article
- Generalized functional linear models for gene-based case-control association studies.Genetic epidemiology · 2014Article
- Functional linear models for association analysis of quantitative traits.Genetic epidemiology · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Fast and cheaper next-generation sequencing technologies will generate unprecedentedly massive and highly dimensional genetic variation data that allow nearly complete evaluation of genetic variation including both common and rare variants. There are two types of association tests: variant-by-variant test and group test. The variant-by-variant test is designed to test the association of common variants, while the group test is suitable to collectively test the association of multiple rare variants. We propose here a smoothed functional principal component analysis (SFPCA) statistic as a general approach for testing association of the entire allelic spectrum of genetic variation (both common and rare variants), which utilizes the merits of both variant-by-variant analysis and group tests. By intensive simulations, we demonstrate that the SFPCA statistic has the correct type 1 error rates and much higher power than the existing methods to detect association of (1) common variants, (2) rare variants, (3) both common and rare variants and (4) variants with opposite directions of effects. To further evaluate its performance, the SFPCA statistic is applied to ANGPTL4 sequence and six continuous phenotypes data from the Dallas Heart Study as an example for testing association of rare variants and a GWAS of schizophrenia data as an example for testing association of common variants. The results show that the SFPCA statistic has much smaller P-values than many existing statistics in both real data analysis examples.
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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.