ArticleJournal of medical genetics2012
Quantitative trait locus analysis for next-generation sequencing with the functional linear models.
Article in Journal of medical genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 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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- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
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- Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model.Frontiers in genetics · 2022Article
- 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
- Identifying rare variants for quantitative traits in extreme samples of population via Kullback-Leibler distance.BMC genetics · 2020Article
- Gene-based association analysis for bivariate time-to-event data through functional regression with copula models.Biometrics · 2020Article
- [Ultrasound subgingival scaling combined with manual root planing for treatment of chronic periodontitis in elderly patients].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2020Article
- Adaptive Fisher method detects dense and sparse signals in association analysis of SNV sets.BMC medical genomics · 2020Article
- Gene-based association analysis of survival traits via functional regression-based mixed effect cox models for related samples.Genetic epidemiology · 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
- Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.Genetic epidemiology · 2019Article
- Article
- Detecting Multiethnic Rare Variants.Methods in molecular biology (Clifton, N.J.) · 2017Article
- A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.Genetic epidemiology · 2017Article
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3 authors.
Funding
Abstract
backgroundAlthough in the past few years we have witnessed the rapid development of novel statistical methods for association studies of qualitative traits using next generation sequencing (NGS) data, only a few statistics are proposed for testing the association of rare variants with quantitative traits. The quantitative trait locus (QTL) analysis of rare variants remains challenging. Analysis from low dimensional data to high dimensional genomic data demands changes in statistical methods from multivariate data analysis to functional data analysis.
methodsWe propose a functional linear model (FLM) as a general principle for developing novel and powerful QTL analysis methods designed for resequencing data. By simulations we calculated the type I error rates and evaluated the power of the FLM and other eight existing statistical methods, even in the presence of both positive and negative signs of effects.
resultsSince the FLM retains all of the genetic information in the data and explores the merits of both variant-by-variant and collective analysis and overcomes their limitation, the FLM has a much higher power than other existing statistics in all the scenarios considered. To evaluate its performance further, the FLM was applied to association analysis of six quantitative traits in the Dallas Heart Study, and RNA-seq eQTL analysis with genetic variation in the low coverage resequencing data of the 1000 Genomes Project. Real data analysis showed that the FLM had much smaller p values to identify significantly associated variants than other existing methods.
conclusionsThe FLM is expected to open a new route for QTL analysis.
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