ArticleGenome research2011
Association studies for next-generation sequencing.
Article in Genome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
63 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
- Research on multi-trait genome association study method based on Shannon information entropy.BMC bioinformatics · 2026Article
- An overview of recent technological developments in bovine genomics.Veterinary and animal science · 2024Review
- Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework.Mathematics (Basel, Switzerland) · 2023Article
- A tree-based gene-environment interaction analysis with rare features.Statistical analysis and data mining · 2022Article
- VIVID: A Web Application for Variant Interpretation and Visualization in Multi-dimensional Analyses.Molecular biology and evolution · 2022Article
- Protein Sequencing, One Molecule at a Time.Annual review of biophysics · 2022Review
- Integrative functional linear model for genome-wide association studies with multiple traits.Biostatistics (Oxford, England) · 2022Article
- A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation.Frontiers in genetics · 2021Review
- 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
- Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis.Cellular immunology · 2020Review
- Gene-based association analysis for bivariate time-to-event data through functional regression with copula models.Biometrics · 2020Article
- Targeted next generation sequencing of nine osteoporosis-related genes in the Wnt signaling pathway among Chinese postmenopausal women.Endocrine · 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
- Linear mixed models for association analysis of quantitative traits with next-generation sequencing data.Genetic epidemiology · 2019Article
- Variants in FAT1 and COL9A1 genes in male population with or without substance use to assess the risk factors for oral malignancy.PloS one · 2019Article
- Longitudinal data analysis for rare variants detection with penalized quadratic inference function.Scientific reports · 2017Article
- A comparison study of multivariate fixed models and Gene Association with Multiple Traits (GAMuT) for next-generation sequencing.Genetic epidemiology · 2017Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Genome-wide association studies (GWAS) have become the primary approach for identifying genes with common variants influencing complex diseases. Despite considerable progress, the common variations identified by GWAS account for only a small fraction of disease heritability and are unlikely to explain the majority of phenotypic variations of common diseases. A potential source of the missing heritability is the contribution of rare variants. Next-generation sequencing technologies will detect millions of novel rare variants, but these technologies have three defining features: identification of a large number of rare variants, a high proportion of sequence errors, and a large proportion of missing data. These features raise challenges for testing the association of rare variants with phenotypes of interest. In this study, we use a genome continuum model and functional principal components as a general principle for developing novel and powerful association analysis methods designed for resequencing data. We use simulations to calculate the type I error rates and the power of nine alternative statistics: two functional principal component analysis (FPCA)-based statistics, the multivariate principal component analysis (MPCA)-based statistic, the weighted sum (WSS), the variable-threshold (VT) method, the generalized T(2), the collapsing method, the CMC method, and individual tests. We also examined the impact of sequence errors on their type I error rates. Finally, we apply the nine statistics to the published resequencing data set from ANGPTL4 in the Dallas Heart Study. We report that FPCA-based statistics have a higher power to detect association of rare variants and a stronger ability to filter sequence errors than the other seven methods.
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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 Socratic graph.