ArticlePLoS genetics2011
Hierarchical generalized linear models for multiple groups of rare and common variants: jointly estimating group and individual-variant effects.
Article in PLoS genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- The associations of human genetic variations with airway microbiome, environmental exposures, and respiratory health.mSystems · 2026Article
- How being synanthropic affects the gut bacteriome and mycobiome: comparison of two mouse species with contrasting ecologies.BMC microbiology · 2020Article
- A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.Bioinformatics (Oxford, England) · 2020Article
- Identifying individual risk rare variants using protein structure guided local tests (POINT).PLoS computational biology · 2019Article
- Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.Genetic epidemiology · 2017Article
- On Robust Association Testing for Quantitative Traits and Rare Variants.G3 (Bethesda, Md.) · 2016Article
- Genetic Variant Selection: Learning Across Traits and Sites.Genetics · 2016Article
- Identifying rare and common variants with Bayesian variable selection.BMC proceedings · 2016Article
- Associating Multivariate Quantitative Phenotypes with Genetic Variants in Family Samples with a Novel Kernel Machine Regression Method.Genetics · 2015Article
- Article
- Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples.Human heredity · 2015Article
- Reproducible simulations of realistic samples for next-generation sequencing studies using Variant Simulation Tools.Genetic epidemiology · 2015Article
- A Sequence Kernel Association Test for Dichotomous Traits in Family Samples under a Generalized Linear Mixed Model.Human heredity · 2015Article
- A unified sparse representation for sequence variant identification for complex traits.Genetic epidemiology · 2014Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- Article
- FamLBL: detecting rare haplotype disease association based on common SNPs using case-parent triads.Bioinformatics (Oxford, England) · 2014Article
- A powerful and adaptive association test for rare variants.Genetics · 2014Article
- Kernel-machine testing coupled with a rank-truncation method for genetic pathway analysis.Genetic epidemiology · 2014Article
- Genetic association analysis of 30 genes related to obesity in a European American population.International journal of obesity (2005) · 2014Article
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4 authors.
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Abstract
Complex diseases and traits are likely influenced by many common and rare genetic variants and environmental factors. Detecting disease susceptibility variants is a challenging task, especially when their frequencies are low and/or their effects are small or moderate. We propose here a comprehensive hierarchical generalized linear model framework for simultaneously analyzing multiple groups of rare and common variants and relevant covariates. The proposed hierarchical generalized linear models introduce a group effect and a genetic score (i.e., a linear combination of main-effect predictors for genetic variants) for each group of variants, and jointly they estimate the group effects and the weights of the genetic scores. This framework includes various previous methods as special cases, and it can effectively deal with both risk and protective variants in a group and can simultaneously estimate the cumulative contribution of multiple variants and their relative importance. Our computational strategy is based on extending the standard procedure for fitting generalized linear models in the statistical software R to the proposed hierarchical models, leading to the development of stable and flexible tools. The methods are illustrated with sequence data in gene ANGPTL4 from the Dallas Heart Study. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
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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.