ArticleGenetics2011
Bayesian detection of expression quantitative trait loci hot spots.
Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.Genetic epidemiology · 2016Pooled it
- Integration of expression QTLs with fine mapping via SuSiE.PLoS genetics · 2024Article
- Integration of Expression QTLs with fine mapping via SuSiE.medRxiv : the preprint server for health sciences · 2023Article
- A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery.Journal of the Royal Statistical Society. Series C, Applied statistics · 2021Article
- A trans locus causes a ribosomopathy in hypertrophic hearts that affects mRNA translation in a protein length-dependent fashion.Genome biology · 2021Article
- EPISPOT: An epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies.American journal of human genetics · 2021Article
- A simple new approach to variable selection in regression, with application to genetic fine mapping.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2020Article
- A fully joint Bayesian quantitative trait locus mapping of human protein abundance in plasma.PLoS computational biology · 2020Article
- A Global-Local Approach for Detecting Hotspots in Multiple-Response Regression.The annals of applied statistics · 2020Article
- WWP2 regulates pathological cardiac fibrosis by modulating SMAD2 signaling.Nature communications · 2019Article
- Systems genetics identifies a macrophage cholesterol network associated with physiological wound healing.JCI insight · 2019Article
- Systems Genetics Approaches in Rat Identify Novel Genes and Gene Networks Associated With Cardiac Conduction.Journal of the American Heart Association · 2018Article
- Genomic approaches for the elucidation of genes and gene networks underlying cardiovascular traits.Biophysical reviews · 2018Review
- A two-step method for variable selection in the analysis of a case-cohort study.International journal of epidemiology · 2018Article
- Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data.Bioinformatics (Oxford, England) · 2017Article
- The nephrologist of tomorrow: towards a kidney-omic future.Pediatric nephrology (Berlin, Germany) · 2017Review
- From integrative genomics to systems genetics in the rat to link genotypes to phenotypes.Disease models & mechanisms · 2016Review
- Statistical Methods in Integrative Genomics.Annual review of statistics and its application · 2016Article
- MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.Bioinformatics (Oxford, England) · 2016Article
- Genetic Variant Selection: Learning Across Traits and Sites.Genetics · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
High-throughput genomics allows genome-wide quantification of gene expression levels in tissues and cell types and, when combined with sequence variation data, permits the identification of genetic control points of expression (expression QTL or eQTL). Clusters of eQTL influenced by single genetic polymorphisms can inform on hotspots of regulation of pathways and networks, although very few hotspots have been robustly detected, replicated, or experimentally verified. Here we present a novel modeling strategy to estimate the propensity of a genetic marker to influence several expression traits at the same time, based on a hierarchical formulation of related regressions. We implement this hierarchical regression model in a Bayesian framework using a stochastic search algorithm, HESS, that efficiently probes sparse subsets of genetic markers in a high-dimensional data matrix to identify hotspots and to pinpoint the individual genetic effects (eQTL). Simulating complex regulatory scenarios, we demonstrate that our method outperforms current state-of-the-art approaches, in particular when the number of transcripts is large. We also illustrate the applicability of HESS to diverse real-case data sets, in mouse and human genetic settings, and show that it provides new insights into regulatory hotspots that were not detected by conventional methods. The results suggest that the combination of our modeling strategy and algorithmic implementation provides significant advantages for the identification of functional eQTL hotspots, revealing key regulators underlying pathways.
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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.