ArticlePLoS genetics2017
Joint genetic analysis using variant sets reveals polygenic gene-context interactions.
Article in PLoS genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 21 citations in OpenAlex.
- A genome-wide association study to elucidate the genetic basis of the cottonseed protein content in Upland cotton (Breeding science · 2026Article
- HistoGWAS: an AI-enabled framework for automated genetic analysis of tissue phenotypes in histology cohorts.Genome biology · 2026Article
- IIIVmrMLM.QEI: An effective tool for indirect detection of QTN-by-environment interactions in genome-wide association studies.Computational and structural biotechnology journal · 2024Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS.Plants (Basel, Switzerland) · 2022Review
- sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression.Genome biology · 2020Article
- Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.Molecular systems biology · 2020Article
- A Robust Method Uncovers Significant Context-Specific Heritability in Diverse Complex Traits.American journal of human genetics · 2020Article
- Modeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis.Cell reports · 2019Article
- Article
- eQTL discovery and their association with severe equine asthma in European Warmblood horses.BMC genomics · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Joint genetic models for multiple traits have helped to enhance association analyses. Most existing multi-trait models have been designed to increase power for detecting associations, whereas the analysis of interactions has received considerably less attention. Here, we propose iSet, a method based on linear mixed models to test for interactions between sets of variants and environmental states or other contexts. Our model generalizes previous interaction tests and in particular provides a test for local differences in the genetic architecture between contexts. We first use simulations to validate iSet before applying the model to the analysis of genotype-environment interactions in an eQTL study. Our model retrieves a larger number of interactions than alternative methods and reveals that up to 20% of cases show context-specific configurations of causal variants. Finally, we apply iSet to test for sub-group specific genetic effects in human lipid levels in a large human cohort, where we identify a gene-sex interaction for C-reactive protein that is missed by alternative methods.
Indexed as
Identifiers
What OpenQuestion holds
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.