ArticleAmerican journal of human genetics2019
Using Transcriptomic Hidden Variables to Infer Context-Specific Genotype Effects in the Brain.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- A Pathway-Level Information ExtractoR (PLIER) framework to gain mechanistic insights into obesity in Down syndrome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Genotype × environment interactions in gene regulation and complex traits.Nature genetics · 2024Review
- Genetic variation and pesticide exposure influence blood DNA methylation signatures in females with early-stage Parkinson's disease.NPJ Parkinson's disease · 2024Article
- Mitochondrial DNA variation in Alzheimer's disease reveals a unique microprotein called SHMOOSE.Molecular psychiatry · 2023Article
- Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases.Nature genetics · 2023Article
- DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk.Genome biology · 2022Article
- Likelihood ratio statistics for gene set enrichment in Alzheimer's disease pathways.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2021Article
- Considerations for integrative multi-omic approaches to explore Alzheimer's disease mechanisms.Brain pathology (Zurich, Switzerland) · 2020Review
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
Deciphering the environmental contexts at which genetic effects are most prominent is central for making full use of GWAS results in follow-up experiment design and treatment development. However, measuring a large number of environmental factors at high granularity might not always be feasible. Instead, here we propose extracting cellular embedding of environmental factors from gene expression data by using latent variable (LV) analysis and taking these LVs as environmental proxies in detecting gene-by-environment (GxE) interaction effects on gene expression, i.e., GxE expression quantitative trait loci (eQTLs). Applying this approach to two largest brain eQTL datasets (n = 1,100), we show that LVs and GxE eQTLs in one dataset replicate well in the other dataset. Combining the two samples via meta-analysis, 895 GxE eQTLs are identified. On average, GxE effect explains an additional ∼4% variation in expression of each gene that displays a GxE effect. Ten of these 52 genes are associated with cell-type-specific eQTLs, and the remaining genes are multi-functional. Furthermore, after substituting LVs with expression of transcription factors (TF), we found 91 TF-specific eQTLs, which demonstrates an important use of our brain GxE eQTLs.
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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.