ArticleNature communications2022
SUMMIT: An integrative approach for better transcriptomic data imputation improves causal gene identification.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- A large-scale genome-wide association meta-analysis for nevus count provides direct insights into the genetics of melanoma.Nature communications · 2026Pooled it
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.bioRxiv : the preprint server for biology · 2026Article
- Uncovering the Genetic Architecture of Optic Nerve Integrity Estimates through Genome-wide Association Study Meta-analyses.medRxiv : the preprint server for health sciences · 2026Article
- Transcriptome-wide association studies at cell-state level using single-cell eQTL data.Cell genomics · 2026Article
- Personalized gene expression prediction in the era of deep learning: a review.Briefings in bioinformatics · 2026Review
- AlphaGenome Enhances Personal Gene Expression Prediction but Retains Key Limitations.Research square · 2025Article
- TransferTWAS: A transfer learning framework for cross-tissue transcriptome-wide association study.American journal of human genetics · 2025Article
- The integration of genome-wide and transcriptome-wide association studies in neurodegenerative diseases: opportunities, challenges, and current methodological innovations.Briefings in bioinformatics · 2025Review
- An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment.Cell genomics · 2025Article
- Integrative Multi-Omics Approach for Improving Causal Gene Identification.Genetic epidemiology · 2025Article
- Multiome-wide Association Studies: Novel Approaches for Understanding Diseases.Genomics, proteomics & bioinformatics · 2024Review
- MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations.Epigenetics · 2024Article
- Omnibus proteome-wide association study identifies 43 risk genes for Alzheimer disease dementia.American journal of human genetics · 2024Article
- The goldmine of GWAS summary statistics: a systematic review of methods and tools.BioData mining · 2024Article
- Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes.Nature communications · 2024Article
- SUMMIT-FA: a new resource for improved transcriptome imputation using functional annotations.Human molecular genetics · 2024Article
- A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.American journal of human genetics · 2024Article
- Penalized Regression Based Proteome-Wide Association Study Reveals Potential Population-Specific Drug Targets for Alzheimer's Disease in European and African American Cohorts.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective.Human genetics · 2023Review
- Transcriptome-wide association studies: recent advances in methods, applications and available databases.Communications biology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the relatively small available reference datasets for developing expression genetic prediction models to capture the moderate to low genetically regulated components of gene expression. Here, we introduce a method, the Summary-level Unified Method for Modeling Integrated Transcriptome (SUMMIT), to improve the expression prediction model accuracy and the power of TWAS by using a large expression quantitative trait loci (eQTL) summary-level dataset. We apply SUMMIT to the eQTL summary-level data provided by the eQTLGen consortium. Through simulation studies and analyses of genome-wide association study summary statistics for 24 complex traits, we show that SUMMIT improves the accuracy of expression prediction in blood, successfully builds expression prediction models for genes with low expression heritability, and achieves higher statistical power than several benchmark methods. Finally, we conduct a case study of COVID-19 severity with SUMMIT and identify 11 likely causal genes associated with COVID-19 severity.
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.