ArticleAmerican journal of human genetics2020
A Multi-tissue Transcriptome Analysis of Human Metabolites Guides Interpretability of Associations Based on Multi-SNP Models for Gene Expression.
Article in American journal of human genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Multiomic integration analysis identifies atherogenic metabolites mediating between novel immune genes and cardiovascular risk.Genome medicine · 2024Pooled it
- Therapeutic Targets for Heart Failure Identified Using Proteomics and Mendelian Randomization.Circulation · 2022Pooled it
- Missing Regulation Between Genetic Association and Transcriptional Abundance for Hypercholesterolemia Genes.Genes · 2025Article
- A scoping review of statistical methods to investigate colocalization between genetic associations and microRNA expression in osteoarthritis.Osteoarthritis and cartilage open · 2024Article
- Leveraging large-scale multi-omics evidences to identify therapeutic targets from genome-wide association studies.BMC genomics · 2024Article
- Adjusting for genetic confounders in transcriptome-wide association studies improves discovery of risk genes of complex traits.Nature genetics · 2024Article
- Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits.Nature communications · 2023Article
- Multiomic Analysis and CRISPR Perturbation Screens Identify Endothelial Cell Programs and Novel Therapeutic Targets for Coronary Artery Disease.Arteriosclerosis, thrombosis, and vascular biology · 2023Review
- Stressing the importance of plant specialized metabolites: omics-based approaches for discovering specialized metabolism in plant stress responses.Frontiers in plant science · 2023Review
- Integrating transcriptomics, metabolomics, and GWAS helps reveal molecular mechanisms for metabolite levels and disease risk.American journal of human genetics · 2022Article
- Multi-context genetic modeling of transcriptional regulation resolves novel disease loci.Nature communications · 2022Article
- Genetic regulation of RNA splicing in human pancreatic islets.Genome biology · 2022Article
- Analyzing and reconciling colocalization and transcriptome-wide association studies from the perspective of inferential reproducibility.American journal of human genetics · 2022Article
- Cis-regulatory architecture of human ESC-derived hypothalamic neuron differentiation aids in variant-to-gene mapping of relevant complex traits.Nature communications · 2021Article
- Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression.American journal of human genetics · 2021Article
- Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders.Nature neuroscience · 2021Article
- Identifying therapeutic drug targets using bidirectional effect genes.Nature communications · 2021Article
- Genomic-Metabolomic Associations Support the Role ofOphthalmology science · 2021Article
- Genome-wide association study implicates novel loci and reveals candidate effector genes for longitudinal pediatric bone accrual.Genome biology · 2021Article
- Genetic analyses support the contribution of mRNA NNature genetics · 2020Article
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
There is particular interest in transcriptome-wide association studies (TWAS) gene-level tests based on multi-SNP predictive models of gene expression-for identifying causal genes at loci associated with complex traits. However, interpretation of TWAS associations may be complicated by divergent effects of model SNPs on phenotype and gene expression. We developed an iterative modeling scheme for obtaining multi-SNP models of gene expression and applied this framework to generate expression models for 43 human tissues from the Genotype-Tissue Expression (GTEx) Project. We characterized the performance of single- and multi-SNP models for identifying causal genes in GWAS data for 46 circulating metabolites. We show that: (A) multi-SNP models captured more variation in expression than did the top cis-eQTL (median 2-fold improvement); (B) predicted expression based on multi-SNP models was associated (false discovery rate < 0.01) with metabolite levels for 826 unique gene-metabolite pairs, but, after stepwise conditional analyses, 90% were dominated by a single eQTL SNP; (C) among the 35% of associations where a SNP in the expression model was a significant cis-eQTL and metabolomic-QTL (met-QTL), 92% demonstrated colocalization between these signals, but interpretation was often complicated by incomplete overlap of QTLs in multi-SNP models; and (D) using a "truth" set of causal genes at 61 met-QTLs, the sensitivity was high (67%), but the positive predictive value was low, as only 8% of TWAS associations (19% when restricted to colocalized associations at met-QTLs) involved true causal genes. These results guide the interpretation of TWAS and highlight the need for corroborative data to provide confident assignment of causality.
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