ArticleJournal of the American Society of Nephrology : JASN2020
Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits.
Article in Journal of the American Society of Nephrology : JASN, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals.Nature communications · 2021Pooled it
- Integrating bulk and single-cell RNA sequencing with GWAS reveals regulatory networks underpinning complex traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- Single-cell RNA sequencing in diabetic kidney disease: a literature review.Renal failure · 2024Review
- Article
- Mother-child correlation of kidney function: data from the Yamanashi Adjunct Study of Japan Environment and Children's Study (JECS).Pediatric nephrology (Berlin, Germany) · 2024Article
- KidneyNetwork: using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease.European journal of human genetics : EJHG · 2023Article
- Article
- Transcriptome- and proteome-wide association studies nominate determinants of kidney function and damage.Genome biology · 2023Article
- Regulon analysis identifies protective FXR and CREB5 in proximal tubules in early diabetic kidney disease.BMC nephrology · 2023Article
- Migration Rates on Swim Plates Vary between Escherichia coli Soil Isolates: Differences Are Associated with Variants in Metabolic Genes.Applied and environmental microbiology · 2023Article
- Integration of artificial intelligence and multi-omics in kidney diseases.Fundamental research · 2023Review
- Review
- Singling out motor neurons in the age of single-cell transcriptomics.Trends in genetics : TIG · 2022Review
- Mapping the single-cell transcriptomic response of murine diabetic kidney disease to therapies.Cell metabolism · 2022Article
- Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease.Nature genetics · 2022Article
- Integrating variant functional annotation scores have varied abilities to improve power of genome-wide association studies.Scientific reports · 2022Article
- Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease.Frontiers in nephrology · 2022Review
- Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments.Nature genetics · 2021Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.
methodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score-based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.
resultsAcross 53 tissues, genes in kidney function-associated GWAS loci were enriched in kidney (
conclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.
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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 Socratic graph.