ArticleAmerican journal of human genetics2013
Sherlock: detecting gene-disease associations by matching patterns of expression QTL and GWAS.
Article in American journal of human genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 164 papers, 8 of them syntheses that pooled it.
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Who cites it
164 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- A systematic review of single nucleotide polymorphisms affecting allopurinol pharmacokinetics and serum uric acid level.Pharmacogenomics · 2024Pooled it
- Integrative Genome-Wide Association Studies of eQTL and GWAS Data for Gout Disease Susceptibility.Scientific reports · 2019Pooled it
- Genetic regulation of gene expression in the epileptic human hippocampus.Human molecular genetics · 2017Pooled it
- Common variants at PVT1, ATG13-AMBRA1, AHI1 and CLEC16A are associated with selective IgA deficiency.Nature genetics · 2016Pooled it
- Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes.Scientific reports · 2016Pooled it
- A meta-analysis strategy for gene prioritization using gene expression, SNP genotype, and eQTL data.BioMed research international · 2015Pooled it
- A meta-analysis of gene expression quantitative trait loci in brain.Translational psychiatry · 2014Pooled it
- Bayesian test for colocalisation between pairs of genetic association studies using summary statistics.PLoS genetics · 2014Pooled it
- Integrating single-cell sQTL mapping with deep-learning splicing prediction identifies causal variants under influenza infection.Genome biology · 2026Article
- Beyond the baseline: mapping the context-specific regulatory landscape of disease.Trends in genetics : TIG · 2026Review
- Host genotypes interact with microbial communities to modulate gene expression in the human intestine.medRxiv : the preprint server for health sciences · 2026Article
- Convergence and divergence of genes informed by common and rare variants of autism spectrum disorders in tissue-specific pathways and gene networks.Translational psychiatry · 2026Article
- Causal splicing variants revealed by deep-learning integration of single-cell sQTL mapping under influenza infection.Research square · 2026Article
- A review of post-GWAS studies in schizophrenia.Translational psychiatry · 2025Review
- Genetic and transcriptional insights reveal hepatitis C virus as a driver of kidney cancer.Infectious agents and cancer · 2025Article
- Genetic and immune landscape of keratoconus: insights from Mendelian randomization analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Deciphering the Potential Causal and Prognostic Relationships Between Gut Microbiota and Brain Tumors: Insights from Genetics Analysis and Machine Learning.Exploration (Beijing, China) · 2025Article
- Dynamics of mitochondrial DNA copy number regulation in relation to gastric cancer survival.Discover oncology · 2025Article
- Trans-eQTL hotspots shape complex traits by modulating cellular states.Cell genomics · 2025Article
- Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brain.medRxiv : the preprint server for health sciences · 2025Article
104 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Genetic mapping of complex diseases to date depends on variations inside or close to the genes that perturb their activities. A strong body of evidence suggests that changes in gene expression play a key role in complex diseases and that numerous loci perturb gene expression in trans. The information in trans variants, however, has largely been ignored in the current analysis paradigm. Here we present a statistical framework for genetic mapping by utilizing collective information in both cis and trans variants. We reason that for a disease-associated gene, any genetic variation that perturbs its expression is also likely to influence the disease risk. Thus, the expression quantitative trait loci (eQTL) of the gene, which constitute a unique "genetic signature," should overlap significantly with the set of loci associated with the disease. We translate this idea into a computational algorithm (named Sherlock) to search for gene-disease associations from GWASs, taking advantage of independent eQTL data. Application of this strategy to Crohn disease and type 2 diabetes predicts a number of genes with possible disease roles, including several predictions supported by solid experimental evidence. Importantly, predicted genes are often implicated by multiple trans eQTL with moderate associations. These genes are far from any GWAS association signals and thus cannot be identified from the GWAS alone. Our approach allows analysis of association data from a new perspective and is applicable to any complex phenotype. It is readily generalizable to molecular traits other than gene expression, such as metabolites, noncoding RNAs, and epigenetic modifications.
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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 OpenQuestion graph.