ArticleGenome biology2021
Optimizing expression quantitative trait locus mapping workflows for single-cell studies.
Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed, 73 citations in OpenAlex.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models.medRxiv : the preprint server for health sciences · 2026Article
- plinkQC: an integrated tool for ancestry inference, sample selection, and quality control in population genetics.Bioinformatics (Oxford, England) · 2026Article
- Assessing molecular gene by treatment interactions using a population of neural progenitors exposed to valproic acid and lithium.Molecular psychiatry · 2026Article
- Article
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Identifying Single-Cell Expression Quantitative Trait Loci Using a Bootstrap Penalized Hurdle Model.Genes · 2026Article
- Adipose single cell epigenome and transcriptome localize genetic risk for cardiometabolic disease and accelerated aging.Nature communications · 2026Article
- Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology.bioRxiv : the preprint server for biology · 2026Article
- scTWAS: a powerful statistical framework for single-cell transcriptome-wide association studies.Nature communications · 2026Article
- scDesignPop generates realistic population-scale single-cell RNA-seq for power analysis, benchmarking, and privacy protection.bioRxiv : the preprint server for biology · 2026Article
- Federated single-cell QTL meta-analysis reveals novel disease mechanisms.bioRxiv : the preprint server for biology · 2026Article
- STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.Frontiers in bioinformatics · 2026Article
- High cell-type specificity of eQTLs revealed by single-nucleus analyses of brain and blood.bioRxiv : the preprint server for biology · 2025Article
- Article
- scTWAS: A powerful statistical framework for single-cell transcriptome-wide association studies.Research square · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- simPIC:flexible simulation of paired-insertion counts for single-cell ATAC sequencing data.bioRxiv : the preprint server for biology · 2025Article
- ti-scMR: trajectory-inference-based dynamic single-cell Mendelian randomization identifies causal genes underlying phenotypic differences.NAR genomics and bioinformatics · 2025Article
- Unveiling genetic signatures of immune response in immune-related diseases through single-cell eQTL analysis across diverse conditions.Nature communications · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSingle-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease.
resultsWhile previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches.
conclusionWe provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.
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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.