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From peas to GWAS
Published:
From peas to GWAS
A database of single-cell eQTL studies
Published:
Curating a database for single-cell eQTL studies
publications
Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression
Published in Nature Communications, 2020
We studied the effect of common genetic variant on endoderm differentiation of iPSCs at single cell resolution
Recommended citation: Cuomo ASE*, Seaton DD*, McCarthy DJ* et al. (2020). Nature Communications. 11, 810. https://www.nature.com/articles/s41467-020-14457-z
Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation
Published in Nature Genetics, 2021
We studied iPSC differentiation toward dopaminergic neurons across over 200 individuals at single cell resolution.
Recommended citation: Jerber J*, Seaton DD*, Cuomo ASE* et al (2021). "Population-scale single-cell RNA-seq profiling across dopaminergic neuron differentiation." Nature Genetics. 53, 304-312. https://www.nature.com/articles/s41588-021-00801-6
Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
Published in Nature Medicine, 2021
Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics
Recommended citation: Muus C et al. (2021). Nature Medicine. 27, 546–559. https://www.nature.com/articles/s41591-020-01227-z
Computational principles and challenges in single-cell data integration
Published in Nature Biotechnology, 2021
In this review, we provide a framework to categorise different forms of single-cell data integration and explore future challenges for the field.
Recommended citation: Argelaguet R, Cuomo ASE, Stegle O and Marioni JC (2021). "Computational principles and challenges in single-cell data integration." Nature Biotechnology. 39, 1202-1215. https://www.nature.com/articles/s41587-021-00895-7
Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Published in Genome Biology, 2021
We evaluate the role of different normalisation and aggregation strategies, covariate adjustments techniques, and multiple testing correction methods to optimise adaptation of standard (bulk) eQTL methods to single-cell data
Recommended citation: Cuomo ASE*, Alvari G*, Azodi CB* et al. (2021). "Optimizing expression quantitative trait locus mapping workflows for single-cell studies." Genome Biology. 22, 188 https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02407-x
CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq
Published in Molecular Systems Biology, 2022
We propose a novel method to map context-specific eQTLs using single-cell data, without the need for individual cells to be grouped a priori into discrete cell types
Recommended citation: Cuomo ASE*, Heinen T* et al. (2022). "CellRegMap: A statistical framework for mapping context-specific regulatory variants using scRNA-seq." Molecular Systems Biology. 18:e10663 https://www.embopress.org/doi/full/10.15252/msb.202110663
Single-cell genomics meets human genetics
Published in Nature Reviews Genetics, 2023
In this review, we provide an overview of current single-cell genetic studies and explore future challenges and opportunities for the field.
Recommended citation: Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG and Powell JE (2023). "Single-cell genomics meets human genetics." Nature Reviews Genetics. https://www.nature.com/articles/s41576-023-00599-5
Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases
Published in Nature Genetics, 2024
In this review, we provide a perspective of the powerful combination of stem cell systems, single cell technologies, and population studies to better understand the biology of traits and diseases.
Recommended citation: Farbehi N, Neavin DR, Cuomo ASE, Studer L, MacArthur DG and Powell JE (2024). "Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases." Nature Genetics. https://www.nature.com/articles/s41588-024-01731-9