Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Anna S E Cuomo,Anna S E Cuomo,Giordano Alvari,Christina B. Azodi,Christina B. Azodi,Davis J. McCarthy,Davis J. McCarthy,Marc Jan Bonder +7 more
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TLDR
In this article, the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to optimize single-cell expression quantitative trait locus (sc-eQTL) mapping is evaluated.Abstract:
Background Single-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. Results While 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. Conclusion We 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.read more
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A compendium of uniformly processed human gene expression and splicing quantitative trait loci.
Nurlan Kerimov,James D. Hayhurst,Kateryna Peikova,Jonathan R. Manning,Peter Walter,Liis Kolberg,Marija Samoviča,Manoj Pandian Sakthivel,Ivan Kuzmin,Stephen J. Trevanion,Tony Burdett,Simon Jupp,Helen Parkinson,Irene Papatheodorou,Andrew D. Yates,Daniel R. Zerbino,Kaur Alasoo +16 more
TL;DR: The eQTL Catalogue as discussed by the authors is a set of gene expression quantitative trait locus (eQTL) studies published their summary statistics, which can be used to gain insight into complex human traits by downstream analyses, such as fine mapping and co-localization.
Journal ArticleDOI
Single-cell eQTL models reveal dynamic T cell state dependence of disease loci
Aparna Nathan,Samira Asgari,Kazuyoshi Ishigaki,Cristian Valencia,Tiffany Amariuta,Yang Luo,Jessica I. Beynor,Yuriy Baglaenko,Sara Suliman,Alkes L. Price,Leonid Lecca,Megan Murray,D. Branch Moody,Soumya Raychaudhuri +13 more
TL;DR: In this article , a single-cell Poisson model is used to analyse quantitative trait loci in memory T cells across continuous, dynamic cell states, revealing that the cell context is critical to understanding variation in eQTLs and their association with disease.
Journal ArticleDOI
Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation
TL;DR: In this article , the authors used single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines.
Journal ArticleDOI
<scp>CellRegMap</scp> : a statistical framework for mapping context‐specific regulatory variants using <scp>scRNA</scp> ‐seq
TL;DR: The Cell Regulatory Map (CellRegMap) as discussed by the authors is a statistical framework to identify and characterize genotype-context interactions of known eQTL variants using scRNA-seq data.
Journal ArticleDOI
PCA outperforms popular hidden variable inference methods for molecular QTL mapping
TL;DR: Zhou et al. as discussed by the authors used principal component analysis (PCA) for molecular quantitative trait locus (molecular QTL) analysis for improving the power of QTL identification.
References
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Posted ContentDOI
splatPop: simulating population scale single-cell RNA sequencing data
Christina B. Azodi,Christina B. Azodi,Luke Zappia,Alicia Oshlack,Alicia Oshlack,Davis J. McCarthy,Davis J. McCarthy +6 more
TL;DR: The splatPop model as mentioned in this paper allows for the simulation of complex batch effects, cell group effects, and conditional effects between individuals from different cohorts with known expression quantitative trait loci effects.
Posted ContentDOI
Genome-wide scale analyses identify novel BMI genotype-environment interactions using a conditional false discovery rate
Rachel Moore,Rachel Moore,Rachel Moore,L. Georgatou-Politou,James Liley,James Liley,Oliver Stegle,Oliver Stegle,Inês Barroso,Inês Barroso,Inês Barroso +10 more
TL;DR: This work combines the conditional false discovery rate with interaction test results obtained from StructLMM to test for G×E effects on BMI on a genome-wide scale whilst leveraging information from marginal associations in a flexible manner.