Genetic mapping of cell type specificity for complex traits
| Autor: | Danielle Posthuma, Christiaan de Leeuw, Martijn P. van den Heuvel, Maša Umićević Mirkov, Kyoko Watanabe |
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| Přispěvatelé: | Complex Trait Genetics, Amsterdam Neuroscience - Complex Trait Genetics, Human genetics, Amsterdam Reproduction & Development (AR&D) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Multifactorial Inheritance Cell type Computer science Science General Physics and Astronomy Genome-wide association study 02 engineering and technology Computational biology Sensitivity and Specificity Genome-wide association studies General Biochemistry Genetics and Molecular Biology Article Workflow Transcriptome 03 medical and health sciences Gene mapping RNA Small Cytoplasmic Cluster Analysis Humans Functional studies Author Correction lcsh:Science Genetic association Multidisciplinary Base Sequence Sequence Analysis RNA Gene Expression Profiling Chromosome Mapping Computational Biology General Chemistry 021001 nanoscience & nanotechnology Computational biology and bioinformatics Gene expression profiling 030104 developmental biology lcsh:Q Gene expression Databases Nucleic Acid 0210 nano-technology Algorithms Genome-Wide Association Study |
| Zdroj: | Nature Communications, Vol 10, Iss 1, Pp 1-13 (2019) Nature Communications Watanabe, K, Umićević Mirkov, M, de Leeuw, C A, van den Heuvel, M P & Posthuma, D 2019, ' Genetic mapping of cell type specificity for complex traits ', Nature Communications, vol. 10, no. 1, 3222 . https://doi.org/10.1038/s41467-019-11181-1 Nature Communications, 10(1):3222. Nature Publishing Group |
| ISSN: | 2041-1723 |
| Popis: | Single-cell RNA sequencing (scRNA-seq) data allows to create cell type specific transcriptome profiles. Such profiles can be aligned with genome-wide association studies (GWASs) to implicate cell type specificity of the traits. Current methods typically rely only on a small subset of available scRNA-seq datasets, and integrating multiple datasets is hampered by complex batch effects. Here we collated 43 publicly available scRNA-seq datasets. We propose a 3-step workflow with conditional analyses within and between datasets, circumventing batch effects, to uncover associations of traits with cell types. Applying this method to 26 traits, we identify independent associations of multiple cell types. These results lead to starting points for follow-up functional studies aimed at gaining a mechanistic understanding of these traits. The proposed framework as well as the curated scRNA-seq datasets are made available via an online platform, FUMA, to facilitate rapid evaluation of cell type specificity by other researchers. Tissue- and cell type-specific information helps to interpret findings from genome-wide association studies. Here, the authors leverage multiple single cell expression datasets to infer cell type specificity of traits. |
| Databáze: | OpenAIRE |
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