Genetic mapping of cell type specificity for complex traits

• Published in: Nature communications Vol. 10; no. 1; pp. 3222 - 13
• Main Authors: Watanabe, Kyoko, Umićević Mirkov, Maša, de Leeuw, Christiaan A., van den Heuvel, Martijn P., Posthuma, Danielle
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.07.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/208/199; 631/208/205/2138; Algorithms; Base Sequence; Chromosome Mapping; Cluster Analysis; Computational Biology; Databases, Nucleic Acid; Datasets; Gene expression; Gene Expression - genetics; Gene Expression Profiling; Gene mapping; Gene sequencing; Genome-wide association studies; Genome-Wide Association Study; Humanities and Social Sciences; Humans; Mapping; multidisciplinary; Multifactorial Inheritance; Ribonucleic acid; RNA; RNA, Small Cytoplasmic - genetics; Science; Science (multidisciplinary); Sensitivity and Specificity; Sequence Analysis, RNA - methods; Transcriptome; Workflow
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-11181-1

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.