Biology subjects

Rouhana, J. M.

Publications and source records attributed to Rouhana, J. M..

3 recordsLinked to original sources

bioRxiv2021

ECLIPSER: identifying causal cell types and genes for complex traits through single cell enrichment of e/sQTL-mapped genes in GWAS loci

SummaryECLIPSER was developed to identify pathogenic cell types and cell type-specific genes that may affect complex disease susceptibility and trait variation by integrating single cell data with known GWAS loci. ECLIPSER maps genes to GWAS loci for a given complex trait based on expression and splicing quantitative trait loci (e/sQTLs) and other functional data, and tests whether the mapped genes are enriched for cell type-specific expression in particular cell types using single-cell/nucleus RNA-seq data from one or more tissues of interest. A Bayesian Fishers exact test is used to compute fold-enrichment significance. We demonstrate the application of ECLIPSER on various skin diseases and traits using snRNA-seq of healthy human skin samples. Availability and ImplementationThe source code and documentation for ECLIPSER and a Jupyter notebook for generating output tables and figures are available at https://github.com/segrelabgenomics/ECLIPSER. The source code for GWASvar2gene that maps genes to GWAS loci based on e/sQTLs is available at https://github.com/segrelabgenomics/GWASvar2gene. The analysis presented here used data from GTEx (https://gtexportal.org/home/datasets) and Open Targets Genetics (https://genetics-docs.opentargets.org/data-access/graphql-api), but can also be applied to other GWAS variant lists and QTL studies. Data used to reproduce the results of the paper are available in Supplementary data.

bioinformatics↗

bioRxiv2021

Single-nucleus cross-tissue molecular reference maps to decipher disease gene function

Understanding the function of genes and their regulation in tissue homeostasis and disease requires knowing the cellular context in which genes are expressed in tissues across the body. Single cell genomics allows the generation of detailed cellular atlases in human tissues, but most efforts are focused on single tissue types. Here, we establish a framework for profiling multiple tissues across the human body at single-cell resolution using single nucleus RNA-Seq (snRNA-seq), and apply it to 8 diverse, archived, frozen tissue types (three donors per tissue). We apply four snRNA-seq methods to each of 25 samples from 16 donors, generating a cross-tissue atlas of 209,126 nuclei profiles, and benchmark them vs. scRNA-seq of comparable fresh tissues. We use a conditional variational autoencoder (cVAE) to integrate an atlas across tissues, donors, and laboratory methods. We highlight shared and tissue-specific features of tissue-resident immune cells, identifying tissue-restricted and non-restricted resident myeloid populations. These include a cross-tissue conserved dichotomy between LYVE1- and HLA class II-expressing macrophages, and the broad presence of LAM-like macrophages across healthy tissues that is also observed in disease. For rare, monogenic muscle diseases, we identify cell types that likely underlie the neuromuscular, metabolic, and immune components of these diseases, and biological processes involved in their pathology. For common complex diseases and traits analyzed by GWAS, we identify the cell types and gene modules that potentially underlie disease mechanisms. The experimental and analytical frameworks we describe will enable the generation of large-scale studies of how cellular and molecular processes vary across individuals and populations.

cell biology↗

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗