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Genome Biology Papers on GWAS Fine-Mapping Method, COVID-19 Susceptibility, Rheumatoid Arthritis

A University of Wisconsin at Madison-led team outlines a model organism omics-informed method for focusing in from genome-wide association study variants in humans to potential effector genes — an approach applied to mouse pancreatic islet cells to look for diabetes effector genes from risk SNPs found in prior GWAS. The "integrative fine-mapping," or INFIMA, approach brings together RNA sequence, transposase-accessible chromatin using sequencing (ATAC-seq), in silico mutation, and other data from mice to help fine-map causal variants and expression quantitative trait loci (eQTL) in diversity outbred (DO) mouse model populations based on chromatin accessibility, transcriptomic, and other clues, the researchers explain, noting that INFIMA "maps genetic variants within the DO founder strains to eQTL genes by quantifying how robustly the multi-omics data explains the allelic patterns observed in the eQTL analysis."

Researchers at the New York Genome Center, Columbia University, New York University, and Icahn School of Medicine at Mount Sinai dig into a chromosome 3 locus linked to COVID-19 severity in prior genome-wide association studies. Using insights from a CRISPR loss-of-function screen SARS-CoV-2-exposed, ACE2-expressing lung epithelial-like cells and more than 100 eQTL datasets from GTEx and the eQTL Catalogue, the team searched for causal contributors to SARS-CoV-2 infection effects in a wide range of tissues and cell types, narrowing in on the SLC6A20 and CXCR6 genes. "By integrating the results of [a] CRISPR screen and cis-eQTLs, we have identified SLC6A20 and CXCR6 as potential protein-coding genes in the 3p21.31 locus through non-coding variants associated with COVID-19 risk in human patients may function," they write. "This integrative approach should prove useful for other human diseases and pathogens to bridge the divide between correlational and causal studies of human biology."

Finally, a team from Switzerland, the UK, and Slovenia presents findings from a functional analysis of rheumatoid arthritis-associated variants in synovial fibroblast cell types. By bringing together three-dimensional chromatin interaction profiles, cell type-specific gene expression data, rheumatoid arthritis variant fine-mapping clues, and DNA architecture and accessibility insights, the investigators teased out suspected rheumatoid arthritis causal genes and related mechanisms in synovial fibroblast cells, which appeared to contribute to as much as 24 percent of the heritability for the autoimmune disease. "Overall, our research significantly advances the knowledge about putative causal SNPs, enhancers, genes, and cell types affected by genetic risk loci in [rheumatoid arthritis]," the authors report. "Our analysis can direct future studies to investigate pathways that are genetically affected in a cell type-specific way."

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