Integrating autoimmune risk loci with gene-expression data identifies specific pathogenic immune cell subsets

Area of Science:

• Immunology
• Genetics
• Computational Biology

Background:

• Genome-wide association studies (GWAS) identify numerous genetic loci associated with complex diseases, but pinpointing causal alleles and relevant cell types is challenging.
• Understanding the specific cell types impacted by genetic variants is crucial for elucidating disease mechanisms, particularly in autoimmune conditions like rheumatoid arthritis.

Purpose of the Study:

• To develop and apply a statistical approach for identifying potentially pathogenic immune cell types in autoimmune diseases.
• To pinpoint specific cell types implicated in rheumatoid arthritis, systemic lupus erythematosus, and Crohn disease.

Main Methods:

• Utilized a gene expression dataset of 223 murine-sorted immune cells from the Immunological Genome Consortium.
• Applied a statistical method to identify enrichment of cell-type-specific genes within disease-associated loci from GWAS data.
• Validated findings by examining enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci, including novel putative loci.

Main Results:

• Identified enrichment of transitional B cell genes in systemic lupus erythematosus (p = 5.9 × 10⁻⁶).
• Found enrichment of epithelial-associated stimulated dendritic cell genes in Crohn disease (p = 1.6 × 10⁻⁵).
• Demonstrated significant enrichment of CD4+ effector memory T cell genes within rheumatoid arthritis loci (p < 10⁻⁶), including novel loci (p = 1.25 × 10⁻⁴).

Conclusions:

• CD4+ effector memory T cells are identified as primary candidates for functional studies in rheumatoid arthritis.
• The developed statistical approach is effective for identifying pathogenic cell types in autoimmune diseases.
• This methodology has broader applications for other complex phenotypes with available GWAS and cell-type-specific gene expression data.