A study published today in Nature Genetics brings scientists closer to unraveling the genetic mechanisms underpinning autoimmune disease risk.
The work Prioritization of autoimmune disease-associated genetic variants that perturb regulatory element activity in T cells pinpoints many regions of the genome that vary between individuals (termed genetic variants) that could potentially cause autoimmune disease. Most of these regions lay outside of genes, making them difficult to study without the use of cutting-edge technologies employed by lead corresponding author John Ray, PhD, of Benaroya Research Institute (BRI) and colleagues.
"In our study we have exhaustively tested more than 18,000 autoimmune-associated genetic variants for their effects on gene expression in T cells, uncovering 60 variants that may cause disease through perturbing gene regulatory region activity in T cells," said Dr. Ray.
In collaboration with Ryan Tewhey, PhD, at the Jackson Laboratory and Nir Hacohen, PhD, at the Broad Institute, Dr. Ray and his team utilized massively parallel reporter assays (MPRA) and chromatin accessibility – where DNA is accessible for gene regulating proteins to bind – prioritizing genetic variants associated with common forms of type 1 diabetes, inflammatory bowel disease, ulcerative colitis, rheumatoid arthritis and multiple sclerosis.
The team identified 313 variants with statistically significant differences in expression and found 60 in accessible chromatin in T cells. Many of these are likely to cause disease, according to statistical genetics approaches. The team then inserted one of these variants into human T cells, which reduced expression of BACH2, a gene that suppresses inflammatory T cell responses. The team modified the same variant region in mice, which made T cells more inflammatory in response to viral infection.
"We believe this to be a major step forward in work related to variant-to-function initiatives," Dr. Ray said. "It serves as a roadmap for how to effectively identify variants that likely cause disease and how to understand their in vivo impact in a model organism."
Few previous studies have analyzed genome-wide genetic associations and extensively followed up variants in models, he said. Researchers anticipate that once genetic risk for autoimmune diseases is understood it may be leveraged to create preventive measures and effective treatments for patients.
For more about the research team’s approach to this work and its implications, see this post on the non-coding genome from The Jackson Laboratory.
