Identification of Causative Variants in Autoimmune Risk Loci

Genome-wide Association Studies (GWAS) have emerged as a transformative approach in deciphering the complex genetic underpinnings of autoimmune diseases. Autoimmunity arises from intricate interactions between genetic susceptibility and environmental triggers. A remarkable aspect of GWAS findings is the identification of genetic variants that are associated with multiple autoimmune diseases. These shared variants suggest overlapping pathways and mechanisms underlying these diseases. For example, the HLA gene region is consistently implicated in multiple diseases such as type 1 diabetes, rheumatoid arthritis, lupus, and celiac disease. Using the Million Veterans Project (MVP) data, we created a cohort of Veterans with at least two reports of any one of 71 different autoimmune phenotypes (n= 83156) and a cohort of controls (n=335647) that did not have any autoimmune phenotypes and performed a GWAS to identify common autoimmune risk loci. Twenty-six regions of the genome were identified with variants surpassing genome wide significance (5x10-8). The largest region identified spanned the HLA region on chr 6 from 29.5 to 32.7 Mb. In the non-HLA loci, linkage disequilibrium (LD) pruning identified 44 independent associations. Thirty-one of these associations have been identified in previous autoimmune GWAS, showing the robustness of our results. Interestingly, the 13 remaining associations have not been previously identified in the GWAS catalog for an autoimmune disorder. These results suggest that these 13 associations are novel associations and provide new insight into the genetic mechanisms of autoimmune risk. GWASs only identify associations between variants and disease, they do not identify which of the associated variants cause increased risk of disease or identify which gene(s) are involved in increased risk. To fully understand the genetic mechanisms involved, we must identify the variants that impact cellular biology in an allele specific manner and which genes are impacted. The goal of this project will focus on these two specific issues. Most associations detected in autoimmunity, and in our results, are in non-protein coding regions of the genome, which suggests that disruption of gene regulation is the major mechanism increasing risk of disease. To identify all potential “causative” variants, we have used LD expansion to identify 2108 variants with an r2>0.4 with each of the non-HLA independent associations. We propose using a Massively Parallel Reporter Assay (MPRA) to screen these variants in multiple cell types relevant to autoimmunity with and without stimulation. From these experiments we will be able to determine not only which of the variants have the capacity to alter gene expression, but also the type of cells showing allele specific expression and the relevant environmental conditions that alter gene expression in an allele specific manner. While these variants would be strong candidates to be causative, the MPRA does not identify which genes the variants target. To address this issue, we propose applying a combination of bioinformatic tools and Perturb-seq techniques using either dCAS9-KRAB (suppressor) or dCAS9-PVR (activator), variant targeted gRNA’s and RNAseq to identify the genes that are regulated by our top candidate autoimmune risk variants. Four variants were identified that alter the amino acid sequence of proteins. Functional assays will be performed to determine the effect of the risk allele on protein function. Combined, these data sets will provide a deeper understanding of the genetic mechanisms that promote increased risk of autoimmunity and could lead to the design of potential therapeutics. In addition, by examining the genetic associations common to multiple autoimmune diseases, we will be able to identify Veterans that have the greatest risk of developing autoimmune disorders and potentially identify which disease will develop. This will allow earlier clinical identification of disease as Project Number: 1I01BX006507-01A1 | Fiscal Year: 2026 | NIH Institute/Center: Veterans Affairs (VA) | Principal Investigator: KENNETH KAUFMAN | Institution: CINCINNATI VA MEDICAL CENTER RESEARCH, CINCINNATI, OH | Activity Code: I01 | Study Section: Special Emphasis Panel[ZRD1 IMMA-G (01)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11045217