Many genetic risk factors for lupus have been identified, but it isn’t clear how or why many of them are linked to the disease. Some lupus risk factors may be responsible for specific clinical symptoms, while others are associated with the severity of disease. Some genes may not actually be linked to the development of lupus at all, or to its severity. Environmental conditions such as medications and illness may cause normally neutral genes to play a harmful role. These environmental factors make it difficult to determine what effects are caused by the genes compared to the environment. Dr. Kawai’s research focuses on developing methods to isolate the genetic components of disease from those exerted by environmental factors. In this study, she will compare the genes of patients with lupus to healthy controls to identify gene changes that are unique to lupus patients. In addition, she will identify whether these differences in genes lead to important differences in the amount of proteins produced by the body. Dr. Kawai’s study will also determine which genes are associated with lupus severity.

What this study means for people with lupus

Dr. Kawai’s study will help researchers better understand the genetic risk factors for lupus as well as its severity. Her findings may also help doctors predict which lupus patients are likely to develop more severe lupus over time.

Systemic lupus erythematosus (SLE) affects approximately 6 million people worldwide and results in substantial morbidity and mortality. However, the disease is extremely heterogeneous and only some patients develop renal involvement or severe disease. SLE results from the complex interaction between genetic and environmental factors but the mechanisms underlying SLE heterogeneity are not known, and we cannot identify subgroups at risk for a particular SLE manifestation. Large genome-wide association studies (GWAS) have identified more than 100 risk loci that are associated with susceptibility to SLE. However, the mechanisms through which these risk loci cause disease or influence its heterogeneity are not known. More recently, our group and others have developed methods that integrate genomic, transcriptomic and proteomic data to estimate the genetic component of the transcriptome (virtual transcriptome) and of the proteome (virtual proteome) and use these powerful tools to identify potential causal genes in complex traits, facilitate a precision medicine approach, and identify new therapeutic targets. Our goal is to use genetic information to estimate genetically-predicted gene expression and protein levels as tools to better understand the biology and the direction of causality behind SLE heterogeneity. Our hypothesis is that genetic information can be used to define which signatures and pathways play a role in the pathogenesis of 1) SLE and 2) severe lupus. Thus, we will use Vanderbilt’s biobank – BioVU and the linked electronic health records (EHRs) to identify patients with SLE and controls with genome-wide genotype data available to: Specific Aim 1: Test the hypothesis that genetically-predicted gene expression and protein levels: 1a) are enriched with signatures and pathways previously identified in SLE; and 1b) define novel signatures and pathways associated with SLE. Specific Aim 2: Test the hypothesis that genetically-predicted gene expression and protein levels associated with SLE can stratify patients with severe disease and identify underlying mechanistic pathways. We will use bioinformatics algorithms to select patients with SLE and controls and perform deep phenotyping of SLE patients to identify those with severe lupus and lupus nephritis. We will estimate genetically-predicted gene expression and protein levels from GWAS data using validated models trained in datasets in which the transcriptome and proteome were measured. We will use the virtual transcriptome and the virtual proteome in SLE and severe lupus as tools to better understand disease biology and disease heterogeneity. The mechanisms underlying the biology of SLE and its heterogeneity are poorly understood. Thus, defining these mechanisms are important to identify key biological pathways that determine disease heterogeneity and progression, and to uncover novel disease mechanisms that can be targeted with new therapies and a personalized approach.