Study Explores How One’s Genes May Guide Future Lupus Diagnosis and Care
To explore how genetic data may help enhance modern approaches to lupus diagnosis and evaluation, researchers examined different genetic data sets to determine whether specific gene expressions might help predict lupus activity. They assessed 6,440 unique differentially expressed genes across multiple data sets and employed several different methods to identify any genetic markers that might be commonly shared across people with lupus.
Although the study found that identifying genetic signatures linked with lupus activity is extremely difficult, underscoring the complexity and diversity of the disease, raw genetic expression data with 10-fold cross-validation simulated a more standardized diagnostic test when compared against other genetic analysis methods. Researchers also concluded that nonlinear, decision tree-based methods of gene classification may be best suited to lupus diagnostics.
This is significant, since a wealth of genetic information has emerged in the past few years, yet none has been integrated to produce a tool for diagnosing lupus or predicting disease activity (flares). Instead, physicians still rely on clinical evaluation and a few laboratory tests, using the same diagnostic approaches that doctors have used for decades.
These findings may point the way toward increasingly personalized and precise lupus treatment and care, as physicians may soon be able to identify the cause of a person’s symptoms and select appropriate treatment based on the individual’s genetic information.
Learn about understanding the genetics of lupus.