Skip to main content
Premium Trial:

Request an Annual Quote

Science Papers Present Algorithm to Diagnose Slowly Progressing Diseases, Korean Genome Project

By combining genetic information with genetic risk scoring, a Brigham and Women's Hospital-led team has developed an algorithm for quickly diagnosing slowly progressing diseases. The researchers evaluated the method — called G-PROB, short for Genetic Probability tool — with the inflammatory arthritis-causing diseases rheumatoid arthritis, systemic lupus erythematosus, spondyloarthropathy, psoriatic arthritis, and gout. Three cohorts were tested: 1,211 patients identified by International Classification of Diseases (ICD) codes within the eMERGE database; 245 patients identified through ICD codes and medical record review within the Partners Biobank; and 243 patients first presenting with unexplained inflammatory arthritis and with final diagnoses by record review within the Partners Biobank. The scientists find that G-PROB could rule out at least one disease in all patients, identify a likely diagnosis in 45 percent of the patients, and identify incorrect clinical diagnoses in 35 percent of cases. "Our results demonstrate that genetic data can provide probabilistic information to discriminate between multiple diseases presenting with similar clinical signs and symptoms," they write.

The first phase of the Korean Genome Project (Korea1K), including 1,094 whole genomes and data for 79 quantitative clinical traits, is reported in Science Advances this week. Project researchers identified 39 million single-nucleotide variants and indels, of which half were singleton or doubleton, and detected Korean-specific patterns based on several types of genomic variation. A genome-wide association study, meanwhile, identified nine more significant candidate alleles than previously reported from the same linkage disequilibrium blocks, highlighting the power of whole-genome sequences for analyzing clinical traits, the investigators write. Demonstrating Korea1K's utility, germline variants in cancer samples could also be filtered out more effectively when the Korea1K variome was used as a panel of normal controls compared to non-Korean variome sets. "This kind of personal whole-genome dataset combined with common health check-derived clinical information is possibly a good exemplary path for an ethnicity-relevant reference panel for future personalized medical applications for Koreans," the investigators write. GenomeWeb has more on this study, here.