Genetics of JIA: New tools bring new approaches

• Type of Material: Article
• Authors: Halima Moncrieffe, Cincinnati Children's Hospital Medical Center; Sampath Prahalad, Emory University; Susan D. Thompson, Cincinnati Children's Hospital Medical Center
• Language: English
• Date: 2014-09-01
• Publisher: Lippincott, Williams & Wilkins
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Wolters Kluwer Health; Lippincott Williams & Wilkins.
• Final Published Version (URL): http://dx.doi.org/10.1097/BOR.0000000000000094
• Title of Journal or Parent Work: Current Opinion in Rheumatology
• ISSN: 1040-8711
• Volume: 26
• Issue: 5
• Start Page: 579
• End Page: 584
• Grant/Funding Information: NIH Funding: P01AR048929, P30AR047363
• Keywords: GENES; Science & Technology; functional variation; COMMON; MISSING HERITABILITY; causal risk variants; DNA METHYLATION; Rheumatology; LANDSCAPE; CELLS; SEQUENCE; SUSCEPTIBILITY LOCUS; TRANSCRIPTION; juvenile idiopathic arthritis; Life Sciences & Biomedicine; ASSOCIATION; complex disease
• Research Categories: Biology, Genetics; Health Sciences, Medicine and Surgery

Abstract

PURPOSE OF REVIEW: In juvenile idiopathic arthritis (JIA), there are now more than 25 regions represented by single nucleotide polymorphisms that show strong genetic associations. The causal variants and corresponding functions have not yet been defined for the majority of these regions. Here, we review current JIA association findings and the recent progress in the annotation of noncoding portion of the human genome as well as the new technologies necessary to apply this knowledge to JIA association findings. RECENT FINDINGS: An international collaboration was able to amass sufficient numbers of JIA and control samples to identify significantly robust genetic associations for JIA. The Encyclopedia of DNA Elements project and the National Institutes of Health (NIH) Roadmap Epigenetics Program have now annotated more than 80% of the noncoding genome, important in understanding the impact of risk loci, the majority of which fall outside of protein coding regions. Recent technological advances in high throughput sequencing, chromatin structure determination, transcription factor and enhancer binding site mapping and genome editing will likely provide a basis for understanding JIA genetic risk. SUMMARY: Understanding the role of genetic variation in the cause of JIA will provide insight for disease mechanism and may explain disease heterogeneity between JIA subtypes and between autoimmune diseases in general.