Clinical and laboratory interpretation of mitochondrial mRNA variants. Issue 10 (22nd July 2020)

• Record Type: Journal Article
• Authors: Wong, Lee‐Jun C.; Chen, Ting; Schmitt, Eric S.; Wang, Jing; Tang, Sha; Landsverk, Megan; Li, Fangyuan; Zhang, Shulin; Wang, Yue; Zhang, Victor W.; Craigen, William J.
• Is Part Of: Human mutation. Volume 41:Issue 10(2020)
• Page Start: 1783
• Page End: 1796
• Publication Date: 2020-07-22
• Subjects: mitochondrial disorders -- MITOMAP -- mRNA variants classification criteria -- mt‐mRNA variants interpretation; Human chromosome abnormalities -- Periodicals; Mutation (Biology) -- Periodicals; 616.04205
• DOI: 10.1002/humu.24082
• Languages: English
• ISSNs: 1059-7794

Abstract

Abstract: Interpretation of mitochondrial protein‐encoding (mt‐mRNA) variants has been challenging due to mitochondrial characteristics that have not been addressed by American College of Medical Genetics and Genomics guidelines. We developed criteria for the interpretation of mt‐mRNA variants via literature review of reported variants, tested and refined these criteria by using our new cases, followed by interpreting 421 novel variants in our clinical database using these verified criteria. A total of 32 of 56 previously reported pathogenic (P) variants had convincing evidence for pathogenicity. These variants are either null variants, well‐known disease‐causing variants, or have robust functional data or strong phenotypic correlation with heteroplasmy levels. Based on our criteria, 65.7% (730/1, 111) of variants of unknown significance (VUS) were reclassified as benign (B) or likely benign (LB), and one variant was scored as likely pathogenic (LP). Furthermore, using our criteria we classified 2, 12, and 23 as P, LP, and LB, respectively, among 421 novel variants. The remaining stayed as VUS (91.2%). Appropriate interpretation of mt‐mRNA variants is the basis for clinical diagnosis and genetic counseling. Mutation type, heteroplasmy levels in different tissues of the probands and matrilineal relatives, in silico predictions, population data, as well as functional studies are key points for pathogenicity assessments.