Genomic Medicine in Diabetes: Improving the Diagnostic Rate of Monogenic diabetes
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AbstractMonogenic diabetes is an uncommon type of diabetes caused by genetic defects in one of several genes, and it accounts for 1-2% of all diabetes. The primary subtypes are Maturity Onset Diabetes of the Young (MODY), neonatal diabetes, and syndromic diabetes. The correct treatment of each subtype of monogenic diabetes depends on the corresponding disease etiology that can only be confirmed by genetic testing. However, the diagnostic rate of monogenic diabetes is inadequate, mainly due to the overlapping phenotype of monogenic diabetes with type 1 diabetes and type 2 diabetes and lack of awareness among patients and physicians. To improve the diagnostic rate of monogenic diabetes, this project focuses on three aspects: 1) systematically screening of patients for genetic testing; 2) comprehensively re-analyzing next-generation sequencing (NGS) data from multiple diabetes cohorts; 3) assessing the ability of Direct-to-Consumer Genetic Testing (DTC-GT) raw data in detecting GCK-MODY variants. The Personalized Diabetes Medicine Program (PDMP) screened 2,522 patients with diabetes with a simple questionnaire, assigned patients to different algorithm criteria groups based on clinical features, and performed genetic testing on suspected patients. Overall, 38 of 313 patients suspected of monogenic diabetes were tested positive for causative variants. The group of patients diagnosed before age 30 who were not treated with insulin had the highest pick-up rate. The comprehensive re-analysis of NGS panel data in PDMP, including re-classification and updating variant calling algorithm, improved the diagnostic rate from 11.82% to 13.10%. Also, the comparison between exome sequencing (ES) and NGS panel or Sanger sequencing of the Progress for Diabetes Genetics in Youth samples showed ES failed to identify all MODY-causing variants, but re-analysis of ES unfiltered data discovered the missing variants. By analyzing the GCK variants in the 23andMe DTC-GT raw data from 3,044 anonymous volunteers and calculating the ancestry-specific allele frequency of GCK-MODY variants, some of the variants showed higher-than-expected minor allele frequency compared with the large population database. Such inconsistency suggests customers should not use DTC-GT as a supplementary method of clinical genetic testing for GCK-MODY. In a summary, these studies provide practical approaches to improve the diagnostic rate of monogenic diabetes.
University of Maryland, Baltimore
High-Throughput Nucleotide Sequencing
Sequence Analysis, DNA