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dc.contributor.authorZhang, Haichen
dc.date.accessioned2021-09-03T18:38:41Z
dc.date.available2021-09-03T18:38:41Z
dc.date.issued2021
dc.identifier.urihttp://hdl.handle.net/10713/16558
dc.descriptionHuman Genetics
dc.descriptionUniversity of Maryland, Baltimore
dc.descriptionPh.D.
dc.description.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.
dc.subjectbioinformaticsen_US
dc.subjectgenomic medicineen_US
dc.subjectmonogenic diabetesen_US
dc.subjectNGSen_US
dc.subject.lcshDiabetesen_US
dc.subject.meshComputational Biologyen_US
dc.subject.meshHigh-Throughput Nucleotide Sequencingen_US
dc.subject.meshSequence Analysis, DNAen_US
dc.titleGenomic Medicine in Diabetes: Improving the Diagnostic Rate of Monogenic diabetesen_US
dc.typedissertationen_US
dc.date.updated2021-08-31T22:07:13Z
dc.language.rfc3066en
dc.contributor.advisorPollin, Toni
dc.contributor.orcid0000-0002-0615-2836en_US
refterms.dateFOA2021-09-03T18:38:41Z


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