Maria J Redondo1, Richard A Oram2, Andrea K Steck3. 1. Texas Children's Hospital/Baylor College of Medicine, 6701 Fannin Street, CC1020, Houston, TX, 77030, USA. redondo@bcm.edu. 2. University of Exeter Medical School, Institute of Biomedical and Clinical Science, RILD Building, Royal Devon and Exeter Hospital, Barrack Road, Exeter, EX2 5DW, UK. 3. Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, 1775 Aurora Ct, Aurora, CO, 80045, USA.
Abstract
PURPOSE OF REVIEW: About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment. RECENT FINDINGS: T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.
PURPOSE OF REVIEW: About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment. RECENT FINDINGS: T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.
Entities:
Keywords:
Genetic; HLA; Prediction; Risk; Score; Type 1 diabetes
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