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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Pediatric Diabetesarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Pediatric Diabetes
Article . 2022 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
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Improved genetic risk scoring algorithm for type 1 diabetes prediction

Authors: Hui‐Qi Qu; Jingchun Qu; Joseph Glessner; Yichuan Liu; Frank Mentch; Xiao Chang; Michael March; +5 Authors

Improved genetic risk scoring algorithm for type 1 diabetes prediction

Abstract

Precise risk prediction of type 1 diabetes (T1D) facilitates early intervention and identification of risk factors prior to irreversible beta-islet cell destruction, and can significantly improve T1D prevention and clinical care. Sharp et al. developed a genetic risk scoring (GRS) system for T1D (T1D-GRS2) capable of predicting T1D risk in children of European ancestry. The T1D-GRS2 was developed on the basis of causal genetic variants, thus may be applicable to minor populations, while a trans-ethnic GRS for T1D may avoid the exacerbation of health disparities due to the lack of genomic information in minorities.Here, we describe a T1D-GRS2 calculator validated in two independent cohorts, including African American children and European American children. Participants were recruited by the Center for Applied Genomics at the Children's Hospital of Philadelphia.It demonstrates that GRS2 is applicable to the T1D risk prediction in the AA cohort, while population-specific thresholds are needed for different populations.The study highlights the potential to further improve T1D-GRS2 performance with the inclusion of additional genetic markers.

Keywords

Genetic Markers, Diabetes Mellitus, Type 1, Risk Factors, Humans, Genetic Predisposition to Disease, Child, Polymorphism, Single Nucleotide, Algorithms

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    popularity
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Powered by OpenAIRE graph
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Average
Top 10%