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Review
. 2019 Dec 1;40(6):1500-1520.
doi: 10.1210/er.2019-00088.

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

Miriam S Udler  1   2   3   4 , Mark I McCarthy  5   6   7 , Jose C Florez  1   2   3   4 , Anubha Mahajan  6
Affiliations
Review

Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine

Miriam S Udler et al. Endocr Rev. .

Abstract

During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.

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Conflict of interest statement

Current Affiliation: M. I. McCarthy’s current affiliation is Genentech, South San Francisco, California 94080.

Disclosure Summary: M.I.M. has served on advisory panels for Pfizer, Novo Nordisk, and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; has stock options in Zoe Global; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, and Servier & Takeda. As of June 2019, M.I.M. is an employee of Genentech and holds stock in Roche. J.C.F. has received a consulting honorarium from Janssen. The remaining authors have nothing to disclose.

All data generated or analyzed during this study are included in this published article or in the data repositories listed in References.

Figures

Figure 1.
Figure 1.
How polygenic scores are derived. The orange dashed line in the graph represents the threshold for genome-wide significance in a GWAS study. The filled red dots in the rsPS and gePS sections represent genetic variants reaching genome-wide significance, and the filled blue dots variants that have not reached genome-wide significance. In the pPS section, open dots reflect variants that have been assigned to one of the four groups of partitioned loci. For full explanation see text.
Figure 2.
Figure 2.
Comparison of rsPS and gePS for T2D using data from Mahajan et al. (9). rsPS and gePS were generated using a T2D GWAS meta-analysis of 455,313 European individuals and used to predict incident T2D in 13,480 cases and 311,390 controls from the UK Biobank. (a) AUROC curves for models predicting incident T2D: each model was adjusted for genotyping array and the first six principal components of ancestry. (b) Prevalence of T2D according to 40 groups binned according to the polygenic scores, with each grouping representing 2.5% of the population. (c) Distribution of rsPS and gePS in the cases and controls. The x-axis represents polygenic score, with values scaled to a mean of 0 and standard deviation of 1. Both rsPS and gePS in UK Biobank individuals is normally distributed with a shift toward the right, observed for T2D cases.
See this image and copyright information in PMC

References

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    1. Florez JC. Precision medicine in diabetes: is it time? Diabetes Care. 2016;39(7):1085–1088. - PubMed

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