Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

Billings, Liana K.; Shi, Zhuqing; Wei, Jun; Rifkin, Andrew S.; Zheng, S. Lilly; Helfand, Brian T.; Ilbawi, Nadim; Dunnenberger, Henry M.; Hulick, Peter J.; Qamar, Arman; Xu, Jianfeng

doi:10.1210/clinem/dgad456

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…Second, the potential for clinical utility of polygenic prediction (and thereby the BPC approach) strongly depends on the magnitude of the PGS's R 2 liability , which is currently prohibitively small for most traits. However, there are some traits, such as cardiovascular disease and type 2 diabetes, for which current PGSs may already be sufficiently powered to find clinical application [41][42][43] and be economically effective [44][45][46][47][48][49] . Moreover, as GWAS sample sizes grow, the PGS's R 2 liability is expected to approach the disorder's h 2 SNP , and therefore their clinical applicability will become more likely.…”

Section: Discussionmentioning

confidence: 99%

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Uffelmann,

Price,

Posthuma

et al. 2024

Preprint

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Polygenic Scores (PGSs) summarize an individual's genetic propensity for a given trait in a single value, based on SNP effect sizes derived from Genome-Wide Association Study (GWAS) results. Methods have been developed that apply Bayesian approaches to improve the prediction accuracy of PGSs through optimization of estimated effect sizes. While these methods are generally well-calibrated for continuous traits (implying the predicted values are on average equal to the true trait values), they are not well-calibrated for binary disorder traits in ascertained samples. This is a problem because well-calibrated PGSs are needed to reliably compute the absolute disorder probability for an individual to facilitate future clinical implementation. Here we introduce the Bayesian polygenic score Probability Conversion (BPC) approach, which computes an individual's predicted disorder probability using GWAS summary statistics, an existing Bayesian PGS method (e.g. PRScs, SBayesR), the individual's genotype data, and a prior disorder probability. The BPC approach transforms the PGS to its underlying liability scale, computes the variances of the PGS in cases and controls, and applies Bayes' Theorem to compute the absolute disorder probability; it is practical in its application as it does not require a tuning dataset with both genotype and phenotype data. We applied the BPC approach to extensive simulated data and empirical data of nine disorders. The BPC approach yielded well-calibrated results that were consistently better than the results of another recently published approach.

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Section: Discussionmentioning

confidence: 99%

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Uffelmann,

Price,

Posthuma

et al. 2024

Preprint

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“…The development and clinical implementation of polygenic risk score (PRS) is a core translational research focus of PMED. Initial efforts focused on prostate cancer PRS and its clinical validity in multiple ancestral populations ( Shi et al, 2021 ; Shi et al, 2022 ) and then expanded to additional conditions ( Ahmed et al, 2022 ; Wei et al, 2022a ; Glaser et al, 2022 ; Billings et al, 2023 ; Patel et al, 2023 ; Shi et al, 2023 ), including other cancer types ( Shi et al, 2019 ; Northcutt et al, 2021 ; Wei et al, 2022b ). Recently, our efforts have centered on the clinical implementation of PRS.…”

Section: Methodsmentioning

confidence: 99%

Personalized medicine in a community health system: the NorthShore experience

David,

Dunnenberger,

Choi

et al. 2023

Front. Genet.

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Genomic and personalized medicine implementation efforts have largely centered on specialty care in tertiary health systems. There are few examples of fully integrated care systems that span the healthcare continuum. In 2014, NorthShore University HealthSystem launched the Center for Personalized Medicine to catalyze the delivery of personalized medicine. Successful implementation required the development of a scalable family history collection tool, the Genetic and Wellness Assessment (GWA) and Breast Health Assessment (BHA) tools; integrated pharmacogenomics programming; educational programming; electronic medical record integration; and robust clinical decision support tools. To date, more than 225,000 patients have been screened for increased hereditary conditions, such as cancer risk, through these tools in primary care. More than 35,000 patients completed clinical genetic testing following GWA or BHA completion. An innovative program trained more than 100 primary care providers in genomic medicine, activated with clinical decision support and access to patient genetic counseling services and digital healthcare tools. The development of a novel bioinformatics platform (FLYPE) enabled the incorporation of genomics data into electronic medical records. To date, over 4,000 patients have been identified to have a pathogenic or likely pathogenic variant in a gene with medical management implications. Over 33,000 patients have clinical pharmacogenomics data incorporated into the electronic health record supported by clinical decision support tools. This manuscript describes the evolution, strategy, and successful multispecialty partnerships aligned with health system leadership that enabled the implementation of a comprehensive personalized medicine program with measurable patient outcomes through a genomics-enabled learning health system model that utilizes implementation science frameworks.

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“…In addition to the well-documented large-effect HLA contribution, non-HLA susceptibility also plays a role 2 . Large-scale genome-wide association studies (GWAS) have identified dozens of independent non-HLA signals across the genome 15–18 , that together with HLA polymorphisms can be used to construct polygenic scores (PGS) to evaluate genetic predisposition of T1D prior to the disease onset 19–21 as well as better differentiate T1D from other types of diabetes 22–24 .…”

mentioning

confidence: 99%

Effect of parental autoimmune diseases on type 1 diabetes in offspring can be partially explained by HLA and non-HLA polymorphisms: a nationwide registry and biobank-based study in 7.2M Finns

Wang,

Liu,

Yang

et al. 2024

Preprint

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Type 1 diabetes (T1D) and other autoimmune diseases (AIDs) co-occur in families. We studied the aggregation of 50 parental AIDs with T1D in offspring and the contribution of a shared genetic background, which was partitioned into HLA and non-HLA variation. Leveraging nationwide registers of 7.2M Finns, including 58,284 family trios, we observed that 15 parental AIDs, such as coeliac disease and rheumatoid arthritis, were associated with an increased risk of T1D in offspring. The identified epidemiological associations were then tested by comprehensive genetic analyses performed on 470K Finns genotyped in the FinnGen study (12,563 trios). The within-family genetic transmission analysis further demonstrated that the aggregation of parental AIDs with offspring T1D could be partially explained by HLA and non-HLA polymorphisms in a disease-dependent manner. For example, the associations with offspring T1D for coeliac disease and psoriasis were mainly driven by HLA while autoimmune hypothyroidism and rheumatoid arthritis also had non-HLA contributors. We, therefore, proposed a novel parental polygenic score (PGS), integrating variations in both HLA and non-HLA genes, to understand the cumulative risk pattern of T1D in offspring. This raises an intriguing possibility of considering parental PGS, in conjunction with clinical diagnoses, to inform individuals about T1D risk in their offspring.

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Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes

Cited by 4 publications

References 38 publications

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Estimating Disorder Probability Based on Polygenic Prediction Using the BPC Approach

Personalized medicine in a community health system: the NorthShore experience

Effect of parental autoimmune diseases on type 1 diabetes in offspring can be partially explained by HLA and non-HLA polymorphisms: a nationwide registry and biobank-based study in 7.2M Finns

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