Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

Lewis, Anna C.F.; Chisholm, Rex L.; Connolly, John J.; Esplin, Edward D.; Glessner, Joe; Gordon, Adam; Green, Robert C.; Hakonarson, Hakon; Harr, Margaret; Holm, Ingrid A.; Jarvik, Gail P.; Karlson, Elizabeth; Kenny, Eimear E.; Kottyan, Leah; Lennon, Niall; Linder, Jodell E.; Luo, Yuan; Martin, Lisa J.; Perez, Emma; Puckelwartz, Megan J.; Rasmussen-Torvik, Laura J.; Sabatello, Maya; Sharp, Richard R.; Smoller, Jordan W.; Sterling, Rene; Terek, Shannon; Wei, Wei-Qi; Fullerton, Stephanie M.

doi:10.1016/j.ajhg.2024.04.005

Cited by 4 publications

References 22 publications

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Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Rroji,

Spasovski

2024

Proteomics

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Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi‐omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

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Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Rroji,

Spasovski

2024

Proteomics

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Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit

Nagpal,

Gibson

2024

Preprint

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The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGSxE interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.

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Comparative Performance of Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease Subtypes in theAll of UsResearch Program

Smith,

Norland,

Hamed

et al. 2024

Preprint

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Background: Performance and portability of contemporary polygenic risk scores (PRS) for atherosclerotic cardiovascular disease (ASCVD) phenotypes vary based on different methods, training data, and trait ascertainment. Objectives: We aimed to investigate performance and portability of contemporary PRS for ASCVD subtypes: coronary heart disease (CHD), abdominal aortic aneurysm (AAA), ischemic stroke (IS), and peripheral artery disease (PAD), using the All of Us Workbench which provides access to a large diverse cohort with phenotype and whole genome sequence data. We also developed and evaluated a multi-trait PRS for each subtype. Methods: Performance of PRS for 4 ASCVD traits and related risk factors was compared across genetic ancestry groups in 245,388 All of Us participants. Genetic EUR, African (AFR), Admixed American (AMR), and remaining ancestry groups (combined as Other, OTH) were defined by All of Us based on principal components. PRS for CHD, IS, AAA, PAD, and multi-trait (combining PRS for the 4 traits as well as PRS for ASCVD risk factors) were assessed for portability across genetic ancestry groups using hazard ratios (HR) per SD increase. Results: For CHD, CHDPGS003725 performed the best (HR for 1 SD increase [95% CI]), across 4 genetic ancestry groups (EUR: 1.72[1.67-1.78], AFR: 1.24[1.18-1.31], AMR: 1.48[1.37-1.59], OTH: 1.65[1.52-1.79]). The best performing PRS for AAA was AAAPGS003972 (EUR: 1.68[1.59-1.78], AFR: 1.29[1.13-1.48], AMR: 1.30[1.06-1.60], OTH: 1.45[1.20-1.75]). The best performing IS PRS was ISPGS000039 in AFR (1.12[1.06-1.17]), AMR (1.11[1.04-1.19]), and OTH (1.23[1.09-1.38]), and ISPGS004939 in EUR (1.16[1.12-1.20]). For PAD, PADPGS004940 performed best in EUR (1.26[1.22-1.30]), AFR (1.11[1.05-1.18]), AMR (1.08[1.01-1.16]), and OTH (1.13[1.04-1.22]). Multi-trait PRS performed better than individual trait PRS for each ASCVD phenotype. Also, PRS derived from multi-ancestry cohorts performed better than those derived from single ancestry. Conclusions: PRS for ASCVD developed from multi-ancestry cohorts and multiple related traits performed best across ancestrally diverse and admixed individuals. PRS for CHD and AAA performed better than those for IS and PAD.

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Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

Cited by 4 publications

References 22 publications

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit

Comparative Performance of Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease Subtypes in theAll of UsResearch Program

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