Polygenic scoring accuracy varies across the genetic ancestry continuum

Ding, Yi; Hou, Kangcheng; Xu, Ziqi; Pimplaskar, Aditya; Petter, Ella; Privé, Florian; Vilhjálmsson, Bjarni J.; Loohuis, Loes Olde; Paşaniuc, Bogdan

doi:10.1038/s41586-023-06079-4

Cited by 71 publications

(39 citation statements)

References 69 publications

(89 reference statements)

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“…Admix-kit holds significant potentials in the development of Polygenic Risk Scores (PRS). The efficacy of PRS is known to hinge on the similarity of the target population to the training population (Ding et al , 2023). With the PRIMED consortium working on methods to improve the performance of PRS in diverse populations, simulations will be pivotal for method evaluation (Kachuri et al , 2023).…”

Section: Discussionmentioning

confidence: 99%

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Hou,

Gogarten,

Kim

et al. 2023

Preprint

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SummaryAdmixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.Availability and implementationAdmix-kit package is open-source and available athttps://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available athttps://github.com/UW-GAC/admix-kit_workflow.

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Section: Discussionmentioning

confidence: 99%

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Hou,

Gogarten,

Kim

et al. 2023

Preprint

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“…The current model is designed for single ancestry analysis. While a large portion of GWAS data currently comes from individuals of European ancestry, it is well-known that polygenic risk scores do not transfer well between individuals of different ancestries, which can impact their utility for patients of non-European ancestry [64][65][66][67][68][69] . Many methods that utilize summary data from multiple populations have already been proposed and demonstrate improved prediction in under-represented populations 25,29,30,[70][71][72][73][74] .…”

Section: Discussionmentioning

confidence: 99%

Ensembled best subset selection using summary statistics for polygenic risk prediction

Chen,

Zhang,

Mazumder

et al. 2023

Preprint

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Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L0L2penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.

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“…The biomedical and human genetics field has extensively studied model fairness [30][31][32] , but most studies lack information on sensitive/protected attributes. While electronic health records provide ample information on race and ethnicity, other socioeconomic characteristics are often unavailable, leading to a focus on fairness considering only race/ethnicity, age, and sex in most papers.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based prediction of one-year mortality in the entire Finnish population is an accurate but unfair digital marker of aging

Vabalas,

Hartonen,

Vartiainen

et al. 2023

Preprint

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Background: Accurately predicting short-term mortality is important for optimizing healthcare resource allocation, developing risk-reducing interventions, and improving end-of-life care. Moreover, short-term mortality risk reflects individual frailty and can serve as digital aging marker. Previous studies have focused on specific, high-risk populations. Predicting all-cause mortality in an unselected population incorporating both health and socioeconomic factors has direct public health relevance but requires careful fairness considerations. Methods: We developed a deep learning model to predict 1-year mortality using nationwide longitudinal data from the Finnish population (N = 5.4 million), including >8,000 features and spanning back up to 50 years. We used the area under the receiver operating characteristic curve (AUC) as a primary metric to assess model performance and fairness. Findings: The model achieved an AUC of 0.944 with strong calibration, outperforming a baseline model that only included age and sex (AUC = 0.897). The model generalized well to different causes of death (AUC > 0.800 for 45 out of 50 causes), including COVID-19 which was not present in the training data. The model performed best among young females and worst in older males (AUC = 0.910 vs. AUC = 0.718). Extensive fairness analyses revealed that individuals belonging to multiple disadvantaged groups had the worst model performance, not explained by age and sex differences, reduced healthcare contact, or smaller training set sizes within these groups. Conclusion: A deep learning model based on nationwide longitudinal multi-modal data accurately identified short-term mortality risk holding the potential for developing a population-wide in-silico aging marker. Unfairness in model predictions represents a major challenge to the equitable integration of these approaches in public health interventions.

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Polygenic scoring accuracy varies across the genetic ancestry continuum

Cited by 71 publications

References 69 publications

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations

Ensembled best subset selection using summary statistics for polygenic risk prediction

Deep learning-based prediction of one-year mortality in the entire Finnish population is an accurate but unfair digital marker of aging

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