Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-specific effects via GAUDI

Sun, Quan; Rowland, Bryce; Chen, Jiawen; Mikhaylova, Anna V.; Avery, Christy L.; Peters, Ulrike; Lundin, Jessica; Matise, Tara C.; Buyske, Steven; Tao, Ran; Mathias, Rasika A.; Reiner, Alexander P.; Auer, Paul L.; Cox, Nancy J.; Kooperberg, Charles; Thornton, Timothy A.; Raffield, Laura M.; Li, Yun

doi:10.1101/2022.10.06.511219

Cited by 11 publications

(15 citation statements)

References 40 publications

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“…However, there is an emerging need to consider the underlying continuum of genetic diversity across populations in both the development and implementational of PRS in diverse populations in the future 52 . Towards this goal, a recent method called GAUDI 53 has been proposed based on the fused lasso penalty for developing PRS in admixed population using individual-level data. While GAUDI shares similarities with PROSPER in terms of the use of the lasso-penalty function, the two methods are distinct in terms of the specification of tuning parameters and use of the ensemble step.…”

Section: Discussionmentioning

confidence: 99%

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Zhang

Zhan

Jin

et al. 2023

Preprint

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Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination of L1(lasso) and L2(ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

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Section: Discussionmentioning

confidence: 99%

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Zhang

Zhan

Jin

et al. 2023

Preprint

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“…This is an issue when we consider that genomic models developed and trained in one population have poor portability outside that population (45)(46)(47). Recently, strides have been made to address this gap, but most have been limited to cross-continental resolution, due to limitations in LAI accuracy (12)(13)(14)(15)(16).…”

Section: Discussionmentioning

confidence: 99%

Retracing Human Genetic Histories and Natural Selection Using Precise Local Ancestry Inference

Lerga-Jaso,

Novković,

Unnikrishnan

et al. 2023

Preprint

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In an increasingly diverse world, including admixed individuals in genomic studies is imperative for equity and portability. A crucial first step is precise local ancestry inference (LAI). We have developed Orchestra, a LAI model with unprecedented accuracy, and trained on over 10,000 single-origin individuals from 35 worldwide populations. We employed Orchestra to delve into genetic relationships and demographic histories, with a focus on Latin Americans, a prime example of admixture, and the Ashkenazi Jewish, whose origins have long been debated. Finally, Orchestra enabled us to map signatures of selection, notably identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections. Our work advances the field of LAI and holds promise for improvements in future applications for admixed populations.

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“…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] . As genetics research moves towards greater diversity, ALL-Sum serves as a valuable foundation for extension to incorporating data from multiple ancestries, as well as admixed individuals [75][76][77] .…”

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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Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-specific effects via GAUDI

Cited by 11 publications

References 40 publications

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction

Retracing Human Genetic Histories and Natural Selection Using Precise Local Ancestry Inference

Ensembled best subset selection using summary statistics for polygenic risk prediction

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