@article {9100, title = {A multi-ethnic polygenic risk score is associated with hypertension prevalence and progression throughout adulthood.}, journal = {Nat Commun}, volume = {13}, year = {2022}, month = {2022 Jun 21}, pages = {3549}, abstract = {

In a multi-stage analysis of 52,436 individuals aged 17-90 across diverse cohorts and biobanks, we train, test, and evaluate a polygenic risk score (PRS) for hypertension risk and progression. The PRS is trained using genome-wide association studies (GWAS) for systolic, diastolic blood pressure, and hypertension, respectively. For each trait, PRS is selected by optimizing the coefficient of variation (CV) across estimated effect sizes from multiple potential PRS using the same GWAS, after which the 3 trait-specific PRSs are combined via an unweighted sum called "PRSsum", forming the HTN-PRS. The HTN-PRS is associated with both prevalent and incident hypertension at 4-6 years of follow up. This association is further confirmed in age-stratified analysis. In an independent biobank of 40,201 individuals, the HTN-PRS is confirmed to be predictive of increased risk for coronary artery disease, ischemic stroke, type 2 diabetes, and chronic kidney disease.

}, keywords = {Adult, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hypertension, Multifactorial Inheritance, Prevalence, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-022-31080-2}, author = {Kurniansyah, Nuzulul and Goodman, Matthew O and Kelly, Tanika N and Elfassy, Tali and Wiggins, Kerri L and Bis, Joshua C and Guo, Xiuqing and Palmas, Walter and Taylor, Kent D and Lin, Henry J and Haessler, Jeffrey and Gao, Yan and Shimbo, Daichi and Smith, Jennifer A and Yu, Bing and Feofanova, Elena V and Smit, Roelof A J and Wang, Zhe and Hwang, Shih-Jen and Liu, Simin and Wassertheil-Smoller, Sylvia and Manson, JoAnn E and Lloyd-Jones, Donald M and Rich, Stephen S and Loos, Ruth J F and Redline, Susan and Correa, Adolfo and Kooperberg, Charles and Fornage, Myriam and Kaplan, Robert C and Psaty, Bruce M and Rotter, Jerome I and Arnett, Donna K and Morrison, Alanna C and Franceschini, Nora and Levy, Daniel and Sofer, Tamar} } @article {9161, title = {Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.}, journal = {Commun Biol}, volume = {5}, year = {2022}, month = {2022 08 22}, pages = {856}, abstract = {

Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22\% for height, 27\% for HDL cholesterol, 43\% for body mass index, 50\% for sleep duration, 58\% for systolic blood pressure, 64\% for total cholesterol, 66\% for triglycerides, 77\% for LDL cholesterol, and 100\% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.

}, keywords = {Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide}, issn = {2399-3642}, doi = {10.1038/s42003-022-03812-z}, author = {Elgart, Michael and Lyons, Genevieve and Romero-Brufau, Santiago and Kurniansyah, Nuzulul and Brody, Jennifer A and Guo, Xiuqing and Lin, Henry J and Raffield, Laura and Gao, Yan and Chen, Han and de Vries, Paul and Lloyd-Jones, Donald M and Lange, Leslie A and Peloso, Gina M and Fornage, Myriam and Rotter, Jerome I and Rich, Stephen S and Morrison, Alanna C and Psaty, Bruce M and Levy, Daniel and Redline, Susan and Sofer, Tamar} } @article {9379, title = {Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.}, journal = {Nat Commun}, volume = {14}, year = {2023}, month = {2023 Jun 02}, pages = {3202}, abstract = {

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

}, keywords = {Blood Pressure, Ethnicity, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Population Health, Risk Factors}, issn = {2041-1723}, doi = {10.1038/s41467-023-38990-9}, author = {Kurniansyah, Nuzulul and Goodman, Matthew O and Khan, Alyna T and Wang, Jiongming and Feofanova, Elena and Bis, Joshua C and Wiggins, Kerri L and Huffman, Jennifer E and Kelly, Tanika and Elfassy, Tali and Guo, Xiuqing and Palmas, Walter and Lin, Henry J and Hwang, Shih-Jen and Gao, Yan and Young, Kendra and Kinney, Gregory L and Smith, Jennifer A and Yu, Bing and Liu, Simin and Wassertheil-Smoller, Sylvia and Manson, JoAnn E and Zhu, Xiaofeng and Chen, Yii-Der Ida and Lee, I-Te and Gu, C Charles and Lloyd-Jones, Donald M and Z{\"o}llner, Sebastian and Fornage, Myriam and Kooperberg, Charles and Correa, Adolfo and Psaty, Bruce M and Arnett, Donna K and Isasi, Carmen R and Rich, Stephen S and Kaplan, Robert C and Redline, Susan and Mitchell, Braxton D and Franceschini, Nora and Levy, Daniel and Rotter, Jerome I and Morrison, Alanna C and Sofer, Tamar} } @article {9702, title = {Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 May 30}, pages = {12436}, abstract = {

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model{\textquoteright}s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1\% (SBP) and 14.3\% to 17.4\% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1\% (SBP) and 4.7 to 5\% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3\% (SBP) and 5.7\% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

}, keywords = {Blood Pressure, Female, Genetic Predisposition to Disease, Genetic Risk Score, Genome-Wide Association Study, Humans, Hypertension, Machine Learning, Male, Middle Aged, Models, Genetic, Multifactorial Inheritance, Phenotype, Risk Factors}, issn = {2045-2322}, doi = {10.1038/s41598-024-62945-9}, author = {Hrytsenko, Yana and Shea, Benjamin and Elgart, Michael and Kurniansyah, Nuzulul and Lyons, Genevieve and Morrison, Alanna C and Carson, April P and Haring, Bernhard and Mitchell, Braxton D and Psaty, Bruce M and Jaeger, Byron C and Gu, C Charles and Kooperberg, Charles and Levy, Daniel and Lloyd-Jones, Donald and Choi, Eunhee and Brody, Jennifer A and Smith, Jennifer A and Rotter, Jerome I and Moll, Matthew and Fornage, Myriam and Simon, Noah and Castaldi, Peter and Casanova, Ramon and Chung, Ren-Hua and Kaplan, Robert and Loos, Ruth J F and Kardia, Sharon L R and Rich, Stephen S and Redline, Susan and Kelly, Tanika and O{\textquoteright}Connor, Timothy and Zhao, Wei and Kim, Wonji and Guo, Xiuqing and Ida Chen, Yii-Der and Sofer, Tamar} }