@article {8997, title = {Association of mitochondrial DNA copy number with cardiometabolic diseases.}, journal = {Cell Genom}, volume = {1}, year = {2021}, month = {2021 Oct 13}, abstract = {

Mitochondrial DNA (mtDNA) is present in multiple copies in human cells. We evaluated cross-sectional associations of whole blood mtDNA copy number (CN) with several cardiometabolic disease traits in 408,361 participants of multiple ancestries in TOPMed and UK Biobank. Age showed a threshold association with mtDNA CN: among younger participants (<65 years of age), each additional 10 years of age was associated with 0.03 standard deviation (s.d.) higher level of mtDNA CN ( = 0.0014) versus a 0.14 s.d. lower level of mtDNA CN ( = 1.82 {\texttimes} 10) among older participants (>=65 years). At lower mtDNA CN levels, we found age-independent associations with increased odds of obesity ( = 5.6 {\texttimes} 10), hypertension ( = 2.8 {\texttimes} 10), diabetes ( = 3.6 {\texttimes} 10), and hyperlipidemia ( = 6.3 {\texttimes} 10). The observed decline in mtDNA CN after 65 years of age may be a key to understanding age-related diseases.

}, issn = {2666-979X}, doi = {10.1016/j.xgen.2021.100006}, author = {Liu, Xue and Longchamps, Ryan J and Wiggins, Kerri L and Raffield, Laura M and Bielak, Lawrence F and Zhao, Wei and Pitsillides, Achilleas and Blackwell, Thomas W and Yao, Jie and Guo, Xiuqing and Kurniansyah, Nuzulul and Thyagarajan, Bharat and Pankratz, Nathan and Rich, Stephen S and Taylor, Kent D and Peyser, Patricia A and Heckbert, Susan R and Seshadri, Sudha and Cupples, L Adrienne and Boerwinkle, Eric and Grove, Megan L and Larson, Nicholas B and Smith, Jennifer A and Vasan, Ramachandran S and Sofer, Tamar and Fitzpatrick, Annette L and Fornage, Myriam and Ding, Jun and Correa, Adolfo and Abecasis, Goncalo and Psaty, Bruce M and Wilson, James G and Levy, Daniel and Rotter, Jerome I and Bis, Joshua C and Satizabal, Claudia L and Arking, Dan E and Liu, Chunyu} } @article {8838, title = {BinomiRare: A robust test for association of a rare genetic variant with a binary outcome for mixed models and any case-control proportion.}, journal = {HGG Adv}, volume = {2}, year = {2021}, month = {2021 Jul 08}, abstract = {

Whole-genome sequencing (WGS) and whole-exome sequencing studies have become increasingly available and are being used to identify rare genetic variants associated with health and disease outcomes. Investigators routinely use mixed models to account for genetic relatedness or other clustering variables (e.g., family or household) when testing genetic associations. However, no existing tests of the association of a rare variant with a binary outcome in the presence of correlated data control the type 1 error where there are (1) few individuals harboring the rare allele, (2) a small proportion of cases relative to controls, and (3) covariates to adjust for. Here, we address all three issues in developing a framework for testing rare variant association with a binary trait in individuals harboring at least one risk allele. In this framework, we estimate outcome probabilities under the null hypothesis and then use them, within the individuals with at least one risk allele, to test variant associations. We extend the BinomiRare test, which was previously proposed for independent observations, and develop the Conway-Maxwell-Poisson (CMP) test and study their properties in simulations. We show that the BinomiRare test always controls the type 1 error, while the CMP test sometimes does not. We then use the BinomiRare test to test the association of rare genetic variants in target genes with small-vessel disease (SVD) stroke, short sleep, and venous thromboembolism (VTE), in whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program.

}, issn = {2666-2477}, doi = {10.1016/j.xhgg.2021.100040}, author = {Sofer, Tamar and Lee, Jiwon and Kurniansyah, Nuzulul and Jain, Deepti and Laurie, Cecelia A and Gogarten, Stephanie M and Conomos, Matthew P and Heavner, Ben and Hu, Yao and Kooperberg, Charles and Haessler, Jeffrey and Vasan, Ramachandran S and Cupples, L Adrienne and Coombes, Brandon J and Seyerle, Amanda and Gharib, Sina A and Chen, Han and O{\textquoteright}Connell, Jeffrey R and Zhang, Man and Gottlieb, Daniel J and Psaty, Bruce M and Longstreth, W T and Rotter, Jerome I and Taylor, Kent D and Rich, Stephen S and Guo, Xiuqing and Boerwinkle, Eric and Morrison, Alanna C and Pankow, James S and Johnson, Andrew D and Pankratz, Nathan and Reiner, Alex P and Redline, Susan and Smith, Nicholas L and Rice, Kenneth M and Schifano, Elizabeth D} } @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 {9101, title = {Targeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea.}, journal = {Am J Respir Crit Care Med}, year = {2022}, month = {2022 Jul 13}, abstract = {

INTRODUCTION: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epi-demiologic evidence supporting the importance of genetic factors influencing OSA, but limited data implicating specific genes.

METHODS: Leveraging high depth genomic sequencing data from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the Cleve-land Family Study (CFS) followed by multi-stage gene-based association analyses in independent cohorts to search for rare variants contributing to OSA severity as assessed by the apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry.

RESULTS: Linkage analysis in CFS identified a suggestive linkage peak on chromosome 7q31 (LOD=2.31). Gene-based analysis identified 21 non-coding rare variants in Caveolin-1 (CAV1) associated with lower AHI after accounting for multiple comparisons (p=7.4{\texttimes}10-8). These non-coding variants together significantly contributed to the linkage evidence (p<10-3). Follow-up anal-ysis revealed significant associations between these variants and increased CAV1 expression, and increased CAV1 expression in peripheral monocytes was associated with lower AHI (p=0.024) and higher minimum overnight oxygen saturation (p=0.007).

CONCLUSION: Rare variants in CAV1, a membrane scaffolding protein essential in multiple cellular and metabolic functions, are associated with higher CAV1 gene expression and lower OSA severity, suggesting a novel target for modulating OSA severity.

}, issn = {1535-4970}, doi = {10.1164/rccm.202203-0618OC}, author = {Liang, Jingjing and Wang, Heming and Cade, Brian E and Kurniansyah, Nuzulul and He, Karen Y and Lee, Jiwon and Sands, Scott A and Brody, Jennifer and Chen, Han and Gottlieb, Daniel J and Evans, Daniel S and Guo, Xiuqing and Gharib, Sina A and Hale, Lauren and Hillman, David R and Lutsey, Pamela L and Mukherjee, Sutapa and Ochs-Balcom, Heather M and Palmer, Lyle J and Purcell, Shaun and Saxena, Richa and Patel, Sanjay R and Stone, Katie L and Tranah, Gregory J and Boerwinkle, Eric and Lin, Xihong and Liu, Yongmei and Psaty, Bruce M and Vasan, Ramachandran S and Manichaikul, Ani and Rich, Stephen S and Rotter, Jerome I and Sofer, Tamar and Redline, Susan and Zhu, Xiaofeng} } @article {9323, title = {Association of Mitochondrial DNA Copy Number With Brain MRI Markers and Cognitive Function: A Meta-analysis of Community-Based Cohorts.}, journal = {Neurology}, year = {2023}, month = {2023 Mar 16}, abstract = {

BACKGROUND AND OBJECTIVES: Previous studies suggest lower mitochondrial DNA (mtDNA) copy number (CN) is associated with neurodegenerative diseases. However, whether mtDNA CN in whole blood is related to endophenotypes of Alzheimer{\textquoteright}s disease (AD) and AD related dementia (AD/ADRD) needs further investigation. We assessed the association of mtDNA CN with cognitive function and MRI measures in community-based samples of middle-aged to older adults.

METHODS: We included dementia-free participants from nine diverse community-based cohorts with whole-genome sequencing in the Trans-Omics for Precision Medicine (TOPMed) program. Circulating mtDNA CN was estimated as twice the ratio of the average coverage of mtDNA to nuclear DNA. Brain MRI markers included total brain, hippocampal, and white matter hyperintensity volumes. General cognitive function was derived from distinct cognitive domains. We performed cohort-specific association analyses of mtDNA CN with AD/ADRD endophenotypes assessed within {\textpm}5 years (i.e., cross-sectional analyses) or 5 to 20 years after blood draw (i.e., prospective analyses) adjusting for potential confounders. We further explored associations stratified by sex and age (<60 vs. >=60 years). Fixed-effects or sample size-weighted meta-analyses were performed to combine results. Finally, we performed Mendelian randomization (MR) analyses to assess causality.

RESULTS: We included up to 19,152 participants (mean age 59 years, 57\% women). Higher mtDNA CN was cross-sectionally associated with better general cognitive function (Beta=0.04; 95\% CI 0.02, 0.06) independent of age, sex, batch effects, race/ethnicity, time between blood draw and cognitive evaluation, cohort-specific variables, and education. Additional adjustment for blood cell counts or cardiometabolic traits led to slightly attenuated results. We observed similar significant associations with cognition in prospective analyses, although of reduced magnitude. We found no significant associations between mtDNA CN and brain MRI measures in meta-analyses. MR analyses did not reveal a causal relation between mtDNA CN in blood and cognition.

DISCUSSION: Higher mtDNA CN in blood is associated with better current and future general cognitive function in large and diverse communities across the US. Although MR analyses did not support a causal role, additional research is needed to assess causality. Circulating mtDNA CN could serve nevertheless as a biomarker of current and future cognitive function in the community.

}, issn = {1526-632X}, doi = {10.1212/WNL.0000000000207157}, author = {Zhang, Yuankai and Liu, Xue and Wiggins, Kerri L and Kurniansyah, Nuzulul and Guo, Xiuqing and Rodrigue, Amanda L and Zhao, Wei and Yanek, Lisa R and Ratliff, Scott M and Pitsillides, Achilleas and Aguirre Pati{\~n}o, Juan Sebastian and Sofer, Tamar and Arking, Dan E and Austin, Thomas R and Beiser, Alexa S and Blangero, John and Boerwinkle, Eric and Bressler, Jan and Curran, Joanne E and Hou, Lifang and Hughes, Timothy M and Kardia, Sharon L and Launer, Lenore and Levy, Daniel and Mosley, Tom H and Nasrallah, Ilya M and Rich, Stephen S and Rotter, Jerome I and Seshadri, Sudha and Tarraf, Wassim and Gonz{\'a}lez, Kevin A and Ramachandran, Vasan and Yaffe, Kristine and Nyquist, Paul A and Psaty, Bruce M and DeCarli, Charles S and Smith, Jennifer A and Glahn, David C and Gonz{\'a}lez, Hector M and Bis, Joshua C and Fornage, Myriam and Heckbert, Susan R and Fitzpatrick, Annette L and Liu, Chunyu and Satizabal, Claudia L} } @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 {9586, title = {Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.}, journal = {medRxiv}, year = {2023}, month = {2023 Dec 14}, 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.

}, doi = {10.1101/2023.12.13.23299909}, 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 Mitchel, 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 Der Ida Chen, Yii and Sofer, Tamar} } @article {9851, title = {The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.}, journal = {medRxiv}, year = {2024}, month = {2024 Dec 20}, abstract = {

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one{\textquoteright}s global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

}, doi = {10.1101/2024.03.05.24303738}, author = {Huang, Yu-Jyun and Kurniansyah, Nuzulul and Goodman, Matthew O and Spitzer, Brian W and Wang, Jiongming and Stilp, Adrienne and Laurie, Cecelia and de Vries, Paul S and Chen, Han and Min, Yuan-I and Sims, Mario and Peloso, Gina M and Guo, Xiuqing and Bis, Joshua C and Brody, Jennifer A and Raffield, Laura M and Smith, Jennifer A and Zhao, Wei and Rotter, Jerome I and Rich, Stephen S and Redline, Susan and Fornage, Myriam and Kaplan, Robert and Franceschini, Nora and Levy, Daniel and Morrison, Alanna C and Boerwinkle, Eric and Smith, Nicholas L and Kooperberg, Charles and Psaty, Bruce M and Z{\"o}llner, Sebastian and Sofer, Tamar} } @article {9861, title = {Gene-Excessive Sleepiness Interactions Suggest Treatment Targets for Obstructive Sleep Apnea Subtype.}, journal = {medRxiv}, year = {2024}, month = {2024 Oct 28}, abstract = {

Obstructive sleep apnea (OSA) is a multifactorial sleep disorder characterized by a strong genetic basis. Excessive daytime sleepiness (EDS) is a symptom that is reported by a subset of OSA patients, persisting even after treatment with continuous positive airway pressure (CPAP). It is recognized as a clinical subtype underlying OSA carrying alarming heightened cardiovascular risk. Thus, conceptualizing EDS as an exposure variable, we sought to investigate EDS{\textquoteright}s influence on genetic variation linked to apnea-hypopnea index (AHI), a diagnostic measure of OSA severity. This study serves as the first large-scale genome-wide gene x environment interaction analysis for AHI, investigating the interplay between its genetic markers and EDS across and within specific sex. Our work pools together whole genome sequencing data from seven cohorts, enabling a diverse dataset (four population backgrounds) of over 11,500 samples. Among the total 16 discovered genetic targets with interaction evidence with EDS, eight are previously unreported for OSA, including , , and identified in all sexes; , , and identified in males; and and identified in females. We discuss connections to insulin resistance, thiamine deficiency, and resveratrol use that may be worthy of therapeutic consideration for excessively sleepy OSA patients.

}, doi = {10.1101/2024.10.25.24316158}, author = {Nagarajan, Pavithra and Kurniansyah, Nuzulul and Lee, Jiwon and Gharib, Sina A and Xu, Yushan and Zhang, Yiyan and Spitzer, Brian and Faquih, Tariq and Zhou, Hufeng and Boerwinkle, Eric and Chen, Han and Gottlieb, Daniel J and Guo, Xiuqing and Heard-Costa, Nancy L and Hidalgo, Bertha A and Levy, Daniel and Liu, Peter Y and Mei, Hao and Montalvan, Rebecca and Mukherjee, Sutapa and North, Kari E and O{\textquoteright}Conner, George T and Palmer, Lyle J and Patel, Sanjay R and Psaty, Bruce M and Purcell, Shaun M and Raffield, Laura M and Rich, Stephen S and Rotter, Jerome I and Saxena, Richa and Smith, Albert V and Stone, Katie L and Zhu, Xiaofeng and Cade, Brian E and Sofer, Tamar and Redline, Susan and Wang, Heming} } @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} }