@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 {9749, title = {The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.}, journal = {medRxiv}, year = {2024}, month = {2024 Aug 26}, abstract = {

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg ( , , ), and one locus associated with a sex-associated mutation pathway ( . We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of and implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

}, doi = {10.1101/2024.08.22.24312319}, author = {Weinstock, Joshua S and Chaudhry, Sharjeel A and Ioannou, Maria and Viskadourou, Maria and Reventun, Paula and Jakubek, Yasminka A and Alexander Liggett, L and Laurie, Cecelia and Broome, Jai G and Khan, Alyna and Taylor, Kent D and Guo, Xiuqing and Peyser, Patricia A and Boerwinkle, Eric and Chami, Nathalie and Kenny, Eimear E and Loos, Ruth J and Psaty, Bruce M and Russell, Tracy P and Brody, Jennifer A and Yun, Jeong H and Cho, Michael H and Vasan, Ramachandran S and Kardia, Sharon L and Smith, Jennifer A and Raffield, Laura M and Bidulescu, Aurelian and O{\textquoteright}Brien, Emily and de Andrade, Mariza and Rotter, Jerome I and Rich, Stephen S and Tracy, Russell P and Chen, Yii Der Ida and Gu, C Charles and Hsiung, Chao A and Kooperberg, Charles and Haring, Bernhard and Nassir, Rami and Mathias, Rasika and Reiner, Alex and Sankaran, Vijay and Lowenstein, Charles J and Blackwell, Thomas W and Abecasis, Goncalo R and Smith, Albert V and Kang, Hyun M and Natarajan, Pradeep and Jaiswal, Siddhartha and Bick, Alexander and Post, Wendy S and Scheet, Paul and Auer, Paul and Karantanos, Theodoros and Battle, Alexis and Arvanitis, Marios} } @article {9865, title = {Human Plasma Proteomic Profile of Clonal Hematopoiesis.}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 31}, abstract = {

Plasma proteomic profiles associated with subclinical somatic mutations in blood cells may offer novel insights into downstream clinical consequences. Here, we explore such patterns in clonal hematopoiesis of indeterminate potential (CHIP), which is linked to several cancer and non-cancer outcomes, including coronary artery disease (CAD). Among 61,833 ancestrally diverse participants (3,881 with CHIP) from NHLBI TOPMed and UK Biobank with blood-based DNA sequencing and proteomic measurements (1,148 proteins by SomaScan in TOPMed and 2,917 proteins by Olink in UK Biobank), we identified 32 and 345 unique proteins from TOPMed and UK Biobank, respectively, associated with the most prevalent driver genes (, , and ). These associations showed substantial heterogeneity by driver genes, sex, and race, and were enriched for immune response and inflammation pathways. Mendelian randomization in humans, coupled with ELISA in hematopoietic -/- vs wild-type mice validation, disentangled causal proteomic perturbations from CHIP. Lastly, we identified plasma proteins shared between CHIP and CAD.

}, issn = {2692-8205}, doi = {10.1101/2023.07.25.550557}, author = {Yu, Zhi and Vromman, Am{\'e}lie and Nguyen, Ngoc Quynh H and Schuermans, Art and Rentz, Thiago and Vellarikkal, Shamsudheen K and Uddin, Md Mesbah and Niroula, Abhishek and Griffin, Gabriel and Honigberg, Michael C and Lin, Amy E and Gibson, Christopher J and Katz, Daniel H and Tahir, Usman and Fang, Shi and Haidermota, Sara and Ganesh, Shriienidhie and Antoine, Tajmara and Weinstock, Joshua and Austin, Thomas R and Ramachandran, Vasan S and Peloso, Gina M and Hornsby, Whitney and Ganz, Peter and Manson, JoAnn E and Haring, Bernhard and Kooperberg, Charles L and Reiner, Alexander P and Bis, Joshua C and Psaty, Bruce M and Min, Yuan-I and Correa, Adolfo and Lange, Leslie A and Post, Wendy S and Rotter, Jerome I and Rich, Stephen S and Wilson, James G and Ebert, Benjamin L and Yu, Bing and Ballantyne, Christie M and Coresh, Josef and Sankaran, Vijay G and Bick, Alexander G and Jaiswal, Siddhartha and Gerszten, Robert E and Libby, Peter and Gupta, Rajat M and Natarajan, Pradeep} } @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} }