Elizabeth Jordan, Hanyu Ni, Patricia Parker, Daniel D Kinnamon, Anjali Owens, Brian Lowes, Chetan Shenoy, Cindy M Martin, Daniel P Judge, Daniel P Fishbein, Douglas Stoller, Elina Minami, Evan P Kransdorf, Frank Smart, Garrie J Haas, Gordon S Huggins, Gregory A Ewald, Jamie Diamond, Jane E Wilcox, Javier Jimenez, Jessica Wang, Jose Tallaj, Mark H Drazner, Mark Hofmeyer, Matthew T Wheeler, Omar Wever Pinzon, Palak Shah, Stephen S Gottlieb, Stuart Katz, Supriya Shore, W H Wilson Tang, Ray E Hershberger
{"title":"Implementing Precision Medicine for Dilated Cardiomyopathy: Insights From the DCM Consortium.","authors":"Elizabeth Jordan, Hanyu Ni, Patricia Parker, Daniel D Kinnamon, Anjali Owens, Brian Lowes, Chetan Shenoy, Cindy M Martin, Daniel P Judge, Daniel P Fishbein, Douglas Stoller, Elina Minami, Evan P Kransdorf, Frank Smart, Garrie J Haas, Gordon S Huggins, Gregory A Ewald, Jamie Diamond, Jane E Wilcox, Javier Jimenez, Jessica Wang, Jose Tallaj, Mark H Drazner, Mark Hofmeyer, Matthew T Wheeler, Omar Wever Pinzon, Palak Shah, Stephen S Gottlieb, Stuart Katz, Supriya Shore, W H Wilson Tang, Ray E Hershberger","doi":"10.1161/CIRCGEN.125.005078","DOIUrl":"https://doi.org/10.1161/CIRCGEN.125.005078","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e005078"},"PeriodicalIF":6.0,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Min Seo Kim, Shaan Khurshid, Shinwan Kany, Lu-Chen Weng, Sarah Urbut, Carolina Roselli, Leonoor Wijdeveld, Sean J Jurgens, Joel T Rämö, Patrick T Ellinor, Akl C Fahed
{"title":"Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.","authors":"Min Seo Kim, Shaan Khurshid, Shinwan Kany, Lu-Chen Weng, Sarah Urbut, Carolina Roselli, Leonoor Wijdeveld, Sean J Jurgens, Joel T Rämö, Patrick T Ellinor, Akl C Fahed","doi":"10.1161/CIRCGEN.124.004943","DOIUrl":"https://doi.org/10.1161/CIRCGEN.124.004943","url":null,"abstract":"<p><strong>Background: </strong>Clinical factors discriminate incident atrial fibrillation (AF) risk with moderate accuracy, with only modest improvement after incorporation of polygenic risk scores. Whether emerging large-scale proteomic profiling can augment AF risk estimation is unknown.</p><p><strong>Methods: </strong>In the UK Biobank cohort, we derived and validated a machine learning model to predict incident AF risk using serum proteins (Pro-AF). We compared Pro-AF to a validated clinical risk score (Cohorts for Aging and Genomic Epidemiology-Atrial Fibrillation) and an AF polygenic risk score. Models were evaluated in a multiply resampled test set from nested cross-validation (internal test set), and a sample of UK Biobank participants separate from model development (hold-out test set). Metrics included discrimination of 5-year incident AF using time-dependent area under the receiver operating characteristic curve and net reclassification.</p><p><strong>Results: </strong>Trained in 32 631 UK Biobank participants, Pro-AF predicts incident AF using 121 protein levels (out of 2911 protein analytes). When assessed in the internal test set comprising 30 632 individuals (mean age 57±8 years, 54% women, 2045 AF events) and hold-out test set comprising 13 998 individuals (mean age 57±8 years, 54% women, 870 AF events), discrimination of 5-year incident AF was highest using Pro-AF (area under the receiver operating characteristic curve internal: 0.761 [95% CI, 0.745-0.780], hold-out: 0.763 [0.734-0.784]), followed by Cohorts for Aging and Genomic Epidemiology-Atrial Fibrillation (0.719 [0.700-0.737]; 0.702 [0.668-0.730]) and the polygenic risk score (0.686 [0.668-0.702]; 0.682 [0.660-0.710]). AF risk estimates were well-calibrated, and the addition of Pro-AF led to substantial continuous net reclassification improvement over Cohorts for Aging and Genomic Epidemiology-Atrial Fibrillation (eg, internal test set 0.410 [0.330-0.492]). A simplified Pro-AF including only the 5 most influential proteins (NT-proBNP, EDA2R [ectodysplasin A2 receptor], NPPB [B-type natriuretic peptide], BCAN [brevican core protein], and GDF15 [growth/differentiation factor 15]), retained favorable discriminative value (area under the receiver operating characteristic curve internal: 0.750 [0.733-0.768]; hold-out: 0.759 [0.732-0.790]).</p><p><strong>Conclusions: </strong>A machine learning-based protein score discriminates 5-year incident AF risk favorably compared with clinical and genetic risk factors. Large-scale proteomic analysis may assist in the prioritization of individuals at risk for AF for screening and related preventive interventions.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004943"},"PeriodicalIF":6.0,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144309596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Usman A Tahir, Daniel Reichart, Anisha Purohit, Jacob L Barber, Gaurav Tiwari, Laurie Farrell, Julia Marine, Darius Roy, Joshen Patel, Catherine Ireland, Carolyn Y Ho, Christine E Seidman, Robert E Gerszten, Neal K Lakdawala
{"title":"Plasma Proteomics Reveals Dysregulated Pathways Across the Spectrum <i>LMNA</i> Cardiomyopathy.","authors":"Usman A Tahir, Daniel Reichart, Anisha Purohit, Jacob L Barber, Gaurav Tiwari, Laurie Farrell, Julia Marine, Darius Roy, Joshen Patel, Catherine Ireland, Carolyn Y Ho, Christine E Seidman, Robert E Gerszten, Neal K Lakdawala","doi":"10.1161/CIRCGEN.124.004924","DOIUrl":"https://doi.org/10.1161/CIRCGEN.124.004924","url":null,"abstract":"<p><strong>Background: </strong>Pathogenic variants in the <i>lamin A/C</i> (<i>LMNA</i>) gene cause an aggressive form of dilated cardiomyopathy (DCM), marked by higher rates of advanced conduction disease, malignant ventricular tachyarrhythmias, and advanced heart failure compared with other causes of nonischemic cardiomyopathy. However, the mechanisms that drive the development and progression of <i>LMNA</i> DCM are incompletely understood.</p><p><strong>Methods: </strong>To identify proteins and biological pathways associated with likely pathogenic/pathogenic <i>LMNA</i> variants, we measured ≈3000 plasma proteins using the OLINK platform in a genetic DCM cohort consisting of <i>LMNA</i> (n=41) and sarcomeric (n=18) DCM, along with phenotype-negative individuals from family-based cascade screening (n=55) with (<i>LMNA</i>, n=16; sarcomere, n=12) or without the family variant (genotype negative, n=27).</p><p><strong>Results: </strong>We identified several novel proteins associated with <i>LMNA</i> DCM compared with sarcomeric DCM, including EDA2R (ectodysplasin A2 receptor; per log2 fold change in relative protein abundance, β=3.0; <i>P</i>=4×10<sup>-</sup>³) and <i>MYL4</i> (myosin light chain 4; β=2.32; <i>P</i>=5×10<sup>-</sup>³). Among the proteins associated with <i>LMNA</i> DCM, 26 showed concordant differential gene expression from single-cell sequencing in cardiomyocytes from myocardial biopsies in advanced <i>LMNA</i> heart failure compared with control hearts (false discovery rate, <5%). We performed principal component analyses on these 26 proteins to identify proteomic signatures of <i>LMNA</i> DCM and found the first principal component to be associated with left ventricular ejection fraction and complete heart block in the <i>LMNA</i> DCM cohort. Six proteins-EDA2R, MYL4, CRIM1 (cysteine-rich transmembrane BMP regulator 1), TPR (translocated promoter region), FSTL3 (follistatin-like 3), and NFYA (nuclear transcription factor Y)-were associated with <i>LMNA</i> pathogenic variants across phenotype-negative individuals, DCM, and their respective cardiomyocyte RNA expression profiles in advanced heart failure.</p><p><strong>Conclusions: </strong>Proteomic profiling in individuals with likely pathogenic/pathogenic <i>LMNA</i> variants illuminated integral pathways across the spectrum of <i>LMNA</i> DCM. These findings may help advance genotype-driven biomarker discovery and tailored therapeutic development in <i>LMNA</i> DCM.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004924"},"PeriodicalIF":6.0,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144282639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C Anwar A Chahal, Fares Alahdab, Babken Asatryan, Daniel Addison, Nay Aung, Mina K Chung, Spiros Denaxas, Jessilyn Dunn, Jennifer L Hall, Nathalie Pamir, David J Slotwiner, Jose D Vargas, Antonis A Armoundas
{"title":"Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine.","authors":"C Anwar A Chahal, Fares Alahdab, Babken Asatryan, Daniel Addison, Nay Aung, Mina K Chung, Spiros Denaxas, Jessilyn Dunn, Jennifer L Hall, Nathalie Pamir, David J Slotwiner, Jose D Vargas, Antonis A Armoundas","doi":"10.1161/CIRCGEN.124.004624","DOIUrl":"10.1161/CIRCGEN.124.004624","url":null,"abstract":"<p><p>Despite advances in cardiovascular care and improved outcomes, fragmented healthcare systems, nonequitable access to health care, and nonuniform and unbiased collection and access to healthcare data have exacerbated disparities in healthcare provision and further delayed the technological-enabled implementation of precision medicine. Precision medicine relies on a foundation of accurate and valid omics and phenomics that can be harnessed at scale from electronic health records. Big data approaches in noncardiovascular healthcare domains have helped improve efficiency and expedite the development of novel therapeutics; therefore, applying such an approach to cardiovascular precision medicine is an opportunity to further advance the field. Several endeavors, including the American Heart Association Precision Medicine platform and public-private partnerships (such as BigData@Heart in Europe), as well as cloud-based platforms, such as Terra used for the National Institutes of Health All of Us, are attempting to temporally and ontologically harmonize data. This state-of-the-art review summarizes best practices used in cardiovascular genomic and precision medicine and provides recommendations for systems' requirements that could enhance and accelerate the integration of these platforms.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004624"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143982592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robin M Perelli, Madeleine J Sitton, Joel D Bohning, Adrian Pickar-Oliver, K Tyler McCullough, Mary E Moya-Mendez, Scott Zheng, Heather Daniels, Garth Devlin, Aravind Asokan, Charles A Gersbach, Andrew P Landstrom
{"title":"Deletion of Exon 51 in a Humanized Duchenne Muscular Dystrophy Mouse Model Abolishes Ventricular Arrhythmia Predisposition.","authors":"Robin M Perelli, Madeleine J Sitton, Joel D Bohning, Adrian Pickar-Oliver, K Tyler McCullough, Mary E Moya-Mendez, Scott Zheng, Heather Daniels, Garth Devlin, Aravind Asokan, Charles A Gersbach, Andrew P Landstrom","doi":"10.1161/CIRCGEN.124.004867","DOIUrl":"10.1161/CIRCGEN.124.004867","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004867"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173797/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Identification and Functional Assessment of Candidate Causal <i>Cis</i>-Regulatory Variants Underlying Electrocardiographic QT Interval GWAS Loci.","authors":"Supraja Kadagandla, Lavanya Gunamalai, Dongwon Lee, Ashish Kapoor","doi":"10.1161/CIRCGEN.124.005032","DOIUrl":"10.1161/CIRCGEN.124.005032","url":null,"abstract":"<p><strong>Background: </strong>Identifying causal variants among tens or hundreds of associated variants at each locus in genome-wide association studies is challenging. As the vast majority of genome-wide association studies variants are noncoding, sequence variation at <i>cis</i>-regulatory elements (CREs) affecting transcriptional expression of specific genes is a widely accepted molecular hypothesis. Following this hypothesis, combined with the observation that open chromatin is a universal hallmark of all types of CREs, we aimed to identify candidate causal <i>cis</i>-regulatory variants underlying QT interval genome-wide association studies loci.</p><p><strong>Methods: </strong>Common variants in high linkage disequilibrium with genome-wide significant variants were identified using variant call format tools. Genome-wide maps of cardiac putative CREs were generated by MACS2-based peak calling in human cardiac left ventricular DNase I sequencing and Assay for Transposase-Accessible Chromatin using sequencing data sets (<i>n</i>=13). Variant-CRE overlap was performed using custom tracks in the Table Browser tool at the UCSC Genome Browser. Luciferase reporter-based enhancer assays for variant-centered test elements were performed in mouse HL1 cardiomyocyte cells. Reporter activities of allelic pairs were compared using the Wilcoxon rank-sum test.</p><p><strong>Results: </strong>At a dozen genome-wide association studies loci, selected for higher effect sizes and better understanding of the likely causal genes, we identified all genome-wide significant variants (<i>n</i>=1401) and included all common variants (minor allele frequency >1%) in high linkage disequilibrium (<i>r</i><sup>2</sup>>0.9) with them as candidate variants (<i>n</i>=3482). Candidate variants were filtered for overlap with cardiac left ventricular putative CREs to identify candidate causal <i>cis</i>-regulatory variants (<i>n</i>=476), which were further assessed for being a known cardiac expression quantitative trait locus variant as additional functional evidence (<i>n</i>=243). Functional evaluation of a subset of seven candidate variants by luciferase reporter-based enhancer assays in HL1 cells using variant-centered test elements led to the identification of 6 enhancer variants with significant allelic differences.</p><p><strong>Conclusions: </strong>These efforts have generated a comprehensive set of candidate causal variants expected to be enriched for <i>cis</i>-regulatory potential and thereby, explaining the observed genetic associations.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e005032"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Parag R Gajendragadkar, Adam Von Ende, Federico Murgia, Alison Offer, C Fielder Camm, Rohan S Wijesurendra, Barbara Casadei, Jemma C Hopewell
{"title":"Mechanistic Pathways Underlying Genetic Predisposition to Atrial Fibrillation Are Associated With Different Cardiac Phenotypes and Cardioembolic Stroke Risk.","authors":"Parag R Gajendragadkar, Adam Von Ende, Federico Murgia, Alison Offer, C Fielder Camm, Rohan S Wijesurendra, Barbara Casadei, Jemma C Hopewell","doi":"10.1161/CIRCGEN.124.004932","DOIUrl":"10.1161/CIRCGEN.124.004932","url":null,"abstract":"<p><strong>Background: </strong>Genome-wide association studies have clustered candidate genes associated with atrial fibrillation (AF) into biological pathways reflecting different pathophysiological mechanisms. We investigated whether these pathways associate with distinct intermediate phenotypes and confer differing risks of cardioembolic stroke.</p><p><strong>Methods: </strong>Three distinct subsets of AF-associated genetic variants, each representing a different mechanistic pathway, that is, the cardiac muscle function and integrity pathway (15 variants), the cardiac developmental pathway (25 variants), and the cardiac ion channels pathway (12 variants), were identified from previous AF genome-wide association studies. Using genetic epidemiological methods and large-scale datasets such as UK Biobank, deCODE, and GIGASTROKE, we investigated the associations of these pathways with AF-related cardiac intermediate phenotypes, which included electrocardiogram parameters (≈16 500 electrocardiograms), left atrial and ventricular size and function (≈36 000 cardiac magnetic resonance imaging scans), and relevant plasma biomarkers (N-terminal pro-B-type natriuretic peptide, ≈70 000 samples; high-sensitivity troponin I and T, ≈87 000 samples), as well as with subtypes of ischemic stroke (≈11 000 cases).</p><p><strong>Results: </strong>Genetic variants representing distinct AF-related mechanistic pathways had significantly different effects on several AF-related phenotypes. In particular, the muscle pathway was associated with a longer PR interval (<i>P</i> for heterogeneity between pathways [<i>P</i><sub>het</sub>]=1×10<sup>-10</sup>), lower left atrial emptying fraction (<i>P</i><sub>het</sub>=5×10<sup>-5</sup>), and higher N-terminal pro-B-type natriuretic peptide (<i>P</i><sub>het</sub>=2×10<sup>-3</sup>) per log-odds higher risk of AF compared with the developmental and ion-channel pathways. In contrast, the ion-channel pathway was associated with a lower risk of cardioembolic stroke (<i>P</i><sub>het</sub>=0.04 in European, and 7×10<sup>-</sup><sup>3</sup> in multiancestry populations) compared with the other pathways.</p><p><strong>Conclusions: </strong>Genetic variants representing specific mechanistic pathways for AF are associated with distinct intermediate cardiac phenotypes and a different risk of cardioembolic stroke. These findings provide a better understanding of the etiological heterogeneity underlying the development of AF and its downstream impact on disease and may offer a route to more targeted treatment strategies.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":"18 3","pages":"e004932"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144316024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ebuka Onyenobi, Michael Zhong, Opeyemi Soremekun, Abram Kamiza, Romuald Boua, Tinashe Chikowore, Segun Fatumo, Ananyo Choudhury, Scott Hazelhurst, Clement Adebamowo, Michèle Ramsay, Bamidele Tayo, Jennifer S Albrecht, Timothy D O'Connor, Yuji Zhang, Braxton D Mitchell, Sally N Adebamowo
{"title":"Development and Validation of Polygenic Risk Scores for Blood Pressure Traits in Continental African Populations.","authors":"Ebuka Onyenobi, Michael Zhong, Opeyemi Soremekun, Abram Kamiza, Romuald Boua, Tinashe Chikowore, Segun Fatumo, Ananyo Choudhury, Scott Hazelhurst, Clement Adebamowo, Michèle Ramsay, Bamidele Tayo, Jennifer S Albrecht, Timothy D O'Connor, Yuji Zhang, Braxton D Mitchell, Sally N Adebamowo","doi":"10.1161/CIRCGEN.124.005048","DOIUrl":"10.1161/CIRCGEN.124.005048","url":null,"abstract":"<p><strong>Background: </strong>Most polygenic risk scores (PRS) have been developed in European populations, frequently leading to limited transferability across diverse ancestry populations. This study aimed to develop and evaluate PRS for blood pressure (BP) traits in continental African populations and investigate how African genetic diversity influences PRS performance.</p><p><strong>Methods: </strong>We generated PRS for systolic BP, diastolic BP, pulse pressure, and hypertension. We used a pan-African cohort as the target population and compared single-ancestry and multi-ancestry PRS methods. We compared the performance of African ancestry-derived PRS against multi-ancestry PRS on the entire data set and within South, East, and West African subpopulations.</p><p><strong>Results: </strong>Multi-ancestry PRS demonstrated significantly higher predictive accuracy compared with single-ancestry PRS models. PRS predictive accuracy varied across different African regions, with the highest performance observed in East Africa. In the combined population, the difference in mean BP values between the first multi-ancestry PRS quartile and the top quartile was 6.53 (95% CI, 5.3-7.74), 3.81 (95% CI, 3.9-4.52), and 3.59 (95% CI, 2.4-4.32) mm Hg for systolic BP, diastolic BP, and pulse pressure, respectively. Individuals in the highest PRS risk quartile had odds of hypertension that were 1.47 (95% CI, 1.7-1.69) times greater than those in the lowest risk quartile.</p><p><strong>Conclusions: </strong>These findings highlight the importance of integrating diverse ancestries in PRS development and accounting for subpopulation genetic variation to improve the predictive accuracy of BP PRS in African populations.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e005048"},"PeriodicalIF":6.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173169/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144149501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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