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Your search keyword '"Tiwari, Hemant K"' showing total 14 results
Start Over Author "Tiwari, Hemant K" Journal frontiers in genetics
14 results on '"Tiwari, Hemant K"'

1. Metabolite profiles and DNA methylation in metabolic syndrome: a two-sample, bidirectional Mendelian randomization

Author

Jones, Alana C., primary, Ament, Zsuzsanna, additional, Patki, Amit, additional, Chaudhary, Ninad S., additional, Srinivasasainagendra, Vinodh, additional, Kijpaisalratana, Naruchorn, additional, Absher, Devin M., additional, Tiwari, Hemant K., additional, Arnett, Donna K., additional, Kimberly, W. Taylor, additional, and Irvin, Marguerite R., additional

Published
2023
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2. Genetic Contributors of Incident Stroke in 10,700 African Americans With Hypertension: A Meta-Analysis From the Genetics of Hypertension Associated Treatments and Reasons for Geographic and Racial Differences in Stroke Studies

Author

Armstrong, Nicole D., Srinivasasainagendra, Vinodh, Patki, Amit, Tanner, Rikki M., Hidalgo, Bertha A., Tiwari, Hemant K., Limdi, Nita A., Lange, Ethan M., Lange, Leslie A., Arnett, Donna K., and Irvin, Marguerite R.

Subjects
genome wide association studies, hypertension, polygenic risk score, Genetics, Molecular Medicine, antihypertensives, QH426-470, incident stroke, Genetics (clinical), Original Research, disparities
Abstract

Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals.Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs aiming to evaluate cross-ethnic performance.Results: We identified 10 statistically significant (p < 5.00E-08) and 90 additional suggestive (p < 1.00E-06) variants associated with incident stroke in the meta-analysis. Six of the top 10 variants were located in an intergenic region on chromosome 18 (LINC01443-LOC644669). Additional variants of interest were located in or near the COL12A1, SNTG1, PCDH7, TMTC1, and NTM genes. Replication was conducted in the Warfarin Pharmacogenomics Cohort (WPC), and while none of the variants were directly validated, seven intronic variants of NTM proximal to our target variants, had a p-value Conclusion: This study highlights biologically plausible genetic determinants for incident stroke in hypertensive AAs. Ultimately, a better understanding of genetic risk factors for stroke in AAs may give new insight into stroke burden and potential clinical tools for those among the highest at risk.

Published
2021

3. Whole-Exome Sequencing and hiPSC Cardiomyocyte Models Identify MYRIP, TRAPPC11, and SLC27A6 of Potential Importance to Left Ventricular Hypertrophy in an African Ancestry Population

Author

Irvin, Marguerite R., primary, Aggarwal, Praful, additional, Claas, Steven A., additional, de las Fuentes, Lisa, additional, Do, Anh N., additional, Gu, C. Charles, additional, Matter, Andrea, additional, Olson, Benjamin S., additional, Patki, Amit, additional, Schwander, Karen, additional, Smith, Joshua D., additional, Srinivasasainagendra, Vinodh, additional, Tiwari, Hemant K., additional, Turner, Amy J., additional, Nickerson, Deborah A., additional, Rao, Dabeeru C., additional, Broeckel, Ulrich, additional, and Arnett, Donna K., additional

Published
2021
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4. An Exome-Wide Sequencing Study of the GOLDN Cohort Reveals Novel Associations of Coding Variants and Fasting Plasma Lipids

Author

Geng, Xin, primary, Irvin, Marguerite R., additional, Hidalgo, Bertha, additional, Aslibekyan, Stella, additional, Srinivasasainagendra, Vinodh, additional, An, Ping, additional, Frazier-Wood, Alexis C., additional, Tiwari, Hemant K., additional, Dave, Tushar, additional, Ryan, Kathleen, additional, Ordovas, Jose M., additional, Straka, Robert J., additional, Feitosa, Mary F., additional, Hopkins, Paul N., additional, Borecki, Ingrid, additional, Province, Michael A., additional, Mitchell, Braxton D., additional, Arnett, Donna K., additional, and Zhi, Degui, additional

Published
2019
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8. PCSK9 variation and association with blood pressure in African Americans: preliminary findings from the HyperGEN and REGARDS studies

Author

Tran, Ngan T., primary, Aslibekyan, Stella, additional, Tiwari, Hemant K., additional, Zhi, Degui, additional, Sung, Yun Ju, additional, Hunt, Steven C., additional, Rao, DC, additional, Broeckel, Ulrich, additional, Judd, Suzanne E., additional, Muntner, Paul, additional, Kent, Shia T., additional, Arnett, Donna K., additional, and Irvin, Marguerite R., additional

Published
2015
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14. Integrated genomic and BMI analysis for type 2 diabetes risk assessment.

Author

Lebrón-Aldea, Dayanara, Dhurandhar, Emily J., Pérez-Rodríguez, Paulino, Klimentidis, Yann C., Tiwari, Hemant K., and Vazquez, Ana I.

Subjects
GENETICS of type 2 diabetes, GENOMICS, INSULIN, OBESITY, PREVENTIVE medicine
Abstract

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by GenomeWide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy. [ABSTRACT FROM AUTHOR]

Published
2015
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