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Showing 1 to 8 of 8 entries
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Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases.

Journal of the American Society of Nephrology : JASN

Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J.
PMID: 34548389
J Am Soc Nephrol. 2021 Sep 21; doi: 10.1681/ASN.2020111599. Epub 2021 Sep 21.

BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known.METHODS: We developed a genome-wide polygenic risk...

Cite
Yu Z, Jin J, Tin A, et al. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol. 2021;doi: 10.1681/ASN.2020111599.
Yu, Z., Jin, J., Tin, A., Köttgen, A., Yu, B., Chen, J., Surapaneni, A., Zhou, L., Ballantyne, C. M., Hoogeveen, R. C., Arking, D. E., Chatterjee, N., Grams, M. E., & Coresh, J. (2021). Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. Journal of the American Society of Nephrology : JASN, . https://doi.org/10.1681/ASN.2020111599
Yu, Zhi, et al. "Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases." Journal of the American Society of Nephrology : JASN vol. (2021). doi: https://doi.org/10.1681/ASN.2020111599
Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol. 2021 Sep 21; doi: 10.1681/ASN.2020111599. Epub 2021 Sep 21. PMID: 34548389; PMCID: PMC8638405.
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A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.

Biostatistics (Oxford, England)

Zhang H, Zhao N, Ahearn TU, Wheeler W, García-Closas M, Chatterjee N.
PMID: 32112086
Biostatistics. 2021 Oct 13;22(4):772-788. doi: 10.1093/biostatistics/kxz065.

Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling...

Cite
Zhang H, Zhao N, Ahearn TU, et al. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics. 2021;22(4):772-788doi: 10.1093/biostatistics/kxz065.
Zhang, H., Zhao, N., Ahearn, T. U., Wheeler, W., García-Closas, M., & Chatterjee, N. (2021). A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics (Oxford, England), 22(4), 772-788. https://doi.org/10.1093/biostatistics/kxz065
Zhang, Haoyu, et al. "A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics." Biostatistics (Oxford, England) vol. 22,4 (2021): 772-788. doi: https://doi.org/10.1093/biostatistics/kxz065
Zhang H, Zhao N, Ahearn TU, Wheeler W, García-Closas M, Chatterjee N. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics. 2021 Oct 13;22(4):772-788. doi: 10.1093/biostatistics/kxz065. PMID: 32112086; PMCID: PMC8511944.
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A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics.

Biostatistics (Oxford, England)

Zhang H, Zhao N, Ahearn TU, Wheeler W, García-Closas M, Chatterjee N.
PMID: 32112086
Biostatistics. 2021 Oct 13;22(4):772-788. doi: 10.1093/biostatistics/kxz065.

Cancers are routinely classified into subtypes according to various features, including histopathological characteristics and molecular markers. Previous genome-wide association studies have reported heterogeneous associations between loci and cancer subtypes. However, it is not evident what is the optimal modeling...

Cite
Zhang H, Zhao N, Ahearn TU, et al. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics. 2021;22(4):772-788doi: 10.1093/biostatistics/kxz065.
Zhang, H., Zhao, N., Ahearn, T. U., Wheeler, W., García-Closas, M., & Chatterjee, N. (2021). A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics (Oxford, England), 22(4), 772-788. https://doi.org/10.1093/biostatistics/kxz065
Zhang, Haoyu, et al. "A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics." Biostatistics (Oxford, England) vol. 22,4 (2021): 772-788. doi: https://doi.org/10.1093/biostatistics/kxz065
Zhang H, Zhao N, Ahearn TU, Wheeler W, García-Closas M, Chatterjee N. A mixed-model approach for powerful testing of genetic associations with cancer risk incorporating tumor characteristics. Biostatistics. 2021 Oct 13;22(4):772-788. doi: 10.1093/biostatistics/kxz065. PMID: 32112086; PMCID: PMC8511944.
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Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited.

Circulation

Arvanitis M, Qi G, Bhatt DL, Post WS, Chatterjee N, Battle A, McEvoy JW.
PMID: 33249881
Circulation. 2021 Mar 02;143(9):895-906. doi: 10.1161/CIRCULATIONAHA.120.049819. Epub 2020 Nov 30.

BACKGROUND: Recent clinical guidelines support intensive blood pressure treatment targets. However, observational data suggest that excessive diastolic blood pressure (DBP) lowering might increase the risk of myocardial infarction (MI), reflecting a J- or U-shaped relationship.METHODS: We analyzed 47 407...

Cite
Arvanitis M, Qi G, Bhatt DL, et al. Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation. 2020;143(9):895-906doi: 10.1161/CIRCULATIONAHA.120.049819.
Arvanitis, M., Qi, G., Bhatt, D. L., Post, W. S., Chatterjee, N., Battle, A., & McEvoy, J. W. (2021). Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation, 143(9), 895-906. https://doi.org/10.1161/CIRCULATIONAHA.120.049819
Arvanitis, Marios, et al. "Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited." Circulation vol. 143,9 (2021): 895-906. doi: https://doi.org/10.1161/CIRCULATIONAHA.120.049819
Arvanitis M, Qi G, Bhatt DL, Post WS, Chatterjee N, Battle A, McEvoy JW. Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation. 2021 Mar 02;143(9):895-906. doi: 10.1161/CIRCULATIONAHA.120.049819. Epub 2020 Nov 30. PMID: 33249881; PMCID: PMC7920937.
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A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.

Journal of the American Statistical Association

Chen TH, Chatterjee N, Landi MT, Shi J.
PMID: 34483403
J Am Stat Assoc. 2021;116(533):133-143. doi: 10.1080/01621459.2020.1764849. Epub 2020 Oct 12.

Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance...

Cite
Chen TH, Chatterjee N, Landi MT, et al. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc. 2020;116(533):133-143doi: 10.1080/01621459.2020.1764849.
Chen, T. H., Chatterjee, N., Landi, M. T., & Shi, J. (2021). A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. Journal of the American Statistical Association, 116(533), 133-143. https://doi.org/10.1080/01621459.2020.1764849
Chen, Ting-Huei, et al. "A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information." Journal of the American Statistical Association vol. 116,533 (2021): 133-143. doi: https://doi.org/10.1080/01621459.2020.1764849
Chen TH, Chatterjee N, Landi MT, Shi J. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc. 2021;116(533):133-143. doi: 10.1080/01621459.2020.1764849. Epub 2020 Oct 12. PMID: 34483403; PMCID: PMC8414872.
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Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited.

Circulation

Arvanitis M, Qi G, Bhatt DL, Post WS, Chatterjee N, Battle A, McEvoy JW.
PMID: 33249881
Circulation. 2021 Mar 02;143(9):895-906. doi: 10.1161/CIRCULATIONAHA.120.049819. Epub 2020 Nov 30.

BACKGROUND: Recent clinical guidelines support intensive blood pressure treatment targets. However, observational data suggest that excessive diastolic blood pressure (DBP) lowering might increase the risk of myocardial infarction (MI), reflecting a J- or U-shaped relationship.METHODS: We analyzed 47 407...

Cite
Arvanitis M, Qi G, Bhatt DL, et al. Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation. 2020;143(9):895-906doi: 10.1161/CIRCULATIONAHA.120.049819.
Arvanitis, M., Qi, G., Bhatt, D. L., Post, W. S., Chatterjee, N., Battle, A., & McEvoy, J. W. (2021). Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation, 143(9), 895-906. https://doi.org/10.1161/CIRCULATIONAHA.120.049819
Arvanitis, Marios, et al. "Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited." Circulation vol. 143,9 (2021): 895-906. doi: https://doi.org/10.1161/CIRCULATIONAHA.120.049819
Arvanitis M, Qi G, Bhatt DL, Post WS, Chatterjee N, Battle A, McEvoy JW. Linear and Nonlinear Mendelian Randomization Analyses of the Association Between Diastolic Blood Pressure and Cardiovascular Events: The J-Curve Revisited. Circulation. 2021 Mar 02;143(9):895-906. doi: 10.1161/CIRCULATIONAHA.120.049819. Epub 2020 Nov 30. PMID: 33249881; PMCID: PMC7920937.
Copy Download .nbib Format: NLM
A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information.

Journal of the American Statistical Association

Chen TH, Chatterjee N, Landi MT, Shi J.
PMID: 34483403
J Am Stat Assoc. 2021;116(533):133-143. doi: 10.1080/01621459.2020.1764849. Epub 2020 Oct 12.

Large-scale genome-wide association (GWAS) studies provide opportunities for developing genetic risk prediction models that have the potential to improve disease prevention, intervention or treatment. The key step is to develop polygenic risk score (PRS) models with high predictive performance...

Cite
Chen TH, Chatterjee N, Landi MT, et al. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc. 2020;116(533):133-143doi: 10.1080/01621459.2020.1764849.
Chen, T. H., Chatterjee, N., Landi, M. T., & Shi, J. (2021). A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. Journal of the American Statistical Association, 116(533), 133-143. https://doi.org/10.1080/01621459.2020.1764849
Chen, Ting-Huei, et al. "A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information." Journal of the American Statistical Association vol. 116,533 (2021): 133-143. doi: https://doi.org/10.1080/01621459.2020.1764849
Chen TH, Chatterjee N, Landi MT, Shi J. A penalized regression framework for building polygenic risk models based on summary statistics from genome-wide association studies and incorporating external information. J Am Stat Assoc. 2021;116(533):133-143. doi: 10.1080/01621459.2020.1764849. Epub 2020 Oct 12. PMID: 34483403; PMCID: PMC8414872.
Copy Download .nbib Format: NLM
Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases.

Journal of the American Society of Nephrology : JASN

Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J.
PMID: 34548389
J Am Soc Nephrol. 2021 Sep 21; doi: 10.1681/ASN.2020111599. Epub 2021 Sep 21.

BACKGROUND: Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known.METHODS: We developed a genome-wide polygenic risk...

Cite
Yu Z, Jin J, Tin A, et al. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol. 2021;doi: 10.1681/ASN.2020111599.
Yu, Z., Jin, J., Tin, A., Köttgen, A., Yu, B., Chen, J., Surapaneni, A., Zhou, L., Ballantyne, C. M., Hoogeveen, R. C., Arking, D. E., Chatterjee, N., Grams, M. E., & Coresh, J. (2021). Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. Journal of the American Society of Nephrology : JASN, . https://doi.org/10.1681/ASN.2020111599
Yu, Zhi, et al. "Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases." Journal of the American Society of Nephrology : JASN vol. (2021). doi: https://doi.org/10.1681/ASN.2020111599
Yu Z, Jin J, Tin A, Köttgen A, Yu B, Chen J, Surapaneni A, Zhou L, Ballantyne CM, Hoogeveen RC, Arking DE, Chatterjee N, Grams ME, Coresh J. Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases. J Am Soc Nephrol. 2021 Sep 21; doi: 10.1681/ASN.2020111599. Epub 2021 Sep 21. PMID: 34548389; PMCID: PMC8638405.
Copy Download .nbib Format: NLM
Showing 1 to 8 of 8 entries