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On powerful GWAS in admixed populations.

Nature genetics

Hou K, Bhattacharya A, Mester R, Burch KS, Pasaniuc B.
PMID: 34824480
Nat Genet. 2021 Dec;53(12):1631-1633. doi: 10.1038/s41588-021-00953-5. Epub 2021 Nov 25.

No abstract available.

Cite
Hou K, Bhattacharya A, Mester R, et al. On powerful GWAS in admixed populations. Nat Genet. 2021;53(12):1631-1633doi: 10.1038/s41588-021-00953-5.
Hou, K., Bhattacharya, A., Mester, R., Burch, K. S., & Pasaniuc, B. (2021). On powerful GWAS in admixed populations. Nature genetics, 53(12), 1631-1633. https://doi.org/10.1038/s41588-021-00953-5
Hou, Kangcheng, et al. "On powerful GWAS in admixed populations." Nature genetics vol. 53,12 (2021): 1631-1633. doi: https://doi.org/10.1038/s41588-021-00953-5
Hou K, Bhattacharya A, Mester R, Burch KS, Pasaniuc B. On powerful GWAS in admixed populations. Nat Genet. 2021 Dec;53(12):1631-1633. doi: 10.1038/s41588-021-00953-5. Epub 2021 Nov 25. PMID: 34824480.
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Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification.

Nature genetics

Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B.
PMID: 34931067
Nat Genet. 2022 Jan;54(1):30-39. doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20.

Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of...

Cite
Ding Y, Hou K, Burch KS, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2021;54(1):30-39doi: 10.1038/s41588-021-00961-5.
Ding, Y., Hou, K., Burch, K. S., Lapinska, S., Privé, F., Vilhjálmsson, B., Sankararaman, S., & Pasaniuc, B. (2022). Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nature genetics, 54(1), 30-39. https://doi.org/10.1038/s41588-021-00961-5
Ding, Yi, et al. "Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification." Nature genetics vol. 54,1 (2022): 30-39. doi: https://doi.org/10.1038/s41588-021-00961-5
Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2022 Jan;54(1):30-39. doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20. PMID: 34931067; PMCID: PMC8758557.
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Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

HGG advances

Chen H, Majumdar A, Wang L, Kar S, Brown KM, Feng H, Turman C, Dennis J, Easton D, Michailidou K, Simard J, Bishop T, Cheng IC, Huyghe JR, Schmit SL, O'Mara TA, Spurdle AB, Gharahkhani P, Schumacher J, Jankowski J, Gocke I, Bondy ML, Houlston RS, Jenkins RB, Melin B, Lesseur C, Ness AR, Diergaarde B, Olshan AF, Amos CI, Christiani DC, Landi MT, McKay JD, Brossard M, Iles MM, Law MH, MacGregor S, Beesley J, Jones MR, Tyrer J, Winham SJ, Klein AP, Petersen G, Li D, Wolpin BM, Eeles RA, Haiman CA, Kote-Jarai Z, Schumacher FR, Brennan P, Chanock SJ, Gaborieau V, Purdue MP, Pharoah P, Hung RJ, Amundadottir LT, Kraft P, Pasaniuc B, Lindström S.
PMID: 34355204
HGG Adv. 2021 Jul 08;2(3). doi: 10.1016/j.xhgg.2021.100041. Epub 2021 Jun 12.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we...

Cite
Chen H, Majumdar A, Wang L, et al. Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG Adv. 2021;2(3)doi: 10.1016/j.xhgg.2021.100041.
Chen, H., Majumdar, A., Wang, L., Kar, S., Brown, K. M., Feng, H., Turman, C., Dennis, J., Easton, D., Michailidou, K., Simard, J., Bishop, T., Cheng, I. C., Huyghe, J. R., Schmit, S. L., O'Mara, T. A., Spurdle, A. B., Gharahkhani, P., Schumacher, J., Jankowski, J., Gocke, I., Bondy, M. L., Houlston, R. S., Jenkins, R. B., Melin, B., Lesseur, C., Ness, A. R., Diergaarde, B., Olshan, A. F., Amos, C. I., Christiani, D. C., Landi, M. T., McKay, J. D., Brossard, M., Iles, M. M., Law, M. H., MacGregor, S., Beesley, J., Jones, M. R., Tyrer, J., Winham, S. J., Klein, A. P., Petersen, G., Li, D., Wolpin, B. M., Eeles, R. A., Haiman, C. A., Kote-Jarai, Z., Schumacher, F. R., Brennan, P., Chanock, S. J., Gaborieau, V., Purdue, M. P., Pharoah, P., Hung, R. J., Amundadottir, L. T., Kraft, P., Pasaniuc, B., & Lindström, S. (2021). Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG advances, 2(3), . https://doi.org/10.1016/j.xhgg.2021.100041
Chen, Hongjie, et al. "Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals." HGG advances vol. 2,3 (2021). doi: https://doi.org/10.1016/j.xhgg.2021.100041
Chen H, Majumdar A, Wang L, Kar S, Brown KM, Feng H, Turman C, Dennis J, Easton D, Michailidou K, Simard J, Bishop T, Cheng IC, Huyghe JR, Schmit SL, O'Mara TA, Spurdle AB, Gharahkhani P, Schumacher J, Jankowski J, Gocke I, Bondy ML, Houlston RS, Jenkins RB, Melin B, Lesseur C, Ness AR, Diergaarde B, Olshan AF, Amos CI, Christiani DC, Landi MT, McKay JD, Brossard M, Iles MM, Law MH, MacGregor S, Beesley J, Jones MR, Tyrer J, Winham SJ, Klein AP, Petersen G, Li D, Wolpin BM, Eeles RA, Haiman CA, Kote-Jarai Z, Schumacher FR, Brennan P, Chanock SJ, Gaborieau V, Purdue MP, Pharoah P, Hung RJ, Amundadottir LT, Kraft P, Pasaniuc B, Lindström S. Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG Adv. 2021 Jul 08;2(3). doi: 10.1016/j.xhgg.2021.100041. Epub 2021 Jun 12. PMID: 34355204; PMCID: PMC8336922.
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A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes.

Bioinformatics (Oxford, England)

Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS.
PMID: 33453114
Bioinformatics. 2021 Jan 16; doi: 10.1093/bioinformatics/btaa1083. Epub 2021 Jan 16.

MOTIVATION: While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a...

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Majumdar A, Burch KS, Haldar T, et al. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btaa1083.
Majumdar, A., Burch, K. S., Haldar, T., Sankararaman, S., Pasaniuc, B., Gauderman, W. J., & Witte, J. S. (2021). A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btaa1083
Majumdar, Arunabha, et al. "A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btaa1083
Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics. 2021 Jan 16; doi: 10.1093/bioinformatics/btaa1083. Epub 2021 Jan 16. PMID: 33453114.
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Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms.

Cell

Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, de la Torre-Ubieta L, Pasaniuc B, Stein JL, Geschwind DH.
PMID: 32359439
Cell. 2020 Apr 30;181(3):745. doi: 10.1016/j.cell.2020.04.016.

No abstract available.

Cite
Walker RL, Ramaswami G, Hartl C, et al. Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell. 2020;181(3):745doi: 10.1016/j.cell.2020.04.016.
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., de la Torre-Ubieta, L., Pasaniuc, B., Stein, J. L., & Geschwind, D. H. (2020). Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell, 181(3), 745. https://doi.org/10.1016/j.cell.2020.04.016
Walker, Rebecca L, et al. "Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms." Cell vol. 181,3 (2020): 745. doi: https://doi.org/10.1016/j.cell.2020.04.016
Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, de la Torre-Ubieta L, Pasaniuc B, Stein JL, Geschwind DH. Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell. 2020 Apr 30;181(3):745. doi: 10.1016/j.cell.2020.04.016. PMID: 32359439.
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Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification.

Nature genetics

Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B.
PMID: 34931067
Nat Genet. 2021 Dec 20; doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20.

Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of...

Cite
Ding Y, Hou K, Burch KS, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2021;doi: 10.1038/s41588-021-00961-5.
Ding, Y., Hou, K., Burch, K. S., Lapinska, S., Privé, F., Vilhjálmsson, B., Sankararaman, S., & Pasaniuc, B. (2021). Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nature genetics, . https://doi.org/10.1038/s41588-021-00961-5
Ding, Yi, et al. "Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification." Nature genetics vol. (2021). doi: https://doi.org/10.1038/s41588-021-00961-5
Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2021 Dec 20; doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20. PMID: 34931067.
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Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms.

Cell

Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, Torre-Ubieta L, Pasaniuc B, Stein JL, Geschwind DH.
PMID: 32302575
Cell. 2020 Apr 16;181(2):484. doi: 10.1016/j.cell.2020.04.003.

No abstract available.

Cite
Walker RL, Ramaswami G, Hartl C, et al. Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell. 2020;181(2):484doi: 10.1016/j.cell.2020.04.003.
Walker, R. L., Ramaswami, G., Hartl, C., Mancuso, N., Gandal, M. J., Torre-Ubieta, L., Pasaniuc, B., Stein, J. L., & Geschwind, D. H. (2020). Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell, 181(2), 484. https://doi.org/10.1016/j.cell.2020.04.003
Walker, Rebecca L, et al. "Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms." Cell vol. 181,2 (2020): 484. doi: https://doi.org/10.1016/j.cell.2020.04.003
Walker RL, Ramaswami G, Hartl C, Mancuso N, Gandal MJ, Torre-Ubieta L, Pasaniuc B, Stein JL, Geschwind DH. Genetic Control of Expression and Splicing in Developing Human Brain Informs Disease Mechanisms. Cell. 2020 Apr 16;181(2):484. doi: 10.1016/j.cell.2020.04.003. PMID: 32302575.
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Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials.

medRxiv : the preprint server for health sciences

Hazlett C, Wulf DA, Pasaniuc B, Arah OA, Erlandson KM, Montague BT.
PMID: 33330889
medRxiv. 2020 Dec 08; doi: 10.1101/2020.12.06.20244798.

OBJECTIVES: To investigate the effectiveness of hydroxychloroquine and dexamethasone on coronavirus disease (COVID-19) mortality using patient data outside of randomized trials.DESIGN: Phenotypes derived from electronic health records were analyzed using the stability-controlled quasi-experiment (SCQE) to provide a range of...

Cite
Hazlett C, Wulf DA, Pasaniuc B, et al. Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials. medRxiv. 2020;doi: 10.1101/2020.12.06.20244798.
Hazlett, C., Wulf, D. A., Pasaniuc, B., Arah, O. A., Erlandson, K. M., & Montague, B. T. (2020). Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.12.06.20244798
Hazlett, Chad, et al. "Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.12.06.20244798
Hazlett C, Wulf DA, Pasaniuc B, Arah OA, Erlandson KM, Montague BT. Credible learning of hydroxychloroquine and dexamethasone effects on COVID-19 mortality outside of randomized trials. medRxiv. 2020 Dec 08; doi: 10.1101/2020.12.06.20244798. PMID: 33330889; PMCID: PMC7743100.
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Fast estimation of genetic correlation for biobank-scale data.

American journal of human genetics

Wu Y, Burch KS, Ganna A, Pajukanta P, Pasaniuc B, Sankararaman S.
PMID: 34861179
Am J Hum Genet. 2021 Nov 24; doi: 10.1016/j.ajhg.2021.11.015. Epub 2021 Nov 24.

Genetic correlation is an important parameter in efforts to understand the relationships among complex traits. Current methods that analyze individual genotype data for estimating genetic correlation are challenging to scale to large datasets. Methods that analyze summary data, while...

Cite
Wu Y, Burch KS, Ganna A, et al. Fast estimation of genetic correlation for biobank-scale data. Am J Hum Genet. 2021;doi: 10.1016/j.ajhg.2021.11.015.
Wu, Y., Burch, K. S., Ganna, A., Pajukanta, P., Pasaniuc, B., & Sankararaman, S. (2021). Fast estimation of genetic correlation for biobank-scale data. American journal of human genetics, . https://doi.org/10.1016/j.ajhg.2021.11.015
Wu, Yue, et al. "Fast estimation of genetic correlation for biobank-scale data." American journal of human genetics vol. (2021). doi: https://doi.org/10.1016/j.ajhg.2021.11.015
Wu Y, Burch KS, Ganna A, Pajukanta P, Pasaniuc B, Sankararaman S. Fast estimation of genetic correlation for biobank-scale data. Am J Hum Genet. 2021 Nov 24; doi: 10.1016/j.ajhg.2021.11.015. Epub 2021 Nov 24. PMID: 34861179.
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Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification.

Nature genetics

Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B.
PMID: 34931067
Nat Genet. 2022 Jan;54(1):30-39. doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20.

Although the cohort-level accuracy of polygenic risk scores (PRSs)-estimates of genetic value at the individual level-has been widely assessed, uncertainty in PRSs remains underexplored. In the present study, we show that Bayesian PRS methods can estimate the variance of...

Cite
Ding Y, Hou K, Burch KS, et al. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2021;54(1):30-39doi: 10.1038/s41588-021-00961-5.
Ding, Y., Hou, K., Burch, K. S., Lapinska, S., Privé, F., Vilhjálmsson, B., Sankararaman, S., & Pasaniuc, B. (2022). Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nature genetics, 54(1), 30-39. https://doi.org/10.1038/s41588-021-00961-5
Ding, Yi, et al. "Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification." Nature genetics vol. 54,1 (2022): 30-39. doi: https://doi.org/10.1038/s41588-021-00961-5
Ding Y, Hou K, Burch KS, Lapinska S, Privé F, Vilhjálmsson B, Sankararaman S, Pasaniuc B. Large uncertainty in individual polygenic risk score estimation impacts PRS-based risk stratification. Nat Genet. 2022 Jan;54(1):30-39. doi: 10.1038/s41588-021-00961-5. Epub 2021 Dec 20. PMID: 34931067; PMCID: PMC8758557.
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Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals.

HGG advances

Chen H, Majumdar A, Wang L, Kar S, Brown KM, Feng H, Turman C, Dennis J, Easton D, Michailidou K, Simard J, Bishop T, Cheng IC, Huyghe JR, Schmit SL, O'Mara TA, Spurdle AB, Gharahkhani P, Schumacher J, Jankowski J, Gocke I, Bondy ML, Houlston RS, Jenkins RB, Melin B, Lesseur C, Ness AR, Diergaarde B, Olshan AF, Amos CI, Christiani DC, Landi MT, McKay JD, Brossard M, Iles MM, Law MH, MacGregor S, Beesley J, Jones MR, Tyrer J, Winham SJ, Klein AP, Petersen G, Li D, Wolpin BM, Eeles RA, Haiman CA, Kote-Jarai Z, Schumacher FR, Brennan P, Chanock SJ, Gaborieau V, Purdue MP, Pharoah P, Hung RJ, Amundadottir LT, Kraft P, Pasaniuc B, Lindström S.
PMID: 34355204
HGG Adv. 2021 Jul 08;2(3). doi: 10.1016/j.xhgg.2021.100041. Epub 2021 Jun 12.

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we...

Cite
Chen H, Majumdar A, Wang L, et al. Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG Adv. 2021;2(3)doi: 10.1016/j.xhgg.2021.100041.
Chen, H., Majumdar, A., Wang, L., Kar, S., Brown, K. M., Feng, H., Turman, C., Dennis, J., Easton, D., Michailidou, K., Simard, J., Bishop, T., Cheng, I. C., Huyghe, J. R., Schmit, S. L., O'Mara, T. A., Spurdle, A. B., Gharahkhani, P., Schumacher, J., Jankowski, J., Gocke, I., Bondy, M. L., Houlston, R. S., Jenkins, R. B., Melin, B., Lesseur, C., Ness, A. R., Diergaarde, B., Olshan, A. F., Amos, C. I., Christiani, D. C., Landi, M. T., McKay, J. D., Brossard, M., Iles, M. M., Law, M. H., MacGregor, S., Beesley, J., Jones, M. R., Tyrer, J., Winham, S. J., Klein, A. P., Petersen, G., Li, D., Wolpin, B. M., Eeles, R. A., Haiman, C. A., Kote-Jarai, Z., Schumacher, F. R., Brennan, P., Chanock, S. J., Gaborieau, V., Purdue, M. P., Pharoah, P., Hung, R. J., Amundadottir, L. T., Kraft, P., Pasaniuc, B., & Lindström, S. (2021). Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG advances, 2(3), . https://doi.org/10.1016/j.xhgg.2021.100041
Chen, Hongjie, et al. "Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals." HGG advances vol. 2,3 (2021). doi: https://doi.org/10.1016/j.xhgg.2021.100041
Chen H, Majumdar A, Wang L, Kar S, Brown KM, Feng H, Turman C, Dennis J, Easton D, Michailidou K, Simard J, Bishop T, Cheng IC, Huyghe JR, Schmit SL, O'Mara TA, Spurdle AB, Gharahkhani P, Schumacher J, Jankowski J, Gocke I, Bondy ML, Houlston RS, Jenkins RB, Melin B, Lesseur C, Ness AR, Diergaarde B, Olshan AF, Amos CI, Christiani DC, Landi MT, McKay JD, Brossard M, Iles MM, Law MH, MacGregor S, Beesley J, Jones MR, Tyrer J, Winham SJ, Klein AP, Petersen G, Li D, Wolpin BM, Eeles RA, Haiman CA, Kote-Jarai Z, Schumacher FR, Brennan P, Chanock SJ, Gaborieau V, Purdue MP, Pharoah P, Hung RJ, Amundadottir LT, Kraft P, Pasaniuc B, Lindström S. Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals. HGG Adv. 2021 Jul 08;2(3). doi: 10.1016/j.xhgg.2021.100041. Epub 2021 Jun 12. PMID: 34355204; PMCID: PMC8336922.
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Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System.

medRxiv : the preprint server for health sciences

Chang TS, Ding Y, Freund MK, Johnson R, Schwarz T, Yabu JM, Hazlett C, Chiang JN, Wulf A, Geschwind DH, Butte MJ, Pasaniuc B.
PMID: 32637977
medRxiv. 2020 Jul 09; doi: 10.1101/2020.07.03.20145581.

With the continuing coronavirus disease 2019 (COVID-19) pandemic coupled with phased reopening, it is critical to identify risk factors associated with susceptibility and severity of disease in a diverse population to help shape government policies, guide clinical decision making,...

Cite
Chang TS, Ding Y, Freund MK, et al. Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System. medRxiv. 2020;doi: 10.1101/2020.07.03.20145581.
Chang, T. S., Ding, Y., Freund, M. K., Johnson, R., Schwarz, T., Yabu, J. M., Hazlett, C., Chiang, J. N., Wulf, A., Geschwind, D. H., Butte, M. J., & Pasaniuc, B. (2020). Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System. medRxiv : the preprint server for health sciences, . https://doi.org/10.1101/2020.07.03.20145581
Chang, Timothy S, et al. "Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System." medRxiv : the preprint server for health sciences vol. (2020). doi: https://doi.org/10.1101/2020.07.03.20145581
Chang TS, Ding Y, Freund MK, Johnson R, Schwarz T, Yabu JM, Hazlett C, Chiang JN, Wulf A, Geschwind DH, Butte MJ, Pasaniuc B. Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System. medRxiv. 2020 Jul 09; doi: 10.1101/2020.07.03.20145581. PMID: 32637977; PMCID: PMC7340203.
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Showing 1 to 12 of 35 entries