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2. Multi-ancestry genome-wide association study of kidney cancer identifies 63 susceptibility regions

Author

Purdue, Mark P., Dutta, Diptavo, Machiela, Mitchell J., Gorman, Bryan R., Winter, Timothy, Okuhara, Dayne, Cleland, Sara, Ferreiro-Iglesias, Aida, Scheet, Paul, Liu, Aoxing, Wu, Chao, Antwi, Samuel O., Larkin, James, Zequi, Stênio C., Sun, Maxine, Hikino, Keiko, Hajiran, Ali, Lawson, Keith A., Cárcano, Flavio, Blanchet, Odile, Shuch, Brian, Nepple, Kenneth G., Margue, Gaëlle, Sundi, Debasish, Diver, W. Ryan, Folgueira, Maria A. A. K., van Bokhoven, Adrie, Neffa, Florencia, Brown, Kevin M., Hofmann, Jonathan N., Rhee, Jongeun, Yeager, Meredith, Cole, Nathan R., Hicks, Belynda D., Manning, Michelle R., Hutchinson, Amy A., Rothman, Nathaniel, Huang, Wen-Yi, Linehan, W. Marston, Lori, Adriana, Ferragu, Matthieu, Zidane-Marinnes, Merzouka, Serrano, Sérgio V., Magnabosco, Wesley J., Vilas, Ana, Decia, Ricardo, Carusso, Florencia, Graham, Laura S., Anderson, Kyra, Bilen, Mehmet A., Arciero, Cletus, Pellegrin, Isabelle, Ricard, Solène, Scelo, Ghislaine, Banks, Rosamonde E., Vasudev, Naveen S., Soomro, Naeem, Stewart, Grant D., Adeyoju, Adebanji, Bromage, Stephen, Hrouda, David, Gibbons, Norma, Patel, Poulam, Sullivan, Mark, Protheroe, Andrew, Nugent, Francesca I., Fournier, Michelle J., Zhang, Xiaoyu, Martin, Lisa J., Komisarenko, Maria, Eisen, Timothy, Cunningham, Sonia A., Connolly, Denise C., Uzzo, Robert G., Zaridze, David, Mukeria, Anush, Holcatova, Ivana, Hornakova, Anna, Foretova, Lenka, Janout, Vladimir, Mates, Dana, Jinga, Viorel, Rascu, Stefan, Mijuskovic, Mirjana, Savic, Slavisa, Milosavljevic, Sasa, Gaborieau, Valérie, Abedi-Ardekani, Behnoush, McKay, James, Johansson, Mattias, Phouthavongsy, Larry, Hayman, Lindsay, Li, Jason, Lungu, Ilinca, Bezerra, Stephania M., Souza, Aline G., Sares, Claudia T. G., Reis, Rodolfo B., Gallucci, Fabio P., Cordeiro, Mauricio D., Pomerantz, Mark, Lee, Gwo-Shu M., Freedman, Matthew L., Jeong, Anhyo, Greenberg, Samantha E., Sanchez, Alejandro, Thompson, R. Houston, Sharma, Vidit, Thiel, David D., Ball, Colleen T., Abreu, Diego, Lam, Elaine T., Nahas, William C., Master, Viraj A., Patel, Alpa V., Bernhard, Jean-Christophe, Freedman, Neal D., Bigot, Pierre, Reis, Rui M., Colli, Leandro M., Finelli, Antonio, Manley, Brandon J., Terao, Chikashi, Choueiri, Toni K., Carraro, Dirce M., Houlston, Richard, Eckel-Passow, Jeanette E., Abbosh, Philip H., Ganna, Andrea, Brennan, Paul, Gu, Jian, and Chanock, Stephen J.

Published
2024
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3. A linear adjustment based approach to posterior drift in transfer learning

Author

Maity, Subha, Dutta, Diptavo, Terhorst, Jonathan, Sun, Yuekai, and Banerjee, Moulinath

Subjects
Statistics - Methodology
Abstract

We present a new model and methods for the posterior drift problem where the regression function in the target domain is modeled as a linear adjustment (on an appropriate scale) of that in the source domain, an idea that inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature, and study the theoretical properties of our proposed estimator in the binary classification problem. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted to transfer learning problems in various domains including epidemiology, genetics and biomedicine. As a concrete application, we illustrate the power of our approach through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data.

Published
2021

4. Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma

Author

Purdue, Mark P., Dutta, Diptavo, Machiela, Mitchell J., Gorman, Bryan R., Winter, Timothy, Okuhara, Dayne, Cleland, Sara, Ferreiro-Iglesias, Aida, Scheet, Paul, Liu, Aoxing, Wu, Chao, Antwi, Samuel O., Larkin, James, Zequi, Stênio C., Sun, Maxine, Hikino, Keiko, Hajiran, Ali, Lawson, Keith A., Cárcano, Flavio, Blanchet, Odile, Shuch, Brian, Nepple, Kenneth G., Margue, Gaëlle, Sundi, Debasish, Diver, W. Ryan, Folgueira, Maria A.A.K., van Bokhoven, Adrie, Neffa, Florencia, Brown, Kevin M., Hofmann, Jonathan N., Rhee, Jongeun, Yeager, Meredith, Cole, Nathan R., Hicks, Belynda D., Manning, Michelle R., Hutchinson, Amy A., Rothman, Nathaniel, Huang, Wen-Yi, Linehan, W. Marston, Lori, Adriana, Ferragu, Matthieu, Zidane-Marinnes, Merzouka, Serrano, Sérgio, Magnabosco, Wesley J., BioBank Japan Project Consortium, Vilas, Ana, Decia, Ricardo, Carusso, Florencia, Graham, Laura S., Anderson, Kyra, Bilen, Mehmet A., Arciero, Cletus, Pellegrin, Isabelle, Ricard, Solène, FinnGen, Scelo, Ghislaine, Banks, Rosamonde E., Vasudev, Naveen S., Soomro, Naeem, Stewart, Grant D., Adeyoju, Adebanji, Bromage, Stephen, Hrouda, David, Gibbons, Norma, Patel, Poulam, Sullivan, Mark, Protheroe, Andrew, Nugent, Francesca I., Fournier, Michelle J., Zhang, Xiaoyu, Martin, Lisa J., Komisarenko, Maria, Eisen, Timothy, Cunningham, Sonia A., Connolly, Denise C., Uzzo, Robert G., Zaridze, David, Mukeria, Anush, Holcatova, Ivana, Hornakova, Anna, Foretova, Lenka, Janout, Vladimir, Mates, Dana, Jinga, Viorel, Rascu, Stefan, Mijuskovic, Mirjana, Savic, Slavisa, Milosavljevic, Sasa, Gaborieau, Valérie, Abedi-Ardekani, Behnoush, McKay, James, Johansson, Mattias, Phouthavongsy, Larry, Hayman, Lindsay, Li, Jason, Lungu, Ilinca, Bezerra, Stephania M., de Souza, Aline G., Sares, Claudia T.G., Reis, Rodolfo B., Gallucci, Fabio P., Cordeiro, Mauricio D., Pomerantz, Mark, Lee, Gwo-Shu M., Freedman, Matthew L., Jeong, Anhyo, Greenberg, Samantha E., Sanchez, Alejandro, Thompson, R. Houston, Sharma, Vidit, Thiel, David D., Ball, Colleen T., Abreu, Diego, Lam, Elaine T., Nahas, William C., Master, Viraj A., Patel, Alpa V., Bernhard, Jean-Christophe, Freedman, Neal D., Bigot, Pierre, Reis, Rui M., Colli, Leandro M., Finelli, Antonio, Manley, Brandon J., Terao, Chikashi, Choueiri, Toni K., Carraro, Dirce M., Houlston, Richard, Eckel-Passow, Jeanette E., Abbosh, Philip H., Ganna, Andrea, Brennan, Paul, Gu, Jian, Chanock, Stephen J., Guo, Xinyu, Winter, Timothy D., Jahagirdar, Om, Ha, Eunji, and Susztak, Katalin

Published
2024
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5. Trans-ethnic genome-wide association study of blood metabolites in the Chronic Renal Insufficiency Cohort (CRIC) study

Author

Rhee, Eugene P, Surapaneni, Aditya, Zheng, Zihe, Zhou, Linda, Dutta, Diptavo, Arking, Dan E, Zhang, Jingning, Duong, ThuyVy, Chatterjee, Nilanjan, Luo, Shengyuan, Schlosser, Pascal, Mehta, Rupal, Waikar, Sushrut S, Saraf, Santosh L, Kelly, Tanika N, Hamm, Lee L, Rao, Panduranga S, Mathew, Anna V, Hsu, Chi-yuan, Parsa, Afshin, Vasan, Ramachandran S, Kimmel, Paul L, Clish, Clary B, Coresh, Josef, Feldman, Harold I, Grams, Morgan E, and Investigators, CKD Biomarkers Consortium and the Chronic Renal Insufficiency Cohort Study

Subjects
Human Genome, Genetics, Kidney Disease, Aetiology, 2.1 Biological and endogenous factors, Renal and urogenital, Cohort Studies, Ethnicity, Female, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Polymorphism, Single Nucleotide, Renal Insufficiency, Chronic, GWAS, metabolomics, trans-ethnic meta-analysis, CKD Biomarkers Consortium and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators, Clinical Sciences, Urology & Nephrology
Abstract

Metabolomics genome wide association study (GWAS) help outline the genetic contribution to human metabolism. However, studies to date have focused on relatively healthy, population-based samples of White individuals. Here, we conducted a GWAS of 537 blood metabolites measured in the Chronic Renal Insufficiency Cohort (CRIC) Study, with separate analyses in 822 White and 687 Black study participants. Trans-ethnic meta-analysis was then applied to improve fine-mapping of potential causal variants. Mean estimated glomerular filtration rate was 44.4 and 41.5 mL/min/1.73m2 in the White and Black participants, respectively. There were 45 significant metabolite associations at 19 loci, including novel associations at PYROXD2, PHYHD1, FADS1-3, ACOT2, MYRF, FAAH, and LIPC. The strength of associations was unchanged in models additionally adjusted for estimated glomerular filtration rate and proteinuria, consistent with a direct biochemical effect of gene products on associated metabolites. At several loci, trans-ethnic meta-analysis, which leverages differences in linkage disequilibrium across populations, reduced the number and/or genomic interval spanned by potentially causal single nucleotide polymorphisms compared to fine-mapping in the White participant cohort alone. Across all validated associations, we found strong concordance in effect sizes of the potentially causal single nucleotide polymorphisms between White and Black study participants. Thus, our study identifies novel genetic determinants of blood metabolites in chronic kidney disease, demonstrates the value of diverse cohorts to improve causal inference in metabolomics GWAS, and underscores the shared genetic basis of metabolism across race.

Published
2022

6. Rare Genetic Variants Associated With Myocardial Fibrosis: Multi-Ethnic Study of Atherosclerosis

Author

Shabani, Mahsima, Dutta, Diptavo, Ambale-Venkatesh, Bharath, Post, Wendy S, Taylor, Kent D, Rich, Stephen S, Wu, Colin O, Pereira, Naveen L, Shah, Sanjiv J, Chatterjee, Nilanjan, Rotter, Jerome I, Arking, Dan E, and Lima, Joao AC

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Biomedical Imaging, Cardiovascular, Genetics, Clinical Research, Heart Disease, Atherosclerosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, cardiomyopathy, genetics, fibrosis, magnetic resonance imaging, T1, interstitial, rare, Cardiovascular medicine and haematology
Abstract

BackgroundRare pathogenic variants in cardiomyopathy (CM) genes can predispose to cardiac remodeling or fibrosis. We studied the carrier status for such variants in adults without clinical cardiovascular disease (CVD) in whom cardiac MRI (CMR)-derived measures of myocardial fibrosis were obtained in the Multi-Ethnic Study of Atherosclerosis (MESA).ObjectivesTo identify CM-associated pathogenic variants and assess their relative prevalence in participants with extensive myocardial fibrosis by CMR.MethodsMESA whole-genome sequencing data was evaluated to capture variants in CM-associated genes (n = 82). Coding variants with a frequency of

Published
2022

8. Genome-wide Association Study of Bladder Cancer Reveals New Biological and Translational Insights

Author

Koutros, Stella, Kiemeney, Lambertus A., Pal Choudhury, Parichoy, Milne, Roger L., Lopez de Maturana, Evangelina, Ye, Yuanqing, Joseph, Vijai, Florez-Vargas, Oscar, Dyrskjøt, Lars, Figueroa, Jonine, Dutta, Diptavo, Giles, Graham G., Hildebrandt, Michelle A.T., Offit, Kenneth, Kogevinas, Manolis, Weiderpass, Elisabete, McCullough, Marjorie L., Freedman, Neal D., Albanes, Demetrius, Kooperberg, Charles, Cortessis, Victoria K., Karagas, Margaret R., Johnson, Alison, Schwenn, Molly R., Baris, Dalsu, Furberg, Helena, Bajorin, Dean F., Cussenot, Olivier, Cancel-Tassin, Geraldine, Benhamou, Simone, Kraft, Peter, Porru, Stefano, Carta, Angela, Bishop, Timothy, Southey, Melissa C., Matullo, Giuseppe, Fletcher, Tony, Kumar, Rajiv, Taylor, Jack A., Lamy, Philippe, Prip, Frederik, Kalisz, Mark, Weinstein, Stephanie J., Hengstler, Jan G., Selinski, Silvia, Harland, Mark, Teo, Mark, Kiltie, Anne E., Tardón, Adonina, Serra, Consol, Carrato, Alfredo, García-Closas, Reina, Lloreta, Josep, Schned, Alan, Lenz, Petra, Riboli, Elio, Brennan, Paul, Tjønneland, Anne, Otto, Thomas, Ovsiannikov, Daniel, Volkert, Frank, Vermeulen, Sita H., Aben, Katja K., Galesloot, Tessel E., Turman, Constance, De Vivo, Immaculata, Giovannucci, Edward, Hunter, David J., Hohensee, Chancellor, Hunt, Rebecca, Patel, Alpa V., Huang, Wen-Yi, Thorleifsson, Gudmar, Gago-Dominguez, Manuela, Amiano, Pilar, Golka, Klaus, Stern, Mariana C., Yan, Wusheng, Liu, Jia, Li, Shengchao Alfred, Katta, Shilpa, Hutchinson, Amy, Hicks, Belynda, Wheeler, William A., Purdue, Mark P., McGlynn, Katherine A., Kitahara, Cari M., Haiman, Christopher A., Greene, Mark H., Rafnar, Thorunn, Chatterjee, Nilanjan, Chanock, Stephen J., Wu, Xifeng, Real, Francisco X., Silverman, Debra T., Garcia-Closas, Montserrat, Stefansson, Kari, Prokunina-Olsson, Ludmila, Malats, Núria, and Rothman, Nathaniel

Published
2023
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14. A linear adjustment-based approach to posterior drift in transfer learning.

Author

Maity, Subha, Dutta, Diptavo, Terhorst, Jonathan, Sun, Yuekai, and Banerjee, Moulinath

Subjects
*CLASSICAL literature, *WATER birds, *GENETICS
Abstract

We present new models and methods for the posterior drift problem where the regression function in the target domain is modelled as a linear adjustment, on an appropriate scale, of that in the source domain, and study the theoretical properties of our proposed estimators in the binary classification problem. The core idea of our model inherits the simplicity and the usefulness of generalized linear models and accelerated failure time models from the classical statistics literature. Our approach is shown to be flexible and applicable in a variety of statistical settings, and can be adopted for transfer learning problems in various domains including epidemiology, genetics and biomedicine. As concrete applications, we illustrate the power of our approach (i) through mortality prediction for British Asians by borrowing strength from similar data from the larger pool of British Caucasians, using the UK Biobank data, and (ii) in overcoming a spurious correlation present in the source domain of the Waterbirds dataset. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Rare Genetic Variants Associated With Myocardial Fibrosis: Multi-Ethnic Study of Atherosclerosis

Author

Shabani, Mahsima, primary, Dutta, Diptavo, additional, Ambale-Venkatesh, Bharath, additional, Post, Wendy S., additional, Taylor, Kent D., additional, Rich, Stephen S., additional, Wu, Colin O., additional, Pereira, Naveen L., additional, Shah, Sanjiv J., additional, Chatterjee, Nilanjan, additional, Rotter, Jerome I., additional, Arking, Dan E., additional, and Lima, Joao A. C., additional

Published
2022
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21. Abstract 11049: Rare Genetic Variants in Individuals with Low ASCVD Risk and Hard Chd or High Coronary Artery Disease: Multi-Ethnic Study of Atherosclerosis

Author

Shabani, Mahsima, primary, Dutta, Diptavo, additional, Post, Wendy, additional, Lowenstein, Charles J, additional, Ambale Venkatesh, Bharath, additional, Chatterjee, Nilanjan, additional, Budoff, Matthew, additional, Rich, Stephen S, additional, Rotter, Jerome I, additional, Arking, Dan E, additional, and Lima, Joao, additional

Published
2021
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23. Proteins Associated with Risk of Kidney Function Decline in the General Population

Author

Grams, Morgan E., primary, Surapaneni, Aditya, additional, Chen, Jingsha, additional, Zhou, Linda, additional, Yu, Zhi, additional, Dutta, Diptavo, additional, Welling, Paul A., additional, Chatterjee, Nilanjan, additional, Zhang, Jingning, additional, Arking, Dan E., additional, Chen, Teresa K., additional, Rebholz, Casey M., additional, Yu, Bing, additional, Schlosser, Pascal, additional, Rhee, Eugene P., additional, Ballantyne, Christie M., additional, Boerwinkle, Eric, additional, Lutsey, Pamela L., additional, Mosley, Thomas, additional, Feldman, Harold I., additional, Dubin, Ruth F., additional, Ganz, Peter, additional, Lee, Hongzhe, additional, Zheng, Zihe, additional, and Coresh, Josef, additional

Published
2021
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33. Genome-wide association and multi-omic analyses reveal ACTN2 as a gene linked to heart failure.

Author

Arvanitis, Marios, Tampakakis, Emmanouil, Yanxiao Zhang, Wei Wang, Auton, Adam, Dutta, Diptavo, Glavaris, Stephanie, Keramati, Ali, Chatterjee, Nilanjan, Chi, Neil C., Bing Ren, Post, Wendy S., and Battle, Alexis

Subjects
HEART failure, EMBRYONIC stem cells, MYOCARDIUM, CYTOSKELETAL proteins, VENTRICULAR remodeling, SARCOLEMMA
Abstract

Heart failure is a major public health problem affecting over 23 million people worldwide. In this study, we present the results of a large scale meta-analysis of heart failure GWAS and replication in a comparable sized cohort to identify one known and two novel loci associated with heart failure. Heart failure sub-phenotyping shows that a new locus in chromosome 1 is associated with left ventricular adverse remodeling and clinical heart failure, in response to different initial cardiac muscle insults. Functional characterization and fine-mapping of that locus reveal a putative causal variant in a cardiac muscle specific regulatory region activated during cardiomyocyte differentiation that binds to the ACTN2 gene, a crucial structural protein inside the cardiac sarcolemma (Hi-C interaction p-value = 0.00002). Genomeediting in human embryonic stem cell-derived cardiomyocytes confirms the influence of the identified regulatory region in the expression of ACTN2. Our findings extend our understanding of biological mechanisms underlying heart failure. [ABSTRACT FROM AUTHOR]

Published
2020
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38. Statistical Methods for Multiple Phenotypes and Gene-Set Association Analysis

Author

Dutta, Diptavo

Subjects
Multiple phenotypes, Gene-set, Kernel regression
Abstract

As association studies continue to advance, more efficient statistical methods are required to fully utilize existing data and to provide insight into genetic architecture of complex traits. Identifying association for a set of phenotypes or with respect to a set of variants can be particularly useful for understanding how biological networks might be affecting the patho-physiology of outcomes. In this dissertation, I attempt to develop computationally efficient statistical methods that facilitate insights into the mechanism of complex traits and understanding of their underlying biology. In Chapter II, I propose a generalized framework for gene-based tests with multiple correlated phenotypes. In genetic association analysis, a joint test of multiple correlated phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants and the violation of these assumptions can reduce power to detect association. In this project we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated and exome-array data from the METSIM study, we show that Multi-SKAT can increase power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate. In Chapter III, I extend Multi-SKAT to a meta-analysis strategy, namely Meta-MultiSKAT, to combine results from several studies. Our method involves extracting score statistics and phenotype adjusted variant relationship matrix from individual studies which are then combined using a kernel that models the heterogeneity of effects between the studies. The proposed method accommodates situations where one or more phenotypes have not been observed in a particular study and studies have different correlation patterns among the phenotypes. With minor modifications our method can be used to test the combined effects of common and rare variants in a region, as well as incorporate functional information on individual variants. Meta-analysis of 4 white blood cell subtype traits from the MGI and SardiNIA studies show that Meta-MultiSKAT can identify associations which were not identified by existing methods and were not significant in individual studies. In Chapter IV, I propose a subset based approach for gene-set (pathway) association analysis using variant level summary statistics. Existing gene-set association (GSA) methods can have low statistical power when only a small fraction of the genes is associated with the phenotype and interpreting results in terms of the underlying genetic mechanism can be challenging since they cannot identify possible active genes within the set. For this, we propose a maximum-type statistic that selects the subset of genes with maximal evidence of association as the driver-genes and evaluates its significance using efficient simulation techniques. Through the analysis of summary statistics from the UK Biobank data for 1201 phenotypes with 10679 gene-sets, we show that our method can be used to identify novel associations across a large number of phenotypes and gene-sets.

Published
2019

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