Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Bratati, Kahali"'
Publikováno v:
HGG Advances, Vol 5, Iss 3, Pp 100285- (2024)
Summary: Structural variations (SV) are large (>50 base pairs) genomic rearrangements comprising deletions, duplications, insertions, inversions, and translocations. Studying SVs is important because they play active and critical roles in regulating
Externí odkaz:
https://doaj.org/article/e32030aa3f5f44df9a459236b858227e
Publikováno v:
Informatics in Medicine Unlocked, Vol 49, Iss , Pp 101466- (2024)
Externí odkaz:
https://doaj.org/article/7c69b0dace004ddcbe9a8d44ccae2129
Autor:
Shreya Chakraborty, Bratati Kahali
Publikováno v:
HGG Advances, Vol 4, Iss 3, Pp 100208- (2023)
Summary: Cognitive functioning is heritable, with metabolic risk factors known to accelerate age-associated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial. Here, we undertake single-variant and gene-based associatio
Externí odkaz:
https://doaj.org/article/90b7fce6ea3f4296af7a764e9c2653ec
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract Genome wide association studies (GWAS) have focused on elucidating the genetic architecture of complex traits by assessing single variant effects in additive genetic models, albeit explaining a fraction of the trait heritability. Epistasis h
Externí odkaz:
https://doaj.org/article/046f54443f7b4e4a80cdaaa582a9cb8d
Publikováno v:
Informatics in Medicine Unlocked, Vol 25, Iss , Pp 100684- (2021)
Whole Genome Sequencing (WGS) provides information for each base of the entire 3.2 billion base pairs of the diploid human genome. Therefore, WGS plays an important role in identifying genetic variations for populations and understanding disease sign
Externí odkaz:
https://doaj.org/article/345cbb9d295640a9b95aa7bdf8378d33
Autor:
Abhay Gupta, Bratati Kahali
Publikováno v:
Alzheimer’s & Dementia: Translational Research & Clinical Interventions, Vol 6, Iss 1, Pp n/a-n/a (2020)
Abstract Introduction An extensive battery of neuropsychological tests is currently used to classify individuals as healthy (HV), mild cognitively impaired (MCI), and with Alzheimer's disease (AD). We used machine learning models for effective cognit
Externí odkaz:
https://doaj.org/article/d6c5ce1fdf6e4901bd05d79d8e03342c
Autor:
Peter T. Campbell, Li Hsu, Emily White, Hermann Brenner, Michael Hoffmeister, Polly A. Newcomb, John D. Potter, Cornelia M. Ulrich, John L. Hopper, Mark A. Jenkins, Aung Ko Win, Michelle Cotterchio, Stephen N. Thibodeau, Kana Wu, Mingyang Song, Daniela Seminara, Noralane M. Lindor, Loïc Le Marchand, David J. Duggan, Mark Thornquist, Tabitha A. Harrison, Mengmeng Du, Robert W. Haile, Graham Casey, Stephen J. Chanock, Sonja I. Berndt, Shuji Ogino, Bette J. Caan, John A. Baron, Richard B. Hayes, Steven Gallinger, Tune H. Pers, Anne E. Justice, Bratati Kahali, Adam E. Locke, Andrew T. Chan, Martha L. Slattery, Anja Rudolph, Jenny Chang-Claude, Ulrike Peters, Jian Gong, Aaron P. Thrift
Supplementary Tables S1-3. Supplementary Table S1 Description of the characteristics by study. Supplementary Table S2 Associations between confounders and body mass index and the weighted genetic risk score among controls. Supplementary Table S3 Odds
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ede2303064aa05ef14606d4a71b0b10
https://doi.org/10.1158/1055-9965.22436557.v1
https://doi.org/10.1158/1055-9965.22436557.v1
Autor:
Peter T. Campbell, Li Hsu, Emily White, Hermann Brenner, Michael Hoffmeister, Polly A. Newcomb, John D. Potter, Cornelia M. Ulrich, John L. Hopper, Mark A. Jenkins, Aung Ko Win, Michelle Cotterchio, Stephen N. Thibodeau, Kana Wu, Mingyang Song, Daniela Seminara, Noralane M. Lindor, Loïc Le Marchand, David J. Duggan, Mark Thornquist, Tabitha A. Harrison, Mengmeng Du, Robert W. Haile, Graham Casey, Stephen J. Chanock, Sonja I. Berndt, Shuji Ogino, Bette J. Caan, John A. Baron, Richard B. Hayes, Steven Gallinger, Tune H. Pers, Anne E. Justice, Bratati Kahali, Adam E. Locke, Andrew T. Chan, Martha L. Slattery, Anja Rudolph, Jenny Chang-Claude, Ulrike Peters, Jian Gong, Aaron P. Thrift
Background: High body mass index (BMI) is consistently linked to increased risk of colorectal cancer for men, whereas the association is less clear for women. As risk estimates from observational studies may be biased and/or confounded, we conducted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65efc5bffef3765cd4528773e15ec1c7
https://doi.org/10.1158/1055-9965.c.6515479.v1
https://doi.org/10.1158/1055-9965.c.6515479.v1
Autor:
Shreya Chakraborty, Bratati Kahali
Cognitive functioning is heritable, with metabolic risk factors known to accelerate ageassociated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial.Here, we undertake single-variant and gene-based association analyses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::459814abd339c913837ae0a5c1eea252
https://doi.org/10.1101/2022.10.12.511871
https://doi.org/10.1101/2022.10.12.511871
Autor:
Vilmundur Gudnason, Solomon K. Musani, Yi-Ping Fu, Albert V. Smith, Yii-Der Ida Chen, Annapurna Kuppa, Xiuqing Guo, Matthew A. Allison, Sharon L.R. Kardia, Donald W. Bowden, Gudny Eirksdottir, Jill M. Norris, Jian Yang, Bratati Kahali, Yanhua Chen, Thomas H. Mosley, Elizabeth K. Speliotes, Lenore J. Launer, James G. Terry, Brian D. Halligan, Jerome I. Rotter, Matthew J. Budoff, Kent D. Taylor, Kathleen A. Ryan, Breland F. Crudup, Adolfo Correa, Lawrence F. Bielak, Jeffrey R. O'Connell, Michael A. Province, Patricia A. Peyser, Nicholette D. Palmer, Christopher J. O'Donnell, Mary F. Feitosa, Lynne E. Wagenknecht, J. Jeffrey Carr, Xiaomeng Du
Publikováno v:
Human molecular genetics, vol 30, iss 15
Hum Mol Genet
Hum Mol Genet
Nonalcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver disease and is highly correlated with metabolic disease. NAFLD results from environmental exposures acting on a susceptible polygenic background. This study performed the la
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a1410659c19fddb97f8c0e22ad4dda1
https://doi.org/10.1093/hmg/ddab096
https://doi.org/10.1093/hmg/ddab096