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https://www.arca.fiocruz.br/handle/icict/18298
CHALLENGES AND DISPARITIES IN THE APPLICATION OF PERSONALIZED GENOMIC MEDICINE TO POPULATIONS WITH AFRICAN ANCESTRY
Author
Kessler, Michael D
Armstrong, Laura Yerges
Taub, Margaret A
Shetty, Amol C
Maloney, Kristin
Jeng, Linda Jo Bone
Ruczinski, Ingo
Levin, Albert M
Williams, L Keoki
Beaty, Terri H
Mathias, Rasika A
Barnes, Kathleen C
O'Connor, Timothy D
Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA)
Armstrong, Laura Yerges
Taub, Margaret A
Shetty, Amol C
Maloney, Kristin
Jeng, Linda Jo Bone
Ruczinski, Ingo
Levin, Albert M
Williams, L Keoki
Beaty, Terri H
Mathias, Rasika A
Barnes, Kathleen C
O'Connor, Timothy D
Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA)
Affilliation
University of Maryland School of Medicine. Institute for Genome Sciences. Baltimore, Maryland, USA
University of Maryland School ofMedicine. Department of Medicine. Baltimore, Maryland, USA / University of Maryland School of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Biostatistics, Bloomberg School of Public Health. Baltimore, Maryland, USA
University of Maryland School of Medicine. Institute for Genome Sciences. Baltimore, Maryland, USA
University of Maryland School of Medicine. Baltimore, Maryland, USA
University of Maryland School of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Biostatistics, Bloomberg School of Public Health. Baltimore, Maryland, USA
Henry Ford Health System. Department of Public Health Sciences. Detroit, Michigan, USA
Henry Ford Health System. Center for Health Policy & Health Services Research. Detroit, Michigan, USA / Henry Ford Health System. Department of Internal Medicine. Detroit, Michigan, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA / Johns Hopkins University. Department of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA / Johns Hopkins University. Department of Medicine. Baltimore, Maryland, USA / University of Colorado. Department of Medicine.Aurora, Colorado, USA
University of Maryland School of Medicine. Institute for Genome Sciences. Baltimore, Maryland, USA / University of Maryland School ofMedicine. Department of Medicine. Baltimore, Maryland, USA / University of Maryland School of Medicine. Baltimore, Maryland, USA. Múltipla - ver em Notas
University of Maryland School ofMedicine. Department of Medicine. Baltimore, Maryland, USA / University of Maryland School of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Biostatistics, Bloomberg School of Public Health. Baltimore, Maryland, USA
University of Maryland School of Medicine. Institute for Genome Sciences. Baltimore, Maryland, USA
University of Maryland School of Medicine. Baltimore, Maryland, USA
University of Maryland School of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Biostatistics, Bloomberg School of Public Health. Baltimore, Maryland, USA
Henry Ford Health System. Department of Public Health Sciences. Detroit, Michigan, USA
Henry Ford Health System. Center for Health Policy & Health Services Research. Detroit, Michigan, USA / Henry Ford Health System. Department of Internal Medicine. Detroit, Michigan, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA / Johns Hopkins University. Department of Medicine. Baltimore, Maryland, USA
Johns Hopkins University. Department of Epidemiology, Bloomberg School of Public Health. Baltimore, Maryland, USA / Johns Hopkins University. Department of Medicine. Baltimore, Maryland, USA / University of Colorado. Department of Medicine.Aurora, Colorado, USA
University of Maryland School of Medicine. Institute for Genome Sciences. Baltimore, Maryland, USA / University of Maryland School ofMedicine. Department of Medicine. Baltimore, Maryland, USA / University of Maryland School of Medicine. Baltimore, Maryland, USA. Múltipla - ver em Notas
Abstract
To characterize the extent and impact of ancestry-related biases in precision genomic medicine, we use 642 whole-genome sequences from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) project to evaluate typical filters and databases. We find significant correlations between estimated African ancestry proportions and the number of variants per individual in all variant classification sets but one. The source of these correlations is highlighted in more detail by looking at the interaction between filtering criteria and the ClinVar and Human Gene Mutation databases. ClinVar's correlation, representing African ancestry-related bias, has changed over time amidst monthly updates, with the most extreme switch happening between March and April of 2014 (r=0.733 to r=-0.683). We identify 68 SNPs as the major drivers of this change in correlation. As long as ancestry-related bias when using these clinical databases is minimally recognized, the genetics community will face challenges with implementation, interpretation and cost-effectiveness when treating minority populations.
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