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Challenges and disparities in the application of personalized genomic medicine to populations with African ancestry

Author

Listed:
  • Michael D. Kessler

    (Institute for Genome Sciences, University of Maryland School of Medicine)

  • Laura Yerges-Armstrong

    (University of Maryland School of Medicine
    Program in Personalized and Genomic Medicine, University of Maryland School of Medicine)

  • Margaret A. Taub

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Amol C. Shetty

    (Institute for Genome Sciences, University of Maryland School of Medicine)

  • Kristin Maloney

    (Program in Personalized and Genomic Medicine, University of Maryland School of Medicine)

  • Linda Jo Bone Jeng

    (Program in Personalized and Genomic Medicine, University of Maryland School of Medicine)

  • Ingo Ruczinski

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Albert M. Levin

    (Henry Ford Health System)

  • L. Keoki Williams

    (Center for Health Policy & Health Services Research, Henry Ford Health System
    Henry Ford Health System)

  • Terri H. Beaty

    (Bloomberg School of Public Health, Johns Hopkins University)

  • Rasika A. Mathias

    (Bloomberg School of Public Health, Johns Hopkins University
    Johns Hopkins University)

  • Kathleen C. Barnes

    (Bloomberg School of Public Health, Johns Hopkins University
    Johns Hopkins University
    University of Colorado)

  • Timothy D. O’Connor

    (Institute for Genome Sciences, University of Maryland School of Medicine
    University of Maryland School of Medicine
    Program in Personalized and Genomic Medicine, University of Maryland School of Medicine)

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.

Suggested Citation

  • Michael D. Kessler & Laura Yerges-Armstrong & Margaret A. Taub & Amol C. Shetty & Kristin Maloney & Linda Jo Bone Jeng & Ingo Ruczinski & Albert M. Levin & L. Keoki Williams & Terri H. Beaty & Rasika , 2016. "Challenges and disparities in the application of personalized genomic medicine to populations with African ancestry," Nature Communications, Nature, vol. 7(1), pages 1-8, November.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms12521
    DOI: 10.1038/ncomms12521
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    Cited by:

    1. Fábio Duarte & Ricardo Álvarez, 2019. "The data politics of the urban age," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.
    2. Michel S. Naslavsky & Marilia O. Scliar & Guilherme L. Yamamoto & Jaqueline Yu Ting Wang & Stepanka Zverinova & Tatiana Karp & Kelly Nunes & José Ricardo Magliocco Ceroni & Diego Lima Carvalho & Carlo, 2022. "Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

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