Disease risk and healthcare utilization among ancestrally diverse groups in the Los Angeles region

Christa Caggiano; Arya Boudaie; Ruhollah Shemirani; Joel Mefford; Ella Petter; Alec Chiu; Defne Ercelen; Rosemary He; Daniel Tward; Kimberly C Paul; Timothy S Chang; Bogdan Pasaniuc; Eimear E Kenny; Jonathan A Shortt; Christopher R Gignoux; Brunilda Balliu; Valerie A Arboleda; Gillian Belbin; Noah Zaitlen

doi:10.1038/s41591-023-02425-1

Disease risk and healthcare utilization among ancestrally diverse groups in the Los Angeles region

Nat Med. 2023 Jul;29(7):1845-1856. doi: 10.1038/s41591-023-02425-1. Epub 2023 Jul 18.

Authors

Christa Caggiano^{1

2}, Arya Boudaie³, Ruhollah Shemirani⁴, Joel Mefford⁵, Ella Petter⁶, Alec Chiu¹, Defne Ercelen⁷, Rosemary He^{6

8}, Daniel Tward^{2

8}, Kimberly C Paul², Timothy S Chang², Bogdan Pasaniuc^{8

9

10

11}, Eimear E Kenny⁴, Jonathan A Shortt^{12

13}, Christopher R Gignoux^{12

13}, Brunilda Balliu⁸, Valerie A Arboleda^{10

11}, Gillian Belbin⁴, Noah Zaitlen^{14

15

16}

Affiliations

¹ Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, USA.
² Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA.
³ Oscar Health, Inc., New York, NY, USA.
⁴ Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
⁵ Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA.
⁶ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA.
⁷ Computational and Systems Biology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA.
⁸ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
⁹ Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, USA.
¹⁰ Department of Pathology and Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
¹¹ Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
¹² Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹³ Division of Bioinformatics and Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
¹⁴ Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA. nzaitlen@ucla.edu.
¹⁵ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA. nzaitlen@ucla.edu.
¹⁶ Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA. nzaitlen@ucla.edu.

PMID: 37464048
DOI: 10.1038/s41591-023-02425-1

Abstract

An individual's disease risk is affected by the populations that they belong to, due to shared genetics and environmental factors. The study of fine-scale populations in clinical care is important for identifying and reducing health disparities and for developing personalized interventions. To assess patterns of clinical diagnoses and healthcare utilization by fine-scale populations, we leveraged genetic data and electronic medical records from 35,968 patients as part of the UCLA ATLAS Community Health Initiative. We defined clusters of individuals using identity by descent, a form of genetic relatedness that utilizes shared genomic segments arising due to a common ancestor. In total, we identified 376 clusters, including clusters with patients of Afro-Caribbean, Puerto Rican, Lebanese Christian, Iranian Jewish and Gujarati ancestry. Our analysis uncovered 1,218 significant associations between disease diagnoses and clusters and 124 significant associations with specialty visits. We also examined the distribution of pathogenic alleles and found 189 significant alleles at elevated frequency in particular clusters, including many that are not regularly included in population screening efforts. Overall, this work progresses the understanding of health in understudied communities and can provide the foundation for further study into health inequities.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, Non-U.S. Gov't

MeSH terms

Delivery of Health Care*
Ethnicity
Humans
Iran
Los Angeles
Patient Acceptance of Health Care*

Abstract

Publication types

MeSH terms

Grants and funding