Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Ruth Johnson; Yi Ding; Vidhya Venkateswaran; Arjun Bhattacharya; Kristin Boulier; Alec Chiu; Sergey Knyazev; Tommer Schwarz; Malika Freund; Lingyu Zhan; Kathryn S Burch; Christa Caggiano; Brian Hill; Nadav Rakocz; Brunilda Balliu; Christopher T Denny; Jae Hoon Sul; Noah Zaitlen; Valerie A Arboleda; Eran Halperin; Sriram Sankararaman; Manish J Butte; UCLA Precision Health Data Discovery Repository Working Group, UCLA Precision Health ATLAS Working Group; Clara Lajonchere; Daniel H Geschwind; Bogdan Pasaniuc

doi:10.1186/s13073-022-01106-x

Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative

Genome Med. 2022 Sep 9;14(1):104. doi: 10.1186/s13073-022-01106-x.

Authors

Ruth Johnson^{1

2}, Yi Ding^{3

4}, Vidhya Venkateswaran^{3

5}, Arjun Bhattacharya^{3

6}, Kristin Boulier^{4

7}, Alec Chiu⁴, Sergey Knyazev^{3

6}, Tommer Schwarz^{3

4}, Malika Freund^{8

9}, Lingyu Zhan¹⁰, Kathryn S Burch^{3

4}, Christa Caggiano^{3

11}, Brian Hill¹², Nadav Rakocz¹², Brunilda Balliu¹³, Christopher T Denny^{14

15

16}, Jae Hoon Sul¹⁷, Noah Zaitlen^{11

13}, Valerie A Arboleda^{3

8

13}, Eran Halperin^{12

13

18}, Sriram Sankararaman^{12

8

13}, Manish J Butte¹⁹; UCLA Precision Health Data Discovery Repository Working Group, UCLA Precision Health ATLAS Working Group; Clara Lajonchere^{11

20}, Daniel H Geschwind^{8

11

20}, Bogdan Pasaniuc^{21

22

23

24

25}

Affiliations

¹ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA. ruthjohnson@g.ucla.edu.
² Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. ruthjohnson@g.ucla.edu.
³ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁴ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁵ Department of Oral Biology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁶ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁷ Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁸ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
⁹ Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA.
¹⁰ Molecular Biology Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹¹ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹² Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹³ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁴ Division of Hematology/Oncology, Department of Pediatrics, Gwynne Hazen Cherry Memorial Laboratories, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁵ Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁶ Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁷ Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁸ Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
¹⁹ Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
²⁰ Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
²¹ Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. pasaniuc@g.ucla.edu.
²² Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA. pasaniuc@g.ucla.edu.
²³ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. pasaniuc@g.ucla.edu.
²⁴ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. pasaniuc@g.ucla.edu.
²⁵ Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA. pasaniuc@g.ucla.edu.

Abstract

Background: Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736).

Methods: We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.

Results: We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10^-16, EAA p-value=6.73×10^-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group.

Conclusions: Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.

Keywords: Biobank; Electronic health records; Genetic ancestry; Genome-wide association studies; Phenome-wide association studies.

Publication types

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

MeSH terms

Asian People
Biological Specimen Banks
Electronic Health Records*
Genomics
Humans
Public Health*

Abstract

Publication types

MeSH terms

Grants and funding