. 2022 Sep 9;14(1):104.

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

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.

PMID: 36085083
PMCID: PMC9461263
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

Ruth Johnson et al. Genome Med. 2022.

. 2022 Sep 9;14(1):104.

doi: 10.1186/s13073-022-01106-x.

Authors

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.

PMID: 36085083
PMCID: PMC9461263
DOI: 10.1186/s13073-022-01106-x

Erratum in

Author Correction: Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative.
Johnson R, Ding Y, Venkateswaran V, Bhattacharya A, Boulier K, Chiu A, Knyazev S, Schwarz T, Freund M, Zhan L, Burch KS, Caggiano C, Hill B, Rakocz N, Balliu B, Denny CT, Sul JH, Zaitlen N, Arboleda VA, Halperin E, Sankararaman S, Butte MJ; UCLA Precision Health Data Discovery Repository Working Group, UCLA Precision Health ATLAS Working Group; Lajonchere C, Geschwind DH, Pasaniuc B. Johnson R, et al. Genome Med. 2022 Nov 16;14(1):128. doi: 10.1186/s13073-022-01128-5. Genome Med. 2022. PMID: 36384576 Free PMC article. No abstract available.

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Self-identified race/ethnicity (SIRE) and genetically inferred ancestry (GIA) are not analogous. We show a Sankey diagram visualizing the sample size breakdown of individuals in each genetically inferred ancestry group and SIRE groups for all individuals in ATLAS (N = 36,736)

**Fig. 2**
Global PCA reflects self-identified race/ethnicity and language of ATLAS participants. A Genetic PCs 1 and 2 of individuals in ATLAS (N=36,736) shaded by continental GIA as inferred from 1000 Genomes. **B, C** The first two genetic PCs of the ATLAS participants shaded by SIRE and preferred language, respectively. To improve visualization in C, only languages with >10 responses were assigned a color

**Fig. 3**
PCA of individuals with inferred East Asian American, European American, and Hispanic Latino American genetic ancestry in ATLAS captures fine-scale subcontinental ancestry groupings. PCA was performed separately within each continental GIA in ATLAS with the corresponding subcontinental ancestry samples from 1000 Genomes: A East Asian American, B European American, C Hispanic Latino American. Cluster annotation labels were determined using a combination of samples from 1000 Genomes and self-identified race, ethnicity, and language information from the EHR

**Fig. 4**
IBD sharing between ATLAS participants. InfoMap community membership is indicated by color for all communities with >100 individuals (20 communities total) and individuals with a degree >30. Community membership indicates elevated shared IBD within that community. Community identity is labeled adjacent to the network plot in the corresponding color

**Fig. 5**
Disease associations vary across continental genetically inferred ancestry groups in ATLAS. We show the odds ratio computed from associating each phenotype with individuals’ genetically inferred ancestry in ATLAS (N=36,736) under a logistic regression model. Error bars represent 95% confidence intervals

**Fig. 6**
Global ancestry correlates with disease prevalence in admixed individuals. Individuals by SIRE who have had a diagnosis of A chronic nonalcoholic liver disease, B uterine leiomyoma, or C liver/intrahepatic bile duct cancer are binned by their proportions of either European, African, Native American, or East Asian ancestry estimated using ADMIXTURE. Within each bin, we plot the prevalence of the diagnoses and provide standard errors (+/− 1.96 SE) of the computed frequencies

**Fig. 7**
Recapitulating known associations for chronic nonalcoholic liver disease in ancestry-specific and multi-ancestry meta-analyses in ATLAS. GWAS Manhattan plots for chronic nonalcoholic liver disease in the A European American, B Hispanic Latino American, C African American, D East Asian American GIA groups in ATLAS, and E the meta-analysis performed across all 4 GIA groups. The red dashed line denotes genome-wide significance (p-value < 5×10^-8). We recapitulate a known association at the 22q13.31 locus

**Fig. 8**
Identifying correlated phenotypes at rs2294915 in both the Hispanic Latino American and European American GIA groups in ATLAS. We show a PheWAS plot at rs2294915 for the Hispanic Latino American (top) and European American (bottom) GIA groups. The red dashed line denotes p-value=4.09×10⁻⁵, the significance threshold after adjusting for the number of tested phenotypes. The red dotted line denotes the significance threshold after correcting for both genome-wide significance and the number of tested phenotypes (p-value=4.09×10⁻¹¹)

See this image and copyright information in PMC

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

Affiliations

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

Authors

Affiliations

Erratum in

Abstract

Conflict of interest statement

Figures

References

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MeSH terms

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