The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank

Ruth Johnson; Yi Ding; Arjun Bhattacharya; Sergey Knyazev; Alec Chiu; Clara Lajonchere; Daniel H Geschwind; Bogdan Pasaniuc

doi:10.1016/j.xgen.2022.100243

The UCLA ATLAS Community Health Initiative: Promoting precision health research in a diverse biobank

Cell Genom. 2023 Jan 11;3(1):100243. doi: 10.1016/j.xgen.2022.100243.

Authors

Ruth Johnson^{1

2}, Yi Ding^{3

2}, Arjun Bhattacharya^{2

4}, Sergey Knyazev², Alec Chiu³, Clara Lajonchere^{5

6}, Daniel H Geschwind^{7

5

6}, Bogdan Pasaniuc^{3

2

7

8

5}

Affiliations

¹ Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.
² Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
³ Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁴ Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁵ Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁶ Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁷ Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.
⁸ Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.

Abstract

The UCLA ATLAS Community Health Initiative (ATLAS) has an initial target to recruit 150,000 participants from across the UCLA Health system with the goal of creating a genomic database to accelerate precision medicine efforts in California. This initiative includes a biobank embedded within the UCLA Health system that comprises de-identified genomic data linked to electronic health records (EHRs). The first freeze of data from September 2020 contains 27,987 genotyped samples imputed to 7.9 million SNPs across the genome and is linked with de-identified versions of the EHRs from UCLA Health. Here, we describe a centralized repository of the genotype data and provide tools and pipelines to perform genome- and phenome-wide association studies across a wide range of EHR-derived phenotypes and genetic ancestry groups. We demonstrate the utility of this resource through the analysis of 7 well-studied traits and recapitulate many previous genetic and phenotypic associations.

Keywords: GWAS; Global-Biobank Meta-analysis Initiative; PheWAS; biobanks; electronic health records; multi-ancestry.

Abstract

Grants and funding