Dataset Information

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

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.

SUBMITTER: Johnson R

PROVIDER: S-EPMC9903668 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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

Johnson Ruth R Ding Yi Y Bhattacharya Arjun A Knyazev Sergey S Chiu Alec A Lajonchere Clara C Geschwind Daniel H DH Pasaniuc Bogdan B

Cell genomics 20230111 1

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 milli ...[more]

PMID: 36777178

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Project description:BackgroundLarge 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).MethodsWe 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.ResultsWe 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.ConclusionsOverall, 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.

Project description:IntroductionGlobal migration and immigration are increasing, and migrants and immigrants (im/migrants) have specific health needs and healthcare experiences. Yet, im/migrant involvement in immigration and health research in Canada is inconsistent. Heretofore, involvement has primarily been in research planning, data collection and analysis, with little community involvement during knowledge exchange or through training and colearning opportunities. Community engagement has been especially uncommon in mixed-method and quantitative research in Canada.ObjectiveThis article describes lessons learnt from the Evaluating Inequities in Refugee & Immigrants' Health Access (IRIS) project from 2018 to 2023, an ongoing mixed-method, community-based research project in British Columbia, Canada. Specifically, we share our core community engagement project structures, Commitments to Community and our Community Engagement Backbone, both collaboratively developed with im/migrant community memebers.ParticipantsPeople with varied experiences of im/migration and connections to multiple, specific im/migrant communities participate in the project as participants, community researchers, community advisory board members, faculty members and students. Core research activities are supported in English, Farsi, Spanish and Tigrinya. We engage community members throughout the research process, from identifying research topics to knowledge exchange.ConclusionWe found that these structures offer an accessible visual representation of the project's commitments to community engagement, and the ways these commitments are demonstrated through values and action. Our training opportunities, colearning activities and knowledge exchange efforts also confirmed the accuracy of interpretation, prompted additional analysis to clarify or add depth to findings, and helped us identify additional research topics. We hope these learnings can be used to expand engagement with diverse im/migrant communities in health and immigration research.

Project description:AimsTo systematically identify global research gaps and resource priorities for integrated community case management (iCCM).MethodsAn iCCM Child Health and Nutrition Research Initiative (CHNRI) Advisory Group, in collaboration with the Community Case Management Operational Research Group (CCM ORG) identified experts to participate in a CHNRI research priority setting exercise. These experts generated and systematically ranked research questions for iCCM. Research questions were ranked using a "Research Priority Score" (RPS) and the "Average Expert Agreement" (AEA) was calculated for every question. Our groups of experts were comprised of both individuals working in Ministries of Health or Non Governmental Organizations (NGOs) in low- and middle-income countries (LMICs) and individuals working in high-income countries (HICs) in academia or NGO headquarters. A Spearman's Rho was calculated to determine the correlation between the two groups' research questions' ranks.ResultsThe overall RPS ranged from 64.58 to 89.31, with a median score of 81.43. AEA scores ranged from 0.54 to 0.86. Research questions involving increasing the uptake of iCCM services, research questions concerning the motivation, retention, training and supervision of Community Health Workers (CHWs) and concerning adding additional responsibilities including counselling for infant and young child feeding (IYCF) and treatment of severe acute malnutrition (SAM) ranked highly. There was weak to moderate, statistically significant, correlation between scores by representatives of high-income countries and those working in-country or regionally (Spearman's ρ = 0.35034, P < 0.01).ConclusionsOperational research to determine optimal training, supervision and modes of motivation and retention for the CHW is vital for improving iCCM, globally, as is research to motivate caregivers to take advantage of iCCM services. Experts working in-country or regionally in LMICs prioritized different research questions than those working in organization headquarters in HICs. Further exploration is needed to determine the nature of this divergence.

Dataset Information

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

Publications

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

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