Leveraging genomic diversity for discovery in an EHR-linked biobank: the UCLA ATLAS Community Health Initiative

Johnson, Ruth; Ding, Yi; Venkateswaran, Vidhya; Bhattacharya, Arjun; Chiu, Alec; Schwarz, Tommer; Freund, Malika K.; Zhan, Lingyu; Burch, Kathryn S.; Caggiano, Christa; Hill, Brian; Rakocz, Nadav; Balliu, Brunilda; Sul, Jae Hoon; Zaitlen, Noah; Arboleda, Valerie A.; Halperin, Eran; Sankararaman, Sriram; Butte, Manish J.; Lajonchere, Clara; Geschwind, Daniel H.; Paşaniuc, Bogdan

doi:10.1101/2021.09.22.21263987

Cited by 14 publications

(17 citation statements)

References 84 publications

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“…MGI exists within a broad family of single-health system biobanks, e.g. at Vanderbilt University (Roden et al, 2008), Geisinger (Carey et al, 2016) and UCLA (Johnson et al, 2021). Even within this group major differences exist in recruitment strategy.…”

Section: Discussionmentioning

confidence: 99%

The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Zawistowski

Fritsche

Pandit

et al. 2021

Preprint

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The recent wave of biobank repositories linking individual-level genetic data with dense clinical health history has introduced a dramatic paradigm shift in phenotyping for human genetic studies. The mechanism by which biobanks recruit participants can vary dramatically according to factors such as geographic catchment and sampling strategy. These enrollment differences leave an imprint on the cohort, defining the demographics and the utility of the biobank for research purposes. Here we introduce the Michigan Genomics Initiative (MGI), a rolling enrollment, single health system biobank currently consisting of >85,000 participants recruited primarily through surgical encounters at Michigan Medicine. A strong ascertainment effect is introduced by focusing recruitment on individuals in Southeast Michigan undergoing surgery. MGI participants are, on average, less healthy than the general population, which produces a biobank enriched for case counts of many disease outcomes, making it well suited for a disease genetics cohort. A comparison to the much larger UK Biobank, which uses population representative sampling, reveals that MGI has higher prevalence for nearly all diagnosis- code-based phenotypes, and larger absolute numbers of cases for many phenotypes. GWAS of these phenotypes replicate many known findings, validating the genetic and clinical data and their proper linkage. Our results illustrate that single health-system biobanks that recruit participants through opportunistic sampling, such as surgical encounters, produce distinct patient profiles that provide an ideal resource for exploring the genetics of complex diseases.

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Section: Discussionmentioning

confidence: 99%

The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Zawistowski

Fritsche

Pandit

et al. 2021

Preprint

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“…We used a version of the ATLAS genotype data that was imputed to an external dataset, as detailed in [67]. Briefly, after performing quality control, genotypes were uploaded the Michigan Imputation Server [70].…”

Section: Methodsmentioning

confidence: 99%

Methylation risk scores are associated with a collection of phenotypes within electronic health record systems

Thompson

Hill

Rokocz

et al. 2022

Preprint

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Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. Since methylation states are influenced by both environmental and genetic factors, we hypothesized that MRS would complement PRS and EHR-based machine-learning methods, improving overall prediction accuracy. To evaluate this hypothesis, we performed the largest assessment of methylation risk scores in clinical datasets to be conducted to date. We measured methylation across a large cohort (n=831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10-7) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. In summary, our work provides a comprehensive evaluation of MRS in comparison to PRS and EHR imputation on the largest dataset consisting of methylation, genotype, and EHR data. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.

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“…Participants were recruited through University of California, Los Angeles (UCLA) Health System. Written informed consent was obtained from the participants for the study of remnant biosamples in the UCLA ATLAS Precision Health Biobank ( Chang et al, 2021 ; GenomicsDB, 2021 ; Johnson et al, 2021 ). Genetic data obtained from remnant biosamples as described below were linked to the deidentified EHR from the UCLA Health System known as the UCLA Data Discovery Repository (DDR), developed under the auspices of the UCLA Health Office of Health Informatics Analytics and the UCLA Institute of Precision Health.…”

Section: Methodsmentioning

confidence: 99%

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer’s Disease in Electronic Health Records

2022

Front. Aging Neurosci.

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Alzheimer’s disease (AD) is the most common form of dementia and a growing public health burden in the United States. Significant progress has been made in identifying genetic risk for AD, but limited studies have investigated how AD genetic risk may be associated with other disease conditions in an unbiased fashion. In this study, we conducted a phenome-wide association study (PheWAS) by genetic ancestry groups within a large academic health system using the polygenic risk score (PRS) for AD. PRS was calculated using LDpred2 with genome-wide association study (GWAS) summary statistics. Phenotypes were extracted from electronic health record (EHR) diagnosis codes and mapped to more clinically meaningful phecodes. Logistic regression with Firth’s bias correction was used for PRS phenotype analyses. Mendelian randomization was used to examine causality in significant PheWAS associations. Our results showed a strong association between AD PRS and AD phenotype in European ancestry (OR = 1.26, 95% CI: 1.13, 1.40). Among a total of 1,515 PheWAS tests within the European sample, we observed strong associations of AD PRS with AD and related phenotypes, which include mild cognitive impairment (MCI), memory loss, and dementias. We observed a phenome-wide significant association between AD PRS and gouty arthropathy (OR = 0.90, adjusted p = 0.05). Further causal inference tests with Mendelian randomization showed that gout was not causally associated with AD. We concluded that genetic predisposition of AD was negatively associated with gout, but gout was not a causal risk factor for AD. Our study evaluated AD PRS in a real-world EHR setting and provided evidence that AD PRS may help to identify individuals who are genetically at risk of AD and other related phenotypes. We identified non-neurodegenerative diseases associated with AD PRS, which is essential to understand the genetic architecture of AD and potential side effects of drugs targeting genetic risk factors of AD. Together, these findings expand our understanding of AD genetic and clinical risk factors, which provide a framework for continued research in aging with the growing number of real-world EHR linked with genetic data.

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Leveraging genomic diversity for discovery in an EHR-linked biobank: the UCLA ATLAS Community Health Initiative

Cited by 14 publications

References 84 publications

The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients

The Michigan Genomics Initiative: a biobank linking genotypes and electronic clinical records in Michigan Medicine patients

Methylation risk scores are associated with a collection of phenotypes within electronic health record systems

Phenome-Wide Association Study of Polygenic Risk Score for Alzheimer’s Disease in Electronic Health Records

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