Dataset Information

Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank.

ABSTRACT: Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, we generated principal components for patients in our dataset, which were used to cluster patients into groups based on genetic ancestry. Consistent with other studies, we find a strong overall agreement (Kappa = 0.872) between genetic ancestry and assigned race, with higher rates of agreement for African-descent and European-descent assignments, and reduced agreement for Hispanic, East Asian-descent, and South Asian-descent assignments. These results suggest caution when selecting study samples of non-African and non-European backgrounds when administratively-assigned race from biobanks is used.

SUBMITTER: Hall JB

PROVIDER: S-EPMC4045967 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank.

Hall Jacob B JB Dumitrescu Logan L Dilks Holli H HH Crawford Dana C DC Bush William S WS

PloS one 20140604 6

Recently, the development of biobanks linked to electronic medical records has presented new opportunities for genetic and epidemiological research. Studies based on these resources, however, present unique challenges, including the accurate assignment of individual-level population ancestry. In this work we examine the accuracy of administratively-assigned race in diverse populations by comparing assigned races to genetically-defined ancestry estimates. Using 220 ancestry informative markers, w ...[more]

PMID: 24896101

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Project description:ImportanceWhile dosing reminders for glaucoma medications have been shown to increase drug adherence, an outstanding limitation preventing broad adoption is difficulty linking reminders to medication in each patient's electronic health record (EHR).ObjectiveTo examine the feasibility of implementing an EHR-linked, automated reminder system for glaucoma medications and assess patient satisfaction with it.Design, setting, and participantsIn this prospective, cross-sectional study, patients receiving glaucoma medications were recruited from a university-based glaucoma clinic from April 2017 to January 2018. Analysis began in February 2018.ExposuresA web-based application was added to the EHR patient portal, allowing patients to configure telecommunication-based reminders for their glaucoma medications. For all potential participants, basic information was collected to determine risk of nonadherence with glaucoma medications. Those consenting to participate then answered questions on medication adherence, reminders, and their likelihood of using EHR-linked reminders. They then configured reminders using the EHR patient portal. After 3 months, they answered questions about their satisfaction with the system.Main outcomes and measuresProportions of patients and high-risk patients willing to use the reminders, and patient satisfaction with the system.ResultsOf 147 patients approached, 100 (68%) agreed to participate, of whom the majority were men (54 [54%]) and white (51 [51%]), with a mean (SD) age of 65 (12.4) years. There were no differences between those willing and unwilling to participate, except for a slightly lower self-reported medication adherence rate among those who participated (mean [SD], 91% [13.6%] vs 97% [6.0%]). Based on a previously validated risk assessment score, 9% (9 of 100) of participants were categorized as being at high risk for poor adherence (≥50% probability of nonadherence) compared with 11% (5 of 47) of nonparticipants. Of 100 participants, 94 ultimately configured reminders, of whom 89 (95%) completed follow-up. On follow-up, 74% (n = 66; 95% CI, 65-83) of participants found the reminders to be useful, 15% (n = 13) were neutral, and 11% (n = 10) found them not useful. Most participants (72 [81%]; 95% CI, 77-85) had help configuring reminders.Conclusions and relevanceElectronic health records-linked reminders had good uptake and were well received among patients at a tertiary glaucoma clinic. These portals may represent a new and convenient method of allowing patients to link their glaucoma medications to automated reminders, although the generalizability of these results and the effect on glaucoma outcomes remains unknown.

Project description:We aimed to detect cross-sectional phenotype and polygenic risk score (PRS) associations between sleep duration and prevalent diseases using the Partners Biobank, a hospital-based cohort study linking electronic medical records (EMR) with genetic information. Disease prevalence was determined from EMR, and sleep duration was self-reported. A PRS for sleep duration was derived using 78 previously associated SNPs from genome-wide association studies (GWAS) for self-reported sleep duration. We tested for associations between (1) self-reported sleep duration and 22 prevalent diseases (n = 30 251), (2) the PRS and self-reported sleep duration (n = 6903), and (3) the PRS and the 22 prevalent diseases (n = 16 033). For observed PRS-disease associations, we tested causality using two-sample Mendelian randomization (MR). In the age-, sex-, and race-adjusted model, U-shaped associations were observed for sleep duration and asthma, depression, hypertension, insomnia, obesity, obstructive sleep apnea, and type 2 diabetes, where both short and long sleepers had higher odds for these diseases than normal sleepers (p < 2.27 × 10-3). Next, we confirmed associations between the PRS and longer sleep duration (0.65 ± 0.19 SD minutes per effect allele; p = 7.32 × 10-04). The PRS collectively explained 1.4% of the phenotypic variance in sleep duration. After adjusting for age, sex, genotyping array, and principal components of ancestry, we observed that the PRS was also associated with congestive heart failure (CHF; p = 0.015), obesity (p = 0.019), hypertension (p = 0.039), restless legs syndrome (RLS; p = 0.041), and insomnia (p = 0.049). Associations were maintained following additional adjustment for obesity status, except for hypertension and insomnia. For all diseases, except RLS, carrying a higher genetic burden of the 78 sleep duration-increasing alleles (i.e. higher sleep duration PRS) associated with lower odds for prevalent disease. In MR, we estimated causal associations between genetically defined longer sleep duration with decreased risk of CHF (inverse variance weighted [IVW] OR per minute of sleep [95% CI] = 0.978 [0.961-0.996]; p = 0.019) and hypertension (IVW OR [95% CI] = 0.993 [0.986-1.000]; p = 0.049), and increased risk of RLS (IVW OR [95% CI] = 1.018 [1.000-1.036]; p = 0.045). By validating the PRS for sleep duration and identifying cross-phenotype associations, we lay the groundwork for future investigations on the intersection between sleep, genetics, clinical measures, and diseases using large EMR datasets.

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.

Dataset Information

Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank.

Publications

Accuracy of administratively-assigned ancestry for diverse populations in an electronic medical record-linked biobank.

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