Accuracy of Administratively-Assigned Ancestry for Diverse Populations in an Electronic Medical Record-Linked Biobank

Hall, Jacob B.; Dumitrescu, Logan; Dilks, Holli H.; Crawford, Dana C.; Bush, William S.

doi:10.1371/journal.pone.0099161

Cited by 28 publications

(27 citation statements)

References 15 publications

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“…The EHRs used for the development of the algorithms were updated in 2015 to include current information. Race/ethnicity is administratively reported in BioVU and strongly correlated with genetic ancestry 23,24 . The majority (81%) of patients in the dataset are black individuals with an average age of 50 years (Table 1).…”

Section: Methodsmentioning

confidence: 99%

Development and Performance of Text-Mining Algorithms to Extract Socioeconomic Status From De-Identified Electronic Health Records

et al. 2016

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Socioeconomic status (SES) is a fundamental contributor to health, and a key factor underlying racial disparities in disease. However, SES data are rarely included in genetic studies due in part to the difficultly of collecting these data when studies were not originally designed for that purpose. The emergence of large clinic-based biobanks linked to electronic health records (EHRs) provides research access to large patient populations with longitudinal phenotype data captured in structured fields as billing codes, procedure codes, and prescriptions. SES data however, are often not explicitly recorded in structured fields, but rather recorded in the free text of clinical notes and communications. The content and completeness of these data vary widely by practitioner. To enable gene-environment studies that consider SES as an exposure, we sought to extract SES variables from racial/ethnic minority adult patients (n=9,977) in BioVU, the Vanderbilt University Medical Center biorepository linked to de-identified EHRs. We developed several measures of SES using information available within the de-identified EHR, including broad categories of occupation, education, insurance status, and homelessness. Two hundred patients were randomly selected for manual review to develop a set of seven algorithms for extracting SES information from de-identified EHRs. The algorithms consist of 15 categories of information, with 830 unique search terms. SES data extracted from manual review of 50 randomly selected records were compared to data produced by the algorithm, resulting in positive predictive values of 80.0% (education), 85.4% (occupation), 87.5% (unemployment), 63.6% (retirement), 23.1% (uninsured), 81.8% (Medicaid), and 33.3% (homelessness), suggesting some categories of SES data are easier to extract in this EHR than others. The SES data extraction approach developed here will enable future EHR-based genetic studies to integrate SES information into statistical analyses. Ultimately, incorporation of measures of SES into genetic studies will help elucidate the impact of the social environment on disease risk and outcomes.

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Section: Methodsmentioning

confidence: 99%

Development and Performance of Text-Mining Algorithms to Extract Socioeconomic Status From De-Identified Electronic Health Records

et al. 2016

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“…Race/ethnicity in the SD is administratively assigned, which has been shown to be highly concordant with ancestry informative markers (AIMs) in European Americans (19–20). All individuals in this study were classified as being white based on demographic data obtained from the SD.…”

Section: Methodsmentioning

confidence: 99%

Clinical and Genetic Factors Associated With Cutaneous Squamous Cell Carcinoma in Kidney and Heart Transplant Recipients

Sanders

Karnes

Denny

et al. 2015

Transplantation Direct

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Background Cutaneous squamous cell carcinoma (cSCC) occurs with higher frequency and recurrence rates, increased morbidity and mortality, and more aggressive metastasis in kidney and heart transplant recipients compared to the general population but all transplant recipients do not develop cSCC. In addition, the phenotypic expression of cSCC among transplant recipients can vary between mild disease to extensive recurrent metastatic disease. These clinically observed differences in occurrence and severity of cSCC among transplant recipients suggest the possibility that an underlying genetic component might modify risk. Methods We identified 88 white post-transplant cSCC cases (71 kidney and 17 heart) and 300 white post-transplant controls (265 kidney and 35 heart) using a DNA biobank linked with de-identified electronic medical records. Logistic regression was used to determine adjusted odds ratios (OR) for clinical characteristics and single nucleotide polymorphisms (SNP) associated with cSCC in both a candidate SNP and genome wide analysis. Results Age (OR 1.08 [1.05–1.11], p<0.001) and azathioprine exposure (OR 8.64 [3.92–19.03], p<0.001) were significantly associated while gender, smoking tobacco use, dialysis duration and immunosuppression duration were not. Ten candidate SNPs previously associated with non-melanoma skin cancer in the general population were significantly associated with cSCC in transplant recipients. Genome wide association analysis implicated SNPs in genes previously associated with malignancy, CSMD1 (OR 3.14 [1.90–5.20]) and CACNA1D (OR 2.67 [1.73–4.10]). Conclusions This study shows an association of increasing age and azathioprine exposure with cSCC and confirms a genetic contribution for cSCC development in kidney and heart transplant recipients.

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“…Race/ethnicity was self-reported, except in EAGLE/BioVU, where race/ethnicity was administrator assigned, and in TaiChi, where subjects were assumed to be Taiwanese/Chinese (28,29). Within each study, race/ethnicity outliers were removed using ancestry informative principal components (PCs).…”

Section: Study Population and Phenotypesmentioning

confidence: 99%

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci

Zubair

Graff²,

Ambite

et al. 2016

Human Molecular Genetics

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Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.

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Accuracy of Administratively-Assigned Ancestry for Diverse Populations in an Electronic Medical Record-Linked Biobank

Cited by 28 publications

References 15 publications

Development and Performance of Text-Mining Algorithms to Extract Socioeconomic Status From De-Identified Electronic Health Records

Development and Performance of Text-Mining Algorithms to Extract Socioeconomic Status From De-Identified Electronic Health Records

Clinical and Genetic Factors Associated With Cutaneous Squamous Cell Carcinoma in Kidney and Heart Transplant Recipients

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci

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