Towards a phenome-wide catalog of human clinical traits impacted by genetic ancestry

Background Racial/ethnic differences for commonly measured clinical variables are well documented, and it has been postulated that population-specific genetic factors may play a role. The genetic heterogeneity of admixed populations, such as African Americans, provides a unique opportunity to identify genomic regions and variants associated with the clinical variability observed for diseases and traits across populations. Method To begin a systematic search for these population-specific genomic regions at the phenome-wide scale, we determined the relationship between global genetic ancestry, specifically European and African ancestry, and clinical variables measured in a population of African Americans from BioVU, Vanderbilt University’s biorepository linked to de-identified electronic medical records (EMRs) as part of the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE) study. Through billing (ICD-9) codes, procedure codes, labs, and clinical notes, 36 common clinical and laboratory variables were mined from the EMR, including body mass index (BMI), kidney traits, lipid levels, blood pressure, and electrocardiographic measurements. A total of 15,863 DNA samples from non-European Americans were genotyped on the Illumina Metabochip containing ~200,000 variants, of which 11,166 were from African Americans. Tests of association were performed to examine associations between global ancestry and the phenotype of interest. Results Increased European ancestry, and conversely decreased African ancestry, was most strongly correlated with an increase in QRS duration, consistent with previous observations that African Americans tend to have shorter a QRS duration compared with European Americans. Despite known racial/ethnic disparities in blood pressure, European and African ancestry was neither associated with diastolic nor systolic blood pressure measurements. Conclusion Collectively, these results suggest that this clinical population can be used to identify traits in which population differences may be due, in part, to population-specific genetics. Electronic supplementary material The online version of this article (doi:10.1186/s13040-015-0068-y) contains supplementary material, which is available to authorized users.

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PID https://www.doi.org/10.1186/s13040-015-0068-y
PID pmc:PMC4642611
PID pmid:26566401
URL https://dblp.uni-trier.de/db/journals/biodatamining/biodatamining8.html#DumitrescuRGBFP15
URL http://europepmc.org/articles/PMC4642611
URL http://link.springer.com/article/10.1186/s13040-015-0068-y/fulltext.html
URL https://link.springer.com/article/10.1186/s13040-015-0068-y
URL https://biodatamining.biomedcentral.com/track/pdf/10.1186/s13040-015-0068-y
URL https://academic.microsoft.com/#/detail/2195763479
URL http://link.springer.com/content/pdf/10.1186/s13040-015-0068-y.pdf
URL https://biodatamining.biomedcentral.com/articles/10.1186/s13040-015-0068-y
URL https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4642611/
URL https://core.ac.uk/display/81094151
URL http://dx.doi.org/10.1186/s13040-015-0068-y
URL https://dx.doi.org/10.1186/s13040-015-0068-y
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Access Right Open Access
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Author William Bush, 0000-0002-9729-6519
Author Sarah A Pendergrass, 0000-0002-0565-6522
Author Dana Crawford, 0000-0002-6437-6248
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Collected From Europe PubMed Central; PubMed Central; ORCID; Datacite; UnpayWall; Crossref; Microsoft Academic Graph; CORE (RIOXX-UK Aggregator)
Hosted By Europe PubMed Central; SpringerOpen; BioData Mining
Journal BioData Mining, 8,
Publication Date 2015-11-11
Publisher BioMed Central
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Language English
Resource Type Other literature type; Article; UNKNOWN
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Source https://science-innovation-policy.openaire.eu/search/publication?articleId=dedup_wf_001::c28f89e2cb52b03a0dcc2bf227b45bac
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Last Updated 24 December 2020, 13:18 (CET)
Created 24 December 2020, 13:18 (CET)