Student‐led community health initiative on dementia: perspectives from a clinical educator and medical students

2018 ◽  
Vol 16 (3) ◽  
pp. 283-285
Author(s):  
Chie Hui Leong ◽  
Jun Ming Liew ◽  
Wang Tech Lim ◽  
Mei Lu Lee ◽  
Shyh Poh Teo

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0232239
Author(s):  
Candyce H. Kroenke ◽  
Gem M. Le ◽  
Shannon M. Conroy ◽  
Alison J. Canchola ◽  
Salma Shariff-Marco ◽  
...  


2012 ◽  
Vol 27 (2) ◽  
pp. e59-e68 ◽  
Author(s):  
Allen Cheadle ◽  
Suzanne Rauzon ◽  
Rebecca Spring ◽  
Pamela M. Schwartz ◽  
Scott Gee ◽  
...  


Author(s):  
Michael C. Gibbons ◽  
Samantha L. Illangasekare ◽  
Earnest Smith ◽  
Joan Kub




2009 ◽  
Vol 11 (3) ◽  
pp. 332-339 ◽  
Author(s):  
Leila Kramer ◽  
Pamela Schwartz ◽  
Allen Cheadle ◽  
J. Elaine Borton ◽  
Merrick Wright ◽  
...  




2015 ◽  
Vol 54 (3) ◽  
pp. 1148-1156 ◽  
Author(s):  
Panagis Galiatsatos ◽  
Rebeca Rios ◽  
W. Daniel Hale ◽  
Jessica L. Colburn ◽  
Colleen Christmas


2021 ◽  
Author(s):  
Ruth Dolly Johnson ◽  
Yi Ding ◽  
Vidhya Venkateswaran ◽  
Arjun Bhattacharya ◽  
Alec Chiu ◽  
...  

Large medical centers located in urban areas such as Los Angeles care for a diverse patient population and offer the potential to study the interplay between genomic ancestry and social determinants of health within a single medical system. Here, we introduce the UCLA ATLAS Community Health Initiative-- a biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients. We leverage the unique genomic diversity of the patient population in ATLAS to explore the interplay between self-reported race/ethnicity and genetic ancestry within a disease context using phenotypes extracted from the EHR. First, we identify an extensive amount of continental and subcontinental genomic diversity within the ATLAS data that is consistent with the global diversity of Los Angeles; this includes clusters of ATLAS individuals corresponding to individuals with Korean, Japanese, Filipino, and Middle Eastern genomic ancestries. Most importantly, we find that common diseases and traits stratify across genomic ancestry clusters, thus suggesting their utility in understanding disease biology across diverse individuals. Next, we showcase the power of genetic data linked with EHR to perform ancestry-specific genome and phenome-wide scans to identify genetic factors for a variety of EHR-derived phenotypes (phecodes). For example, we find ancestry-specific associations for liver disease, and link the genetic variants with neurological and neoplastic phenotypes primarily within individuals of admixed ancestries. Overall, our results underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping efforts linked with EHR-based phenotyping.



2007 ◽  
Vol 55 (1) ◽  
pp. S118
Author(s):  
H. Budden ◽  
S. K. Yeong ◽  
L. Taylor ◽  
P. Kretz ◽  
L. Parfitt ◽  
...  


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