We addressed the underrepresentation of non-European populations in genome-wide association studies (GWASs) by building HiGenome, a large-scale genetic resource for the Taiwanese Han population. Using a custom genotyp...
We addressed the underrepresentation of non-European populations in genome-wide association studies (GWASs) by building HiGenome, a large-scale genetic resource for the Taiwanese Han population. Using a custom genotyping array, we integrated deidentified electronic medical records (2003 to 2021) with genomic data to enable GWASs, phenome-wide association studies, and polygenic risk score (PRS) analysis. Among 413,000 participants, 323,397 passed ancestry and quality control filtering. GWASs covered 1085 traits, focusing on diseases prevalent in Taiwan such as type 2 diabetes, chronic kidney disease, gout, and alcoholic liver damage. PRSs were calculated for 238 traits, with the strongest associations observed in musculoskeletal disorders. Incorporating PRS into clinical practice supports early risk prediction and personalized prevention. To further expand translational value, we also conducted pharmacogenomic analysis and human leukocyte antigen typing. HiGenome offers a large-scale genetic and clinical dataset from the Taiwanese Han population, supporting population-specific analyses and precision medicine development in East Asia. The hospital-based design enables continuous follow-up and longitudinal data expansion.
Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often no...
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Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current a...
Background Digital is the new blood, where collection, use, and storage of raw digital data streams enabled by technology-embedded sensors are replacing analog methods. Analyses of digital data extend beyond current applications, analogous to previously stored blood currently be used for omics/biomarker research. Today digital technologies are used to measure Alzheimer’s disease (AD)-related clinical symptoms (e.g., cognition, function, and behavior), but future possibilities remain to be determined. Importantly, digital data collection can go beyond current intermittent sampling by employing internet-connected applications and devices that allow real-time monitoring and accurate detection of changes, even within a widely heterogeneous spectrum. Method Beginning in 2005, the Framingham Heart Study (FHS) began digitally recording participants’ spoken responses to neuropsychological (NP) test questions, and in 2011 extended to a digital pen to record written responses. The emergence of wearable devices and smartphone applications has led to an expanded use of digital technologies beyond recordings during NP assessments. The Boston University Alzheimer’s Disease Research Center (BU ADRC) leverages digital sensors in multiple internet-connected devices that longitudinally collect digital data, in both in-person and remote settings. Result The first publication involving FHS digital data was in 2016; the remaining (n> 17) have been from 2020-current. Digital data measuring cognition, function and behavior are being collected through the BU ADRC, where results to date indicate nearly 100% and 75% uptake of active and passive engagement applications, respectively, and 95% uptake for wearable devices (n = 60). Longitudinal assessments include > 3 months for ∼50 participants; > 1 year for n∼21 participants. Conclusion Opportunities for improved trial outcome measurement are evident in the broad range of outcomes digital sensors capture beyond traditional research methodologi
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