Development and implementation of polygenic risk score in Vietnamese population

he higher
the relative risk of said individual is, the more justified the
medical intervention becomes.
PRS analysis can also be represented based on age
(Figure 2). The cumulative risk of disease stratified by the
PRS can guide the decision at which age an individual can
benefit the most from a screening test [55]. This age-based
criterion can spotlight the balance of average risk of breast
cancer and the risk of harm due to the false-positive result.
4. Validating PRS Performance
A common concern in PRS analysis is whether the most
optimized PRS overfits the training data [56]. As a result,
applying said PRS to the general population can lead to
inflated results and false conclusions. The best strategy
to prevent overfitting of the PRS-based prediction model
is to validate its accuracy on an independent data set.
In the absence of an independent data set, the training
data can be divided into 2 separated data sets, one for
optimizing the PRS and the other for performing out-of-
sample prediction [57].
VI. CONCLUSION
The cost of reading DNA is becoming more and more
affordable through advancement of genotyping and se-
quencing technologies. Alongside the development of data
storage, new computing methods and abundance of dis-
ease databases, the PRS has provided better accuracy to
existing models of risk prediction for common diseases.
Consequently, individual clinical management (e.g., disease
screening and therapeutic intervention) can be personalized
based on individual genetic information. This genetic infor-
mation can be obtained at any point in life with a minimally
invasive procedure (e.g., blood draw or saliva sample)
and a single genotype data can be analyzed to provide
estimations for many diseases simultaneously. Although the
medical community still has doubt and hesitation regarding
implementation of the PRS, it will continue to improve and
have larger impact in the near future.
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Nguyen Tran The Hung received his doc-
tor of medicine degree from Universities of
Medicine and Pharmacy of Ho Chi Minh
city (Viet Nam) in 2016. He then got a
master degree in biomedical science from
China Medical Universities (Taichung, Tai-
wan) in 2019. His research field is human
genetic and diabetes mellitus. He worked
briefly as a pediatrician before pursuing his career in academia as
a research scientist at Vingroup Big Data Institute from 2019 until
now. His thesis on type 2 diabetic nephropathy and the application
of polygenic risk score made him believe in the potential impact
that genetic research can make in healthcare.
Le Duc Hau obtained his PhD degree
in Bioinformatics from University of Ul-
san, Republic of Korea in 2012. He is
now leading the Department of Compu-
tational Biomedicine, Vingroup Big Data
Institute, Vietnam. He has been focus-
ing on proposing computational methods
for disease- and drug-related problems in
personalized medicine, especially on identification of disease-
associated biomarkers, prediction of drug targets and response.
In parallel, he has been developed bioinformatics tools. So far,
he has been published more than fifty papers in well-recognized
journals and conferences, nearly a half of those are in ISI-indexed
journals. In addition, he has been a member of program com-
mittees and reviewer of several international conferences/journals.
Moreover, he is a principal investigator and a key member of some
national/ministry-level projects. Specially, he is the principal in-
vestigator of the biggest genome project in Vietnam (i.e., building
databases of genomic variants for Vietnamese population). Finally,
he has been collaborating with some well-recognized international
research institutes.
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