Development and implementation of polygenic risk score in Vietnamese population
Research and Development on Information and Communication Technology
Development and Implementation of
Polygenic Risk Score in Vietnamese Population
Nguyen Tran The Hung, Le Duc Hau
Department of Computational Biomedicine, Vingroup Big Data Institute, Hanoi, Vietnam
Correspondence: Le Duc Hau, [email protected]
Communication: received 18 October 2019, revised 23 December 2019, accepted 29 December 2019
Digital Object Identifier: 10.32913/mic-ict-research.v2019.n2.893
The Editor coordinating the review of this article and deciding to accept it was Prof. Le Hoang Son
Abstract: Recent technological advancements and availabil-
ity of genetic databases have facilitated the integration of
genetic factors into risk prediction models. A Polygenic Risk
Score (PRS) combines the effect of many Single Nucleotide
Polymorphisms (SNP) into a single score. This score has
lately been shown to have a clinically predictive value in
various common diseases. Some clinical interpretations of
PRS are summarized in this review for coronary artery
disease, breast cancer, prostate cancer, diabetes mellitus, and
Alzheimer’s disease. While these findings gave support to the
implementation of PRS in clinical settings, the populations
of interest were derived mainly from European ancestry.
Therefore, applying these findings to non-European ancestry
(Vietnamese in this context) requires many efforts and cau-
tions. This review aims to articulate the evidence supporting
the clinical use of PRS, the concepts behind the validity of
PRS, approach to implement PRS in Vietnamese population,
and cautions in selecting methods and thresholds to develop
an appropriate PRS.
in combining all the small SNP effects into a single score
2. PRS Calculation
In its simplest form, the PRS of an individual can be
calculated as the sum of all effect sizes of the effective
alleles observed in its genotype. The formula to calculate
the PRS is given as follows:
<
Õ
b
PRS8 =
G8 9 × ꢀ9,
9=1
where PRS8 is the risk score for the 8th individual, < is
the number of SNPs included in the calculation, G8 9 is the
genotype of the 8th individual for the 9th SNP (can be 0, 1,
b
or 2 depending on the inheritance model), and ꢀ9 is the
effect size of the 9th SNP, usually obtained from GWAS
Keywords: Genetic, clinical, single nucleotide polymoyphism
(SNP), polygenic risk score (PRS).
summary statistics [4].
Although the concept of PRS is as old as the finding
of genetic materials (DNA), modern technology allows the
integration of more genetic variants and more precise effect
sizes. Therefore, there are numerous considerations and
thresholds related to developing and validating the formula
of PRS that are still controversial [5].
I. RENEWED INTEREST IN POLYGENIC RISK SCORE
1. Definition
A Polygenic Risk Score (PRS) in the context of genetic
studies is a mathematical aggregation of risk effects con-
ferred by many Single Nucleotide Polymorphisms (SNP).
Each SNP contributes a small effect to the development of
a disease or a complex trait of interest. In the early days
of Genome-Wide Association Study (GWAS), researchers
expected to find genetic variants that have a large effect
already surpassed hundreds of thousand of individuals, they
failed to capture the genetic variants that can explain the
heritability of common diseases, such as breast cancer,
prostate cancer, coronary artery disease, diabetes mellitus,
3. Advancements in the Field of Genetics
At this junction, there are many developments of tech-
nology and findings of new studies that facilitate the
development of PRS. The availability of various popula-
tions’ reference human genomes can be accessed publicly
comprise of up to millions of samples. These GWAS
summary statistics data can be easily accessed through the
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Research and Development on Information and Communication Technology
more than 4000 published studies.
New analysis methods for developing the PRS without
relying solely on genome-wide significant hits continue to
notype data of large longitudinal cohorts becomes eas-
“UK biobank” [11].
II. CLINICAL USAGE OF PRS
While making clinical decisions, doctors often have to
classify the susceptibility of a patient based on known risk
Figure 1. CAD incident by PRS group in statin primary prevention trial.
factors. This disease risk classification is very important in
providing an appropriate recommendation for the patient.
A group of individuals having certain risk factors could
have higher relative risk than the general population to
guarantee different clinical management. If existing medical
intervention can provide more benefits than adverse effects,
with reasonable costs, this group of high-risk individuals
would receive more benefits from it. Recent studies have
suggested that genetic profiling using the PRS can provide
some clinical utilities [12].
with p-value < 0.01. ***: chi-square test with p-value < 0.001.
having high genetic risk for CAD, who can receive more
benefits from primary prevention with statin therapy [17].
Patients having the top quintile of PRS have higher risk of
subclinical atherosclerosis and receive greater absolute risk
reduction of CAD event from statin therapy (Figure 1).
PRS analysis and its interpretation revolved around some
situations: risk prediction performance of PRS indepen-
dently or in combination with other non-genetic risk factors
and estimation of lifetime risk trajectories. Some recent
studies have proposed some clinical interpretations of PRS
that can modify therapeutic intervention, disease screening
findings of PRS in certain common diseases: coronary
artery disease, diabetes mellitus, breast cancer, prostate
cancer and Alzheimer’s disease.
2. Breast Cancer
Breast cancer screening has been recommended for
women older than 50 without major risk factors for a long
in women older than 50 is reducing disease mortality and
decreasing false positive diagnosis. A study by Pashayan et
al. argued that a well-defined risk-stratified screening strat-
egy would improve the quality of life of women and save
prediction model combining clinical risk factors and the
PRS could identify a subgroup of women who had relative
risk of developing breast cancer higher than that of 50-year-
from earlier screening tests and assertive lifestyle change to
reduce certain risk factors. The women in the top quintile
of PRS could have the same relative risk as average 50-
1. Coronary Artery Disease
Clinical risk scores like the Framingham risk score is
a traditional tool in evaluating 10-year coronary artery
to score each individual and infer his chance of developing
CAD in the next 10 years. Abraham et al. have proved that
integrating the PRS in traditional clinical risk model can
better capture the lifetime risk of CAD in patients [15].
This argument was supported by better C-statistic (measure
of goodness-of-fit) in the combined model as compared to
the clinical one. More importantly, men in the top quintile
of PRS had 10% cumulative CAD risk around 15 years
earlier than men in the bottom quintile.
3. Prostate Cancer
Current medical guideline suggests that the age to con-
sider prostate cancer screening is 50 years for average-risk
significant predictor for the age of prostate cancer onset. It
was also a relatively inexpensive evaluation of individual’s
benefits from prostate cancer screening.
In the primary prevention setting, statin can be used to
treat atherosclerosis and reduce the risk of cardiovascular
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Vol. 2019, No. 2, December
additive model tests could avoid multiple comparisons but
overlooked the other inheritance models. Besides, when
the gene-gene interaction and gene-environment exposure
were taken into account, the model became much more
complicated and current statistical methods could not keep
2. Content
In the context of implementation, the PRS was used to
predict the genetic disease risk of common diseases. The
content of the PRS needed to capture all of the genetic
variations with the purpose of reflecting the genetic liabil-
ity of the disease. However, for many common diseases,
genetic variation only accounted for a small portion of the
disease phenotype [2].
Figure 2. Cumulative breast cancer risk stratified by PRS quintile. Adapted
from a study by Maas et al. (2016) [21].
4. Diabetes Mellitus
Early detection of individuals with high risk of type 1
diabetes (T1D) allows better monitoring and prevention of
disease progression. Redondo et al. evaluated the perfor-
mance of PRS on T1D patients’ relatives without diabetes
gression to T1D was best predicted by a combined model
of PRS, number of positive auto-antibodies, DPT-1 Risk
T1D can benefit from monitoring and prevention trials.
factors to T2D development and the way some T2D-risk
prediction model were constructed. Conceptually, the SNPs
having effect on a complex trait such as T2D consist
of SNPs that modified intermediate phenotypes (blood
pressure, BMI), which eventually contribute to the risk of
T2D. These intermediate phenotypes present themselves
as clinical risk factors. T2D is also affected by factors
independent with genetics such as age, lifestyle (Figure 3).
a PRS that had the strongest association with type 2
diabetes (T2D) in a population-based cohort and evaluated
its performance on a prospective individual risk assessment.
The hazard for incident T2D was more than 3 times higher
in the top quintile of PRS, as compared to others.
The conventional risk prediction model used for T2D in
cent findings in the field of genetics suggested that combin-
ing clinical risk factors and the PRS could improve the cur-
rent risk prediction model and the cost-benefit metrics [33].
The caveat of this approach was that many clinical risk
factors are not independent with genetics. The combined
prediction model might have the effect of genetic factors
and clinical factors of the same pathogenic mechanism
counted simultaneously (e.g., the effect of SNPs associated
with blood pressure and the effect of clinical high blood
pressure were both included in the prediction model in
C-statistic, a single mechanism (blood pressure/BMI) was
counted twice: one in the genetic feature and the other
in the clinical one. The outcome would be biased toward
that mechanism. Another approach was to evaluate the
prediction model only with the PRS, stratified by age [26].
This approach highlighted the independent nature of the
PRS and visualized the cumulative risk of the disease of
the high-risk group compared to the general population.
5. Alzheimer’s Disease
One of the most common causes of dementia is
Alzheimer’s disease. Desikan et al. studied the PRS per-
study argued that the PRS could be integrated into screen-
ing for individuals with age-specific high genetic risk for
Alzheimer’s disease. This finding has not been found its
use in clinical settings yet, but may prove to be useful for
therapeutic trials.
III. VALIDITY OF PRS
1. Construct
When the PRS was constructed, it was assumed that
SNPs had additive effects on the disease. Because of the
large number of SNPs and its unexplained characteristics
with the disease of interest, GWAS typically chose the
biological reality is assuredly more complicated than that.
The mode of inheritance of a SNP could be additive,
3. Criterion
Whether the PRS has valid predictive power depends on
the specific disease and population. A review by Duncan et
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Research and Development on Information and Communication Technology
Figure 3. Risk factors of T2D and content of the prediction model for T2D.
European ancestry and East Asian ancestry participants. A
PRS derived from the European population had lower per-
formance in the non-European population. As a result, if we
wanted to apply PRS utilities in the Vietnamese population,
we had to improve methodological choice and threshold to
accommodate the difference in linkage disequilibrium and
variant frequency between Vietnamese and European/East-
Asian. The clinical utility of the PRS needed to be validated
in a prospective cohort study [35].
the genotype of an individual for known variants, as well
as identify variants that may be unique to that person [38].
The cost of genotyping array is lower and, thus, more
suitable for large scale research in the population. However,
the commercial genotyping array used in GWAS has a
strong ascertainment bias because SNPs are chosen from
this problem is to design an SNP-array suitable for the
Vietnamese population.
2. Available Databases
IV. IMPLEMENTING PRS IN VIETNAMESE
POPULATION
The 1000 genome project, which shared reference
genome from diverse populations, contained over 88 million
representation of human genomes in different populations
and ethnicities. This database contained 101 Vietnamese
individuals (Kinh ethnic from Ho Chi Minh city). The
availability of the Vietnamese reference genome is very
important for researchers who are interested in conducting
genetic research in Vietnam. The more references there are,
the better it can represent Vietnamese human genomes.
As a result, risk prediction based on genetic factors will
be more reliable and accurate. To this end, a Vietnamese
prospect of implementing genetic findings in Vietnamese
clinical settings become more imminent.
1. Method to Read DNA
Genotyping is a method of determining which genetic
variants an individual possesses. SNP microarrays allow
detection of hundreds of thousands of pre-determined
SNPs. In genetic research, SNP arrays are most frequently
used for GWASs. A commercial genotyping array included
Sequencing is the process of determining the DNA
sequence, which is the exact order of DNA’s bases: adenine
(A), guanine (G), cytosine (C), and thymine (T). Whole-
genome sequencing and whole-exome sequencing are more
expensive but they allow a more precise detection of
genetic variations (e.g., structural variants). Depending on
the region, a given stretch of sequence may include some
DNA that varies between individuals, in addition to the
constant region. Thus, sequencing can be used to determine
Of course, this is only the first step in genetic research
in Vietnam. In order to catch up with international re-
search and development, Vietnam still has a long way
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Vol. 2019, No. 2, December
Figure 4. Resources of genetic studies and PRS analysis. Adapted from a tutorial by Choi et al. (2018) [36].
to go. There are some established databases of human
These databases allow researchers to share their datasets
and to perform large scale and complex analysis on various
diseases. A genotype-phenotype database of the Vietnamese
population for replicating published genetic findings is
sorely needed. The availability of such a database will
facilitate the replication of PRS research with high applica-
bility and will optimize the predictive performance in the
Vietnamese population. Besides, replication of GWAS with
a large Vietnamese sample size will also provide valuable
information for genetic research.
2) Exclude individuals with genotyping rate greater
than 2%;
3) Exclude individuals with sex discrepancies be-
tween recorded data and their X chromosome’s
heterozygosity;
4) Include SNPs with minor allele frequency (MAF)
above the threshold (based on the sample size of the
study, larger sample size can use lower threshold);
5) Exclude SNPs that deviate from Hardy-Weinberg
equilibr−iu10m (for binary traits HWE p-value is less
than 10 , for quantitative traits HWE p-value is less
than 10−6);
6) Exclude individuals with heterozygosity rate deviated
more than 3 standard deviations from the population
mean;
V. POLYGENIC RISK SCORE ANALYSIS
7) Perform principal component analysis on the training
data and using the first 10 eigenvectors as covariates.
Polygenic risk score calculation can be characterized by
two input data sets: the base data (the summary statistics
of the latest GWAS concerning the disease of interest) and
the training data (the individual genotype-phenotype from
the population of interest) as illustrated in Figure 4.
Because base data and training data come from different
sources, some QC steps need to be taken according to
1) Check the integrity of the transferred file with a
software like md5sum [44];
1. Quality Control of Input Data
Since the base data come from GWAS, input data must
be quality-controlled (QC) according to the standard of
GWAS. According to a tutorial on conducting GWAS by
2) Ensure that input data have genomic position of the
same genome build with LiftOver program [45];
3) Define the effect allele and the reference allele from
the base data since some GWAS summaries cate-
gorize the allele as risk allele/non-risk allele and
major/minor allele;
1) Exclude SNPs that are missing in greater than 2% of
the subjects;
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Research and Development on Information and Communication Technology
TABLE I
DIFFERENT METHODS OF COMPUTING PRS. ADAPTED FROM A STUDY BY CHOI ET AL. (2018) [36]
Clumping + thresholding
Penalized Regression
Bayesian Shrinkage
Shrinkage of SNPs’
Effect Size
P-value threshold
LASSO, penalty parameters, Elastic Net
Fraction of causal SNPs
Handling of Linkage
Disequilibrium
Clumping
LD matrix integral to the algorithm
Shrink effect sizes with respect to LD
LDpred [49]
Software
PRSice [8]
4) Process the ambiguous SNPs due to unknown chro-
mosome strand (sense/antisense) from different DNA
read platform; removing duplicated SNPs;
5) Ensure the independence of base data and training
data by removing overlapping samples and closely
related individuals (1st/2nd degree relatives);
statistics. This technique allows for decreasing the variance
at the cost of introducing some bias. The selection of tuning
parameters _ and B in the elastic net technique results in a
model’s best fit. This model is complicated to calculate but
performs better than the clumping-and-thresholding method
in a small sample size [9].
6) Check chip-heritability from GWAS summary statis-
ing misleading conclusion (because the predictive
power of the PRS cannot surpass the power and
Bayesian shrinkage is a method that infers the mean
effect size of SNP based on a reference LD panel and
assumption of causal SNP fraction in the genotype. Simula-
tion and empirical data prove that this method outperforms
the clumping-and-thresholding method, particularly in a
2. PRS Calculation
Penalized regression and Bayesian shrinkage lead to
millions of SNPs included in the PRS calculation. Most
of these SNPs have a negligible effect on individual risk
score. Their effect sizes are so small and would have been
zero if the effect size would be rounded up to 3 or 4 digits
after the decimal point. This approach aims to maximize the
C-statistics even if the majority of the included SNPs have
however, convolutes the PRS formula beyond the biological
and medical interpretation.
After QC has been performed, PRSs are calculated for all
individuals in the training data. There are many methods to
method has to take into account the following factors. The
first factor is the effect sizes of SNPs taken from GWAS
summary statistics. These effect sizes consist of true associ-
ation with the disease and some degree of unknown random
variations which produce “winner’s curse” effect among the
most significant SNPs. The second one is the number of
SNPs included in the calculation. And the third one is the
correlation among SNPs, i.e., linkage disequilibrium. LD
difference between base and training data can make the
PRS misestimate the risk of disease.
3. Optimizing PRS and Result Presentation
The association between the PRS and the phenotype
can be quantified with goodness-of-fit metrics such as the
estimate of effect size (beta for quantitative phenotype and
OR for binary phenotype), ?-value of the null hypothesis
test of no association, explained variance ('2) and area
under the curve (AUC).
Clumping-and-thresholding is the most common method
to compute the PRS. Clumping in this context means
selecting the most significant SNP in a block of related
SNPs based on LD (usually A2 > 0.2). The ?-value
threshold is usually genome-wide significant 5 × 10−8 or
the threshold that maximizes the AUC is selected. The
clumping-and-thresholding method is fast and easy to use. It
can capture the independent effect of the SNPs. However, a
compelling criticism of this method is the removal of SNPs
in LD with arbitrary A2 value. Clumping-and-thresholding
most common tools for computing the PRS. The processes
of calculating, evaluating and visualizing the result are
automated in the software.
Explained variance is a statistical measure to quantify the
discrepancy between the PRS model and the observed phe-
notype. A higher percentage of explained variance means
a better association of the model, which also means that
the PRS has better predictive performance. In linear regres-
sion analysis for quantitative phenotype, explained variance
is the coefficient of determination. In logistic regression
analysis for binary phenotype, explained variance is the
pseudo-'2. Various types of pseudo-'2 metrics are used in
epidemiology field. Among them, Nagelkerke '2 is perhaps
the most popular [51].
Penalized regression is a method to integrate LD infor-
mation of the population of interest into GWAS summary
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Vol. 2019, No. 2, December
The receiver operating characteristic curve illustrates the
true positive rate (sensitivity) versus false-positive rate
(specificity). The AUC represents the accuracy of the test.
A test with AUC between 0.9–1 is an excellent test. A test
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.
Result presentation of the PRS analysis should emphasize
its relevant usage which is a prognosis of disease liability.
The main goal of the PRS is the identification of a group
of individuals with high-risk of disease based on genetic
factors. Therefore, the result should stratify the population
into distinct tiers of risk based on the percentile rank cut-
off value, usually top 1 percentile, top 10 percentile, top 20
the medical community has already defined the appropriate
level of risk versus benefits that justifies certain medical
have high-risk for a disease, we can infer the relative risk
of said individual compared to the reference. The higher
the relative risk of said individual is, the more justified the
medical intervention becomes.
REFERENCES
[1] T. A. Manolio, “Genomewide association studies and as-
sessment of the risk of disease,” New England Journal of
Medicine, vol. 363, no. 2, pp. 166–176, 2010.
[2] T. A. Manolio, F. S. Collins et al., “Finding the missing
heritability of complex diseases,” Nature, vol. 461, no. 7265,
pp. 747–753, 2009.
[3] N. Chatterjee, B. Wheeler, J. Sampson, P. Hartge, S. J.
Chanock, and J.-H. Park, “Projecting the performance of
risk prediction based on polygenic analyses of genome-wide
association studies,” Nature Genetics, vol. 45, no. 4, pp. 400–
405, 2013.
[4] J. N. Cooke Bailey and R. P. Igo Jr, “Genetic Risk Scores,”
Current Protocols in Human Genetics, vol. 91, no. 1, pp.
1.29.1–1.29.9, 2016.
[5] A. C. J. Janssens, “Validity of Polygenic Risk Scores: Are
we measuring what we think we are?” Human Molecular
Genetics, vol. 28, no. R2, pp. R143–R150, 2019.
[6] 1000 Genomes Project Consortium and others, “A global
reference for human genetic variation,” Nature, vol. 526, no.
7571, pp. 68–74, 2015.
[7] J. MacArthur, E. Bowler et al., “The new NHGRI-EBI cata-
log of published genome-wide association studies (GWAS
catalog),” Nucleic Acids Research, vol. 45, no. D1, pp.
D896–D901, 2017.
PRS analysis can also be represented based on age
PRS can guide the decision at which age an individual can
criterion can spotlight the balance of average risk of breast
cancer and the risk of harm due to the false-positive result.
[8] J. Euesden, C. M. Lewis, and P. F. O’Reilly, “PRSice:
Polygenic risk score software,” Bioinformatics, vol. 31, no. 9,
pp. 1466–1468, 2015.
[9] F. Privé, H. Aschard, and M. G. Blum, “Efficient implemen-
tation of penalized regression for genetic risk prediction,”
Genetics, vol. 212, no. 1, pp. 65–74, 2019.
[10] G. Versmée, L. Versmée, M. Dusenne, N. Jalali, and P. Avil-
lach, “dbgap2x: An R package to explore and extract data
from the database of Genotypes and Phenotypes (dbGaP),”
Bioinformatics, vol. 36, no. 4, pp. 1305–1306, 2020.
[11] C. Bycroft, C. Freeman et al., “The UK biobank resource
with deep phenotyping and genomic data,” Nature, vol. 562,
no. 7726, pp. 203–209, 2018.
[12] A. Torkamani, N. E. Wineinger, and E. J. Topol, “The
personal and clinical utility of polygenic risk scores,” Nature
Reviews Genetics, vol. 19, no. 9, pp. 581–590, 2018.
[13] S. A. Lambert, G. Abraham, and M. Inouye, “Towards
clinical utility of polygenic risk scores,” Human Molecular
Genetics, vol. 28, no. R2, pp. R133–R142, 2019.
[14] P. W. Wilson, R. B. D’Agostino, D. Levy, A. M. Belanger,
H. Silbershatz, and W. B. Kannel, “Prediction of coro-
nary heart disease using risk factor categories,” Circulation,
vol. 97, no. 18, pp. 1837–1847, 1998.
[15] G. Abraham, A. S. Havulinna et al., “Genomic prediction of
coronary heart disease,” European Heart Journal, vol. 37,
no. 43, pp. 3267–3278, 2016.
[16] R. S. Rosenson and C. C. Tangney, “Antiatherothrombotic
properties of statins: Implications for cardiovascular event
reduction,” JAMA, vol. 279, no. 20, pp. 1643–1650, 1998.
[17] P. Natarajan, R. Young et al., “Polygenic risk score identifies
subgroup with higher burden of atherosclerosis and greater
relative benefit from statin therapy in the primary prevention
setting,” Circulation, vol. 135, no. 22, pp. 2091–2101, 2017.
4. Validating PRS Performance
A common concern in PRS analysis is whether the most
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
81
Research and Development on Information and Communication Technology
[18] J. G. Elmore, “Screening for breast cancer: Strategies
and recommendations,” Retrieved from the Up to Date
“Cost-effectiveness and benefit-to-harm ratio of risk-
stratified screening for breast cancer: A life-table model,”
JAMA Oncology, vol. 4, no. 11, pp. 1504–1510, 2018.
[20] P. Maas, M. Barrdahl et al., “Breast cancer risk from mod-
ifiable and nonmodifiable risk factors among white women
in the United States,” JAMA Oncology, vol. 2, no. 10, pp.
1295–1302, 2016.
[21] A. Lee, N. Mavaddat et al., “BOADICEA: A comprehensive
breast cancer risk prediction model incorporating genetic
and non-genetic risk factors,” Genetics in Medicine: Offi-
cial Journal of the American College of Medical Genetics,
vol. 21, no. 8, pp. 1708–1718, 2019.
into clinical practice? Let’s do the science first and
follow the evidence wherever it takes us,” Centers for
Disease Control and Prevention, 2019. [Online]. Available:
performing Polygenic Risk Score analyses,” BioRxiv, 2018.
[37] K. Wetterstrand, “The cost of sequencing a human genome,”
National Human Genome Research Institute, 2019. [On-
line]. Available: https://www.genome.gov/about-genomics/
Genome Research Institute, 2015. [Online]. Avail-
A biased prediction?” Genome Medicine, vol. 10, no. 1, pp.
1–3, 2018.
[22] R. M. Hoffman, “Screening for prostate cancer,”
2019. [Online]. Available: www.uptodate.com/contents/
[40] V. S. Le, K. T. Tran et al., “A Vietnamese human genetic
variation database,” Human Mutation, vol. 40, no. 10, pp.
1664–1675, 2019.
[41] M. D. Mailman, M. Feolo et al., “The NCBI dbGaP database
of genotypes and phenotypes,” Nature Genetics, vol. 39,
no. 10, pp. 1181–1186, 2007.
[42] E. M. Ramos, D. Hoffman et al., “Phenotype–Genotype
Integrator (PheGenI): Synthesizing genome-wide association
study (GWAS) data with existing genomic resources,” Euro-
pean Journal of Human Genetics, vol. 22, no. 1, pp. 144–
147, 2014.
[43] S. Purcell, B. Neale et al., “PLINK: A tool set for whole-
genome association and population-based linkage analyses,”
The American Journal of Human Genetics, vol. 81, no. 3,
pp. 559–575, 2007.
[44] U. Drepper, S. Miller, and D. Madore, “md5sum: Verify
compact digital fingerprint of a file (GNU GPL version
3 or later),” Free Software Foundation, 2010. [Online].
Available: linux.die.net/man/1/md5sum
[45] R. M. Kuhn, D. Haussler, and W. J. Kent, “The UCSC
genome browser and associated tools,” Briefings in Bioin-
formatics, vol. 14, no. 2, pp. 144–161, 2013.
guide screening for aggressive prostate cancer: Development
and validation in large scale cohorts,” BMJ, vol. 360, 2018.
[24] M. J. Redondo, S. Geyer et al., “A type 1 diabetes genetic
risk score predicts progression of islet autoimmunity and
development of type 1 diabetes in individuals at risk,”
Diabetes Care, vol. 41, no. 9, pp. 1887–1894, 2018.
[25] J. M. Sosenko, J. P. Krischer et al., “A risk score for type 1
diabetes derived from autoantibody-positive participants in
the diabetes prevention trial–type 1,” Diabetes Care, vol. 31,
no. 3, pp. 528–533, 2008.
[26] K. Lall, R. Magi, A. Morris, A. Metspalu, and K. Fischer,
“Personalized risk prediction for type 2 diabetes: The poten-
tial of genetic risk scores,” Genetics in Medicine, vol. 19,
no. 3, pp. 322–329, 2017.
[27] R. S. Desikan, C. C. Fan et al., “Genetic assessment of
age-associated Alzheimer disease risk: Development and
validation of a polygenic hazard score,” PLoS Medicine,
vol. 14, no. 3, p. e1002258, 2017.
[28] A. T. Marees, H. de Kluiver et al., “A tutorial on con-
ducting genome-wide association studies: Quality control
and statistical analysis,” International Journal of Methods
in Psychiatric Research, vol. 27, no. 2, p. e1608, 2018.
[29] P. G. Bagos, “Genetic model selection in genome-wide
association studies: Robust methods and the use of meta-
analysis,” Statistical Applications in Genetics and Molecular
Biology, vol. 12, no. 3, pp. 285–308, 2013.
[30] D. Thomas, “Methods for investigating gene-environment
interactions in candidate pathway and genome-wide asso-
ciation studies,” Annual Review of Public Health, vol. 31,
no. 1, pp. 21–36, 2010.
[31] J. H. Moore, “Computational analysis of gene-gene inter-
actions using multifactor dimensionality reduction,” Expert
Review of Molecular Diagnostics, vol. 4, no. 6, pp. 795–803,
2004.
[32] P. W. Wilson, J. B. Meigs, L. Sullivan, C. S. Fox, D. M.
Nathan, and S. D’Agostino, R. B., “Prediction of incident di-
abetes mellitus in middle-aged adults: The framingham off-
spring study,” Archives Internal Medicine, vol. 167, no. 10,
pp. 1068–1074, 2007.
[46] B. K. Bulik-Sullivan, P.-R. Loh et al., “LD score regression
distinguishes confounding from polygenicity in genome-wide
association studies,” Nature Genetics, vol. 47, no. 3, p. 291,
2015.
[47] F. Dudbridge, “Power and predictive accuracy of polygenic
risk scores,” PLoS Genetics, vol. 9, no. 3, 2013.
[48] T. S. H. Mak, R. M. Porsch, S. W. Choi, X. Zhou, and
P. C. Sham, “Polygenic scores via penalized regression on
summary statistics,” Genetic Epidemiology, vol. 41, no. 6,
pp. 469–480, 2017.
[49] B. J. Vilhjálmsson, J. Yang et al., “Modeling linkage dise-
quilibrium increases accuracy of polygenic risk scores,” The
American Journal of Human Genetics, vol. 97, no. 4, pp.
576–592, 2015.
[50] A. V. Khera, M. Chaffin et al., “Genome-wide poly-
genic scores for common diseases identify individuals with
risk equivalent to monogenic mutations,” Nature Genetics,
vol. 50, no. 9, pp. 1219–1224, 2018.
[51] S. H. Lee, M. E. Goddard, N. R. Wray, and P. M. Visscher,
“A better coefficient of determination for genetic profile
analysis,” Genetic Epidemiology, vol. 36, no. 3, pp. 214–
224, 2012.
[52] A. P. Bradley, “The use of the area under the ROC curve
in the evaluation of machine learning algorithms,” Pattern
Recognition, vol. 30, no. 7, pp. 1145–1159, 1997.
[53] P. M. Ridker, J. G. MacFadyen et al., “Rosuvastatin for
primary prevention among individuals with elevated high-
sensitivity C-reactive protein and 5% to 10% and 10% to
[33] B. J. Keating, “Advances in risk prediction of type 2
diabetes: Integrating genetic scores with Framingham risk
models,” Diabetes, vol. 64, no. 5, pp. 1495–1497, 2015.
[34] L. Duncan, H. Shen et al., “Analysis of polygenic risk
score usage and performance in diverse human populations,”
Nature Communications, vol. 10, no. 1, pp. 1–9, 2019.
[35] M. Khoury, “Is it time to integrate polygenic risk scores
82
Vol. 2019, No. 2, December
20% 10-year risk,” Circulation: Cardiovascular Quality and
Outcomes, vol. 3, no. 5, pp. 447–452, 2010.
[54] P. C. Gøtzsche and O. Olsen, “Is screening for breast cancer
with mammography justifiable?” The Lancet, vol. 355, no.
9198, pp. 129–134, January 2000.
[55] G. A. Colditz and B. Rosner, “Cumulative risk of breast
cancer to age 70 years according to risk factor status:
Data from the Nurses’ Health Study,” American Journal of
Epidemiology, vol. 152, no. 10, pp. 950–964, 2000.
[56] B. A. Goldstein, L. Yang, E. Salfati, and T. L. Assimes,
“Contemporary considerations for constructing a genetic
risk score: An empirical approach,” Genetic Epidemiology,
vol. 39, no. 6, pp. 439–445, 2015.
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.
[57] S. Michiels, S. Koscielny, and C. Hill, “Prediction of cancer
outcome with microarrays: A multiple random validation
strategy,” The Lancet, vol. 365, no. 9458, pp. 488–492, 2005.
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.
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