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Astrologo NCN, Gaudillo JD, Albia JR, Roxas-Villanueva RML. Genetic risk assessment based on association and prediction studies. Sci Rep 2023; 13:15230. [PMID: 37709797 PMCID: PMC10502006 DOI: 10.1038/s41598-023-41862-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/01/2023] [Indexed: 09/16/2023] Open
Abstract
The genetic basis of phenotypic emergence provides valuable information for assessing individual risk. While association studies have been pivotal in identifying genetic risk factors within a population, complementing it with insights derived from predictions studies that assess individual-level risk offers a more comprehensive approach to understanding phenotypic expression. In this study, we established personalized risk assessment models using single-nucleotide polymorphism (SNP) data from 200 Korean patients, of which 100 experienced hepatitis B surface antigen (HBsAg) seroclearance and 100 patients demonstrated high levels of HBsAg. The risk assessment models determined the predictive power of the following: (1) genome-wide association study (GWAS)-identified candidate biomarkers considered significant in a reference study and (2) machine learning (ML)-identified candidate biomarkers with the highest feature importance scores obtained by using random forest (RF). While utilizing all features yielded 64% model accuracy, using relevant biomarkers achieved higher model accuracies: 82% for 52 GWAS-identified candidate biomarkers, 71% for three GWAS-identified biomarkers, and 80% for 150 ML-identified candidate biomarkers. Findings highlight that the joint contributions of relevant biomarkers significantly influence phenotypic emergence. On the other hand, combining ML-identified candidate biomarkers into the pool of GWAS-identified candidate biomarkers resulted in the improved predictive accuracy of 90%, demonstrating the capability of ML as an auxiliary analysis to GWAS. Furthermore, some of the ML-identified candidate biomarkers were found to be linked with hepatocellular carcinoma (HCC), reinforcing previous claims that HCC can still occur despite the absence of HBsAg.
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Affiliation(s)
- Nicole Cathlene N Astrologo
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Joverlyn D Gaudillo
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Domingo AI Research Center (DARC Labs), 1606, Pasig, Philippines.
| | - Jason R Albia
- Domingo AI Research Center (DARC Labs), 1606, Pasig, Philippines
- Venn Biosciences Corporation Dba InterVenn Biosciences, Metro Manila, Pasig, Philippines
- Graduate School, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Ranzivelle Marianne L Roxas-Villanueva
- Data Analytics Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
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2
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Luo M, Zhang L, Yang C, Zhou B, Hou J, Jiang DK. CXCL13 variant predicts pegylated-interferon α treatment response in HBeAg-positive chronic hepatitis B patients. J Med Virol 2023; 95:e28963. [PMID: 37470204 DOI: 10.1002/jmv.28963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/17/2023] [Accepted: 07/06/2023] [Indexed: 07/21/2023]
Abstract
As a key immune cytokine, C-X-C motif chemokine ligand 13 (CXCL13) has been reported to play critical roles in immune control of hepatitis B virus (HBV) infection. We aimed to screen single-nucleotide polymorphisms (SNPs) of CXCL13 for predicting response to pegylated interferon-alpha (PegIFNα) therapy of chronic hepatitis B (CHB) patients. Two independent cohorts with a total of 945 (Cohort 1, n = 238; Cohort 2, n = 707) hepatitis B e antigen (HBeAg)-positive CHB patients treated with PegIFNα were enrolled in this retrospective cohort study. Eight candidate SNPs were selected through gene-wide SNP mining within or flanking CXCL13. A polygenic score (PGS) was utilized to assess the cumulative effects of multiple SNPs. The associations of candidate SNPs and PGS with combined response (CR, defined as the combination of HBeAg seroconversion and HBV DNA level <3.3log10 IU/mL) and hepatitis B surface antigen (HBsAg) level were evaluated. Among the eight candidate SNPs, rs76084459 which is located at upstream of CXCL13 was significantly associated with both CR (p = 0.002) and HBsAg level (p = 0.015). A PGS integrating CXCL13_rs76084459 and five other SNPs, which were previously identified as predictors of PegIFNα treatment response, was further strongly correlated with CR (p = 1.759 × 10-10 ) and HBsAg level (p = 0.004). This study demonstrated that CXCL13_rs76084459 can predict response to PegIFNα treatment of HBeAg-positive CHB patients. A PGS composed of six SNPs including CXCL13_rs76084459 predicts PegIFNα treatment response better.
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Affiliation(s)
- Mengqi Luo
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
- The Key Laboratory of Molecular Pathology (Hepatic Diseases) of Guangxi, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China
| | - Lingyan Zhang
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Chou Yang
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Bin Zhou
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jinlin Hou
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - De-Ke Jiang
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Institutes of Liver Diseases Research of Guangdong Province, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, China
- The Key Laboratory of Molecular Pathology (Hepatic Diseases) of Guangxi, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, China
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Silva PP, Gaudillo JD, Vilela JA, Roxas-Villanueva RML, Tiangco BJ, Domingo MR, Albia JR. A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci. Sci Rep 2022; 12:15817. [PMID: 36138111 PMCID: PMC9499949 DOI: 10.1038/s41598-022-19708-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022] Open
Abstract
Identifying disease-associated susceptibility loci is one of the most pressing and crucial challenges in modeling complex diseases. Existing approaches to biomarker discovery are subject to several limitations including underpowered detection, neglect for variant interactions, and restrictive dependence on prior biological knowledge. Addressing these challenges necessitates more ingenious ways of approaching the "missing heritability" problem. This study aims to discover disease-associated susceptibility loci by augmenting previous genome-wide association study (GWAS) using the integration of random forest and cluster analysis. The proposed integrated framework is applied to a hepatitis B virus surface antigen (HBsAg) seroclearance GWAS data. Multiple cluster analyses were performed on (1) single nucleotide polymorphisms (SNPs) considered significant by GWAS and (2) SNPs with the highest feature importance scores obtained using random forest. The resulting SNP-sets from the cluster analyses were subsequently tested for trait-association. Three susceptibility loci possibly associated with HBsAg seroclearance were identified: (1) SNP rs2399971, (2) gene LINC00578, and (3) locus 11p15. SNP rs2399971 is a biomarker reported in the literature to be significantly associated with HBsAg seroclearance in patients who had received antiviral treatment. The latter two loci are linked with diseases influenced by the presence of hepatitis B virus infection. These findings demonstrate the potential of the proposed integrated framework in identifying disease-associated susceptibility loci. With further validation, results herein could aid in better understanding complex disease etiologies and provide inputs for a more advanced disease risk assessment for patients.
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Affiliation(s)
- Princess P Silva
- Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Joverlyn D Gaudillo
- Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines.
- Domingo AI Research Center (DARC Labs), 1606, Pasig City, Philippines.
| | - Julianne A Vilela
- Philippine Genome Center Program for Agriculture, Office of the Vice Chancellor for Research and Extension, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Ranzivelle Marianne L Roxas-Villanueva
- Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Computational Interdisciplinary Research Laboratory (CINTERLabs), University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
| | - Beatrice J Tiangco
- National Institute of Health, UP College of Medicine, Taft Avenue, 1000, Manila, Philippines
- Division of Medicine, The Medical City, 1605, Pasig, Philippines
| | - Mario R Domingo
- Domingo AI Research Center (DARC Labs), 1606, Pasig City, Philippines
| | - Jason R Albia
- Data-Driven Research Laboratory (DARELab), Institute of Mathematical Sciences and Physics, University of the Philippines Los Baños, 4031, Los Baños, Laguna, Philippines
- Domingo AI Research Center (DARC Labs), 1606, Pasig City, Philippines
- Venn Biosciences Corporation Dba InterVenn Biosciences, Metro Manila, Philippines
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Hamilton E, Yang L, Mentzer AJ, Guo Y, Chen Y, Lv J, Fletcher R, Wright N, Lin K, Walters R, Kartsonaki C, Yang Y, Burgess S, Sansome S, Li L, Millwood IY, Chen Z. Conventional and genetic risk factors for chronic Hepatitis B virus infection in a community-based study of 0.5 million Chinese adults. Sci Rep 2022; 12:12075. [PMID: 35840665 PMCID: PMC9287541 DOI: 10.1038/s41598-022-16360-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 07/08/2022] [Indexed: 11/30/2022] Open
Abstract
Despite universal vaccination of newborns, the prevalence of chronic hepatitis virus B (HBV) infection and the associated disease burden remain high among adults in China. We investigated risk factors for chronic HBV infection in a community-based study of 512,726 individuals aged 30-79 years recruited from ten diverse areas during 2004-2008. Multivariable logistic regression was used to estimate odds ratios (ORs) of hepatitis B surface antigen (HBsAg) positivity recorded at baseline by sociodemographic and lifestyle factors, and medical history. In a random subset (n = 69,898) we further assessed the association of 18 single nucleotide polymorphisms (SNPs) previously shown to be associated with HBsAg positivity and development of chronic liver disease (CLD) (1600 cases). Several factors showed strong associations with HBsAg positivity, particularly younger age (< 40 vs. ≥ 60 years: OR 1.48, 95% CI 1.32-1.66), male sex (1.40, 1.34-1.46) and urban residency (1.55, 1.47-1.62). Of the 18 SNPs selected, 17 were associated with HBsAg positivity, and 14 with CLD, with SNPs near HLA-DPB1 were most strongly associated with both outcomes. In Chinese adults a range of genetic and non-genetic factors were associated with chronic HBV infection and CLD, which can inform targeted screening to help prevent disease progression.
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Affiliation(s)
- Elizabeth Hamilton
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
| | - Ling Yang
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK.
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | | | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Yiping Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | | | - Neil Wright
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
| | - Robin Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Christiana Kartsonaki
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yingcai Yang
- NCDs Prevention and Control Department, Shinan CDC, Qingdao, Shandong, China
| | - Sushila Burgess
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
| | - Sam Sansome
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
| | - Liming Li
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing, China
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK.
- Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, BDI Building, Old Road Campus, University of Oxford, Oxford, OX3 7LF, UK
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Wang L, Sun X, He J, Liu Z. Functions and Molecular Mechanisms of Deltex Family Ubiquitin E3 Ligases in Development and Disease. Front Cell Dev Biol 2021; 9:706997. [PMID: 34513839 PMCID: PMC8424196 DOI: 10.3389/fcell.2021.706997] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 08/05/2021] [Indexed: 12/14/2022] Open
Abstract
Ubiquitination is a posttranslational modification of proteins that significantly affects protein stability and function. The specificity of substrate recognition is determined by ubiquitin E3 ligase during ubiquitination. Human Deltex (DTX) protein family, which functions as ubiquitin E3 ligases, comprises five members, namely, DTX1, DTX2, DTX3, DTX3L, and DTX4. The characteristics and functional diversity of the DTX family proteins have attracted significant attention over the last decade. DTX proteins have several physiological and pathological roles and are closely associated with cell signal transduction, growth, differentiation, and apoptosis, as well as the occurrence and development of various tumors. Although they have been extensively studied in various species, data on structural features, biological functions, and potential mechanisms of action of the DTX family proteins remain limited. In this review, recent research progress on each member of the DTX family is summarized, providing insights into future research directions and potential strategies in disease diagnosis and therapy.
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Affiliation(s)
- Lidong Wang
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xiaodan Sun
- Postdoctoral Research Workstation, Jilin Cancer Hospital, Changchun, China
| | - Jingni He
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Zhen Liu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
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Irham LM, Wong HSC, Perwitasari DA, Chou WH, Yang HI, Chang WC. Single-nucleotide polymorphism of rs7944135 (macrophage-expressed gene 1) is associated with hepatitis B surface antigen seroclearance in chronic hepatitis B infection: A cohort study. Medicine (Baltimore) 2019; 98:e17936. [PMID: 31860948 PMCID: PMC6940119 DOI: 10.1097/md.0000000000017936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Clearance of the hepatitis B surface antigen (HBsAg) is the ultimate aim of treatment for patients with chronic hepatitis B (CHB) infection. Genetic, factor age, and gender were reported to be involved in the clearance of HBsAg. However, the rate of HBsAg seroclearance in CHB patients is still low globally and few of the single-nucleotide polymorphism (SNP) had been identified to associated with HBsAg seroclearance in CHB patients.Recently, 3 associated SNPs (rs7944135, rs171941, and rs6462008) were reported in the clearance of HBsAg in the Korean population. However, these SNPs have not been investigated in the CHB Taiwanese population. In present study, these 3 SNPs were genotyped in 2565 Taiwanese CHB patients including 493 CHB patients with HBsAg seroclearance and 2072 without HBsAg seroclearance.We observed that SNP rs7944135 was solely associated with HBsAg seroclearance. Subjects with the AA genotype at rs7944135 of macrophage-expressed gene 1 had a higher susceptibility to HBsAg clearance, compared to those with the AG or GG genotype under the genotypic model (odds ratio [OR] = 1.76. 95% confidence interval [CI] = 1.14-2.72, P = .045). Furthermore, we found a 1.74-fold increased risk of acquiring HBsAg seroclearance associated with the AA genotype compared to AG + GG of rs7944135 under the recessive model (OR = 1.74. 95% CI = 1.13-2.66, P = .014). According to the cumulative fraction curve with the log-rank test revealed that patients with the AA genotype of rs7944135 showed higher susceptibility to occur HBsAg seroclearance (P = .039) and HBV DNA undetectable (P = .0074) compared to those with the AG or GG genotype.This study examined the associations of 3 SNPs (rs7944135, rs171941, and rs6462008) with HBsAg seroclearance, and we identified that rs7944135 is solely associated with HBsAg seroclearance in Taiwanese CHB patients.
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Affiliation(s)
- Lalu Muhammad Irham
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Faculty of Pharmacy, University of Ahmad Dahlan, Yogyakarta, Indonesia
| | - Henry Sung-Ching Wong
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | | | - Wan-Hsuan Chou
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
| | - Hwai-I Yang
- Genomics Research Center, Academia Sinica
- Institute of Clinical Medicine, National Yang-Ming University
| | - Wei-Chiao Chang
- Department of Clinical Pharmacy, School of Pharmacy, College of Pharmacy
- Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei, Taiwan
- Genomics Research Center, Academia Sinica
- Integrative Research Center for Critical Care, Wan fang Hospital, Taipei Medical University, Taipei
- Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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