Multiple polygenic risk scores can improve the prediction of systemic lupus erythematosus in Taiwan.

OBJECTIVE: To identify new genetic variants associated with SLE in Taiwan and establish polygenic risk score (PRS) models to improve the early diagnostic accuracy of SLE. METHODS: The study enrolled 2429 patients with SLE and 48 580 controls from China Medical University Hospital in Taiwan. A genome-wide association study (GWAS) and PRS analyses of SLE and other three SLE markers, namely ANA, anti-double-stranded DNA antibody (dsDNA) and anti-Smith antibody (Sm), were conducted. RESULTS: Genetic variants associated with SLE were identified through GWAS. Some novel genes, which have been previously reported, such as RCC1L and EGLN3, were revealed to be associated with SLE in Taiwan. Multiple PRS models were established, and optimal cut-off points for each PRS were determined using the Youden Index. Combining the PRSs for SLE, ANA, dsDNA and Sm yielded an area under the curve of 0.64 for the optimal cut-off points. An analysis of human leucocyte antigen (HLA) haplotypes in SLE indicated that individuals with HLA-DQA1*01:01 and HLA-DQB1*05:01 were at a higher risk of being classified into the SLE group. CONCLUSIONS: The use of PRSs to predict SLE enables the identification of high-risk patients before abnormal laboratory data were obtained or symptoms were manifested. Our findings underscore the potential of using PRSs and GWAS in identifying SLE markers, offering promise for early diagnosis and prediction of SLE.

Identification of 13 Novel Loci in a Genome-Wide Association Study on Taiwanese with Hepatocellular Carcinoma.

Liver cancer is caused by complex interactions among genetic factors, viral infection, alcohol abuse, and metabolic diseases. We conducted a genome-wide association study and polygenic risk score (PRS) model in Taiwan, employing a nonspecific etiology approach, to identify genetic risk factors for hepatocellular carcinoma (HCC). Our analysis of 2836 HCC cases and 134,549 controls revealed 13 novel associated loci such as the FAM66C gene, noncoding genes, liver-fibrosis-related genes, metabolism-related genes, and HCC-related pathway genes. We incorporated the results from the UK Biobank and Japanese database into our study for meta-analysis to validate our findings. We also identified specific subtypes of the major histocompatibility complex that influence both viral infection and HCC progression. Using this data, we developed a PRS to predict HCC risk in the general population, patients with HCC, and HCC-affected families. The PRS demonstrated higher risk scores in families with multiple HCCs and other cancer cases. This study presents a novel approach to HCC risk analysis, identifies seven new genes associated with HCC development, and introduces a reproducible PRS model for risk assessment.

Genome-Wide Association Study of Alopecia Areata in Taiwan: The Conflict Between Individuals and Hair Follicles.

Purpose: Alopecia areata (AA) is one of the most prevalent autoimmune diseases affecting humans. Given that hair follicles are immune-privileged, autoimmunity can result in disfiguring hair loss. However, the genetic basis for AA in the Taiwanese population remains unknown. Materials and Methods: A genome-wide association study was conducted using a cohort of 408 AA cases and 8167 controls. To link variants to gene relationships, we used 882 SNPs (P<1E-05) within 74 genes that were associated with AA group to build the biological networks by IPA software. HLA diplotypes and haplotypes were analyzed using Attribute Bagging (HIBAG)-R package and chi-square analysis. Results: Seven single nucleotide polymorphisms (SNPs) including LINC02006 (rs531166736, rs187306735), APC (rs112800832_C_CAT), SRP19 (rs139948960, rs144784670), EGFLAM (rs16903975) and LDLRAD3 (rs79874564) were closely associated with the AA phenotype (P<5E-08). Examination of biological networks revealed that these genomic areas are associated with antigen presentation signaling, B cell and T cell development, Th1 and Th2 activation pathways, Notch signaling, crosstalk signaling between dendritic cells and natural killer cells, and phagosome maturation. Based on human leukocyte antigen (HLA) genotype analysis, four HLA genotypes (HLA-B*15:01-*40:01, HLA-DQA1*01:02-*03:03, HLA-DQA1*01:02, and HLA-DQB1*02:01) were found to be associated with AA (adjusted p-value<0.05). HLA-DQA1*01:02 is the most significantly related gene in the Taiwanese population (adjusted p-value = 2.09E-05). Conclusion: This study successfully identified susceptibility loci associated with AA in the Taiwanese population. These findings not only shed light on the origins of AA within the Taiwanese context but also contribute to a comprehensive understanding of the genetic factors influencing AA susceptibility.

Comparison of multiple imputation algorithms and verification using whole-genome sequencing in the CMUH genetic biobank.

A genome-wide association study (GWAS) can be conducted to systematically analyze the contributions of genetic factors to a wide variety of complex diseases. Nevertheless, existing GWASs have provided highly ethnic specific data. Accordingly, to provide data specific to Taiwan, we established a large-scale genetic database in a single medical institution at the China Medical University Hospital. With current technological limitations, microarray analysis can detect only a limited number of single-nucleotide polymorphisms (SNPs) with a minor allele frequency of >1%. Nevertheless, imputation represents a useful alternative means of expanding data. In this study, we compared four imputation algorithms in terms of various metrics. We observed that among the compared algorithms, Beagle5.2 achieved the fastest calculation speed, smallest storage space, highest specificity, and highest number of high-quality variants. We obtained 15,277,414 high-quality variants in 175,871 people by using Beagle5.2. In our internal verification process, Beagle5.2 exhibited an accuracy rate of up to 98.75%. We also conducted external verification. Our imputed variants had a 79.91% mapping rate and 90.41% accuracy. These results will be combined with clinical data in future research. We have made the results available for researchers to use in formulating imputation algorithms, in addition to establishing a complete SNP database for GWAS and PRS researchers. We believe that these data can help improve overall medical capabilities, particularly precision medicine, in Taiwan.

Using Machine Learning Approaches to Predict Target Gene Expression in Rice T-DNA Insertional Mutants.

To change the expression of the flanking genes by inserting T-DNA into the genome is commonly used in rice functional gene research. However, whether the expression of a gene of interest is enhanced must be validated experimentally. Consequently, to improve the efficiency of screening activated genes, we established a model to predict gene expression in T-DNA mutants through machine learning methods. We gathered experimental datasets consisting of gene expression data in T-DNA mutants and captured the PROMOTER and MIDDLE sequences for encoding. In first-layer models, support vector machine (SVM) models were constructed with nine features consisting of information about biological function and local and global sequences. Feature encoding based on the PROMOTER sequence was weighted by logistic regression. The second-layer models integrated 16 first-layer models with minimum redundancy maximum relevance (mRMR) feature selection and the LADTree algorithm, which were selected from nine feature selection methods and 65 classified methods, respectively. The accuracy of the final two-layer machine learning model, referred to as TIMgo, was 99.3% based on fivefold cross-validation, and 85.6% based on independent testing. We discovered that the information within the local sequence had a greater contribution than the global sequence with respect to classification. TIMgo had a good predictive ability for target genes within 20 kb from the 35S enhancer. Based on the analysis of significant sequences, the G-box regulatory sequence may also play an important role in the activation mechanism of the 35S enhancer.

iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules.

Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2.

EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches.

T-DNA activation-tagging technology is widely used to study rice gene functions. When T-DNA inserts into genome, the flanking gene expression may be altered using CaMV 35S enhancer, but the affected genes still need to be validated by biological experiment. We have developed the EAT-Rice platform to predict the flanking gene expression of T-DNA insertion site in rice mutants. The three kinds of DNA sequences including UPS1K, DISTANCE, and MIDDLE were retrieved to encode and build a forecast model of two-layer machine learning. In the first-layer models, the features nucleotide context (N-gram), cis-regulatory elements (Motif), nucleotide physicochemical properties (NPC), and CG-island (CGI) were used to build SVM models by analysing the concealed information embedded within the three kinds of sequences. Logistic regression was used to estimate the probability of gene activation which as feature-encoding weighting within first-layer model. In the second-layer models, the NaiveBayesUpdateable algorithm was used to integrate these first layer-models, and the system performance was 88.33% on 5-fold cross-validation, and 79.17% on independent-testing finally. In the three kinds of sequences, the model constructed by Middle had the best contribution to the system for identifying the activated genes. The EAT-Rice system provided better performance and gene expression prediction at further distances when compared to the TRIM database. An online server based on EAT-rice is available at http://predictor.nchu.edu.tw/EAT-Rice.

Treatment outcomes in patients receiving conventional amphotericin B therapy: a prospective multicentre study in Taiwan.

OBJECTIVES: To evaluate treatment outcomes and healthcare resource use with conventional amphotericin B therapy for invasive fungal infections (IFIs). PATIENTS AND METHODS: A prospective observational study in hospitalized adult patients receiving amphotericin B treatment was undertaken at four hospitals in Taiwan. Patients were observed from the start of therapy to hospital discharge. RESULTS: A total of 108 patients (October 2000 to April 2002) were included in the study. Proven or probable IFIs as defined by the EORTC/MSG criteria were the reasons for the initiation of amphotericin B in 35.2% of the sample. A total of 24.1% patients developed nephrotoxicity (NT) (defined as a 50% increase in the baseline serum creatinine and achieving a peak of at least 2.0 mg/dL). Treatment of proven/probable IFIs [odds ratio (OR) = 4.16, 95% confidence interval (CI) = 1.61-10.75] was a significant predictor of the development of NT. The in-hospital mortality rate was 38.0%. Proven/probable IFIs (OR = 6.93, 95% CI = 2.62-18.29) and the development of NT (OR = 3.68, 95% CI = 1.22-11.04) were independent predictors of in-hospital mortality. For patients alive at discharge, those with NT had a trend of longer hospital stay compared with patients who had not developed NT (mean, 49.3 +/- 18.2 versus 29.3 +/- 22.3 days, P = 0.069). For patients who died, those who had developed NT died sooner (15.5 +/- 16.7 versus 33. 8 +/- 26.9 days, P = 0.0004). CONCLUSIONS: NT was associated with accelerated mortality and increased hospital stay for patients who survived. Using amphotericin B carefully or the use of antifungal agents with less potential for NT might improve patient outcomes.