Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data.

OBJECTIVE: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. METHODS: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. RESULTS: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. CONCLUSION: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. STATEMENT OF SIGNIFICANCE: Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

Develop and Apply Electrocardiography-Based Risk Score to Identify Community-Based Elderly Individuals at High-Risk of Mortality.

With an aging world population, risk stratification of community-based, elderly population is required for primary prevention. This study proposes a combined score developed using electrocardiographic (ECG) parameters and determines its long-term prognostic value for predicting risk of cardiovascular mortality. A cohort-study, conducted from December 2008 to April 2019, enrolled 5,380 subjects in Taiwan, who were examined, using three-serial-12-lead ECGs, and their health/demographic information were recorded. To understand the predictive effects of ECG parameters on overall-survival, Cox hazard regression analysis were performed. The mean age at enrollment was 69.04 ± 8.14 years, and 47.4% were males. ECG abnormalities, LVH [hazard ratio (HR) = 1.39, 95% confidence intervals (CI) = (1.16-1.67), P = 0.0003], QTc [HR = 1.31, CI = (1.07-1.61), P = 0.007] and PR interval [HR = 1.40, CI = (1.01-1.95), P = 0.04], were significantly associated with primary outcome all-cause death. Furthermore, LVH [HR = 2.37, CI = (1.48-3.79), P = 0.0003] was significantly associated with cardiovascular death, while PR interval [HR = 2.63, CI = (1.24- 5.57), P = 0.01] with unexplained death. ECG abnormality (EA) score was defined based on the number of abnormal ECG parameters for each patient, which was used to divide all patients into sub-groups. Competing risk survival analysis using EA score were performed by using the Gray's test, which reported that high-risk EA groups showed significantly higher cumulative incidence for all three outcomes. Prognostic models using the EA score as predictor were developed and a 10-fold cross validation design was adopted to conduct calibration and discrimination analysis, to establish the efficacy of the proposed models. Overall, ECG model could successfully predict people, susceptible to all three death outcomes (P < 0.05), with high efficacy. Statistically significant (P < 0.001) improvement of the c-indices further demonstrated the robustness of the prediction model with ECG parameters, as opposed to a traditional model with no EA predictor. The EA score is highly associated with increased risk of mortality in elderly population and may be successfully used in clinical practice.