Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.

Both adolescent substance use and adolescent depression are major public health problems, and have the tendency to co-occur. Thousands of articles on adolescent substance use or depression have been published. It is labor intensive and time consuming to extract huge amounts of information from the cumulated collections. Topic modeling offers a computational tool to find relevant topics by capturing meaningful structure among collections of documents. In this study, a total of 17,723 abstracts from PubMed published from 2000 to 2014 on adolescent substance use and depression were downloaded as objects, and Latent Dirichlet allocation (LDA) was applied to perform text mining on the dataset. Word clouds were used to visually display the content of topics and demonstrate the distribution of vocabularies over each topic. The LDA topics recaptured the search keywords in PubMed, and further discovered relevant issues, such as intervention program, association links between adolescent substance use and adolescent depression, such as sexual experience and violence, and risk factors of adolescent substance use, such as family factors and peer networks. Using trend analysis to explore the dynamics of proportion of topics, we found that brain research was assessed as a hot issue by the coefficient of the trend test. Topic modeling has the ability to segregate a large collection of articles into distinct themes, and it could be used as a tool to understand the literature, not only by recapturing known facts but also by discovering other relevant topics.

Aims: Several studies suggested that depression might worsen the clinical outcome of diabetes mellitus; however, such association was confounded by duration of illness and baseline complications. This study aimed to assess whether depression increases the risk of diabetes complications and mortality among incident patients with diabetes. Methods: This was a population-based matched cohort study using Taiwan's National Health Insurance Research Database. A total of 38 537 incident patients with diabetes who had depressive disorders and 154 148 incident diabetes patients without depression who were matched by age, sex and cohort entry year were randomly selected. The study endpoint was the development of macrovascular and microvascular complications, all-cause mortality and cause-specific mortality. Results: Among participants, the mean (±SD) age was 52.61 (±12.45) years, and 39.63% were male. The average duration of follow-up for mortality was 5.5 years, ranging from 0 to 14 years. The adjusted hazard ratios were 1.35 (95% confidence interval [CI], 1.32–1.37) for macrovascular complications and 1.08 (95% CI, 1.04–1.12) for all-cause mortality. However, there was no association of depression with microvascular complications, mortality due to cardiovascular diseases or mortality due to diabetes mellitus. The effect of depression on diabetes complications and mortality was more prominent among young adults than among middle-aged and older adults. Conclusions: Depression was associated with macrovascular complications and all-cause mortality in our patient cohort. However, the magnitude of association was less than that in previous studies. Further research should focus on the benefits and risks of treatment for depression on diabetes outcome.

Predicting complex disease risks on the basis of individual genomic profiles is an advancing field in human genetics^1,2. However, most genetic studies have focused on populations of European ancestry, creating a global imbalance in precision medicine and underscoring the need for genomic research in non-European groups^3,4. The Taiwan Precision Medicine Initiative recruited more than half a million Taiwanese residents, providing a large dataset of genetic profiles and electronic medical record data for people with Han Chinese ancestry. Using extensive phenotypic data, we conducted comprehensive genomic analyses across the medical phenome with individuals genetically similar to Han Chinese reference populations. These analyses identified population-specific genetic risk variants and new findings for various complex traits. We developed polygenic risk scores, demonstrating strong predictive performance for conditions such as cardiometabolic diseases, autoimmune disorders, cancers and infectious diseases. We observed consistent findings in an independent dataset, Taiwan Biobank, and among people of East Asian ancestry in the UK Biobank and the All of Us Project. The identified genetic risks accounted for up to 10.3% of the overall health variation in the Taiwan Precision Medicine Initiative cohort. Our approach of characterizing the phenome-wide genomic landscape, developing population-specific risk-prediction models, assessing their performance and identifying the genetic effect on health serves as a model for similar studies in other diverse study populations.