Hsin-Chou Yang

Genome-wide association studies (GWASs) have disproportionately focused on European (EUR) populations, limiting the characterization of genetic architecture in other ancestries. To address this imbalance, we integrated large-scale biobanks from Japan, Korea, Taiwan, and China to perform the largest phenome-wide meta-analysis to date in East Asian (EAS) populations, encompassing over one million individuals across 127 complex traits. We identified 8,010 previously unreported associations and observed substantial genetic sharing across EAS subpopulations, while also detecting cohort-specific heterogeneity within the broader EAS context. Transethnic analyses revealed moderate genetic correlations between EAS and EUR populations, indicating both shared and ancestry-specific components of disease risk. Pleiotropy analyses highlighted prominent signals within the HLA region, supported by protein-protein interaction connectivity and immune-related pathway enrichment. Decomposition of genome-wide association matrices further uncovered structured cross-trait architectures, revealing a predominantly shared polygenic backbone driven by metabolic, biochemical, and anthropometric traits, together with two discrete latent components enriched for immune-related processes. Together, our findings refine the genetic architecture of complex traits in East Asian populations at unprecedented scale and clarify the balance between shared and population-specific determinants of human diseases.

Background: Vegetarian diets might influence cancer risk. Methods: We studied 1,645,555 meat eaters, 57,016 poultry eaters, 42,910 pescatarians, 63,147 vegetarians and 8849 vegans in 9 cohorts (UK, US, Taiwan, India). After a median 16 years follow-up, incident cancers were: 4504 mouth and pharynx, 1308 oesophagus (squamous cell), 2105 oesophagus (adenocarcinoma), 3578 stomach, 30,528 colorectum, 2970 liver, 8030 pancreas, 3077 lung (never smokers), 61,368 breast, 11,220 endometrium, 8076 ovary, 45,946 prostate, 7193 kidney, 6869 bladder, 11,651 non-Hodgkin lymphoma, 4658 multiple myeloma and 7306 leukaemia. Multivariable Cox regression was used to estimate cohort-specific hazard ratios (HRs) and 95% confidence intervals (CIs), and the results were combined using meta-analysis. Results: Compared to meat eaters, poultry eaters had lower risk of prostate cancer (0.93, 0.88–0.98), pescatarians had lower risks of colorectal (0.85, 0.77–0.93), breast (0.93, 0.88–0.98) and kidney cancer (0.73, 0.58–0.93), vegetarians had lower risks of cancers of the pancreas (0.79, 0.65–0.97), breast (0.91, 0.86–0.97), prostate (0.88, 0.79–0.97), kidney (0.72, 0.57–0.92) and multiple myeloma (0.69, 0.51–0.93) but higher risk of squamous cell carcinoma of the oesophagus (1.93, 1.30–2.87), and vegans had higher risk of colorectal cancer (1.40, 1.12–1.75). Conclusions: Vegetarian diets might influence risk for several cancers. The generalisability should be considered cautiously.

Metabolomics has experienced significant growth and increased popularity due to technological advancements. We introduced an integrated tool for untargeted metabolomics analysis, SMART 1.0, that streamlined the entire analysis process, from initial data preprocessing to subsequent association analysis. With SMART 2.0, we enhanced SMART 1.0 by introducing new analytical modules in targeted metabolomics analysis, data normalization, quality control assessment, and advanced dimensionality reduction and classification methods. Additionally, SMART 2.0 offers integrative omics pathway analysis and postanalysis tasks such as peak identification and concentration calibration. We also explored the potential of using large language models for peak annotation and have found the results to be promising. This study employs narcotics data and breast cancer data as demonstrative examples to illustrate the new functionalities. The codes, a user guide, and example data can be downloaded at https://github.com/YuJenL/SMART.

Han Chinese people comprise nearly 20% of the global population but remain under-represented in genetic studies ^1,2 , so there is an urgent need for large-scale cohorts to advance precision medicine. Here we present the Taiwan Precision Medicine Initiative (TPMI), established by Academia Sinica in collaboration with 16 major medical centres around Taiwan, which has recruited 565,390 participants who consent to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMRs) for research. EMR access is both retrospective and prospective, allowing longitudinal studies. Genetic profiling is done with population-optimized arrays of single-nucleotide polymorphisms for people of Han Chinese ancestry, which enable genome-wide association ^3,4 , phenome-wide association ^5,6 and polygenic risk score ^7,8 studies to be performed to evaluate common disease risk and pharmacogenetic response. Participants also agreed to be re-contacted for future research and receive personalized genetic risk profiles with health management recommendations. The TPMI has established the TPMI Data Access Platform, a central database and analysis platform that both safeguards the security of the data and facilitates academic research. As a large cohort of individuals with non-European ancestry that merges genetic profiles with EMR data and enables longitudinal follow-up, TPMI provides a unique resource that could be used to validate genetic risk prediction models, perform clinical trials of risk-based health management and inform health policies. Ultimately, the TPMI cohort will contribute to global genetic research and serve as a model for population-based precision medicine.

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.

Incorporating pharmacogenetics into clinical practice promises to improve therapeutic outcomes by optimizing drug selection and dosage based on genetic factors affecting drug response. A key advantage of PGx-guided therapy is to decrease the likelihood of adverse events. To evaluate the clinical impact of PGx risk variants, we performed a retrospective study using genetic and clinical data from the largest Han Chinese cohort, comprising 486,956 individuals, assembled by the Taiwan Precision Medicine Initiative. We found that nearly all participants carried at least one genetic variant that could affect drug response, with many carrying multiple risk variants. Here we show the detailed analyses of four gene-drug pairs, azathioprine ( NUDT15 / TPMT ), clopidogrel ( CYP2C19 ), statins ( ABCG2 / CYP2C9 / SLCO1B1 ), and NSAIDs ( CYP2C9 ), for which sufficient data exists for statistical power. While the results validate previous findings that PGx risk variants are significantly associated with drug-related adverse events or ineffectiveness, the excess risk of adverse events or lack of efficacy is small compared to that found in those without the PGx risk variants, and most patients with PGx variants do not suffer from adverse events. Our results point to the complexity of implementing PGx in clinical practice and the need for integrative approaches to optimize precision medicine.

DNA sequencing of patients with rare disorders has been highly successful in identifying “causal variants” for numerous conditions. However, there are many reports of healthy individuals who harbor these deleterious variants, leading to the concept of incomplete penetrance and doubt about the utility of genetic testing in clinical practice and population screening. As the deleterious variants are rare, the penetrance of these variants in the population is largely unknown. We analyzed the genetic and clinical data from 486,956 participants of the Taiwan Precision Medicine Initiative (TPMI) to determine the risk difference between those with and without deleterious variants. In all, we analyzed 292 disease-relevant variants and their clinical outcomes to assess their association. We found that only 15 variants show a risk difference exceeding 5% between those with or without the variants. In essence, 87.3% of deleterious variants exhibit minimal risk differences, suggesting a limited impact on the individual and population levels. Our analysis revealed increasing trends with age in six cardiovascular and degenerative diseases and bell-shaped trends in two cancers. Additionally, we identified three clinical outcomes exhibiting a dose-response relationship with the number of deleterious variants. Our findings show that large-scale testing of deleterious variants found in the literature is not warranted, except for those exhibiting large disease risk differences.

The Taiwan Precision Medicine Initiative (TPMI), a project initiated by the Academia Sinica in collaboration with 16 major medical centers around Taiwan, has recruited 565,390 participants who consented to provide DNA samples for genetic profiling and grant access to their electronic medical records (EMR) for studies to develop precision medicine. Access to the EMR is both retrospective and prospective, allowing researchers to conduct prospective studies over time. Genetic profiling is done with population-optimized SNP arrays for the Han Chinese populations that enable genetic analyses such as genome-wide association, phenome-wide association, and polygenic risk score studies to evaluate common disease risk and pharmacogenetic response. Furthermore, the TPMI participants agree to be contacted for future research opportunities related to their genetic risks and receive personalized genetic risk profiles with health management recommendations. TPMI has established the TPMI Data Access Platform (TDAP), a central database and analysis platform that both safeguards the security of the data and facilitates academic research. The TPMI is the largest non-European cohort that merges genetic profiles with EMR in the world. With a cohort that can be followed over time, it can be utilized to validate genetic risk prediction models, conduct clinical trials to show the efficacy of risk-based health management, and optimize health policies based on genetic risks. In this report, we describe the TPMI study design, the population and genetic characteristics of the TPMI cohort, and the power it provides to conduct crucial studies in developing precision medicine on a population and personal level. As Han Chinese represent almost 20% of the world’s population, the results of TPMI studies will benefit >1.4 billion people around the world and serve as a model for developing population-based precision medicine.

Predicting complex disease risks based on individual genomic profiles is an advancing field in human genetics ^1,2 . However, most genetic studies have focused on European populations, 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 (TPMI) recruited over half a million Taiwanese residents, providing a large dataset of genetic profiles and electronic medical record data for the Han Chinese. Using extensive phenotypic data, we conducted comprehensive genomic analyses of Han Chinese across the medical phenome. These analyses identified population-specific genetic risk variants and novel 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 East Asians 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 TPMI cohort. Our approach of characterizing the phenome-wide genomic landscape, developing population-specific risk prediction models, assessing their performance, and identifying the genetic impact on health, serves as a model for similar studies in other diverse study populations.