Taiwan Biobank: A rich biomedical research database of the Taiwanese population

The advancement of artificial intelligence in biomedical research and health innovation: challenges and opportunities in emerging economies

The advancement of artificial intelligence (AI), algorithm optimization and high-throughput experiments has enabled scientists to accelerate the discovery of new chemicals and materials with unprecedented efficiency, resilience and precision. Over the recent years, the so-called autonomous experimentation (AE) systems are featured as key AI innovation to enhance and accelerate research and development (R&D). Also known as self-driving laboratories or materials acceleration platforms, AE systems are digital platforms capable of running a large number of experiments autonomously. Those systems are rapidly impacting biomedical research and clinical innovation, in areas such as drug discovery, nanomedicine, precision oncology, and others. As it is expected that AE will impact healthcare innovation from local to global levels, its implications for science and technology in emerging economies should be examined. By examining the increasing relevance of AE in contemporary R&D activities, this article aims to explore the advancement of artificial intelligence in biomedical research and health innovation, highlighting its implications, challenges and opportunities in emerging economies. AE presents an opportunity for stakeholders from emerging economies to co-produce the global knowledge landscape of AI in health. However, asymmetries in R&D capabilities should be acknowledged since emerging economies suffers from inadequacies and discontinuities in resources and funding. The establishment of decentralized AE infrastructures could support stakeholders to overcome local restrictions and opens venues for more culturally diverse, equitable, and trustworthy development of AI in health-related R&D through meaningful partnerships and engagement. Collaborations with innovators from emerging economies could facilitate anticipation of fiscal pressures in science and technology policies, obsolescence of knowledge infrastructures, ethical and regulatory policy lag, and other issues present in the Global South. Also, improving cultural and geographical representativeness of AE contributes to foster the diffusion and acceptance of AI in health-related R&D worldwide. Institutional preparedness is critical and could enable stakeholders to navigate opportunities of AI in biomedical research and health innovation in the coming years.

Phenomic landscape and pharmacogenomic implications for HLA region in a Taiwan Han Chinese population

BACKGROUND

The human leukocyte antigen (HLA) genes, exhibiting significant genetic diversity, are associated with susceptibility to various clinical diseases and diverse in drug responses. High costs of HLA sequencing and the population-specific architecture of this genetic region necessitate the establishment of a population-specific HLA imputation reference panel. Moreover, there is a lack of understanding about the genetic and phenotypic landscape of HLA variations within the Taiwanese population.

METHODS

We created models for a Taiwanese-specific HLA imputation reference panel. These models were trained with the array genotype data and HLA sequencing data from 845 Taiwanese subjects. HLA imputation was applied for 59,448 Taiwanese subjects to characterize the HLA allele and haplotype frequencies. Additionally, a phenome-wide association study (PheWAS) was conducted to identify the phenotypes associated with HLA variations. The association of the biallelic HLA variants with the binary and quantitative traits were evaluated with additive logistic and linear regression models, respectively. Furthermore, an omnibus test with likelihood-ratio test was applied for each HLA amino acid position in the multiallelic HLA amino acid polymorphisms to compare the difference between a fitted model and a null model following a χ2 distribution of n-1 degree of freedom at a position with n residues. Finally, we estimated the prevalence of adverse drug reactions (ADR)-related HLA alleles in the Taiwanese population.

RESULTS

In this study, the reference panel models displayed remarkable accuracy, with averages of 99.3%, 98.9%, and 99.1% for 2-, 4-, 6-digit alleles of the eight classical HLA genes, respectively. For PheWAS, a total of 18,136 significant associations with HLA variants across 26 phenotypes are identified (p < 5×10-8), highlighting the pleiotropy feature of the HLA region. Among the independent signals, 15 are novel, including the association of HLA-B pos 138 variation with ankylosing spondylitis (AS), and rs9266290 and rs9266292 with allergy. Through an analysis spanning the entire HLA region, we identified clusters of phenotype correlations. Finally, the carriers of pharmacogenomic related HLA alleles, including HLA-C*01:02 (35.86%), HLA-B*58:01 (20.9%), and HLA-B*15:02 (8.38%), were characterized in the Taiwanese general population.

CONCLUSIONS

We successfully delivered the HLA imputation for 59,448 Taiwanese subjects and characterized the genetic and phenotypic landscapes of the HLA variations. In addition, we quantified the estimated prevalence of the ADR-related HLA alleles in the Taiwanese population. The developed HLA imputation reference panel could be used for estimation of population HLA allele frequencies, which can facilitate further studies in the role of HLA variants in a wider range of phenotypes in the population.

Genetic etiology of truncus arteriosus excluding 22q11.2 deletion syndrome and identification of c.1617del, a prevalent variant in TMEM260, in the Japanese population

Truncus Arteriosus (TA) is a congenital heart disease characterized by a single common blood vessel emerging from the right and left ventricles instead of the main pulmonary artery and aorta. TA accounts for 4% of all critical congenital heart diseases. The most common cause of TA is 22q11.2 deletion syndrome, accounting for 12-35% of all TA cases. However, no major causes of TA other than 22q11.2 deletion have been reported. We performed whole-genome sequencing of 11 Japanese patients having TA without 22q11.2 deletion. Among five patients, we identified pathogenic variants in TMEM260; the biallelic loss-of-function variants of which have recently been associated with structural heart defects and renal anomalies syndrome (SHDRA). In one patient, we identified a de novo pathogenic variant in GATA6, and in another patient, we identified a de novo probably pathogenic variant in NOTCH1. Notably, we identified a prevalent variant in TMEM260 (ENST00000261556.6), c.1617del (p.Trp539Cysfs*9), in 8/22 alleles among the 11 patients. The c.1617del variant was estimated to occur approximately 23 kiloyears ago. Based on the allele frequency of the c.1617del variant in the Japanese population (0.36%), approximately 26% of Japanese patients afflicted with TA could harbor homozygous c.1617del variants. This study highlights TMEM260, especially c.1617del, as a major genetic cause of TA in the Japanese population.

Predicting Risks of Dry Eye Disease Development Using a Genome-Wide Polygenic Risk Score Model

Purpose

The purpose of this study was to conduct a large-scale genome-wide association study (GWAS) and construct a polygenic risk score (PRS) for risk stratification in patients with dry eye disease (DED) using the Taiwan Biobank (TWB) databases.

Methods

This retrospective case-control study involved 40,112 subjects of Han Chinese ancestry, sourced from the publicly available TWB. Cases were patients with DED (n = 14,185), and controls were individuals without DED (n = 25,927). The patients with DED were further divided into 8072 young (<60 years old) and 6113 old participants (≥60 years old). Using PLINK (version 1.9) software, quality control was carried out, followed by logistic regression analysis with adjustments for sex, age, body mass index, depression, and manic episodes as covariates. We also built PRS prediction models using the standard clumping and thresholding method and evaluated their performance (area under the curve [AUC]) through five-fold cross-validation.

Results

Eleven independent risk loci were identified for these patients with DED at the genome-wide significance levels, including DNAJB6, MAML3, LINC02267, DCHS1, SIRPB3P, HULC, MUC16, GAS2L3, and ZFPM2. Among these, MUC16 encodes mucin family protein. The PRS model incorporated 932 and 740 genetic loci for young and old populations, respectively. A higher PRS score indicated a greater DED risk, with the top 5% of PRS individuals having a 10-fold higher risk. After integrating these covariates into the PRS model, the area under the receiver operating curve (AUROC) increased from 0.509 and 0.537 to 0.600 and 0.648 for young and old populations, respectively, demonstrating the genetic-environmental interaction.

Conclusions

Our study prompts potential candidates for the mechanism of DED and paves the way for more personalized medication in the future.

Translational Relevance

Our study identified genes related to DED and constructed a PRS model to improve DED prediction.

Syndromic ciliopathy: a taiwanese single-center study

BACKGROUND

Syndromic ciliopathies are a group of congenital disorders characterized by broad clinical and genetic overlap, including obesity, visual problems, skeletal anomalies, mental retardation, and renal diseases. The hallmark of the pathophysiology among these disorders is defective ciliary functions or formation. Many different genes have been implicated in the pathogenesis of these diseases, but some patients still remain unclear about their genotypes.

METHODS

The aim of this study was to identify the genetic causes in patients with syndromic ciliopathy. Patients suspected of or meeting clinical diagnostic criteria for any type of syndromic ciliopathy were recruited at a single diagnostic medical center in Southern Taiwan. Whole exome sequencing (WES) was employed to identify their genotypes and elucidate the mutation spectrum in Taiwanese patients with syndromic ciliopathy. Clinical information was collected at the time of patient enrollment.

RESULTS

A total of 14 cases were molecularly diagnosed with syndromic ciliopathy. Among these cases, 10 had Bardet-Biedl syndrome (BBS), comprising eight BBS2 patients and two BBS7 patients. Additionally, two cases were diagnosed with Alström syndrome, one with Oral-facial-digital syndrome type 14, and another with Joubert syndrome type 10. A total of 4 novel variants were identified. A recurrent splice site mutation, BBS2: c.534 + 1G > T, was present in all eight BBS2 patients, suggesting a founder effect. One BBS2 patient with homozygous c.534 + 1G > T mutations carried a third ciliopathic allele, TTC21B: c.264_267dupTAGA, a nonsense mutation resulting in a premature stop codon and protein truncation.

CONCLUSIONS

Whole exome sequencing (WES) assists in identifying molecular pathogenic variants in ciliopathic patients, as well as the genetic hotspot mutations in specific populations. It should be considered as the first-line genetic testing for heterogeneous disorders characterized by the involvement of multiple genes and diverse clinical manifestations.

Secondhand smoke increases the risk of developing chronic obstructive pulmonary disease

Smoking is the most important risk factor for chronic obstructive pulmonary disease (COPD), however evidence from large-scale studies on whether secondhand smoke (SHS) increases the risk of COPD is still lacking. We conducted this large longitudinal study to investigate the association between SHS and the development of COPD. This is a longitudinal study. Data on 6519 subjects who were never-smokers, had no history of COPD, and had complete lung function records were extracted from the Taiwan Biobank. They were divided into two groups according to SHS exposure: no exposure and exposure groups. Data were collected when participants enrolled in the study and during regular follow-up. Cox proportional hazards regression models were used to estimate the relative risk (RR) and 95% confidence interval (CI) for the association between SHS and the risk of developing COPD. At 48 months of follow-up, 260 (4%) participants in the no exposure group and 34 (7%) participants in the exposure group developed COPD. The RR of incident COPD development was significantly higher in the exposure group than that in the no exposure group after adjusting for confounders (RR = 1.49; 95% CI 1.04 to 2.14; P value = 0.031). There is a dose-response relationship between the duration of exposure to SHS and the risk of incident COPD, which demonstrates that an additional hour of exposure to SHS per week was associated with a 1.03-fold increased likelihood of developing COPD after adjusting for confounders (RR = 1.03; 95% CI 1.00 to 1.05; P value = 0.027). SHS exposure contributes to the development of COPD. This finding can help raise awareness of the harms of SHS and provide a reference for formulating anti-smoking policies.

Shared genetic architectures of educational attainment in East Asian and European populations

Educational attainment (EduYears), a heritable trait often used as a proxy for cognitive ability, is associated with various health and social outcomes. Previous genome-wide association studies (GWASs) on EduYears have been focused on samples of European (EUR) genetic ancestries. Here we present the first large-scale GWAS of EduYears in people of East Asian (EAS) ancestry (n = 176,400) and conduct a cross-ancestry meta-analysis with EduYears GWAS in people of EUR ancestry (n = 766,345). EduYears showed a high genetic correlation and power-adjusted transferability ratio between EAS and EUR. We also found similar functional enrichment, gene expression enrichment and cross-trait genetic correlations between two populations. Cross-ancestry fine-mapping identified refined credible sets with a higher posterior inclusion probability than single population fine-mapping. Polygenic prediction analysis in four independent EAS and EUR cohorts demonstrated transferability between populations. Our study supports the need for further research on diverse ancestries to increase our understanding of the genetic basis of educational attainment.

Hemodynamic and Clinical Profiles of Pulmonary Arterial Hypertension Patients with GDF2 and BMPR2 Variants

Asians have a higher carrier rate of pulmonary arterial hypertension (PAH)-related genetic variants than Caucasians do. This study aimed to identify PAH-related genetic variants using whole exome sequencing (WES) in Asian idiopathic and heritable PAH cohorts. A WES library was constructed, and candidate variants were further validated by polymerase chain reaction and Sanger sequencing in the PAH cohort. In a total of 69 patients, the highest incidence of variants was found in the BMPR2, ATP13A3, and GDF2 genes. Regarding the BMPR2 gene variants, there were two nonsense variants (c.994C>T, p. Arg332*; c.1750C>T, p. Arg584*), one missense variant (c.1478C>T, p. Thr493Ile), and one novel in-frame deletion variant (c.877_888del, p. Leu293_Ser296del). Regarding the GDF2 variants, there was one likely pathogenic nonsense variant (c.259C>T, p. Gln87*) and two missense variants (c.1207G>A, p. Val403Ile; c.38T>C, p. Leu13Pro). The BMPR2 and GDF2 variant subgroups had worse hemodynamics. Moreover, the GDF2 variant patients were younger and had a significantly lower GDF2 value (135.6 ± 36.2 pg/mL, p = 0.002) in comparison to the value in the non-BMPR2/non-GDF2 mutant group (267.8 ± 185.8 pg/mL). The BMPR2 variant carriers had worse hemodynamics compared to the patients with the non-BMPR2/non-GDF2 mutant group. Moreover, there was a significantly lower GDF2 value in the GDF2 variant carriers compared to the control group. GDF2 may be a protective or corrected modifier in certain genetic backgrounds.

The association between hepatic viral infections and cancers: a cross-sectional study in the Taiwan adult population

BACKGROUND

Hepatitis B (HBV) and hepatitis C (HCV) viruses are diseases of global public health concern and are associated with liver cancer. Recent studies have revealed associations between hepatic viral infections and extrahepatic cancers. This study aimed to explore the associations between hepatitis B and C viruses and cancer at baseline in the Taiwan Biobank database while controlling for a wide range of confounding variables.

METHODS

In a cross-sectional study of adults aged > 20 years, we compared the distribution of demographic factors, lifestyle, and comorbidities between viral and nonviral hepatic groups using the chi-square test. Univariate and multivariate logistic regressions were performed to observe the associations between hepatitis B and C viral infections and cancers by estimating the odds ratio (OR) and 95% confidence interval (CI). Multivariate regression analysis was adjusted for sociodemographic factors, lifestyle, and comorbidities.

RESULTS

From the database, 2955 participants were identified as having HCV infection, 15,305 as having HBV infection, and 140,108 as the nonviral group. HBV infection was associated with an increased likelihood of liver cancer (adjusted OR (aOR) = 6.60, 95% CI = 3.21-13.57, P < 0.001) and ovarian cancer (aOR = 4.63, 95% CI = 1.98-10.83, P = 0.001). HCV infection was observed to increase the likelihood of liver cancer (aOR = 4.90, 95% CI = 1.37-17.53, P = 0.015), ovarian cancer (aOR = 8.50, 95% CI = 1.78-40.69, P = 0.007), and kidney cancer (aOR = 12.89, 95% CI = 2.41-69.01, P = 0.003).

CONCLUSION

Our findings suggest that hepatic viral infections are associated with intra- and extrahepatic cancers. However, being cross-sectional, causal inferences cannot be made. A recall-by-genotype study is recommended to further investigate the causality of these associations.

Cigarette smoking and PM2.5 might jointly exacerbate the risk of metabolic syndrome

Background

Cigarette smoking and particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5) are major preventable cardiovascular mortality and morbidity promoters. Their joint role in metabolic syndrome (MS) pathogenesis is unknown. We determined the risk of MS based on PM2.5 and cigarette smoking in Taiwanese adults.

Methods

The study included 126,366 Taiwanese between 30 and 70 years old with no personal history of cancer. The Taiwan Biobank (TWB) contained information on MS, cigarette smoking, and covariates, while the Environmental Protection Administration (EPA), Taiwan, contained the PM2.5 information. Individuals were categorized as current, former, and nonsmokers. PM2.5 levels were categorized into quartiles: PM2.5 ≤ Q1, Q1 < PM2.5 ≤ Q2, Q2 < PM2.5 ≤ Q3, and PM2.5 > Q3, corresponding to PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3.

Results

The prevalence of MS was significantly different according to PM2.5 exposure (p-value = 0.0280) and cigarette smoking (p-value < 0.0001). Higher PM2.5 levels were significantly associated with a higher risk of MS: odds ratio (OR); 95% confidence interval (CI) = 1.058; 1.014-1.104, 1.185; 1.134-1.238, and 1.149; 1.101-1.200 for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. The risk of MS was significantly higher among former and current smokers with OR; 95% CI = 1.062; 1.008-1.118 and 1.531; 1.450-1.616, respectively, and a dose-dependent p-value < 0.0001. The interaction between both exposures regarding MS was significant (p-value = 0.0157). Stratification by cigarette smoking revealed a significant risk of MS due to PM2.5 exposure among nonsmokers: OR (95% CI) = 1.074 (1.022-1.128), 1.226 (1.166-1.290), and 1.187 (1.129-1.247) for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. According to PM2.5 quartiles, current smokers had a higher risk of MS, regardless of PM2.5 levels (OR); 95% CI = 1.605; 1.444-1.785, 1.561; 1.409-1.728, 1.359; 1.211-1.524, and 1.585; 1.418-1.772 for PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. After combining both exposures, the group, current smokers; PM2.5 > 38.205 μg/m3 had the highest odds (1.801; 95% CI =1.625-1.995).

Conclusion

PM2.5 and cigarette smoking were independently and jointly associated with a higher risk of MS. Stratified analyses revealed that cigarette smoking might have a much higher effect on MS than PM2.5. Nonetheless, exposure to both PM2.5 and cigarette smoking could compound the risk of MS.

Evaluating polygenic risk scores for predicting cardiometabolic traits and disease risks in the Taiwan Biobank

Type 2 diabetes (T2D) and hypertension are common comorbidities and, along with hyperlipidemia, serve as risk factors for cardiovascular diseases. This study aimed to evaluate the predictive value of polygenic risk scores (PRSs) on cardiometabolic traits related to T2D, hypertension, and hyperlipidemia and the incidence of these three diseases in Taiwan Biobank samples. Using publicly available, large-scale genome-wide association studies summary statistics, we constructed cross-ethnic PRSs for T2D, hypertension, body mass index, and nine quantitative traits typically used to define the three diseases. A composite PRS (cPRS) for each of the nine traits was constructed by aggregating the significant PRSs of its genetically correlated traits. The associations of each of the nine traits at baseline as well as the change of trait values during a 3- to 6-year follow-up period with its cPRS were evaluated. The predictive performances of cPRSs in predicting future incidences of T2D, hypertension, and hyperlipidemia were assessed. The cPRSs had significant associations with baseline and changes of trait values in 3-6 years and explained a higher proportion of variance for all traits than individual PRSs. Furthermore, models incorporating disease-related cPRSs, along with clinical features and relevant trait measurements achieved area under the curve values of 87.8%, 83.7%, and 75.9% for predicting future T2D, hypertension, and hyperlipidemia in 3-6 years, respectively.

Shared Genetics between Age at Menarche and Type 2 Diabetes Mellitus: Genome-Wide Genetic Correlation Study

Background: Age at menarche (AAM) has been associated with type 2 diabetes mellitus (T2DM). However, little is known about their shared heritability. Methods: Our data comes from the Taiwan Biobank. Genome-wide association studies (GWASs) were conducted to identify single-nucleotide polymorphisms (SNPs) related to AAM-, T2DM-, and T2DM-related phenotypes, such as body fat percentage (BFP), fasting blood glucose (FBG), and hemoglobin A1C (HbA1C). Further, the conditional false discovery rate (cFDR) method was applied to examine the shared genetic signals. Results: Conditioning on AAM, Quantile-quantile plots showed an earlier departure from the diagonal line among SNPs associated with BFP and FBG, indicating pleiotropic enrichments among AAM and these traits. Further, the cFDR analysis found 39 independent pleiotropic loci that may underlie the AAM-T2DM association. Among them, FN3KRP rs1046896 (cFDR = 6.84 × 10-49), CDKAL1 rs2206734 (cFDR = 6.48 × 10-10), B3GNTL1 rs58431774 (cFDR = 2.95 × 10-10), G6PC2 rs1402837 (cFDR = 1.82 × 10-8), and KCNQ1 rs60808706 (cFDR = 9.49 × 10-8) were highlighted for their significant genetic enrichment. The protein-protein interaction analysis revealed a significantly enriched network among novel discovered genes that were mostly found to be involved in the insulin and glucagon signaling pathways. Conclusions: Our study highlights potential pleiotropic effects across AAM and T2DM. This may shed light on identifying the genetic causes of T2DM.

Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits

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.

Risk of gout in Taiwan Biobank participants pertaining to their sex and family history of gout among first-degree relatives

BACKGROUND

Family history of gout and sex are independently associated with gout. However, there is a paucity of research regarding the joint role of both factors in gout pathogenesis. Therefore, we assessed the independent and combined association of family history of gout and sex with gout.

METHODS

Our analysis included 132,311 Taiwan Biobank (TWB)-enrolled individuals comprising 21,159 gout cases and 111,152 controls. We subcategorized the family history of gout as (1) both siblings and parents had gout), (2) only parents had gout, and (3) only siblings had gout.

RESULTS

Generally, sex (men compared to women) and family history of gout were independently associated with a higher risk of gout. The odds ratio (OR); 95% confidence interval (CI) was 9.175; 8.801-9.566 for sex, and 2.306; 2.206-2.410 for family history. For the subcategories 'both siblings and had gout,' 'only parents had gout,' and 'only siblings had gout,' the odds ratios (ORs); 95% confidence intervals (CIs) were 4.944; 4.414-5.538, 2.041; 1.927-2.161, and 2.162; 2.012-2.323, respectively. The interaction between sex and family history was significant (p value = 0.0001). After stratification by sex, family history of gout remained significantly associated with a higher risk of gout in both sexes, even though the odds ratios were higher in men. For the subcategories 'both siblings and parents had gout,' 'only parent had gout,' and 'only siblings had gout,' the corresponding ORs; 95% CIs were 6.279; 5.243-7.520, 2.211; 2.062-2.371, and 2.148; 1.955-2.361 in men and 4.199; 3.566-4.945, 1.827; 1.640-2.035, and 2.093; 1.876-2.336 in women. After integrating sex and family history (reference: women with no family history), the highest risk of gout was observed in men who had at least one parent and sibling with a history of gout (OR; 95% CI 55.774; 46.360-67.101).

CONCLUSION

Sex and family history of gout were independently and interactively associated with gout. Sex-wise, men had a higher risk of gout than women. Family history was associated with a higher risk of gout in both sexes, but men had a higher risk. Notably, men having both siblings and parents with gout had the highest risk of gout.

Population diversity and equity in the genomic era: going global to return to the local

Advances in precision medicine depend on the quantity and quality of available genomic information. Various articles alert about the current disparities between the world's regions regarding the amount of genomic information available and the negative impact this will have on global health. The objective of this paper is to review these articles to describe what aspects they emphasize and highlight some issues that remain to be analyzed from the perspective of a "peripheral" country. Most of these articles come from central countries, where the need for more diversity in genomics is already detected. Several authors analyze lack of human diversity with focus on national, while others analyze the problem from a global perspective. Depending on the country of origin of the research, the claim for greater diversity has different meanings. Broadly, high-income countries advocate for better coverage looking within the boundaries of their own countries. In other regions of the world, where this field of research has not yet been massively developed, the same need for greater inclusiveness of origins in population genomics studies is not detected. An under-analyzed aspect is the unequal starting point between regions regarding the economic resources available for the development of this field of medicine, and for science and health in general. Although this macroeconomic and social aspect is usually absent in scientific analyses, without it solved, it will be impossible to guarantee that all world populations are equally represented in the panels or genomic databases that serve as input for precision medicine development.

Imputation Accuracy Across Global Human Populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.

Boosting the power of genome-wide association studies within and across ancestries by using polygenic scores

Genome-wide association studies (GWASs) have been mostly conducted in populations of European ancestry, which currently limits the transferability of their findings to other populations. Here, we show, through theory, simulations and applications to real data, that adjustment of GWAS analyses for polygenic scores (PGSs) increases the statistical power for discovery across all ancestries. We applied this method to analyze seven traits available in three large biobanks with participants of East Asian ancestry (n = 340,000 in total) and report 139 additional associations across traits. We also present a two-stage meta-analysis strategy whereby, in contributing cohorts, a PGS-adjusted GWAS is rerun using PGSs derived from a first round of a standard meta-analysis. On average, across traits, this approach yields a 1.26-fold increase in the number of detected associations (range 1.07- to 1.76-fold increase). Altogether, our study demonstrates the value of using PGSs to increase the power of GWASs in underrepresented populations and promotes such an analytical strategy for future GWAS meta-analyses.

TMEM132D and VIPR2 Polymorphisms as Genetic Risk Loci for Retinal Detachment: A Genome-Wide Association Study and Polygenic Risk Score Analysis

Purpose

Retinal detachment (RD) is a sight-threatening ocular disease caused by separation of the neurosensory retina from the underlying retinal pigment epithelium layer. Its genetic basis is unclear because of a limited amount of data. In this study, we aimed to identify genetic risk loci associated with RD in participants without diabetes mellitus and to construct a polygenic risk score (PRS) to predict the risk of RD.

Methods

A genome-wide association study was conducted using data from the Taiwan Biobank to identify RD risk loci. A total of 1533 RD cases and 106,270 controls were recruited, all of whom were Han Chinese. Replication studies were performed using data from the UK Biobank and Biobank Japan. To construct the PRS, a traditional clumping and thresholding method was performed and validated by fivefold cross-validation.

Results

Two novel loci with significant associations were identified. These two genes were TMEM132D (lead single nucleotide polymorphism [SNP]: rs264498, adjusted-P = 7.18 × 10-9) and VIPR2 (lead SNP: rs3812305, adjusted-P = 8.38 × 10-9). The developed PRS was effective in discriminating individuals at high risk of RD with a dose-response relationship. The quartile with the highest risk had an odds ratio of 1244.748 compared to the lowest risk group (95% confidence interval, 175.174-8844.892).

Conclusions

TMEM132D and VIPR2 polymorphisms are genetic candidates linked to RD in Han Chinese populations. Our proposed PRS was effective at discriminating high-risk from low-risk individuals.

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestries has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping, which builds on the single-population fine-mapping framework, Sum of Single Effects (SuSiE). SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and LD patterns, accounts for multiple causal variants in a genomic region, and can be applied to GWAS summary statistics. We comprehensively evaluated SuSiEx using simulations, a range of quantitative traits measured in both UK Biobank and Taiwan Biobank, and schizophrenia GWAS across East Asian and European ancestries. In all evaluations, SuSiEx fine-mapped more association signals, produced smaller credible sets and higher posterior inclusion probability (PIP) for putative causal variants, and captured population-specific causal variants.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Genome-wide association study of age at menarche in the Taiwan Biobank suggests NOL4 as a novel associated gene

Sexual maturation is a complex physiological process that involves multiple variables, such as genetic and environmental factors. Among females, age at menarche (AM) is a critical milestone for sexual maturation. This study aimed to identify genetic markers of AM using nationwide population cohort data in Taiwan. Females with self-reported AM between 10 and 16 years (N = 39,827) were eligible for the final analysis. To identify genetic signals related to AM, we conducted a genome-wide association study using a linear regression model and split-half meta-analysis method to verify our findings. The Functional Mapping and Annotation web-based platform was used for positional mapping and gene-based and gene-set analyses. The meta-analysis identified four significant loci, i.e., LIN28B (pooled P = 1.39 × 10^-21), NOL4 (pooled P = 8.94 × 10^-9), GPR45 (pooled P = 4.19 × 10^-11), and LOC105373831 (pooled P = 4.37 × 10^-8), that were associated with AM. MAGMA gene-based analysis revealed that LIN28B (P = 1.13 × 10^-8), NOL4 (P = 2.27 × 10^-7), RXRG (P = 4.34 × 10-⁷), ETV5 (P = 1.75 × 10^-6), and HACE1 (P = 1.82 × 10^-6) were significantly associated with AM, while the gene-set analysis identified a significantly enriched pathway involving mTOR signaling complex (FDR corrected P = 1.28 × 10^-2). The results replicated evidence for several genetic markers associated with AM in the Taiwanese female population. Our analysis identified a novel locus (rs7239368) in NOL4 associated with AM (β = 0.051 ± 0.009 years, pooled P = 8.94 × 10^-9), whereas additional research is needed to validate its molecular role in sexual maturation.

Biobanking as a Tool for Genomic Research: From Allele Frequencies to Cross-Ancestry Association Studies

In recent years, great advances have been made in the field of collection, storage, and analysis of biological samples. Large collections of samples, biobanks, have been established in many countries. Biobanks typically collect large amounts of biological samples and associated clinical information; the largest collections include over a million samples. In this review, we summarize the main directions in which biobanks aid medical genetics and genomic research, from providing reference allele frequency information to allowing large-scale cross-ancestry meta-analyses. The largest biobanks greatly vary in the size of the collection, and the amount of available phenotype and genotype data. Nevertheless, all of them are extensively used in genomics, providing a rich resource for genome-wide association analysis, genetic epidemiology, and statistical research into the structure, function, and evolution of the human genome. Recently, multiple research efforts were based on trans-biobank data integration, which increases sample size and allows for the identification of robust genetic associations. We provide prominent examples of such data integration and discuss important caveats which have to be taken into account in trans-biobank research.