Genotyping and population characteristics of the China Kadoorie Biobank

Expanding scope of genetic studies in the era of biobanks

Biobanks have become pivotal in genetic research, particularly through genome-wide association studies (GWAS), driving transformative insights into the genetic basis of complex diseases and traits through the integration of genetic data with phenotypic, environmental, family history, and behavioral information. This review explores the distinct design and utility of different biobanks, highlighting their unique contributions to genetic research. We further discuss the utility and methodological advances in combining data from disease-specific study or consortia with that of biobanks, especially focusing on summary statistics based meta-analysis. Subsequently we review the spectrum of additional advantages offered by biobanks in genetic studies in representing population differences, calibration of polygenic scores, assessment of pleiotropy and improving post-GWAS in silico analyses. Advances in sequencing technologies, particularly whole-exome and whole-genome sequencing, have further enabled the discovery of rare variants at biobank scale. Among recent developments, the integration of large-scale multi-omics data especially proteomics and metabolomics, within biobanks provides deeper insights into disease mechanisms and regulatory pathways. Despite challenges like ascertainment strategies and phenotypic misclassification, biobanks continue to evolve, driving methodological innovation and enabling precision medicine. We highlight the contributions of biobanks to genetic research, their growing integration with multi-omics, and finally discuss their future potential for advancing healthcare and therapeutic development.

Causal Relevance of Lp(a) for Coronary Heart Disease and Stroke Types in East Asian and European Ancestry Populations: A Mendelian Randomization Study

BACKGROUND

Elevated plasma levels of Lp(a) [lipoprotein(a)] are a causal risk factor for coronary heart disease and stroke in European individuals, but the causal relevance of Lp(a) for different stroke types and in East Asian individuals with different Lp(a) genetic architecture is uncertain.

METHODS

We measured plasma levels of Lp(a) in a nested case-control study of 18 174 adults (mean [SD] age, 57 [10] years; 49% female) in the China Kadoorie Biobank (CKB) and performed a genome-wide association analysis to identify genetic variants affecting Lp(a) levels, with replication in ancestry-specific subsets in UK Biobank. We further performed 2-sample Mendelian randomization analyses, associating ancestry-specific Lp(a)-associated instrumental variants derived from CKB or from published data in European individuals with risk of myocardial infarction (n=17 091), ischemic stroke (IS [n=29 233]) and its subtypes, or intracerebral hemorrhage (n=5845) in East Asian and European individuals using available data from CKB and genome-wide association analysis consortia.

RESULTS

In CKB observational analyses, plasma levels of Lp(a) were log-linearly and positively associated with higher risks of myocardial infarction and IS, but not with ICH. In genome-wide association analysis, we identified 29 single nucleotide polymorphisms independently associated with Lp(a) that together explained 33% of variance in Lp(a) in Chinese individuals. In UK Biobank, the lead Chinese variants identified in CKB were replicated in 1260 Chinese individuals, but explained only 10% of variance in Lp(a) in European individuals. In Mendelian randomization analyses, however, there were highly concordant effects of Lp(a) across both ancestries for all cardiovascular disease outcomes examined. In combined analyses of both ancestries, the proportional reductions in risk per 100 nmol/L lower genetically predicted Lp(a) levels for myocardial infarction were 3-fold greater than for total IS (rate ratio, 0.78 [95% CI, 0.76-0.81] versus 0.94 [0.92-0.96]), but were similar to those for large-artery IS (0.80 [0.73-0.87]; n=8134). There were weaker associations with cardioembolic IS (0.92 [95% CI, 0.86-0.98]; n=11 730), and no association with small-vessel IS (0.99 [0.91-1.07]; n=12 343) or with intracerebral hemorrhage (1.08 [0.96-1.21]; n=5845).

CONCLUSIONS

The effects of Lp(a) on risk of myocardial infarction and large-artery IS were comparable in East Asian and European individuals, suggesting that people with either ancestry could expect comparable proportional benefits for equivalent reductions in Lp(a), but there was little effect on other stroke types.

Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSxEAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSxEAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Natural environmental factors at birth on risk for rheumatoid arthritis: the impact of season, temperature, latitude, and sunlight exposure

BACKGROUND

Environmental factors contribute to approximately 41% of the risk of rheumatoid arthritis (RA). Previous studies have focused on anthropogenic environmental factors, while much less attention has been given to natural environmental factors. Our study explored the potential influence of natural environmental factors at birth on the risk of RA.

METHODS

This large retrospective study utilized data from the China Kadoorie Biobank. A restricted cubic spline (RCS) model was employed to explore nonlinear relationships between natural environmental factors and the risk of RA. Additionally, a multivariable Cox regression model, adjusted for confounding factors, was used to examine correlations between season of birth, geographic, climate, and the risk of RA.

RESULTS

A total of 512,715 participants were included in this study, of which 2889 (0.56%) were diagnosed with RA. The RCS analysis revealed that the monthly average temperature at birth (p < 0.001), the latitude (p = 0.027) of the birthplace, and the sunshine rate (p < 0.001) exhibited a nonlinear relationship with the risk of RA. Multivariable Cox regression analysis revealed that participants born in Spring and Summer (HR 1.13, 95% CI 1.05-1.23) had an increased risk of RA compared to those born in Autumn and Winter. Additionally, participants born at latitudes ≤ 24°N (HR 1.49, 95% CI 1.32-1.68), with sunshine rate ≤ 28% (HR 2.00, 95% CI 1.75-2.29) or ≥ 60% (HR 1.22, 95% CI 1.08-1.38) had an increased risk of RA. Being born in regions with a monthly average temperature ≥ 27 °C (HR 0.82, 95% CI 0.72-0.95) was associated with a decreased risk of RA.

CONCLUSION

Being born in Spring and Summer, as well as early-life exposure to low-latitude regions and extreme sunlight environments increases the risk of RA. Our study revealed significant associations between the risk of RA and natural environmental factors at birth, emphasizing the impact of the early-life environment on the onset of RA.

A mendelian randomization study on the association between 731 types of immune cells and 91 types of blood cells with venous thromboembolism

BACKGROUND

Venous thromboembolism (VTE) is a grave medical condition characterized by the blockage of distant blood vessels due to blood clots or detached vessel wall fragments, leading to ischemia or necrosis of the affected tissues. With the recent introduction of immunothrombosis, the significance of immune cells in the process of thrombus formation has gained prominent attention. Complex cross-talk occurs between immune cells and blood cells during infection or inflammation, with immune cells actively participating in blood clot formation by promoting platelet recruitment and thrombin activation. Nevertheless, comprehensive studies on the genetic association between immune cells phenotypes and VTE remain scarce. This article employed Mendelian randomization (MR) to investigate the association between the incidence of VTE and a range of 731 immune cell types, along with 91 blood cell perturbation phenotypes, utilizing single nucleotide polymorphisms as instrumental variables.

METHODS

Through the utilization of publicly available genetic data, a two-sample bi-directional MR analysis was conducted. Sensitivity analyses included Cochran's Q test, MR-Egger intercept test, MR-pleiotropy residual sum and outlier (MR-PRESSO) and leave-one-out analysis. For significant associations, replication analysis was conducted using GWAS data from deep vein thrombosis (DVT) and pulmonary embolism (PE).

RESULTS

We firstly investigated the causal relationship between 731 immune cells and VTE risk. All the GWAS data were obtained from European populations and from men and women. The IVW analysis revealed that CD20 on naive-mature B cell, CD20 on IgD- CD38dim B cell, and CD20 on unswitched memory B cell may increase the risk of VTE (P < 0.05). CD28- CD8dim T cell %T cell, CD64 on monocyte and CD64 on CD14 + CD16- monocyte may be protective factors against DVT (P < 0.05). Then disturbed blood cells types as exposure were analyzed to examine its association with occurrence of VTE. Initial and replication analysis both revealed that environmental KCl-impacted red blood cells and butyric acid-impacted platelet accelerated incidence of VTE (P < 0.05), while colchicine -impacted eosinophil, KCl-impacted reticulocyte and Lipopolysaccharide (LPS) -impacted neutrophil reduced VTE risk (P < 0.05). Sensitivity analyses confirmed the robustness and reliability of these positive findings.

CONCLUSIONS

Our study presents evidence of a causal link between six immune cell phenotypes and VTE. Additionally, we have identified two types of blood cells that are associated with both VTE and DVT, and three types of blood cells that are relevant to both VTE and PE.

CLINICAL TRIAL NUMBER

Not applicable.

Effect of Long-Term Fine Particulate Matter Exposure on Lung Cancer Incidence and Mortality in Chinese Nonsmokers

Rationale: The association between fine particulate matter (particulate matter ⩽2.5 μm in aerodynamic diameter, PM2.5) and lung cancer incidence in nonsmokers (LCINS) remains inconsistent. Objectives: To investigate the association between long-term PM2.5 exposure and LCINS in a Chinese population and to assess the modifying effect of genetic factors. Methods: Time-dependent Cox proportional hazard models were used to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) of PM2.5 with LCINS risk and LCINS-related mortality. The polygenic risk score was constructed to further explore the interactions between genetic risk and PM2.5 exposure. In addition, the population attributable fraction of PM2.5 to lung cancer risk and mortality was calculated. Measurements and Main Results: The results demonstrated significant associations between PM2.5 exposure and LCINS incidence (HR, 1.10 per 10 μg/m3; 95% CI, 1.04-1.17 per 10 μg/m3) and mortality (HR, 1.17 per 10 μg/m3; 95% CI, 1.08-1.27 per 10 μg/m3). Compared with the lowest-risk group, individuals exposed to the high PM2.5 concentration (⩾50.9 μg/m3) and high genetic risk (top 30%) exhibited the highest LCINS incidence (HR, 2.01; 95% CI, 1.39-2.87) and mortality (HR, 2.30; 95% CI, 1.38-3.82). A significant additive interaction between PM2.5 and genetic risk on LCINS incidence was observed. Approximately 33.6% of LCINS cases and 48.5% of LCINS-related deaths in China could be prevented if PM2.5 concentrations were reduced to meet World Health Organization guidelines. Conclusions: Long-term exposure to outdoor PM2.5 increases LCINS risk and LCINS-related mortality, especially in populations with high genetic risk. Strengthening air pollution control measures in China has the potential to significantly reduce the burden of LCINS.

A genome-wide cross-trait analysis characterizes the shared genetic architecture between lung and gastrointestinal diseases

Lung and gastrointestinal diseases often occur together, leading to more adverse health outcomes than when a disease of one of these systems occurs alone. However, the potential genetic mechanisms underlying lung-gastrointestinal comorbidities remain unclear. Here, we leverage lung and gastrointestinal trait data from individuals of European, East Asian and African ancestries, to perform a large-scale genetic cross trait analysis, followed by functional annotation and Mendelian randomization analysis to explore the genetic mechanisms involved in the development of lung-gastrointestinal comorbidities. Notably, we find significant genetic correlations between 27 trait pairs among the European population. The highest correlation is between chronic bronchitis and peptic ulcer disease. At the variant level, we identify 42 candidate pleiotropic genetic variants (3 of them previously uncharacterized) in 14 trait pairs by integrating cross-trait meta-analysis, fine-mapping and colocalization analyses. We also find 66 candidate pleiotropic genes, most of which were enriched in immune or inflammatory response-related activities. Causal inference approaches result in 4 potential lung-gastrointestinal associations. Introducing the gut microbiota as a variable establishes a relationship between the genus Parasutterella, gastro-oesophageal reflux disease and asthma. In summary, our findings highlight the genetic relationship between lung and gastrointestinal diseases, providing insights into the genetic mechanisms underlying the development of lung gastrointestinal comorbidities.

An integrative network approach for longitudinal stratification in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms resulting from the loss of dopamine-producing neurons in the brain. Currently, there is no cure for the disease which is in part due to the heterogeneity in patient symptoms, trajectories and manifestations. There is a known genetic component of PD and genomic datasets have helped to uncover some aspects of the disease. Understanding the longitudinal variability of PD is essential as it has been theorised that there are different triggers and underlying disease mechanisms at different points during disease progression. In this paper, we perform longitudinal and cross-sectional experiments to identify which data modalities or combinations of modalities are informative at different time points. We use clinical, genomic, and proteomic data from the Parkinson's Progression Markers Initiative. We validate the importance of flexible data integration by highlighting the varying combinations of data modalities for optimal stratification at different disease stages in idiopathic PD. We show there is a shared signal in the DNAm signatures of participants with a mutation in a causal gene of PD and participants with idiopathic PD. We also show that integration of SNPs and DNAm data modalities has potential for use as an early diagnostic tool for individuals with a genetic cause of PD.

Efficient storage and regression computation for population-scale genome sequencing studies

MOTIVATION

The growing availability of large-scale population biobanks has the potential to significantly advance our understanding of human health and disease. However, the massive computational and storage demands of whole genome sequencing (WGS) data pose serious challenges, particularly for underfunded institutions or researchers in developing countries. This disparity in resources can limit equitable access to cutting-edge genetic research.

RESULTS

We present novel algorithms and regression methods that dramatically reduce both computation time and storage requirements for WGS studies, with particular attention to rare variant representation. By integrating these approaches into PLINK 2.0, we demonstrate substantial gains in efficiency without compromising analytical accuracy. In an exome-wide association analysis of 19.4 million variants for the body mass index phenotype in 125 077 individuals (AllofUs project data), we reduced runtime from 695.35 min (11.5 h) on a single machine to 1.57 min with 30 GB of memory and 50 threads (or 8.67 min with 4 threads). Additionally, the framework supports multi-phenotype analyses, further enhancing its flexibility.

AVAILABILITY AND IMPLEMENTATION

Our optimized methods are fully integrated into PLINK 2.0 and can be accessed at: https://www.cog-genomics.org/plink/2.0/.

Large-scale cross-trait genetic analysis highlights shared genetic backgrounds of autoimmune diseases

Disorders associated with the immune system burden multiple organs, although the shared biology exists across the diseases. Preceding family-based studies reveal that immune diseases are heritable to varying degrees, providing the basis for immunogenomics. The recent cost reduction in genetic analysis intensively promotes biobank-scale studies and the development of frameworks for statistical genetics. The accumulating multi-layer omics data, including genome-wide association studies (GWAS) and RNA-sequencing at single-cell resolution, enable us to dissect the genetic backgrounds of immune-related disorders. Although autoimmune and allergic diseases are generally categorized into different disease categories, epidemiological studies reveal the high incidence of autoimmune and allergic disease complications, suggesting the shared genetics and biology between the disease categories. Biobank resources and consortia cover multiple immune-related disorders to accumulate phenome-wide associations of genetic variants and enhance researchers to analyze the shared and heterogeneous genetic backgrounds. The emerging post-GWAS and integrative multi-omics analyses provide genetic and biological insights into the multicategorical disease associations.

Evolution, genetic diversity, and health

Human genetic diversity in today's world has been shaped by evolutionary history, demographic shifts and environmental exposures, influencing complex traits, disease susceptibility and drug responses. Capturing this diversity is essential for advancing precision medicine and promoting equitable healthcare. Despite the great progress achieved with initiatives such as the human Pangenome and large biobanks that aim for a better representation of human diversity, important challenges remain. In this Perspective, we discuss the importance of diversity in clinical genomics through an evolutionary lens. We highlight progress and challenges and outline key clinical applications of diverse genetic data. We argue that diversifying both datasets and methodologies-integrating ancestral and environmental factors-is crucial for fully understanding the genetic basis of human health and disease.

Biobanking with genetics shapes precision medicine and global health

Precision medicine provides patients with access to personally tailored treatments based on individual-level data. However, developing personalized therapies requires analyses with substantial statistical power to map genetic and epidemiologic associations that ultimately create models informing clinical decisions. As one solution, biobanks have emerged as large-scale, longitudinal cohort studies with long-term storage of biological specimens and health information, including electronic health records and participant survey responses. By providing access to individual-level data for genotype-phenotype mapping efforts, pharmacogenomic studies, polygenic risk score assessments and rare variant analyses, biobanks support ongoing and future precision medicine research. Notably, due in part to the geographical enrichment of biobanks in Western Europe and North America, European ancestries have become disproportionately over-represented in precision medicine research. Herein, we provide a genetics-focused review of biobanks from around the world that are in pursuit of supporting precision medicine. We discuss the limitations of their designs, ongoing efforts to diversify genomics research and strategies to maximize the benefits of research leveraging biobanks for all.

Comparative studies of 2168 plasma proteins measured by two affinity-based platforms in 4000 Chinese adults

Proteomics offers unique insights into human biology and drug development, but few studies have directly compared the utility of different proteomics platforms. We measured plasma levels of 2168 proteins in 3976 Chinese adults using both Olink Explore and SomaScan platforms. The correlation of protein levels between platforms was modest (median rho = 0.29), with protein abundance and data quality parameters being key factors influencing correlation. For 1694 proteins with one-to-one matched reagents, 765 Olink and 513 SomaScan proteins had cis-pQTLs, including 400 with colocalising cis-pQTLs. Moreover, 1096 Olink and 1429 SomaScan proteins were associated with BMI, while 279 and 154 proteins were associated with risk of ischaemic heart disease, respectively. Addition of Olink and SomaScan proteins to conventional risk factors for ischaemic heart disease improved C-statistics from 0.845 to 0.862 (NRI: 12.2%) and 0.863 (NRI: 16.4%), respectively. These results demonstrate the utility of these platforms and could inform the design and interpretation of future studies.

White-Matter Structural Connectivity and Alzheimer's Disease: A Mendelian Randomization Study

BACKGROUND

Alzheimer's disease (AD) and white-matter structural connectivity have been linked in some observational studies, although it is unknown if this is a causal relationship. The purpose of this study was to examine the impact of various white-matter structural connectivity on AD via a two-sample multivariate Mendelian randomization (MR) approach.

METHODS

The genome-wide association study (GWAS) of Wainberg et al. provided the summary data on white-matter structural connectivity, and Bellenguez et al.'s study provided the GWAS aggregated data for AD. MR methods included inverse variance weighted, Mendelian randomization Egger, simple mode, weighted median, and weighted mode. Heterogeneity, horizontal pleiotropy, and "leave-one-out" analysis guaranteed the robustness of causation. Finally, reverse MR analysis was conducted on the white-matter structural connectivity that showed positive results in the forward MR analysis.

RESULTS

Among 206 white-matter structural connections, we identified 10 connections were strongly correlated with genetic susceptibility to AD. Right-hemisphere limbic network to thalamus white-matter structural connectivity and Right-hemisphere salience_ventral attention network to accumbens white-matter structural connectivity were positively correlated with the likelihood of AD, while the remaining 8 white-matter structural connections were negatively related with AD. None of the above 10 white-matter structural connections have a reverse causal relationship with AD.

CONCLUSION

Our MR study reveals a certain degree of association between white-matter structural connectivity and AD, which may provide support for future diagnosis and treatment of AD.

Polygenic risk scores for pan-cancer risk prediction in the Chinese population: A population-based cohort study based on the China Kadoorie Biobank

BACKGROUND

Polygenic risk scores (PRSs) have been extensively developed for cancer risk prediction in European populations, but their effectiveness in the Chinese population remains uncertain.

METHODS AND FINDINGS

We constructed 80 PRSs for the 13 most common cancers using seven schemes and evaluated these PRSs in 100,219 participants from the China Kadoorie Biobank (CKB). The optimal PRSs with the highest discriminatory ability were used to define genetic risk, and their site-specific and cross-cancer associations were assessed. We modeled 10-year absolute risk trajectories for each cancer across risk strata defined by PRSs and modifiable risk scores and quantified the explained relative risk (ERR) of PRSs with modifiable risk factors for different cancers. More than 60% (50/80) of the PRSs demonstrated significant associations with the corresponding cancer outcomes. Optimal PRSs for nine common cancers were identified, with each standard deviation increase significantly associated with corresponding cancer risk (hazard ratios (HRs) ranging from 1.20 to 1.76). Compared with participants at low genetic risk and reduced modifiable risk scores, those with high genetic risk and elevated modifiable risk scores had the highest risk of incident cancer, with HRs ranging from 1.97 (95% confidence interval (CI): 1.11-3.48 for cervical cancer, P = 0.020) to 8.26 (95% CI: 1.92-35.46 for prostate cancer, P = 0.005). We observed nine significant cross-cancer associations for PRSs and found the integration of PRSs significantly increased the prediction accuracy for most cancers. The PRSs contributed 2.6%-20.3%, while modifiable risk factors explained 2.3%-16.7% of the ERR in the Chinese population.

CONCLUSIONS

The integration of existing evidence has facilitated the development of PRSs associated with nine common cancer risks in the Chinese population, potentially improving clinical risk assessment.

Efficient storage and regression computation for population-scale genome sequencing studies

In the era of big data in human genetics, large-scale biobanks aggregating genetic data from diverse populations have emerged as important for advancing our understanding of human health and disease. However, the computational and storage demands of whole genome sequencing (WGS) studies pose significant challenges, especially for researchers from underfunded institutions or developing countries, creating a disparity in research capabilities. We introduce new approaches that significantly enhance computational efficiency and reduce data storage requirements for WGS studies. By developing algorithms for compressed storage of genetic data, focusing particularly on optimizing the representation of rare variants, and designing regression methods tailored for the scale and complexity of WGS data, we significantly lower computational and storage costs. We integrate our approach into PLINK 2.0. The implementation demonstrates considerable reductions in storage space and computational time without compromising analytical accuracy, as evidenced by the application to the AllofUs project data. We optimized the runtime of an exome-wide association analysis involving 19.4 million variants and the body mass index phenotype of 125,077 individuals, reducing it from 695.35 minutes (approximately 11.5 hours) on a single machine to just 1.57 minutes using 30 GB of memory and 50 threads (or 8.67 minutes with 4 threads). Additionally, we extended this approach to support multi-phenotype analyses. We anticipate that our approach will enable researchers across the globe to unlock the potential of population biobanks, accelerating the pace of discoveries that can improve our understanding of human health and disease.

Genomic sequencing: the case for equity of care in the era of personalized medicine

Over the past two decades, genomic sequencing (exome and genome) has proven to be critical in providing a faster and more accurate diagnosis as well as tailored treatment plans for a variety of populations. Despite its potential, disparities in access to genomic sequencing persist, predominantly among underrepresented and socioeconomically disadvantaged groups and populations. This inequity stems from factors such as: 1) high costs of sequencing, 2) significant gaps in insurance coverage, 3) limited availability of genetic services in many healthcare institutions and geographic areas, and 4) lack of diversity in genetic research and databases. Addressing these barriers is essential to realizing the full benefits of personalized treatment approaches for all individuals. By doing so, healthcare systems can move towards a more inclusive model that delivers optimal care for everyone. This manuscript emphasizes these issues by considering diverse perspectives from various ethnic groups, summarizing findings across different patient populations (adults, pediatrics, critical/non-critical care), and highlighting the importance of collaboration and workforce training in genomic sequencing and interpretation. IMPACT: Presentation of exemplary studies demonstrating the advantages of genomic sequencing in various clinical settings and a variety of high-risk populations. Review of obstacles in providing equitable genomic medical care and the importance of national and international collaborations An overview of the ethical aspects of genomic sequencing is provided.

Genome-wide association studies in a large Korean cohort identify novel quantitative trait loci for 36 traits and illuminate their genetic architectures

Genome-wide association studies (GWAS) have been predominantly conducted in populations of European ancestry, limiting opportunities for biological discovery in diverse populations. We report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 301 novel genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 4,588 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for a diverse set of traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.

Introduction to Mendelian randomization

Mendelian randomization (MR), i.e. instrumental variable analysis using genetic instruments, is an approach that incorporates population genetics to improve causal inference. Given that genetics are randomly allocated at conception, this resembles the randomization process in randomized controlled trials and hence is more resistant to unobserved confounding compared to conventional observational studies (e.g. cohort studies). The seminar paper briefly described the origin of MR and its underlying assumptions (relevance, independence, and exclusion restriction). This was followed by introducing one sample MR designs (in which instrument-exposure and instrument-outcome associations are derived from the same sample) and one sample MR design (in which instrument-exposure and instrument-outcome associations are derived from different samples). The seminar paper then summarized key aspects of MR studies, such as instrument selection, data sources for conducting MR studies, and statistical analyses. Variations of MR design were also introduced, such as how this design can inform the effect of drug targets (drug target MR). The STROBE-MR checklist and relevant MR guidelines were introduced. The seminar paper concluded by discussing the credibility crisis of MR studies.

Review on GPU accelerated methods for genome-wide SNP-SNP interactions

Detecting genome-wide SNP-SNP interactions (epistasis) efficiently is essential to harnessing the vast data now available from modern biobanks. With millions of SNPs and genetic information from hundreds of thousands of individuals, researchers are positioned to uncover new insights into complex disease pathways. However, this data scale brings significant computational and statistical challenges. To address these, recent approaches leverage GPU-based parallel computing for high-throughput, cost-effective analysis and refine algorithms to improve time and memory efficiency. In this survey, we systematically review GPU-accelerated methods for exhaustive epistasis detection, detailing the statistical models used and the computational strategies employed to enhance performance. Our findings indicate substantial speedups with GPU implementations over traditional CPU approaches. We conclude that while GPU-based solutions hold promise for advancing genomic research, continued innovation in both algorithm design and hardware optimization is necessary to meet future data challenges in the field.

Genomic insights into Neolithic founding paternal lineages around the Qinghai-Xizang Plateau using integrated YanHuang resource

Indigenous populations of the Qinghai-Xizang Plateau exhibit unique high-altitude adaptations, especially within Tibeto-Burman (TB) groups. However, the paternal genetic heritage of eastern Plateau regions remains less explored. We present one integrative Y chromosome dataset of 9,901 modern and ancient individuals, including whole Y chromosome sequences from 1,297 individuals and extensive Y-SNP/STR genotype data. We reveal the Paleolithic common origin and following divergence of Qinghai-Xizang Plateau ancestors from East Asian lowlands, marked by subsequent isolation and Holocene expansion involving local hunter-gatherers and millet-farming communities. We identified two key TB-related founding lineages, D-Z31591 and O-CTS4658, which underwent significant expansions around 5,000 years ago on the Qinghai-Xizang Plateau and its eastern Tibetan-Yi Corridor. The genetic legacy of these TB lineages highlights crucial migration pathways linking the Plateau and lowland southwestern China. Our findings align paternal genetic structures with East Asian geography and linguistic groups, underscoring the utility of Y chromosome analyses in unraveling complex paternal histories.

ICMR-MDRF Diabetes Biosamples: Cohort profile

Background & objectives Biobanks are crucial for biomedical research, enabling new treatments and medical advancements. The biobank at the Madras Diabetes Research Foundation (MDRF) aims to gather, process, store, and distribute biospecimens to assist scientific studies. Methods This article details the profile of two cohorts: the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study and the Registry of people with diabetes in India with young age at onset (ICMR-YDR). The ICMR-INDIAB study is the largest epidemiological study on diabetes in India, encompassing a nationally representative sample of individuals aged 20 yr and older from urban and rural areas in every State across the country. The ICMR-YDR is the first national-level, multicentric clinic-based registry focusing on youth-onset diabetes in India, aiming to understand the disease patterns and variations in youth-onset diabetes across different country regions. Results Key operations at the MDRF biobank include collecting and processing samples, where serum and whole blood samples are aliquoted and transferred through a cold chain to the central laboratory, and then stored in Siruseri (29 km from the capital city of Chennai, Tamil Nadu). Samples are barcoded, linked to subject information, and stored in freezers or liquid nitrogen (LN2) vessels, with inventory tracked via software for easy retrieval. A register records access to the biobank, ensuring sample integrity and compliance with regulatory requirements. The biobank adheres to the ICMR's National Ethical Guidelines for Biomedical and Health Research involving human participants. Interpretation & conclusions The biobank enables the analysis of biomarkers in stored samples, aiding in scientifically sound decisions, treating patients, and potentially curing diabetes.

Proteo-genomic analyses in relatively lean Chinese adults identify proteins and pathways that affect general and central adiposity levels

Adiposity is an established risk factor for multiple diseases, but the causal relationships of different adiposity types with circulating protein biomarkers have not been systematically investigated. We examine the causal associations of general and central adiposity with 2923 plasma proteins among 3977 Chinese adults (mean BMI = 23.9 kg/m²). Genetically-predicted body mass index (BMI), body fat percentage (BF%), waist circumference (WC), and waist-to-hip ratio (WHR) are significantly (FDR < 0.05) associated with 399, 239, 436, and 283 proteins, respectively, with 80 proteins associated with all four and 275 with only one adiposity trait. WHR is associated with the most proteins (n = 90) after adjusting for other adiposity traits. These associations are largely replicated in Europeans (mean BMI = 27.4 kg/m²). Two-sample Mendelian randomisation (MR) analyses in East Asians using cis-protein quantitative trait locus (cis-pQTLs) identified in GWAS find 30/2 proteins significantly affect levels of BMI/WC, respectively, with 10 showing evidence of colocalisation, and seven (inter-alpha-trypsin inhibitor heavy chain H3, complement factor B, EGF-containing fibulin-like extracellular matrix protein 1, thioredoxin domain-containing protein 15, alpha-2-antiplasmin, fibronectin, mimecan) are replicated in separate MR using different cis-pQTLs identified in Europeans. These findings identified potential novel mechanisms and targets, to our knowledge, for improved treatment and prevention of obesity and associated diseases.

Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations

BACKGROUND

The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.

OBJECTIVE AND RATIONALE

This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.

SEARCH METHODS

We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.

OUTCOMES

The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.

WIDER IMPLICATIONS

The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

Joint impact of polygenic risk score and lifestyles on early- and late-onset cardiovascular diseases

Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.

Big data in sarcoidosis

PURPOSE OF REVIEW

This review provides an overview of recent advancements in sarcoidosis research, focusing on collaborative networks, phenotype characterization, and molecular studies. It highlights the importance of collaborative efforts, phenotype characterization, and the integration of multilevel molecular data for advancing sarcoidosis research and paving the way toward personalized medicine.

RECENT FINDINGS

Sarcoidosis exhibits heterogeneous clinical manifestations influenced by various factors. Efforts to define sarcoidosis endophenotypes show promise, while technological advancements enable extensive molecular data generation. Collaborative networks and biobanks facilitate large-scale studies, enhancing biomarker discovery and therapeutic protocols.

SUMMARY

Sarcoidosis presents a complex challenge due to its unknown cause and heterogeneous clinical manifestations. Collaborative networks, comprehensive phenotype delineation, and the utilization of cutting-edge technologies are essential for advancing our understanding of sarcoidosis biology and developing personalized medicine approaches. Leveraging large-scale epidemiological resources and biobanks and integrating multilevel molecular data offer promising avenues for unraveling the disease's heterogeneity and improving patient outcomes.

Promoting equity in polygenic risk assessment through global collaboration

The long delay before genomic technologies become available in low- and middle-income countries is a concern from both scientific and ethical standpoints. Polygenic risk scores (PRSs), a relatively recent advance in genomics, could have a substantial impact on promoting health by improving disease risk prediction and guiding preventive strategies. However, clinical use of PRSs in their current forms might widen global health disparities, as their portability to diverse groups is limited. This Perspective highlights the need for global collaboration to develop and implement PRSs that perform equitably across the world. Such collaboration requires capacity building and the generation of new data in low-resource settings, the sharing of harmonized genotype and phenotype data securely across borders, novel population genetics and statistical methods to improve PRS performance, and thoughtful clinical implementation in diverse settings. All this needs to occur while considering the ethical, legal and social implications, with support from regulatory and funding agencies and policymakers.

Parkinson's disease and the risk of gastrointestinal cancers: a two-sample Mendelian randomization study

Background

The association between Parkinson's disease (PD) and gastrointestinal (GI) cancers remains unknown. This study aims to assess the causal effect of PD on colon cancer (CC), gastric cancer (GC), esophageal cancer (EC), and rectal cancer (RC) using the two-sample Mendelian randomization (MR) method.

Methods

Five pairs of summary datasets of genome-wide association studies (GWAS) from publicly available studies [Integrative Epidemiology Unit (IEU) OpenGWAS project, FinnGen, and GWAS Catalog database] were enrolled. The inverse variance weighted (IVW) method was used as the primary outcome for MR analysis. Cochran Q-derived, MR-Egger intercept test, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) methods, and leave-one-out analysis were used to test heterogeneity and directional pleiotropy.

Results

For the European population, no significant causal effect of PD on the risk of CC was found [odds ratio (OR) =0.9; 95%, confidence interval (CI): 1.00-1.11; P=0.42]. The same applied to the East Asian population (OR =1.05; 95% CI: 0.66-1.66; P=0.63). As for GC, no causal effect was found (OR =0.94, 95% CI: 0.89-0.99; P=0.22). Moreover, the genetic liability for PD was not associated with EC (OR =1.00; 95% CI: 0.99-1.00; P=0.32). Finally, no evidence was found for any causal effect of genetic liability for PD on an increased risk of RC (OR =1.00; 95% CI: 0.99-1.00; P=0.71).

Conclusions

There is no causal effect of genetic liability for PD on an increased risk of GI cancer.

Conventional and genetic associations of BMI with major vascular and non-vascular disease incidence and mortality in a relatively lean Chinese population: U-shaped relationship revisited

BACKGROUND

Higher body mass index (BMI) is associated with higher incidence of cardiovascular and some non-cardiovascular diseases (CVDs/non-CVDs). However, uncertainty remains about its associations with mortality, particularly at lower BMI levels.

METHODS

The prospective China Kadoorie Biobank recruited >512 000 adults aged 30-79 years in 2004-08 and genotyped a random subset of 76 000 participants. In conventional and Mendelian randomization (MR) analyses, Cox regression yielded adjusted hazard ratios (HRs) associating measured and genetically predicted BMI levels with incident risks of major vascular events (MVEs; conventional/MR 68 431/23 621), ischaemic heart disease (IHD; 50 698/12 177), ischaemic stroke (IS; 42 427/11 897) and intracerebral haemorrhage (ICH; 7644/4712), and with mortality risks of CVD (15 427/6781), non-CVD (26 915/4355) and all causes (42 342/6784), recorded during ∼12 years of follow-up.

RESULTS

Overall, the mean BMI was 23.8 (standard deviation: 3.2) kg/m2 and 13% had BMIs of <20 kg/m2. Measured and genetically predicted BMI showed positive log-linear associations with MVE, IHD and IS, but a shallower positive association with ICH in conventional analyses. Adjusted HRs per 5 kg/m2 higher genetically predicted BMI were 1.50 (95% CI 1.41-1.58), 1.49 (1.38-1.61), 1.42 (1.31-1.54) and 1.64 (1.58-1.69) for MVE, IHD, IS and ICH, respectively. These were stronger than associations in conventional analyses [1.21 (1.20-1.23), 1.28 (1.26-1.29), 1.31 (1.29-1.33) and 1.14 (1.10-1.18), respectively]. At BMIs of ≥20 kg/m2, there were stronger positive log-linear associations of BMI with CVD, non-CVD and all-cause mortality in MR than in conventional analyses.

CONCLUSIONS

Among relatively lean Chinese adults, higher genetically predicted BMI was associated with higher risks of incident CVDs. Excess mortality risks at lower BMI in conventional analyses are likely not causal and may reflect residual reverse causality.

gwid: an R package and Shiny application for Genome-Wide analysis of IBD data

Summary

Genome-wide identity by descent (gwid) is an R package developed for the analysis of identity-by-descent (IBD) data pertaining to dichotomous traits. This package offers a set of tools to assess differential IBD levels for the two states of a binary trait, yielding informative and meaningful results. Furthermore, it provides convenient functions to visualize the outcomes of these analyses, enhancing the interpretability and accessibility of the results. To assess the performance of the package, we conducted an evaluation using real genotype data derived from the SNPs to investigate rheumatoid arthritis susceptibility from the Marshfield Clinic Personalized Medicine Research Project.

Availability and implementation

gwid is available as an open-source R package. Release versions can be accessed on CRAN (https://cran.r-project.org/package=gwid) for all major operating systems. The development version is maintained on GitHub (https://github.com/soroushmdg/gwid) and full documentation with examples and workflow templates is provided via the package website (http://tinyurl.com/gwid-tutorial). An interactive R Shiny dashboard is also developed (https://tinyurl.com/gwid-shiny).

Causal relevance of different blood pressure traits on risk of cardiovascular diseases: GWAS and Mendelian randomisation in 100,000 Chinese adults

Elevated blood pressure (BP) is major risk factor for cardiovascular diseases (CVD). Genome-wide association studies (GWAS) conducted predominantly in populations of European ancestry have identified >2,000 BP-associated loci, but other ancestries have been less well-studied. We conducted GWAS of systolic, diastolic, pulse, and mean arterial BP in 100,453 Chinese adults. We identified 128 non-overlapping loci associated with one or more BP traits, including 74 newly-reported associations. Despite strong genetic correlations between populations, we identified appreciably higher heritability and larger variant effect sizes in Chinese compared with European or Japanese ancestry populations. Using instruments derived from these GWAS, multivariable Mendelian randomisation demonstrated that BP traits contribute differently to the causal associations of BP with CVD. In particular, only pulse pressure was independently causally associated with carotid plaque. These findings reinforce the need for studies in diverse populations to understand the genetic determinants of BP traits and their roles in disease risk.

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

PCSK9 genetic variants and risk of vascular and non-vascular diseases in Chinese and UK populations

AIMS

Lowering low-density lipoprotein cholesterol (LDL-C) through PCSK9 inhibition represents a new therapeutic approach to preventing and treating cardiovascular disease (CVD). Phenome-wide analyses of PCSK9 genetic variants in large biobanks can help to identify unexpected effects of PCSK9 inhibition.

METHODS AND RESULTS

In the prospective China Kadoorie Biobank, we constructed a genetic score using three variants at the PCSK9 locus associated with directly measured LDL-C [PCSK9 genetic score (PCSK9-GS)]. Logistic regression gave estimated odds ratios (ORs) for PCSK9-GS associations with CVD and non-CVD outcomes, scaled to 1 SD lower LDL-C. PCSK9-GS was associated with lower risks of carotid plaque [n = 8340 cases; OR = 0.61 (95% confidence interval: 0.45-0.83); P = 0.0015], major occlusive vascular events [n = 15 752; 0.80 (0.67-0.95); P = 0.011], and ischaemic stroke [n = 11 467; 0.80 (0.66-0.98); P = 0.029]. However, PCSK9-GS was also associated with higher risk of hospitalization with chronic obstructive pulmonary disease [COPD: n = 6836; 1.38 (1.08-1.76); P = 0.0089] and with even higher risk of fatal exacerbations amongst individuals with pre-existing COPD [n = 730; 3.61 (1.71-7.60); P = 7.3 × 10-4]. We also replicated associations for a PCSK9 variant, reported in UK Biobank, with increased risks of acute upper respiratory tract infection (URTI) [pooled OR after meta-analysis of 1.87 (1.38-2.54); P = 5.4 × 10-5] and self-reported asthma [pooled OR of 1.17 (1.04-1.30); P = 0.0071]. There was no association of a polygenic LDL-C score with COPD hospitalization, COPD exacerbation, or URTI.

CONCLUSION

The LDL-C-lowering PCSK9 genetic variants are associated with lower risk of subclinical and clinical atherosclerotic vascular disease but higher risks of respiratory diseases. Pharmacovigilance studies may be required to monitor patients treated with therapeutic PCSK9 inhibitors for exacerbations of respiratory diseases or respiratory tract infections.

LAY SUMMARY

Genetic analyses of over 100 000 participants of the China Kadoorie Biobank, mimicking the effect of new drugs intended to reduce cholesterol by targeting the PCSK9 protein, have identified potential severe effects of lower PCSK9 activity in patients with existing respiratory disease.PCSK9 genetic variants that are associated with lower cholesterol and reduced rates of cardiovascular disease are also associated with increased risk of a range of respiratory diseases, including asthma, upper respiratory tract infections, and hospitalization with chronic obstructive pulmonary disease (COPD).These genetic variants are not associated with whether or not individuals have COPD; instead, they are specifically associated with an increase in the chance of those who already have COPD being hospitalized and even dying, suggesting that careful monitoring of such patients should be considered during development of and treatment with anti-PCSK9 medication.

A genome-wide association study based on the China Kadoorie Biobank identifies genetic associations between snoring and cardiometabolic traits

Despite the high prevalence of snoring in Asia, little is known about the genetic etiology of snoring and its causal relationships with cardiometabolic traits. Based on 100,626 Chinese individuals, a genome-wide association study on snoring was conducted. Four novel loci were identified for snoring traits mapped on SLC25A21, the intergenic region of WDR11 and FGFR, NAA25, ALDH2, and VTI1A, respectively. The novel loci highlighted the roles of structural abnormality of the upper airway and craniofacial region and dysfunction of metabolic and transport systems in the development of snoring. In the two-sample bi-directional Mendelian randomization analysis, higher body mass index, weight, and elevated blood pressure were causal for snoring, and a reverse causal effect was observed between snoring and diastolic blood pressure. Altogether, our results revealed the possible etiology of snoring in China and indicated that managing cardiometabolic health was essential to snoring prevention, and hypertension should be considered among snorers.

Associations between type 2 diabetes mellitus and chronic liver diseases: evidence from a Mendelian randomization study in Europeans and East Asians

Objective

Multiple observational studies have demonstrated an association between type 2 diabetes mellitus (T2DM) and chronic liver diseases (CLDs). However, the causality of T2DM on CLDs remained unknown in various ethnic groups.

Methods

We obtained instrumental variables for T2DM and conducted a two-sample mendelian randomization (MR) study to examine the causal effect on nonalcoholic fatty liver disease (NAFLD), hepatocellular carcinoma (HCC), viral hepatitis, hepatitis B virus (HBV) infection, and hepatitis C virus (HCV) infection risk in Europeans and East Asians. The primary analysis utilized the inverse variance weighting (IVW) technique to evaluate the causal relationship between T2DM and CLDs. In addition, we conducted a series of rigorous analyses to bolster the reliability of our MR results.

Results

In Europeans, we found that genetic liability to T2DM has been linked with increased risk of NAFLD (IVW : OR =1.3654, 95% confidence interval [CI], 1.2250-1.5219, p=1.85e-8), viral hepatitis (IVW : OR =1.1173, 95%CI, 1.0271-1.2154, p=0.0098), and a suggestive positive association between T2DM and HCC (IVW : OR=1.2671, 95%CI, 1.0471-1.5333, p=0.0150), HBV (IVW : OR=1.1908, 95% CI, 1.0368-1.3677, p=0.0134). No causal association between T2DM and HCV was discovered. Among East Asians, however, there was a significant inverse association between T2DM and the proxies of NAFLD (ALT: IVW OR=0.9752, 95%CI 0.9597-0.9909, p=0.0021; AST: IVW OR=0.9673, 95%CI, 0.9528-0.9821, p=1.67e-5), and HCV (IVW: OR=0.9289, 95%CI, 0.8852-0.9747, p=0.0027). Notably, no causal association was found between T2DM and HCC, viral hepatitis, or HBV.

Conclusion

Our MR analysis revealed varying causal associations between T2DM and CLDs in East Asians and Europeans. Further research is required to investigate the potential mechanisms in various ethnic groups, which could yield new insights into early screening and prevention strategies for CLDs in T2DM patients.

PGS-Depot: a comprehensive resource for polygenic scores constructed by summary statistics based methods

Polygenic score (PGS) is an important tool for the genetic prediction of complex traits. However, there are currently no resources providing comprehensive PGSs computed from published summary statistics, and it is difficult to implement and run different PGS methods due to the complexity of their pipelines and parameter settings. To address these issues, we introduce a new resource called PGS-Depot containing the most comprehensive set of publicly available disease-related GWAS summary statistics. PGS-Depot includes 5585 high quality summary statistics (1933 quantitative and 3652 binary trait statistics) curated from 1564 traits in European and East Asian populations. A standardized best-practice pipeline is used to implement 11 summary statistics-based PGS methods, each with different model assumptions and estimation procedures. The prediction performance of each method can be compared for both in- and cross-ancestry populations, and users can also submit their own summary statistics to obtain custom PGS with the available methods. Other features include searching for PGSs by trait name, publication, cohort information, population, or the MeSH ontology tree and searching for trait descriptions with the experimental factor ontology (EFO). All scores, SNP effect sizes and summary statistics can be downloaded via FTP. PGS-Depot is freely available at http://www.pgsdepot.net.

Harnessing deep learning for population genetic inference

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

MTHFR and risk of stroke and heart disease in a low-folate population: a prospective study of 156 000 Chinese adults

BACKGROUND

The relevance of folic acid for stroke prevention in low-folate populations such as in China is uncertain. Genetic studies of the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism, which increases plasma homocysteine (tHcy) levels, could clarify the causal relevance of elevated tHcy levels for stroke, ischaemic heart disease (IHD) and other diseases in populations without folic acid fortification.

METHODS

In the prospective China Kadoorie Biobank, 156 253 participants were genotyped for MTHFR and 12 240 developed a stroke during the 12-year follow-up. Logistic regression was used to estimate region-specific odds ratios (ORs) for total stroke and stroke types, IHD and other diseases comparing TT genotype for MTHFR C677T (two thymine alleles at position 677 of MTHFR C677T polymorphism) vs CC (two cytosine alleles) after adjustment for age and sex, and these were combined using inverse-variance weighting.

RESULTS

Overall, 21% of participants had TT genotypes, but this varied from 5% to 41% across the 10 study regions. Individuals with TT genotypes had 13% (adjusted OR 1.13, 95% CI 1.09-1.17) higher risks of any stroke [with a 2-fold stronger association with intracerebral haemorrhage (1.24, 1.17-1.32) than for ischaemic stroke (1.11, 1.07-1.15)] than the reference CC genotype. In contrast, MTHFR C677T was unrelated to risk of IHD or any other non-vascular diseases, including cancer, diabetes and chronic obstructive lung disease.

CONCLUSIONS

In Chinese adults, the MTHFR C677T polymorphism was associated with higher risks of stroke. The findings warrant corroboration by further trials of folic acid and implementation of mandatory folic acid fortification programmes for stroke prevention in low-folate populations.

Alcohol intake and cause-specific mortality: conventional and genetic evidence in a prospective cohort study of 512 000 adults in China

BACKGROUND

Genetic variants that affect alcohol use in East Asian populations could help assess the causal effects of alcohol consumption on cause-specific mortality. We aimed to investigate the associations between alcohol intake and cause-specific mortality using conventional and genetic epidemiological methods among more than 512 000 adults in China.

METHODS

The prospective China Kadoorie Biobank cohort study enrolled 512 724 adults (210 205 men and 302 519 women) aged 30-79 years, during 2004-08. Residents with no major disabilities from ten diverse urban and rural areas of China were invited to participate, and alcohol use was self-reported. During 12 years of follow-up, 56 550 deaths were recorded through linkage to death registries, including 23 457 deaths among 168 050 participants genotyped for ALDH2-rs671 and ADH1B-rs1229984. Adjusted hazard ratios (HRs) for cause-specific mortality by self-reported and genotype-predicted alcohol intake were estimated using Cox regression.

FINDINGS

33% of men drank alcohol most weeks. In conventional observational analyses, ex-drinkers, non-drinkers, and heavy drinkers had higher risks of death from most major causes than moderate drinkers. Among current drinkers, each 100 g/week higher alcohol intake was associated with higher mortality risks from cancers (HR 1·18 [95% CI 1·14-1·22]), cardiovascular disease (CVD; HR 1·19 [1·15-1·24]), liver diseases (HR 1·51 [1·27-1·78]), non-medical causes (HR 1·15 [1·08-1·23]), and all causes (HR 1·18 [1·15-1·20]). In men, ALDH2-rs671 and ADH1B-rs1229984 genotypes predicted 60-fold differences in mean alcohol intake (4 g/week in the lowest group vs 255 g/week in the highest). Genotype-predicted alcohol intake was uniformly and positively associated with risks of death from all causes (n=12 939; HR 1·07 [95% CI 1·05-1·10]) and from pre-defined alcohol-related cancers (n=1274; 1·12 [1·04-1·21]), liver diseases (n=110; 1·31 [1·02-1·69]), and CVD (n=6109; 1·15 [1·10-1·19]), chiefly due to stroke (n=3285; 1·18 [1·12-1·24]) rather than ischaemic heart disease (n=2363; 1·06 [0·99-1·14]). Results were largely consistent using a polygenic score to predict alcohol intake, with higher intakes associated with higher risks of death from alcohol-related cancers, CVD, and all causes. Approximately 2% of women were current drinkers, and although power was low to assess observational associations of alcohol with mortality, the genetic evidence suggested that the excess risks in men were due to alcohol, not pleiotropy.

INTERPRETATION

Higher alcohol intake increased the risks of death overall and from major diseases for men in China. There was no genetic evidence of protection from moderate drinking for all-cause and cause-specific mortality, including CVD.

FUNDING

Kadoorie Charitable Foundation, National Natural Science Foundation of China, British Heart Foundation, Cancer Research UK, GlaxoSmithKline, Wellcome Trust, Medical Research Council, and Chinese Ministry of Science and Technology.

A high-resolution haplotype-resolved Reference panel constructed from the China Kadoorie Biobank Study

Precision medicine depends on high-accuracy individual-level genotype data. However, the whole-genome sequencing (WGS) is still not suitable for gigantic studies due to budget constraints. It is particularly important to construct highly accurate haplotype reference panel for genotype imputation. In this study, we used 10 000 samples with medium-depth WGS to construct a reference panel that we named the CKB reference panel. By imputing microarray datasets, it showed that the CKB panel outperformed compared panels in terms of both the number of well-imputed variants and imputation accuracy. In addition, we have completed the imputation of 100 706 microarrays with the CKB panel, and the after-imputed data is the hitherto largest whole genome data of the Chinese population. Furthermore, in the GWAS analysis of real phenotype height, the number of tested SNPs tripled and the number of significant SNPs doubled after imputation. Finally, we developed an online server for offering free genotype imputation service based on the CKB reference panel (https://db.cngb.org/imputation/). We believe that the CKB panel is of great value for imputing microarray or low-coverage genotype data of Chinese population, and potentially mixed populations. The imputation-completed 100 706 microarray data are enormous and precious resources of population genetic studies for complex traits and diseases.

Genetic influences on alcohol flushing in East Asian populations

BACKGROUND

Although it is known that variation in the aldehyde dehydrogenase 2 (ALDH2) gene family influences the East Asian alcohol flushing response, knowledge about other genetic variants that affect flushing symptoms is limited.

METHODS

We performed a genome-wide association study meta-analysis and heritability analysis of alcohol flushing in 15,105 males of East Asian ancestry (Koreans and Chinese) to identify genetic associations with alcohol flushing. We also evaluated whether self-reported flushing can be used as an instrumental variable for alcohol intake.

RESULTS

We identified variants in the region of ALDH2 strongly associated with alcohol flushing, replicating previous studies conducted in East Asian populations. Additionally, we identified variants in the alcohol dehydrogenase 1B (ADH1B) gene region associated with alcohol flushing. Several novel variants were identified after adjustment for the lead variants (ALDH2-rs671 and ADH1B-rs1229984), which need to be confirmed in larger studies. The estimated SNP-heritability on the liability scale was 13% (S.E. = 4%) for flushing, but the heritability estimate decreased to 6% (S.E. = 4%) when the effects of the lead variants were controlled for. Genetic instrumentation of higher alcohol intake using these variants recapitulated known associations of alcohol intake with hypertension. Using self-reported alcohol flushing as an instrument gave a similar association pattern of higher alcohol intake and cardiovascular disease-related traits (e.g. stroke).

CONCLUSION

This study confirms that ALDH2-rs671 and ADH1B-rs1229984 are associated with alcohol flushing in East Asian populations. Our findings also suggest that self-reported alcohol flushing can be used as an instrumental variable in future studies of alcohol consumption.

bubbleHeatmap: an R package for visualization of nightingale health metabolomics datasets

Summary

We present bubbleHeatmap, an R plotting package which combines elements of a bubble plot and heatmap to conveniently display two numerical variables for each data point across a categorical two dimensional grid. This has particular advantages for visualizing the 251 metabolomic measures produced by the automated, high-throughput, 1H-NMR-based platform provided by Nightingale Health, which includes 12 measures repeated across each of 14 lipoprotein subclasses. As these metabolomic profiles are currently available for large biobanks, we provide a figure template to aid the use of bubbleHeatmap in displaying results from analyses using these data.

Availability and implementation

https://cran.r-project.org/web/packages/bubbleHeatmap.