The Polygenic Score Catalog: new functionality and tools to enable FAIR research.

Polygenic scores (PGS) have transformed human genetic research and have multiple potential clinical applications, including risk stratification for disease prevention and prediction of treatment response. Here, we present a series of recent enhancements to the PGS Catalog (www.PGSCatalog.org), the largest findable, accessible, interoperable, and reusable (FAIR) repository of PGS. These include expansions in data content and ancestral diversity as well as the addition of new features. We further present the PGS Catalog Calculator (pgsc_calc, https://github.com/PGScatalog/pgsc_calc), an open-source, scalable and portable pipeline to reproducibly calculate PGS that securely democratizes equitable PGS applications by implementing genetic ancestry estimation and score normalization using reference data. With the PGS Catalog & calculator users can now quantify an individual's genetic predisposition for hundreds of common diseases and clinically relevant traits. Taken together, these updates and tools facilitate the next generation of PGS, thus lowering barriers to the clinical studies necessary to identify where PGS may be integrated into clinical practice.

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction.

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N~408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N~40,466), where genetically less variable individuals (low vPGS) had increased conventional PGS accuracy (by ~19%) than genetically more variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Integration of polygenic and gut metagenomic risk prediction for common diseases.

Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.

Large-scale releases and establishment of wMel Wolbachia in Aedes aegypti mosquitoes throughout the Cities of Bello, Medellín and Itagüí, Colombia.

BACKGROUND: The wMel strain of Wolbachia has been successfully introduced into Aedes aegypti mosquitoes and has been shown to reduce the transmission of dengue and other Aedes-borne viruses. Here we report the entomological results from phased, large-scale releases of Wolbachia infected Ae. aegypti mosquitoes throughout three contiguous cities located in the Aburrá Valley, Colombia. METHODOLOGY/PRINCIPAL FINDINGS: Local wMel Wolbachia-infected Ae. aegypti mosquitoes were generated and then released in an initial release pilot area in 2015-2016, which resulted in the establishment of Wolbachia in the local mosquito populations. Subsequent large-scale releases, mainly involving vehicle-based releases of adult mosquitoes along publicly accessible roads and streets, were undertaken across 29 comunas throughout Bello, Medellín and Itagüí Colombia between 2017-2022. In 9 comunas these were supplemented by egg releases that were undertaken by staff or community members. By the most recent monitoring, Wolbachia was found to be stable and established at consistent levels in local mosquito populations (>60% prevalence) in the majority (67%) of areas. CONCLUSION: These results, from the largest contiguous releases of wMel Wolbachia mosquitoes to date, highlight the operational feasibility of implementing the method in large urban settings. Based on results from previous studies, we expect that Wolbachia establishment will be sustained long term. Ongoing monitoring will confirm Wolbachia persistence in local mosquito populations and track its establishment in the remaining areas.

Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment.

Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.

An atlas of genetic scores to predict multi-omic traits.

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Differences in gene expression in field populations of Wolbachia-infected Aedes aegypti mosquitoes with varying release histories in northern Australia.

Aedes aegypti is the principal mosquito vector of dengue, yellow fever, Zika and chikungunya viruses. The wMel strain of the endosymbiotic bacteria Wolbachia pipientis was introduced into the vector as a novel biocontrol strategy to stop transmission of these viruses. Mosquitoes with Wolbachia have been released in the field in Northern Queensland, Australia since 2011, at various locations and over several years, with populations remaining stably infected. Wolbachia infection is known to alter gene expression in its mosquito host, but whether (and how) this changes over the long-term in the context of field releases remains unknown. We sampled mosquitoes from Wolbachia-infected populations with three different release histories along a time gradient and performed RNA-seq to investigate gene expression changes in the insect host. We observed a significant impact on gene expression in Wolbachia-infected mosquitoes versus uninfected controls. Fewer genes had significantly upregulated expression in mosquitoes from the older releases (512 and 486 from the 2011 and 2013/14 release years, respectively) versus the more recent releases (1154 from the 2017 release year). Nonetheless, a fundamental signature of Wolbachia infection on host gene expression was observed across all releases, comprising upregulation of immunity (e.g. leucine-rich repeats, CLIPs) and metabolism (e.g. lipid metabolism, iron transport) genes. There was limited downregulation of gene expression in mosquitoes from the older releases (84 and 71 genes from the 2011 and 2013/14 release years, respectively), but significantly more in the most recent release (509 from the 2017 release year). Our findings indicate that at > 8 years post-introgression into field populations, Wolbachia continues to profoundly impact expression of host genes, such as those involved in insect immune response and metabolism. If Wolbachia-mediated virus blocking is underpinned by these differential gene expression changes, our results suggest it may remain stable long-term.

Quality control and removal of technical variation of NMR metabolic biomarker data in ~120,000 UK Biobank participants.

Metabolic biomarker data quantified by nuclear magnetic resonance (NMR) spectroscopy in approximately 121,000 UK Biobank participants has recently been released as a community resource, comprising absolute concentrations and ratios of 249 circulating metabolites, lipids, and lipoprotein sub-fractions. Here we identify and characterise additional sources of unwanted technical variation influencing individual biomarkers in the data available to download from UK Biobank. These included sample preparation time, shipping plate well, spectrometer batch effects, drift over time within spectrometer, and outlier shipping plates. We developed a procedure for removing this unwanted technical variation, and demonstrate that it increases signal for genetic and epidemiological studies of the NMR metabolic biomarker data in UK Biobank. We subsequently developed an R package, ukbnmr, which we make available to the wider research community to enhance the utility of the UK Biobank NMR metabolic biomarker data and to facilitate rapid analysis.

Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction.

BACKGROUND: The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction. METHODS: The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared. RESULTS: Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%). CONCLUSIONS: These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.

Genetically personalised organ-specific metabolic models in health and disease.

Understanding how genetic variants influence disease risk and complex traits (variant-to-function) is one of the major challenges in human genetics. Here we present a model-driven framework to leverage human genome-scale metabolic networks to define how genetic variants affect biochemical reaction fluxes across major human tissues, including skeletal muscle, adipose, liver, brain and heart. As proof of concept, we build personalised organ-specific metabolic flux models for 524,615 individuals of the INTERVAL and UK Biobank cohorts and perform a fluxome-wide association study (FWAS) to identify 4312 associations between personalised flux values and the concentration of metabolites in blood. Furthermore, we apply FWAS to identify 92 metabolic fluxes associated with the risk of developing coronary artery disease, many of which are linked to processes previously described to play in role in the disease. Our work demonstrates that genetically personalised metabolic models can elucidate the downstream effects of genetic variants on biochemical reactions involved in common human diseases.

Substantial Fat Loss in Physique Competitors Is Characterized by Increased Levels of Bile Acids, Very-Long Chain Fatty Acids, and Oxylipins.

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects.

Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease.

For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

A literature review of dispersal pathways of Aedes albopictus across different spatial scales: implications for vector surveillance.

BACKGROUND: Aedes albopictus is a highly invasive species and an important vector of dengue and chikungunya viruses. Indigenous to Southeast Asia, Ae. albopictus has successfully invaded every inhabited continent, except Antarctica, in the past 80 years. Vector surveillance and control at points of entry (PoE) is the most critical front line of defence against the introduction of Ae. albopictus to new areas. Identifying the pathways by which Ae. albopictus are introduced is the key to implementing effective vector surveillance to rapidly detect introductions and to eliminate them. METHODS: A literature review was conducted to identify studies and data sources reporting the known and suspected dispersal pathways of human-mediated Ae. albopictus dispersal between 1940-2020. Studies and data sources reporting the first introduction of Ae. albopictus in a new country were selected for data extraction and analyses. RESULTS: Between 1940-2020, Ae. albopictus was reported via various dispersal pathways into 86 new countries. Two main dispersal pathways were identified: (1) at global and continental spatial scales, maritime sea transport was the main dispersal pathway for Ae. albopictus into new countries in the middle to late 20th Century, with ships carrying used tyres of particular importance during the 1980s and 1990s, and (2) at continental and national spatial scales, the passive transportation of Ae. albopictus in ground vehicles and to a lesser extent the trade of used tyres and maritime sea transport appear to be the major drivers of Ae. albopictus dispersal into new countries, especially in Europe. Finally, the dispersal pathways for the introduction and spread of Ae. albopictus in numerous countries remains unknown, especially from the 1990s onwards. CONCLUSIONS: This review identified the main known and suspected dispersal pathways of human-mediated Ae. albopictus dispersal leading to the first introduction of Ae. albopictus into new countries and highlighted gaps in our understanding of Ae. albopictus dispersal pathways. Relevant advances in vector surveillance and genomic tracking techniques are presented and discussed in the context of improving vector surveillance.

The Emergence of Japanese Encephalitis Virus in Australia in 2022: Existing Knowledge of Mosquito Vectors.

In early 2022, the Japanese encephalitis virus (JEV) was identified as the cause of stillborn and mummified piglets in pig farms in southeastern Australia. Human cases and additional pig farms with infected piglets were subsequently identified across a widespread area encompassing four states. To inform surveillance and control programs, we synthesized existing information on Australian vectors of JEV, much of which was generated in response to incursions of JEV into the northern state of Queensland between 1995 and 2005. Members of the Culex sitiens subgroup, particularly Culex annulirostris, should be considered the primary vectors of JEV in Australia, as they yielded >87% of field detections of JEV, were highly efficient laboratory vectors of the virus, readily fed on pigs and birds (the key amplifying hosts of the virus) when they were available, and are widespread and often occur in large populations. Three introduced species, Culex quinquefasciatus, Culex gelidus and Culex tritaeniorhynchus may also serve as vectors, but more information on their geographical distribution, abundance and bionomics in the Australian context is required. Mosquitoes from other genera, such as Aedes and Verrallina, whilst considered relatively poor vectors, could play a regional or supplemental role in transmission, especially facilitating vertical transmission as a virus overwintering mechanism. Additional factors that could impact JEV transmission, including mosquito survival, dispersal and genetics, are also discussed. Possible directions for investigation are provided, especially in the context of the virus emerging in a region with different mosquito fauna and environmental drivers than northern Australia.

Nucleic Acid Preservation Card Surveillance Is Effective for Monitoring Arbovirus Transmission on Crocodile Farms and Provides a One Health Benefit to Northern Australia.

The Kunjin strain of West Nile virus (WNVKUN) is a mosquito-transmitted flavivirus that can infect farmed saltwater crocodiles in Australia and cause skin lesions that devalue the hides of harvested animals. We implemented a surveillance system using honey-baited nucleic acid preservation cards to monitor WNVKUN and another endemic flavivirus pathogen, Murray Valley encephalitis virus (MVEV), on crocodile farms in northern Australia. The traps were set between February 2018 and July 2020 on three crocodile farms in Darwin (Northern Territory) and one in Cairns (North Queensland) at fortnightly intervals with reduced trapping during the winter months. WNVKUN RNA was detected on all three crocodile farms near Darwin, predominantly between March and May of each year. Two of the NT crocodile farms also yielded the detection of MVE viral RNA sporadically spread between April and November in 2018 and 2020. In contrast, no viral RNA was detected on crocodile farms in Cairns during the entire trapping period. The detection of WNVKUN and MVEV transmission by FTATM cards on farms in the Northern Territory generally correlated with the detection of their transmission to sentinel chicken flocks in nearby localities around Darwin as part of a separate public health surveillance program. While no isolates of WNVKUN or MVEV were obtained from mosquitoes collected on Darwin crocodile farms immediately following the FTATM card detections, we did isolate another flavivirus, Kokobera virus (KOKV), from Culex annulirostris mosquitoes. Our studies support the use of the FTATM card system as a sensitive and accurate method to monitor the transmission of WNVKUN and other arboviruses on crocodile farms to enable the timely implementation of mosquito control measures. Our detection of MVEV transmission and isolation of KOKV from mosquitoes also warrants further investigation of their potential role in causing diseases in crocodiles and highlights a "One Health" issue concerning arbovirus transmission to crocodile farm workers. In this context, the introduction of FTATM cards onto crocodile farms appears to provide an additional surveillance tool to detect arbovirus transmission in the Darwin region, allowing for a more timely intervention of vector control by relevant authorities.

A decade of stability for wMel Wolbachia in natural Aedes aegypti populations.

Mosquitoes carrying Wolbachia endosymbionts are being released in many countries for arbovirus control. The wMel strain of Wolbachia blocks Aedes-borne virus transmission and can spread throughout mosquito populations by inducing cytoplasmic incompatibility. Aedes aegypti mosquitoes carrying wMel were first released into the field in Cairns, Australia, over a decade ago, and with wider releases have resulted in the near elimination of local dengue transmission. The long-term stability of Wolbachia effects is critical for ongoing disease suppression, requiring tracking of phenotypic and genomic changes in Wolbachia infections following releases. We used a combination of field surveys, phenotypic assessments, and Wolbachia genome sequencing to show that wMel has remained stable in its effects for up to a decade in Australian Ae. aegypti populations. Phenotypic comparisons of wMel-infected and uninfected mosquitoes from near-field and long-term laboratory populations suggest limited changes in the effects of wMel on mosquito fitness. Treating mosquitoes with antibiotics used to cure the wMel infection had limited effects on fitness in the next generation, supporting the use of tetracycline for generating uninfected mosquitoes without off-target effects. wMel has a temporally stable within-host density and continues to induce complete cytoplasmic incompatibility. A comparison of wMel genomes from pre-release (2010) and nine years post-release (2020) populations show few genomic differences and little divergence between release locations, consistent with the lack of phenotypic changes. These results indicate that releases of Wolbachia-infected mosquitoes for population replacement are likely to be effective for many years, but ongoing monitoring remains important to track potential evolutionary changes.

Combined effects of host genetics and diet on human gut microbiota and incident disease in a single population cohort.

Human genetic variation affects the gut microbiota through a complex combination of environmental and host factors. Here we characterize genetic variations associated with microbial abundances in a single large-scale population-based cohort of 5,959 genotyped individuals with matched gut microbial metagenomes, and dietary and health records (prevalent and follow-up). We identified 567 independent SNP-taxon associations. Variants at the LCT locus associated with Bifidobacterium and other taxa, but they differed according to dairy intake. Furthermore, levels of Faecalicatena lactaris associated with ABO, and suggested preferential utilization of secreted blood antigens as energy source in the gut. Enterococcus faecalis levels associated with variants in the MED13L locus, which has been linked to colorectal cancer. Mendelian randomization analysis indicated a potential causal effect of Morganella on major depressive disorder, consistent with observational incident disease analysis. Overall, we identify and characterize the intricate nature of host-microbiota interactions and their association with disease.

Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease.

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%-23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases.

Cardiometabolic diseases are frequently polygenic in architecture, comprising a large number of risk alleles with small effects spread across the genome1-3. Polygenic scores (PGS) aggregate these into a metric representing an individual's genetic predisposition to disease. PGS have shown promise for early risk prediction4-7 and there is an open question as to whether PGS can also be used to understand disease biology8. Here, we demonstrate that cardiometabolic disease PGS can be used to elucidate the proteins underlying disease pathogenesis. In 3,087 healthy individuals, we found that PGS for coronary artery disease, type 2 diabetes, chronic kidney disease and ischaemic stroke are associated with the levels of 49 plasma proteins. Associations were polygenic in architecture, largely independent of cis and trans protein quantitative trait loci and present for proteins without quantitative trait loci. Over a follow-up of 7.7 years, 28 of these proteins associated with future myocardial infarction or type 2 diabetes events, 16 of which were mediators between polygenic risk and incident disease. Twelve of these were druggable targets with therapeutic potential. Our results demonstrate the potential for PGS to uncover causal disease biology and targets with therapeutic potential, including those that may be missed by approaches utilizing information at a single locus.