An atlas of genetic scores to predict multi-omic traits

Unravelling disease complexity: integrative analysis of multi-omic data in clinical research

INTRODUCTION

A holistic view on biological systems is today a reality with the application of multi-omic technologies. These technologies allow the profiling of genome, epigenome, transcriptome, proteome, metabolome as well as newly emerging 'omes.' While the multiple layers of data accumulate, their integration and reconciliation in a single system map is a cumbersome exercise that faces many challenges. Application to human health and disease requires large sample sizes, robust methodologies and high-quality standards.

AREAS COVERED

We review the different methods used to integrate multi-omics, as recent ones including artificial intelligence. With proteomics as an anchor technology, we then present selected applications of its data combination with other omics layers in clinical research, mainly covering literature from the last five years in the Scopus and/or PubMed databases.

EXPERT OPINION

Multi-omics is powerful to comprehensively type molecular layers and link them to phenotype. Yet, technologies and data are very diverse and still strategies and methodologies to properly integrate these modalities are needed.

Relationships among immune cells, metabolites, and non-small cell lung cancer: a mediation Mendelian randomization study

BACKGROUND

Immune cells and metabolites significantly impact organism health and disease processes. The interaction between non-small cell lung cancer (NSCLC) and metabolites and immune cells remains underexplored.

METHODS

To investigate the causal relationships between immune cells, metabolites, and NSCLC, we used Mendelian randomization (MR). The research design comprehensively embraced the direct correlation and the role that metabolites play as an intermediate in the interaction between NSCLC and immune cells.

RESULTS

MR analysis allowed us to finally identify one immune cell, there was a substantial causal connection between CD64 on CD14⁻CD16⁻ and NSCLC. Furthermore, we discovered four metabolites that showed strong causal links to NSCLC: 1-(1-enyl-stearoyl)-2-arachidonoyl-GPE (p-18:0/20:4) levels, phenyllactate (PLA) levels in elite athletes, the caffeine to paraxanthine ratio and 1-palmitoyl-2-arachidonoyl-GPC (16:0/20:4n6) levels. Finally, through a two-step MR mediation analysis, we found that CD64 on CD14⁻CD16⁻ mediated the occurrence of NSCLC via 1-palmitoleoyl-2-linolenoyl-GPC (16:1/18:3) levels and the caffeine to paraxanthine ratio, with mediation proportions of - 11.89% and 21.69%, respectively.

CONCLUSION

Our findings show the complicated link between immune cells, metabolites, and NSCLC. The discovered connections and mediating effects provide important insights.

Pancreatic cancer subtyping - the keystone of precision treatment

In recent years, the incidence and mortality rates of pancreatic cancer have been rising, posing a severe threat to human health. Tumor heterogeneity remains a critical barrier to advancing diagnosis and treatment efforts. The lack of specific early symptoms, limited early diagnostic methods, high biological complexity, and restricted therapeutic options contribute to the poor outcomes and prognosis of pancreatic cancer. Therefore, there is an urgent need to explore the different subtypes in-depth and develop personalized therapeutic strategies tailored to each subtype. Increasing evidence highlights the pivotal role of molecular subtyping in treating pancreatic cancer. This review focuses on recent advancements in classifying molecular subtypes and therapeutic approaches, discussed from the perspectives of gene mutations, genomics, transcriptomics, proteomics, metabolomics, and immunomics.

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

Elevated High-Density Lipoprotein Triglycerides Increase Atherosclerotic Risk

The relationship between high-density lipoprotein (HDL) and atherosclerotic risk remains incompletely elucidated, potentially due to the inherent heterogeneity of HDL particles. Hypertriglyceridemia is associated with alterations in HDL composition. This study investigated the impact of elevated triglycerides (TG) on HDL and its association with coronary artery disease (CAD) risk using a large prospective cohort study and Mendelian randomization (MR). We found that elevated TG associated with reduced HDL particle size, decreased concentrations of HDL components, and increased triglycerides in HDL (HDL-TG) (all P for trend < 0.001). The protective effects of HDL particle concentration and HDL cholesterol on CAD are attenuated with increasing serum TG levels. Independent and positive association between HDL-TG levels and incident CAD events (hazard ratio [HR] per 1 standard deviation increase: 1.066, 95% CI: 1.052-1.080, P<0.001) was confirmed even after adjustment for established cardiovascular diseases risk factors. MR analyses supported a causal role for HDL-TG in CAD development (inverse-variance weighted [IVW] method: odds ratios [ORs] of 1.120 (95% CI: 1.053-1.192, P<0.001) and 1.141 (95% CI: 1.032-1.263, P=0.010) for dataset groups 1 and 2, respectively). Drug-target MR analyses suggested a potential association between omega-3 fatty acids (OM3-FA) and lower HDL-TG levels, with LPL and DGAT2 as key pharmacological targets. Our findings suggest that elevated TG contribute to adverse alterations in HDL, elevating CAD risk. HDL-TG is an independent positive risk factor for CAD and a potential causal contributor to CAD development. OM3-FA supplementation may offer a therapeutic strategy for mitigating the CAD risk associated with elevated HDL-TG.

Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Oculomics: Current concepts and evidence

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.

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.

PennPRS: a centralized cloud computing platform for efficient polygenic risk score training in precision medicine

Polygenic risk scores (PRS) are becoming increasingly vital for risk prediction and stratification in precision medicine. However, PRS model training presents significant challenges for broader adoption of PRS, including limited access to computational resources, difficulties in implementing advanced PRS methods, and availability and privacy concerns over individual-level genetic data. Cloud computing provides a promising solution with centralized computing and data resources. Here we introduce PennPRS (https://pennprs.org), a scalable cloud computing platform for online PRS model training in precision medicine. We developed novel pseudo-training algorithms for multiple PRS methods and ensemble approaches, enabling model training without requiring individual-level data. These methods were rigorously validated through extensive simulations and large-scale real data analyses involving over 6,000 phenotypes across various data sources. PennPRS supports online single- and multi-ancestry PRS training with seven methods, allowing users to upload their own data or query from more than 27,000 datasets in the GWAS Catalog, submit jobs, and download trained PRS models. Additionally, we applied our pseudo-training pipeline to train PRS models for over 8,000 phenotypes and made their PRS weights publicly accessible. In summary, PennPRS provides a novel cloud computing solution to improve the accessibility of PRS applications and reduce disparities in computational resources for the global PRS research community.

Genome-scale models in human metabologenomics

Metabologenomics integrates metabolomics with other omics data types to comprehensively study the genetic and environmental factors that influence metabolism. These multi-omics data can be incorporated into genome-scale metabolic models (GEMs), which are highly curated knowledge bases that explicitly account for genes, transcripts, proteins and metabolites. By including all known biochemical reactions catalysed by enzymes and transporters encoded in the human genome, GEMs analyse and predict the behaviour of complex metabolic networks. Continued advancements to the scale and scope of GEMs - from cells and tissues to microbiomes and the whole body - have helped to design effective treatments and develop better diagnostic tools for metabolic diseases. Furthermore, increasing amounts of multi-omics data are incorporated into GEMs to better identify the underlying mechanisms, biomarkers and potential drug targets of metabolic diseases.

The X chromosome's influences on the human brain

Genes on the X chromosome are extensively expressed in the human brain. However, little is known for the X chromosome's impact on the brain anatomy, microstructure, and functional networks. We examined 1045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X chromosome interactions while proposing an atlas outlining dosage compensation for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we found unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.

Photoreceptor metabolic window unveils eye-body interactions

Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.

The common VTE-protective G haplotype of F5 increases factor V-short, TFPI function, and risk of bleeding

ABSTRACT

The G haplotype is a group of co-inherited single nucleotide variants in the F5 gene that reduce venous thromboembolism (VTE) risk. Although 7% of the population is homozygous for the G haplotype (F5-G/G), the underlying mechanism of VTE protection is poorly understood. Using RNA sequencing data from 4651 blood donors in the INTERVAL study, we detected a rare excision event at the factor V (FV)-short splice sites in 5% of F5-G/Gs carriers as compared with 2.16% of homozygotes for the F5 reference sequence (F5-ref; P = .003). Highly elevated (∼10-fold) FV-short, a FV isoform that lacks most of the B-domain, has been linked with increased tissue factor inhibitor α (TFPIα) levels in rare hemorrhagic diathesis, including East Texas bleeding disorder. To ascertain whether the enhanced FV-short splicing seen in F5-G/G INTERVAL participants translated to increased plasma FV-short levels, we analyzed plasma samples from 7 F5-G/G and 13 F5-ref individuals in a recall-by-genotype study. A ∼2.2-fold higher amount of FV-short was found in a plasma pool from F5-G/G participants when compared with the pool of F5-refs (P = .029), but there was no difference in the total FV levels. Although no significant difference in TFPI levels were found, F5-G/Gs showed a ∼1.4-fold TFPI-dependent increase in lag time to thrombin generation than F5-refs (P = .0085). Finally, in an analysis of 117 699 UK Biobank participants, we discovered that, although being protective against VTE, the G haplotype also confers an increase in bleeding episodes (P = .011). Our study provides evidence that the effect of the common G haplotype is mediated by the FV-short/TFPI pathway.

Polygenic Risk Scores in Human Disease

BACKGROUND

Polygenic risk scores (PRS) are measures of genetic susceptibility to human health traits. With the advent of large data repositories combining genetic data and phenotypic information, PRS are providing valuable insights into the genetic architecture of complex diseases and are transforming the landscape of precision medicine.

CONTENT

PRS have emerged as tools with clinical utility in human disease. Herein, details on how to develop PRS are provided, followed by 5 areas in which they can be used to improve human health: (a) augmenting risk prediction, (b) refining diagnosis, (c) guiding treatment choices, (d) making clinical trials more efficient, and (e) improving public health. Finally, some of the ongoing challenges to the clinical implementation of PRS are noted.

SUMMARY

PRS can offer valuable information for providers and patients, including identifying risk of disease earlier in life and before the onset of clinical risk factors, guiding treatment decisions, improving public health outcomes, and making clinical trials more efficient. The future of genomic-informed risk assessments of disease is through integrated risk models that combine genetic factors including PRS, monogenic, and somatic DNA information with nongenetic risk factors such as clinical risk estimators and multiomic data. However, adopting PRS in a clinical setting at scale faces some challenges, including cross-ancestry performance, standardization and calibration of risk models, downstream clinical decision-making from risk information, and seamless integration into existing health systems.

Proteomic analysis identifies novel biological pathways that may link dietary quality to type 2 diabetes risk: evidence from African American and Asian cohorts

BACKGROUND

Diet affects the development of chronic diseases such as type 2 diabetes, but the underlying biological mechanisms are only partly understood.

OBJECTIVES

This study aimed to identify proteomic markers of the Alternative Healthy Eating Index (AHEI) and the Dietary Approaches to Stop Hypertension (DASH) diet and their association with type 2 diabetes risk.

METHODS

We examined the associations between the AHEI and DASH diet quality scores and 1317 plasma proteins in African American participants of the Jackson Heart Study (JHS, n = 1878). These findings were validated in a Singapore Multi-Ethnic Cohort (n = 2395) and examined in relation to type 2 diabetes incidence (n = 539 cases). We adjusted for multiple testing by using false discovery rate-adjusted q values.

RESULTS

We identified 13 proteins consistently associated with the AHEI or DASH scores with the strongest associations for the AHEI score and epidermal growth factor receptor (β:0.089; SE: 0.017; q < 0.001) and for the DASH score and tissue factor (β: -0.114; SE: 0.022; q < 0.001). Most of these proteins were related to inflammation, thrombosis, adipogenesis, and glucose metabolism. Concentrations of myeloperoxidase, epidermal growth factor receptor, hepatocyte growth factor receptor, coagulation factor Xa, contactin 4, kynureninase, neurogenic locus notch homolog protein 1, and vesicular integral-membrane protein VIP36 were associated with the risk of type 2 diabetes in the Asian cohort. The diabetes odds ratio for a 2-fold higher protein abundance concentration ranged from 0.03 (95% CI: 0.01, 0.08) for neurogenic locus notch homolog protein 1 to 3.04 (95% CI: 2.13, 4.33) for kynureninase. Furthermore, genetic markers for myeloperoxidase and hepatocyte growth factor receptor were significantly associated with diabetes risk.

CONCLUSIONS

Our study across geographically and ethnically diverse populations identified robust protein biomarkers for healthy dietary patterns. Furthermore, our findings suggest novel biological mechanisms linking dietary patterns with type 2 diabetes development.

Clinical and Genomic Prediction of Coronary Artery Disease Subtypes

BACKGROUND

Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors.

METHODS

Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs).

RESULTS

Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort.

CONCLUSIONS

In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine.

Proteomic signatures of healthy dietary patterns are associated with lower risks of major chronic diseases and mortality

Healthy dietary patterns have been linked to a decreased risk of chronic diseases. However, it remains uncertain whether proteomic signatures can reflect proteome response to healthy diet patterns, and whether these proteomic signatures are associated with health outcomes. Using data from the UK Biobank including Olink plasma proteins, we identified substantial proteomic variation in relation to adherence to eight healthy dietary patterns. The proteomic signatures, reflecting adherence and proteome response to healthy dietary patterns, were prospectively associated with lower risks of diabetes, cardiovascular diseases, chronic respiratory diseases, chronic kidney diseases and cancers, along with longer life expectancy, even after adjusting for corresponding dietary patterns. These findings suggest proteomic signatures have the potential to complement traditional dietary assessments and deepen our understanding of the relationships between dietary patterns and chronic diseases.

Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle

BACKGROUND

Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging.

METHODS

We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis.

RESULTS

We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database.

CONCLUSIONS

Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle.

A large-scale multi-omics polygenic risk score analysis identified candidate risk locus associated with rheumatoid arthritis

OBJECTIVE

This study aimed to investigate the associations of multi-omics polygenic risk score (PRS) and rheumatoid arthritis (RA) to identify potential genes/proteins and biological pathways.

METHODS

Based on multi-omics data from 48,813 participants in the INTERVAL cohort, we calculated multi-omics PRS for 13,646 mRNAs (RNASeq), 308 proteins (Olink), 2380 proteins (SomaScan), 726 metabolites (Metabolon), and 141 metabolites (Nightingale). Using the generalized linear model, we first evaluated the associations between multi-omics PRS and RA in 58,813 UK Biobank participants. The Gene Ontology (GO) project and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to identify the functional pathways in RA. Furthermore, differential gene expression profile datasets were used to validate the identified genes/proteins in our study.

RESULTS

We identified 59 transcriptomics PRS and 29 proteomics PRS significantly associated with RA. Both proteomics and transcriptomic PRS identified HLA-DQA2 (RNASeq: OR=1.19, P=1.18×10-24; SomaScan: OR=1.24, P=4.43×10-27) and AGER (RNASeq: OR=0.91, P=4.18×10-4; SomaScan: OR=0.93, P=3.97×10-3) were significantly associated with RA. Proteomic PRS from different profiling platforms (SomaScan and Olink) identified a consistent association between TFF3 (SomaScan: OR=0.90, P=4.08×10-6; Olink: OR=0.93, P=4.87×10-3) and RA. The identified gene/proteins were mainly enriched in the NF-kappa B signaling pathway (hsa04064, P=5.06×10-5) and Cytokine-cytokine receptor interaction (hsa04060, P=2.49×10-4). In addition, a total of 12 candidate genes in our study were verified in two independent GEO datasets, such as FLOT1 and ABCF1.

CONCLUSION

Our findings provide novel insights into the involvement of identified genes/proteins and pathways in the pathogenesis of RA from multi-omics levels.

Blood Lipid Polygenic Risk Score Development and Application for Atherosclerosis Ultrasound Parameters

Background: The present study investigates the feasibility of using three previously published genome-wide association studies (GWAS) results on blood lipids to develop polygenic risk scores (PRS) for population samples from the European part of the Russian Federation. Methods: Two population samples were used in the study - one from the Ivanovo region (n = 1673) and one from the Vologda region (n = 817). We investigated three distinct approaches to PRS development: using the straightforward PRS approach with original effect sizes and fine-tuning with PRSice-2 and LDpred2. Results: In total, we constructed 56 PRS scales related to four lipid phenotypes: low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, total cholesterol, and triglyceride levels. Compared with previous results for the Russian population, we achieved an additional R2 increase of 2-4%, depending on the approach and lipid phenotype studied. Overall, the R2 PRS estimates approached those described for other populations. We also evaluated the clinical utility of blood lipid PRS for predicting carotid and femoral artery atherosclerosis. Specifically, we found that PRS for total cholesterol, low-density lipoprotein cholesterol, and triglycerides were positively correlated with ultrasound parameters of carotid and femoral artery atherosclerosis (ρ = 0.09-0.13, p < 0.001), whereas PRS for high-density lipoprotein cholesterol were inversely correlated with the number of plaques in the femoral arteries (ρ = -0.08, p = 8.71 × 10-3). Conclusions: PRS fine-tuning using PRSice-2 add LDpred2 improves the performance of blood lipid PRS. Our study demonstrates the potential for further use of blood lipid PRS for prediction of atherosclerosis risk.

Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts

PURPOSE

To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression.

DESIGN

Multicenter, multiethnic cohort study.

PARTICIPANTS

This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort.

METHODS

Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts.

MAIN OUTCOME MEASURES

DR development and progression and retinal microvascular damage.

RESULTS

Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05).

CONCLUSIONS

Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology.

FINANCIAL DISCLOSURE(S)

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

π-HuB: the proteomic navigator of the human body

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.

Plasma MERTK is causally associated with infection mortality

BACKGROUND

Infectious diseases are a major cause of mortality in spite of existing public health, anti-microbial and vaccine interventions. We aimed to define plasma proteomic associates of infection mortality and then apply Mendelian randomisation (MR) to yield biomarkers that may be causally associated.

METHODS

We used UK Biobank plasma proteomic data to associate 2923 plasma proteins with infection mortality before 31st December 2019 (240 events in 52,520 participants). Since many plasma proteins also predict non-infection mortality, we focussed on those associated with >1.5-fold risk of infection mortality in an analysis excluding survivors. Protein quantitative trait scores (pQTS) were then used to identify whether genetically predicted protein levels also associated with infection mortality. To conduct Two Sample MR, we performed a genome-wide association study (GWAS) of infection mortality using UK Biobank participants without plasma proteomic data (n = 363,953 including 984 infection deaths).

FINDINGS

After adjusting for clinical risk factors, 1142 plasma proteins were associated with risk of infection mortality (false discovery rate <0.05). 259 proteins were associated with >1.5-fold increased risk of infection versus non-infection mortality. Of these, we identified genetically predicted increasing MERTK concentration was associated with increased risk of infection mortality. MR supported a causal association between increasing plasma MERTK protein and infection mortality (odds ratio 1.46 per unit; 95% CI 1.15- 1.85; p = 0.002).

CONCLUSION

Plasma MERTK is causally associated with infection mortality and warrants exploration as a potential therapeutic target.

An open-source framework for end-to-end analysis of electronic health record data

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy's features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.

Estimation of Direct and Indirect Polygenic Effects and Gene-Environment Interactions using Polygenic Scores in Case-Parent Trio Studies

Family-based studies provide a unique opportunity to characterize genetic risks of diseases in the presence of population structure, assortative mating, and indirect genetic effects. We propose a novel framework, PGS-TRI, for the analysis of polygenic scores (PGS) in case-parent trio studies for estimation of the risk of an index condition associated with direct effects of inherited PGS, indirect effects of parental PGS, and gene-environment interactions. Extensive simulation studies demonstrate the robustness of PGS-TRI in the presence of complex population structure and assortative mating compared to alternative methods. We apply PGS-TRI to multi-ancestry trio studies of autism spectrum disorders (Ntrio = 1,517) and orofacial clefts (Ntrio = 1,904) to establish the first transmission-based estimates of risk associated with pre-defined PGS for these conditions and other related traits. For both conditions, we further explored offspring risk associated with polygenic gene-environment interactions, and direct and indirect effects of genetically predicted levels of gene expression and metabolite traits.

Mapping biological influences on the human plasma proteome beyond the genome

Broad-capture proteomic platforms now enable simultaneous assessment of thousands of plasma proteins, but most of these are not actively secreted and their origins are largely unknown. Here we integrate genomic with deep phenomic information to identify modifiable and non-modifiable factors associated with 4,775 plasma proteins in ~8,000 mostly healthy individuals. We create a data-driven map of biological influences on the human plasma proteome and demonstrate segregation of proteins into clusters based on major explanatory factors. For over a third (N = 1,575) of protein targets, joint genetic and non-genetic factors explain 10-77% of the variation in plasma (median 19.88%, interquartile range 14.01-31.09%), independent of technical factors (median 2.48%, interquartile range 0.78-6.41%). Together with genetically anchored causal inference methods, our map highlights potential causal associations between modifiable risk factors and plasma proteins for hundreds of protein-disease associations, for example, COL6A3, which possibly mediates the association between reduced kidney function and cardiovascular disease. We provide a map of biological and technical influences on the human plasma proteome to help contextualize findings from proteomic studies.

Current understanding and management of cardiovascular involvement in rheumatic immune-mediated inflammatory diseases

Immune-mediated inflammatory diseases (IMIDs) are a spectrum of disorders of overlapping immunopathogenesis, with a prevalence of up to 10% in Western populations and increasing incidence in developing countries. Although targeted treatments have revolutionized the management of rheumatic IMIDs, cardiovascular involvement confers an increased risk of mortality and remains clinically under-recognized. Cardiovascular pathology is diverse across rheumatic IMIDs, ranging from premature atherosclerotic cardiovascular disease (ASCVD) to inflammatory cardiomyopathy, which comprises myocardial microvascular dysfunction, vasculitis, myocarditis and pericarditis, and heart failure. Epidemiological and clinical data imply that rheumatic IMIDs and associated cardiovascular disease share common inflammatory mechanisms. This concept is strengthened by emergent trials that indicate improved cardiovascular outcomes with immune modulators in the general population with ASCVD. However, not all disease-modifying therapies that reduce inflammation in IMIDs such as rheumatoid arthritis demonstrate equally beneficial cardiovascular effects, and the evidence base for treatment of inflammatory cardiomyopathy in patients with rheumatic IMIDs is lacking. Specific diagnostic protocols for the early detection and monitoring of cardiovascular involvement in patients with IMIDs are emerging but are in need of ongoing development. This Review summarizes current concepts on the potentially targetable inflammatory mechanisms of cardiovascular pathology in rheumatic IMIDs and discusses how these concepts can be considered for the diagnosis and management of cardiovascular involvement across rheumatic IMIDs, with an emphasis on the potential of cardiovascular imaging for risk stratification, early detection and prognostication.

Hierarchical joint analysis of marginal summary statistics-Part II: High-dimensional instrumental analysis of omics data

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Artificial intelligence and porcine breeding

Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.

Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models

Translational biology posits a strong bi-directional link between clinical phenotypes and a patient's biological profile. By leveraging this bi-directional link, we can efficiently deconvolute pre-existing clinical information into biological profiles. However, traditional computational tools are limited in their ability to resolve this link because of the relatively small sizes of paired clinical-biological datasets for training and the high dimensionality/sparsity of tabular clinical data. Here, we use state-of-the-art foundation models (FMs) for electronic health record (EHR) data to generate proteomics profiles of pregnant patients, thereby deconvoluting pre-existing clinical information into biological profiles without the cost and effort of running large-scale traditional omics studies. We show that FM-derived representations of a patient's EHR data coupled with a fully connected neural network prediction head can generate 206 blood protein expression levels. Interestingly, these proteins were enriched for developmental pathways, while proteins not able to be generated from EHR data were enriched for metabolic pathways. Finally, we show a proteomic signature of gestational diabetes that includes proteins with established and novel links to gestational diabetes. These results showcase the power of FM-derived EHR representations in efficiently generating biological states of pregnant patients. This capability can revolutionize disease understanding and therapeutic development, offering a cost-effective, time-efficient, and less invasive alternative to traditional methods of generating proteomics.

Deciphering the impact of genomic variation on function

Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.

Western diets and chronic diseases

'Westernization', which incorporates industrial, cultural and dietary trends, has paralleled the rise of noncommunicable diseases across the globe. Today, the Western-style diet emerges as a key stimulus for gut microbial vulnerability, chronic inflammation and chronic diseases, affecting mainly the cardiovascular system, systemic metabolism and the gut. Here we review the diet of modern times and evaluate the threat it poses for human health by summarizing recent epidemiological, translational and clinical studies. We discuss the links between diet and disease in the context of obesity and type 2 diabetes, cardiovascular diseases, gut and liver diseases and solid malignancies. We collectively interpret the evidence and its limitations and discuss future challenges and strategies to overcome these. We argue that healthcare professionals and societies must react today to the detrimental effects of the Western diet to bring about sustainable change and improved outcomes in the future.

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Challenges and advances in the management of inflammation in atherosclerosis

INTRODUCTION

Atherosclerosis, traditionally considered a lipid-related disease, is now understood as a chronic inflammatory condition with significant global health implications.

OBJECTIVES

This review aims to delve into the complex interactions among immune cells, cytokines, and the inflammatory cascade in atherosclerosis, shedding light on how these elements influence both the initiation and progression of the disease.

METHODS

This review draws on recent clinical research to elucidate the roles of key immune cells, macrophages, T cells, endothelial cells, and clonal hematopoiesis in atherosclerosis development. It focuses on how these cells and process contribute to disease initiation and progression, particularly through inflammation-driven processes that lead to plaque formation and stabilization. Macrophages ingest oxidized low-density lipoprotein (oxLDL), which partially converts to high-density lipoprotein (HDL) or accumulates as lipid droplets, forming foam cells crucial for plaque stability. Additionally, macrophages exhibit diverse phenotypes within plaques, with pro-inflammatory types predominating and others specializing in debris clearance at rupture sites. The involvement of CD4+ T and CD8+ T cells in these processes promotes inflammatory macrophage states, suppresses vascular smooth muscle cell proliferation, and enhances plaque instability.

RESULTS

The nuanced roles of macrophages, T cells, and the related immune cells within the atherosclerotic microenvironment are explored, revealing insights into the cellular and molecular pathways that fuel inflammation. This review also addresses recent advancements in imaging and biomarker technology that enhance our understanding of disease progression. Moreover, it points out the limitations of current treatment and highlights the potential of emerging anti-inflammatory strategies, including clinical trials for agents such as p38MAPK, tumor necrosis factor α (TNF-α), and IL-1β, their preliminary outcomes, and the promising effects of canakinumab, colchicine, and IL-6R antagonists.

CONCLUSION

This review explores cutting-edge anti-inflammatory interventions, their potential efficacy in preventing and alleviating atherosclerosis, and the role of nanotechnology in delivering drugs more effectively and safely.

Fine-mapping causal tissues and genes at disease-associated loci

Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.

Integrated proteomics and metabolomics analysis of sclerosis-related proteins and femoral head necrosis following internal fixation of femoral neck fractures

Femoral head necrosis (FHN) is a serious complication after femoral neck fractures (FNF), often linked to sclerosis around screw paths. Our study aimed to uncover the proteomic and metabolomic underpinnings of FHN and sclerosis using integrated proteomics and metabolomics analyses. We identified differentially expressed proteins (DEPs) and metabolites (DEMs) among three groups: patients with FNF (Group A), sclerosis (Group B), and FHN (Group C). Using the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses, we examined the roles of these proteins and metabolites. Our findings highlight the significant differences across the groups, with 218 DEPs and 44 DEMs identified between the sclerosis and FNF groups, 247 DEPs and 31 DEMs between the FHN and sclerosis groups, and a stark 682 DEPs and 94 DEMs between the FHN and FNF groups. Activities related to carbonate dehydratase and hydrolase were similar in the FHN and sclerosis groups, whereas extracellular region and lysosome were prevalent in the FHN and FNF groups. Our study also emphasized the involvement of the PI3K-Akt pathway in sclerosis and FHN. Moreover, the key metabolic pathways were implicated in glycerophospholipid metabolism and retrograde endocannabinoid signaling. Using western blotting, we confirmed the pivotal role of specific genes/proteins such as ITGB5, TNXB, CA II, and CA III in sclerosis and acid phosphatase 5 and cathepsin K in FHN. This comprehensive analyses elucidates the molecular mechanisms behind sclerosis and FHN and suggests potential biomarkers and therapeutic targets, paving the way for improved treatment strategies. Further validation of the findings is necessary to strengthen the robustness and reliability of the results.

The pivotal role of the X-chromosome in the genetic architecture of the human brain

Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.

Exploring the Associated Genetic Causes of Diabetic Retinopathy as a Model of Inflammation in Retinal Diseases

To investigate potential biomarkers and biological processes associated with diabetic retinopathy (DR) using transcriptomic and proteomic data. The OmicsPred PheWAS application was interrogated to identify genes and proteins associated with DR and diabetes mellitus (DM) at a false discovery rate (FDR)-adjusted p-value of <0.05 and also <0.005. Gene Ontology PANTHER analysis and STRING database analysis were conducted to explore the biological processes and protein interactions related to the identified biomarkers. The interrogation identified 49 genes and 22 proteins associated with DR and/or DM; these were divided into those uniquely associated with diabetic retinopathy, uniquely associated with diabetes mellitus, and the ones seen in both conditions. The Gene Ontology PANTHER and STRING database analyses highlighted associations of several genes and proteins associated with diabetic retinopathy with adaptive immune response, valyl-TRNA aminoacylation, complement activation, and immune system processes. Our analyses highlight potential transcriptomic and proteomic biomarkers for DR and emphasize the association of known aspects of immune response, the complement system, advanced glycosylation end-product formation, and specific receptor and mitochondrial function with DR pathophysiology. These findings may suggest pathways for future research into novel diagnostic and therapeutic strategies for DR.

Genotype-based "virtual" metabolomics in a clinical biobank identifies novel metabolite-disease associations

Introduction: Circulating metabolites act as biomarkers of dysregulated metabolism and may inform disease pathophysiology. A portion of the inter-individual variability in circulating metabolites is influenced by common genetic variation. We evaluated whether a genetics-based "virtual" metabolomics approach can identify novel metabolite-disease associations. Methods: We examined the association between polygenic scores for 724 metabolites with 1,247 clinical phenotypes in the BioVU DNA biobank, comprising 57,735 European ancestry and 15,754 African ancestry participants. We applied Mendelian randomization (MR) to probe significant relationships and validated significant MR associations using independent GWAS of candidate phenotypes. Results and Discussion: We found significant associations between 336 metabolites and 168 phenotypes in European ancestry and 107 metabolites and 56 phenotypes in African ancestry. Of these metabolite-disease pairs, MR analyses confirmed associations between 73 metabolites and 53 phenotypes in European ancestry. Of 22 metabolitephenotype pairs evaluated for replication in independent GWAS, 16 were significant (false discovery rate p < 0.05). These included associations between bilirubin and X-21796 with cholelithiasis, phosphatidylcholine (16:0/22:5n3,18:1/20:4) and arachidonate with inflammatory bowel disease and Crohn's disease, and campesterol with coronary artery disease and myocardial infarction. These associations may represent biomarkers or potentially targetable mediators of disease risk.

Mendelian Randomization Study With Clinical Follow-Up Links Metabolites to Risk and Severity of Pulmonary Arterial Hypertension

BACKGROUND

Pulmonary arterial hypertension (PAH) exhibits phenotypic heterogeneity and variable response to therapy. The metabolome has been implicated in the pathogenesis of PAH, but previous works have lacked power to implicate specific metabolites. Mendelian randomization (MR) is a method for causal inference between exposures and outcomes.

METHODS AND RESULTS

Using genome-wide association study summary statistics, we implemented MR analysis to test for potential causal relationships between serum concentration of 575 metabolites and PAH. Five metabolites were causally associated with the risk of PAH after multiple testing correction. Next, we measured serum concentration of candidate metabolites in an independent clinical cohort of 449 patients with PAH to check whether metabolite concentrations are correlated with markers of disease severity. Of the 5 candidates nominated by our MR work, serine was negatively associated and homostachydrine was positively associated with clinical severity of PAH via direct measurement in this independent clinical cohort. Finally we used conditional and orthogonal approaches to explore the biology underlying our lead metabolites. Rare variant burden testing was carried out using whole exome sequencing data from 578 PAH cases and 361 675 controls. Multivariable MR is an extension of MR that uses a single set of instrumental single-nucleotide polymorphisms to measure multiple exposures; multivariable MR is used to determine interdependence between the effects of different exposures on a single outcome. Rare variant analysis demonstrated that loss-of-function mutations within activating transcription factor 4, a transcription factor responsible for upregulation of serine synthesis under conditions of serine starvation, are associated with higher risk for PAH. Homostachydrine is a xenobiotic metabolite that is structurally related to l-proline betaine, which has previously been linked to modulation of inflammation and tissue remodeling in PAH. Our multivariable MR analysis suggests that the effect of l-proline betaine is actually mediated indirectly via homostachydrine.

CONCLUSIONS

Our data present a method for study of the metabolome in the context of PAH, and suggests several candidates for further evaluation and translational research.

Elucidating the relationship between metabolites and breast cancer: A Mendelian randomization study

The evidence about the causal roles of metabolites in breast cancer is lacking. This study conducted a systematic evaluation of the potential causal relationship between 1091 human blood metabolites, 309 metabolite ratios, and the likelihood of developing breast cancer and its subtype by employing a two-sample bidirectional Mendelian randomization (MR) approach Four metabolites, including tryptophan betaine (Odds Ratio [OR] = 1.07, 95%CI = 1.04-1.10, Bonferroni-corrected P = 0.007), X-21312 (OR = 0.90, 95%CI = 0.86-0.94, Bonferroni-corrected P = 0.02), 3-bromo-5-chloro-2,6-dihydroxybenzoic acid (OR = 0.94, 95%CI = 0.91-0.96, Bonferroni-corrected P = 0.03) and X-18921 (OR = 0.96, 95%CI = 0.94-0.98, Bonferroni-corrected P = 0.04) were significantly associated with overall breast cancer using inverse-variance weighted (IVW) method. Tryptophan betaine was also significantly associated with estrogen receptor (ER)-positive breast cancer (OR = 1.08, 95%CI = 1.04-1.11, Bonferroni-corrected P = 0.03). X-23680 (OR = 1.10, 95%CI = 1.05-1.15, Bonferroni-corrected P = 0.04) and glycine to phosphate ratio (OR = 1.07, 95%CI = 1.04-1.10, Bonferroni-corrected P = 0.04) were associated with ER-negative breast cancer. Reverse MR analysis showed no significant associations between breast cancer and metabolites. This MR study indicated compelling evidence of a causal association between metabolites and the risk of breast cancer and its subtypes, underscoring the potential impact of metabolic interference on breast cancer risk and indicating the drug targets for breast cancer.

The future of equine semen analysis

We are currently experiencing a period of rapid advancement in various areas of science and technology. The integration of high throughput 'omics' techniques with advanced biostatistics, and the help of artificial intelligence, is significantly impacting our understanding of sperm biology. These advances will have an appreciable impact on the practice of reproductive medicine in horses. This article provides a brief overview of recent advances in the field of spermatology and how they are changing assessment of sperm quality. This article is written from the authors' perspective, using the stallion as a model. We aim to portray a brief overview of the changes occurring in the assessment of sperm motility and kinematics, advances in flow cytometry, implementation of 'omics' technologies, and the use of artificial intelligence/self-learning in data analysis. We also briefly discuss how some of the advances can be readily available to the practitioner, through the implementation of 'on-farm' devices and telemedicine.

Major lipids and lipoprotein levels and risk of blood pressure elevation: a Mendelian Randomisation study

BACKGROUND

Quantitative nuclear magnetic resonance (NMR) metabolomics techniques provide detailed measurements of lipoprotein particle concentration. Metabolic dysfunction often represents a cluster of conditions, including dyslipidaemia, hypertension, and diabetes, that increase the risk of cardiovascular diseases (CVDs). However, the causal relationship between lipid profiles and blood pressure (BP) remains unclear. We performed a Mendelian Randomisation (MR) study to disentangle and prioritize the potential causal effects of major lipids, lipoprotein particles, and circulating metabolites on BP and pulse pressure (PP).

METHODS

We employed single-nucleotide polymorphisms (SNPs) associated with major lipids, lipoprotein particles, and other metabolites from the UK Biobank as instrumental variables. Summary-level data for BP and PP were obtained from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Two-sample MR and MR Bayesian model averaging approaches (MR-BMA) were conducted to analyse and rank causal associations.

FINDINGS

Genetically predicted TG was the most likely causal exposure among the major lipids to increase systolic blood pressure (SBP) and diastolic blood pressure (DBP), with marginal inclusion probabilities (MIPs) of 0.993 and 0.847, respectively. Among the majority of lipoproteins and their containing lipids, including major lipids, genetically elevated TG in small high-density lipoproteins (S_HDL_TG) had the strongest association with the increase of SBP and DBP, with MIPs of 0.416 and 0.397, respectively. HDL cholesterol (HDL_C) and low-density lipoprotein cholesterol (LDL_C) were potential causal factors for PP elevation among the major lipids (MIP = 0.927 for HDL_C and MIP = 0.718 for LDL_C). Within the sub-lipoproteins, genetically predicted atherogenic lipoprotein particles (i.e., sub-very low-density lipoprotein (VLDL), intermediate-density lipoprotein (IDL), and LDL particles) had the most likely causal impact on increasing PP.

INTERPRETATION

This study provides genetic evidence for the causality of lipids on BP indicators. However, the effect size on SBP, DBP, and PP varies depending on the lipids' components and sizes. Understanding this potential relationship may inform the potential benefits of comprehensive management of lipid profiles for BP control.

FUNDING

Key Research and Development Program of Hubei Province, Science and Technology Innovation Project of Huanggang Central Hospital of Yangtze University, the Hubei Industrial Technology Research Institute of Heart-Brain Diseases, and the Hubei Provincial Engineering Research Centre of Comprehensive Care for Heart-Brain Diseases.

Special Issue "Deployment of Proteomics Approaches in Biomedical Research"

Many angles of personalized medicine, such as diagnostic improvements, systems biology [...].

Precision Approaches to Chronic Obstructive Pulmonary Disease Management

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide. COPD heterogeneity has hampered progress in developing pharmacotherapies that affect disease progression. This issue can be addressed by precision medicine approaches, which focus on understanding an individual's disease risk, and tailoring management based on pathobiology, environmental exposures, and psychosocial issues. There is an urgent need to identify COPD patients at high risk for poor outcomes and to understand at a mechanistic level why certain individuals are at high risk. Genetics, omics, and network analytic techniques have started to dissect COPD heterogeneity and identify patients with specific pathobiology. Drug repurposing approaches based on biomarkers of specific inflammatory processes (i.e., type 2 inflammation) are promising. As larger data sets, additional omics, and new analytical approaches become available, there will be enormous opportunities to identify high-risk individuals and treat COPD patients based on their specific pathophysiological derangements. These approaches show great promise for risk stratification, early intervention, drug repurposing, and developing novel therapeutic approaches for COPD.

Subset scanning for multi-trait analysis using GWAS summary statistics

MOTIVATION

Multi-trait analysis has been shown to have greater statistical power than single-trait analysis. Most of the existing multi-trait analysis methods only work with a limited number of traits and usually prioritize high statistical power over identifying relevant traits, which heavily rely on domain knowledge.

RESULTS

To handle diseases and traits with obscure etiology, we developed TraitScan, a powerful and fast algorithm that identifies potential pleiotropic traits from a moderate or large number of traits (e.g. dozens to thousands) and tests the association between one genetic variant and the selected traits. TraitScan can handle either individual-level or summary-level GWAS data. We evaluated TraitScan using extensive simulations and found that it outperformed existing methods in terms of both testing power and trait selection when sparsity was low or modest. We then applied it to search for traits associated with Ewing Sarcoma, a rare bone tumor with peak onset in adolescence, among 754 traits in UK Biobank. Our analysis revealed a few promising traits worthy of further investigation, highlighting the use of TraitScan for more effective multi-trait analysis as biobanks emerge. We also extended TraitScan to search and test association with a polygenic risk score and genetically imputed gene expression.

AVAILABILITY AND IMPLEMENTATION

Our algorithm is implemented in an R package "TraitScan" available at https://github.com/RuiCao34/TraitScan.

Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks

OBJECTIVE

To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity.

METHODS

Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis.

RESULTS

The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection.

CONCLUSION

By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.