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Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype–phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.Despite the success of human genome-wide association studies (GWAS) in associating genetic variants and complex diseases or traits, criticisms of the usefulness of this study design remain. This Review assesses the pros and cons of GWAS, with a focus on the cardiometabolic field.

Purpose of Review ‘Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of ‘omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of ‘omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed. Recent Findings Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of ‘omics data. Studies with multiple types of ‘omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of ‘omics data. For instance, disease-associated loci in the genome can be supplemented with other ‘omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. Summary As technologies improve, costs for ‘omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of ‘omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of ‘omics data to provide insights greater than the sum of its parts.

Current risk assessment and treatment strategies for venous thromboembolism (VTE) consider genetic factors only in a limited way. New work shows a more pervasive role of common variants in VTE risk, inspiring genetic predictors that surpass and complement individual clinical risk factors and monogenic thrombophilia testing.

Our cells are powered by small internal compartments known as mitochondria, which host several copies of their own ‘mitochondrial’ genome. Defects in these semi-autonomous structures are associated with a range of severe, and sometimes fatal conditions: easily checking the health of mitochondria through cheap, quick and non-invasive methods can therefore help to improve human health. Measuring the concentration of mitochondrial DNA molecules in our blood cells can help to estimate the number of mitochondrial genome copies per cell, which in turn act as a proxy for the health of the compartment. In fact, having lower or higher concentration of mitochondrial DNA molecules is associated with diseases such as cancer, stroke, or cardiac conditions. However, current approaches to assess this biomarker are time and resource-intensive; they also do not work well across people with different ancestries, who have slightly different versions of mitochondrial genomes. In response, Chong et al. developed a new method for estimating mitochondrial DNA concentration in blood samples. Called AutoMitoC, the automated pipeline is fast, easy to use, and can be used across ethnicities. Applying this method to nearly 400,000 individuals highlighted 71 genetic regions for which slight sequence differences were associated with changes in mitochondrial DNA concentration. Further investigation revealed that these regions contained genes that help to build, maintain, and organize mitochondrial DNA. In addition, the analyses yield preliminary evidence showing that lower concentration of mitochondrial DNA may be linked to a higher risk of dementia. Overall, the work by Chong et al. demonstrates that AutoMitoC can be used to investigate how mitochondria are linked to health and disease in populations across the world, potentially paving the way for new therapeutic approaches.

Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance. GraBLD yielded prediction R 2 of 0.239 and 0.082 using GIANT summary association statistics for height and BMI in the UK Biobank study ( N  = 130 K; 1.98 M SNPs), explaining 46.9% and 32.7% of the overall polygenic variance, respectively. For diabetes status, the area under the receiver operating characteristic curve was 0.602 in the UK Biobank study using summary-level association statistics from the DIAGRAM consortium. GraBLD outperformed other polygenic score heuristics for the prediction of height ( p  < 2.2 × 10 −16 ) and BMI ( p  < 1.57 × 10 −4 ), and was equivalent to LDpred for diabetes. Results were independently validated in the Health and Retirement Study ( N  = 8,292; 688,398 SNPs). Our report demonstrates the use of machine-learning techniques, coupled with summary-level data from large genome-wide meta-analyses to improve the prediction of polygenic traits.

Purpose of Review This review provides an overview of genetic and non-genetic causes of premature coronary artery disease (pCAD). Recent Findings pCAD refers to coronary artery disease (CAD) occurring before the age of 65 years in women and 55 years in men. Both genetic and non-genetic risk factors may contribute to the onset of pCAD. Recent advances in the genetic epidemiology of pCAD have revealed the importance of both monogenic and polygenic contributions to pCAD. Familial hypercholesterolemia (FH) is the most common monogenic disorder associated with atherosclerotic pCAD. However, clinical overreliance on monogenic genes can result in overlooked genetic causes of pCAD, especially polygenic contributions. Non-genetic factors, notably smoking and drug use, are also important contributors to pCAD. Cigarette smoking has been observed in 25.5% of pCAD patients relative to 12.2% of non-pCAD patients. Finally, myocardial infarction (MI) associated with spontaneous coronary artery dissection (SCAD) may result in similar clinical presentations as atherosclerotic pCAD. Summary Recognizing the genetic and non-genetic causes underlying pCAD is important for appropriate prevention and treatment. Despite recent progress, pCAD remains incompletely understood, highlighting the need for both awareness and research.

Aims/hypothesis Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n =241), severe insulin-deficient diabetes (SIDD, n =1594), severe insulin-resistant diabetes (SIRD, n =914), mild obesity-related diabetes (MOD, n =1595) and mild age-related diabetes (MARD, n =2673). Methods Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. Results A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p <4.3 × 10 −5 ). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. Conclusions/interpretation We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. Trial registration ORIGIN trial, ClinicalTrials.gov NCT00069784. Graphical abstract

Strengthening nutrition routine information system is critical to support nutrition programs with relevant data to inform decision-making. This study analyzed the practices and lessons learned from the implementation in Burkina Faso in strengthening nutrition routine data using institutionalized health management information systems for decision making. Methods This qualitative study was conducted in Burkina Faso in 2022 on the capitalization of best practices after 3 years of implementation through documentary review, semi-structured individual interviews with 64 key implementing informants spread over 2 health districts, 2 regional hospital centers and 2 health regions, and a national triangulation workshop with 40 implementing actors, including 20 from the central level, 15 from the decentralized level, and 5 partners. Results The results of the study show the best practices and progress identified: (i) the integration of new routine data elements and nutrition indicators into District Health Information Software (DHIS2), which filled the data gap for adequate monitoring of the nutrition program; (ii) the design and use of the nutrition indicator dashboard; (iii) data validation and performance review sessions which have improved the quality and use of routine data in decision-making; and (iv) decentralization of data entry of monthly activity reports of health facilities. Lessons learned included: (i) conducting a small-scale phase to test the indicators is an important step to take before national scale-up of the indicators; (ii) a participatory approach involving all actors at different levels is important; (iii) advocacy is important to integrate prevention indicators into health facilities information systems in a more curative-oriented health system; (iv) the decentralized entry of data is a best practice that improves data quality in terms of timeliness, completeness, and internal consistency. Conclusion Beyond the inclusion of indicators, special emphasis should be placed on working on data quality. Future experiences in refining routine data related to nutrition-sensitive interventions in the non-health sectors are key next steps that would further contribute to strengthening the national nutrition information system.

Maternal smoking has been linked to adverse health outcomes in newborns but the extent to which it impacts newborn health has not been quantified through an aggregated cord blood DNA methylation (DNAm) score. Here, we examine the feasibility of using cord blood DNAm scores leveraging large external studies as discovery samples to capture the epigenetic signature of maternal smoking and its influence on newborns in White European and South Asian populations. We first examined the association between individual CpGs and cigarette smoking during pregnancy, and smoking exposure in two White European birth cohorts (n=744). Leveraging established CpGs for maternal smoking, we constructed a cord blood epigenetic score of maternal smoking that was validated in one of the European-origin cohorts (n=347). This score was then tested for association with smoking status, secondary smoking exposure during pregnancy, and health outcomes in offspring measured after birth in an independent White European (n=397) and a South Asian birth cohort (n=504). Several previously reported genes for maternal smoking were supported, with the strongest and most consistent association signal from the gene (6 CpGs with p<5 × 10 ). The epigenetic maternal smoking score was strongly associated with smoking status during pregnancy (OR = 1.09 [1.07, 1.10], p=5.5 × 10 ) and more hours of self-reported smoking exposure per week (1.93 [1.27, 2.58], p=7.8 × 10 ) in White Europeans. However, it was not associated with self-reported exposure (p>0.05) among South Asians, likely due to a lack of smoking in this group. The same score was consistently associated with a smaller birth size (-0.37±0.12 cm, p=0.0023) in the South Asian cohort and a lower birth weight (-0.043±0.013 kg, p=0.0011) in the combined cohorts. This cord blood epigenetic score can help identify babies exposed to maternal smoking and assess its long-term impact on growth. Notably, these results indicate a consistent association between the DNAm signature of maternal smoking and a small body size and low birth weight in newborns, in both White European mothers who exhibited some amount of smoking and in South Asian mothers who themselves were not active smokers. This study was funded by the Canadian Institutes of Health Research Metabolomics Team Grant: MWG-146332.

While conventional LDL-C, HDL-C, and triglyceride measurements reflect aggregate properties of plasma lipoprotein fractions, NMR-based measurements more accurately reflect lipoprotein particle concentrations according to class (LDL, HDL, and VLDL) and particle size (small, medium, and large). The concentrations of these lipoprotein sub-fractions may be related to risk of cardiovascular disease and related metabolic disorders. We performed a genome-wide association study of 17 lipoprotein measures determined by NMR together with LDL-C, HDL-C, triglycerides, ApoA1, and ApoB in 17,296 women from the Women's Genome Health Study (WGHS). Among 36 loci with genome-wide significance (P<5x10(-8)) in primary and secondary analysis, ten (PCCB/STAG1 (3q22.3), GMPR/MYLIP (6p22.3), BTNL2 (6p21.32), KLF14 (7q32.2), 8p23.1, JMJD1C (10q21.3), SBF2 (11p15.4), 12q23.2, CCDC92/DNAH10/ZNF664 (12q24.31.B), and WIPI1 (17q24.2)) have not been reported in prior genome-wide association studies for plasma lipid concentration. Associations with mean lipoprotein particle size but not cholesterol content were found for LDL at four loci (7q11.23, LPL (8p21.3), 12q24.31.B, and LIPG (18q21.1)) and for HDL at one locus (GCKR (2p23.3)). In addition, genetic determinants of total IDL and total VLDL concentration were found at many loci, most strongly at LIPC (15q22.1) and APOC-APOE complex (19q13.32), respectively. Associations at seven more loci previously known for effects on conventional plasma lipid measures reveal additional genetic influences on lipoprotein profiles and bring the total number of loci to 43. Thus, genome-wide associations identified novel loci involved with lipoprotein metabolism-including loci that affect the NMR-based measures of concentration or size of LDL, HDL, and VLDL particles-all characteristics of lipoprotein profiles that may impact disease risk but are not available by conventional assay.