Methods applied to neonatal dried blood spot samples for secondary research purposes: a scoping review.

This scoping review aimed to synthesize the analytical techniques used and methodological limitations encountered when undertaking secondary research using residual neonatal dried blood spot (DBS) samples. Studies that used residual neonatal DBS samples for secondary research (i.e. research not related to newborn screening for inherited genetic and metabolic disorders) were identified from six electronic databases: Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Medline, PubMed and Scopus. Inclusion was restricted to studies published from 1973 and written in or translated into English that reported the storage, extraction and testing of neonatal DBS samples. Sixty-seven studies were eligible for inclusion. Included studies were predominantly methodological in nature and measured various analytes, including nucleic acids, proteins, metabolites, environmental pollutants, markers of prenatal substance use and medications. Neonatal DBS samples were stored over a range of temperatures (ambient temperature, cold storage or frozen) and durations (two weeks to 40.5 years), both of which impacted the recovery of some analytes, particularly amino acids, antibodies and environmental pollutants. The size of DBS sample used and potential contamination were also cited as methodological limitations. Residual neonatal DBS samples retained by newborn screening programs are a promising resource for secondary research purposes, with many studies reporting the successful measurement of analytes even from neonatal DBS samples stored for long periods of time in suboptimal temperatures and conditions.

A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk.

Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

The PREVENT dementia programme: baseline demographic, lifestyle, imaging and cognitive data from a midlife cohort study investigating risk factors for dementia.

PREVENT is a multi-centre prospective cohort study in the UK and Ireland that aims to examine midlife risk factors for dementia and identify and describe the earliest indices of disease development. The PREVENT dementia programme is one of the original epidemiological initiatives targeting midlife as a critical window for intervention in neurodegenerative conditions. This paper provides an overview of the study protocol and presents the first summary results from the initial baseline data to describe the cohort. Participants in the PREVENT cohort provide demographic data, biological samples (blood, saliva, urine and optional cerebrospinal fluid), lifestyle and psychological questionnaires, undergo a comprehensive cognitive test battery and are imaged using multi-modal 3-T MRI scanning, with both structural and functional sequences. The PREVENT cohort governance structure is described, which includes a steering committee, a scientific advisory board and core patient and public involvement groups. A number of sub-studies that supplement the main PREVENT cohort are also described. The PREVENT cohort baseline data include 700 participants recruited between 2014 and 2020 across five sites in the UK and Ireland (Cambridge, Dublin, Edinburgh, London and Oxford). At baseline, participants had a mean age of 51.2 years (range 40-59, SD ± 5.47), with the majority female (n = 433, 61.9%). There was a near equal distribution of participants with and without a parental history of dementia (51.4% versus 48.6%) and a relatively high prevalence of APOEɛ4 carriers (n = 264, 38.0%). Participants were highly educated (16.7 ± 3.44 years of education), were mainly of European Ancestry (n = 672, 95.9%) and were cognitively healthy as measured by the Addenbrookes Cognitive Examination-III (total score 95.6 ± 4.06). Mean white matter hyperintensity volume at recruitment was 2.26 ± 2.77 ml (median = 1.39 ml), with hippocampal volume being 8.15 ± 0.79 ml. There was good representation of known dementia risk factors in the cohort. The PREVENT cohort offers a novel data set to explore midlife risk factors and early signs of neurodegenerative disease. Data are available open access at no cost via the Alzheimer's Disease Data Initiative platform and Dementia Platforms UK platform pending approval of the data access request from the PREVENT steering group committee.

Methylome-wide studies of six metabolic traits.

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised ßrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.

Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts.

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

Epigenetic scores of blood-based proteins as biomarkers of general cognitive function and brain health.

BACKGROUND: Epigenetic Scores (EpiScores) for blood protein levels have been associated with disease outcomes and measures of brain health, highlighting their potential usefulness as clinical biomarkers. They are typically derived via penalised regression, whereby a linear weighted sum of DNA methylation (DNAm) levels at CpG sites are predictive of protein levels. Here, we examine 84 previously published protein EpiScores as possible biomarkers of cross-sectional and longitudinal measures of general cognitive function and brain health, and incident dementia across three independent cohorts. RESULTS: Using 84 protein EpiScores as candidate biomarkers, associations with general cognitive function (both cross-sectionally and longitudinally) were tested in three independent cohorts: Generation Scotland (GS), and the Lothian Birth Cohorts of 1921 and 1936 (LBC1921 and LBC1936, respectively). A meta-analysis of general cognitive functioning results in all three cohorts identified 18 EpiScore associations (absolute meta-analytic standardised estimates ranged from 0.03 to 0.14, median of 0.04, PFDR < 0.05). Several associations were also observed between EpiScores and global brain volumetric measures in the LBC1936. An EpiScore for the S100A9 protein (a known Alzheimer disease biomarker) was associated with general cognitive functioning (meta-analytic standardised beta: - 0.06, P = 1.3 × 10-9), and with time-to-dementia in GS (Hazard ratio 1.24, 95% confidence interval 1.08-1.44, P = 0.003), but not in LBC1936 (Hazard ratio 1.11, P = 0.32). CONCLUSIONS: EpiScores might make a contribution to the risk profile of poor general cognitive function and global brain health, and risk of dementia, however these scores require replication in further studies.

Genetic control of DNA methylation is largely shared across European and East Asian populations.

DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.

A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels.

ABSTRACT: Coagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10-9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.

Validation of biomarkers of aging.

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Comprehensive evaluation of smoking exposures and their interactions on DNA methylation.

BACKGROUND: Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (<1 year), persistence of differential methylation from in utero smoking exposure, and effects of environmental tobacco smoke (ETS). METHODS: We meta-analysed data from up to 15,014 adults across 5 cohorts with DNA methylation measured in blood using Illumina's EPIC array for current smoking (2560 exposed), quit < 1 year (500 exposed), in utero (286 exposed), and ETS exposure (676 exposed). We also evaluated the interaction of current smoking with sex or diet (fibre, folate, and vitamin C). FINDINGS: Using false discovery rate (FDR < 0.05), 65,857 CpGs were differentially methylated in relation to current smoking, 4025 with recent quitting, 594 with in utero exposure, and 6 with ETS. Most current smoking CpGs attenuated within a year of quitting. CpGs related to in utero exposure in adults were enriched for those previously observed in newborns. Differential methylation by current smoking at 4-71 CpGs may be modified by sex or dietary intake. Nearly half (35-50%) of differentially methylated CpGs on the 450 K array were associated with blood gene expression. Current smoking and in utero smoking CpGs implicated 3049 and 1067 druggable targets, including chemotherapy drugs. INTERPRETATION: Many smoking-related methylation sites were identified with Illumina's EPIC array. Most signals revert to levels observed in never smokers within a year of cessation. Many in utero smoking CpGs persist into adulthood. Smoking-related druggable targets may provide insights into cancer treatment response and shared mechanisms across smoking-related diseases. FUNDING: Intramural Research Program of the National Institutes of Health, Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, Chief Scientist Office of the Scottish Government Health Directorates and the Scottish Funding Council, Medical Research Council UK and the Wellcome Trust.

Epigenetic Contributions to Clinical Risk Prediction of Cardiovascular Disease.

BACKGROUND: Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort. METHODS: Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (ncases≥1274; ncontrols≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed. RESULTS: Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (P<0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10-3; 0.3% increase in C-statistic). CONCLUSIONS: EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.

Prospective study design and data analysis in UK Biobank.

Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.

Multiple Cardiac Biomarkers to Improve Prediction of Cardiovascular Events: Findings from the Generation Scotland Scottish Family Health Study.

BACKGROUND: Many studies have investigated whether single cardiac biomarkers improve cardiovascular risk prediction for primary prevention but whether a combined approach could further improve risk prediction is unclear. We aimed to test a sex-specific, combined cardiac biomarker approach for cardiovascular risk prediction. METHODS: In the Generation Scotland Scottish Family Health Study, N-terminal pro-B-type natriuretic peptide (NT-proBNP), growth differentiation factor-15 (GDF-15), cardiac troponin I (cTnI), cardiac troponin T (cTnT), and C-reactive protein (CRP) were measured in stored serum using automated immunoassays. Sex-specific Cox models that included SCORE2 risk factors evaluated addition of single and combined biomarkers for prediction of major adverse cardiovascular events (MACE). Combined biomarker models were compared to a baseline model that included SCORE2 risk factors. RESULTS: The study population comprised 18 383 individuals (58.9% women, median age of 48 years [25th-75th percentile, 35-58 years]). During the median follow up of 11.6 (25th-75th percentile, 10.8-13.0) years, MACE occurred in 942 (5.1%) individuals. The greatest increase in discrimination with addition of individual biomarkers to the base model was for women GDF-15 and for men NT-proBNP (change in c-index: + 0.010 for women and +0.005 for men). For women, combined biomarker models that included GDF-15 and NT-proBNP (+0.012) or GDF-15 and cTnI (+0.013), but not CRP or cTnT, further improved discrimination. For men, combined biomarker models that included NT-proBNP and GDF-15 (+0.007), NT-proBNP and cTnI (+0.006), or NT-proBNP and CRP (+0.008), but not cTnT, further improved discrimination. CONCLUSIONS: A combined biomarker approach, particularly the use of GDF-15, NT-proBNP and cTnI, further refined cardiovascular risk estimates.

Integration of datasets for individual prediction of DNA methylation-based biomarkers.

BACKGROUND: Epigenetic scores (EpiScores) can provide biomarkers of lifestyle and disease risk. Projecting new datasets onto a reference panel is challenging due to separation of technical and biological variation with array data. Normalisation can standardise data distributions but may also remove population-level biological variation. RESULTS: We compare two birth cohorts (Lothian Birth Cohorts of 1921 and 1936 - nLBC1921 = 387 and nLBC1936 = 498) with blood-based DNA methylation assessed at the same chronological age (79 years) and processed in the same lab but in different years and experimental batches. We examine the effect of 16 normalisation methods on a novel BMI EpiScore (trained in an external cohort, n = 18,413), and Horvath's pan-tissue DNA methylation age, when the cohorts are normalised separately and together. The BMI EpiScore explains a maximum variance of R2=24.5% in BMI in LBC1936 (SWAN normalisation). Although there are cross-cohort R2 differences, the normalisation method makes a minimal difference to within-cohort estimates. Conversely, a range of absolute differences are seen for individual-level EpiScore estimates for BMI and age when cohorts are normalised separately versus together. While within-array methods result in identical EpiScores whether a cohort is normalised on its own or together with the second dataset, a range of differences is observed for between-array methods. CONCLUSIONS: Normalisation methods returning similar EpiScores, whether cohorts are analysed separately or together, will minimise technical variation when projecting new data onto a reference panel. These methods are important for cases where raw data is unavailable and joint normalisation of cohorts is computationally expensive.

Blood-based genome-wide DNA methylation correlations across body-fat- and adiposity-related biochemical traits.

The recent increase in obesity levels across many countries is likely to be driven by nongenetic factors. The epigenetic modification DNA methylation (DNAm) may help to explore this, as it is sensitive to both genetic and environmental exposures. While the relationship between DNAm and body-fat traits has been extensively studied, there is limited literature on the shared associations of DNAm variation across such traits. Akin to genetic correlation estimates, here, we introduce an approach to evaluate the similarities in DNAm associations between traits: DNAm correlations. As DNAm can be both a cause and consequence of complex traits, DNAm correlations have the potential to provide insights into trait relationships above that currently obtained from genetic and phenotypic correlations. Utilizing 7,519 unrelated individuals from Generation Scotland with DNAm from the EPIC array, we calculated DNAm correlations between body-fat- and adiposity-related traits by using the bivariate OREML framework in the OSCA software. For each trait, we also estimated the shared contribution of DNAm between sexes. We identified strong, positive DNAm correlations between each of the body-fat traits (BMI, body-fat percentage, and waist-to-hip ratio, ranging from 0.96 to 1.00), finding larger associations than those identified by genetic and phenotypic correlations. We identified a significant deviation from 1 in the DNAm correlations for BMI between males and females, with sex-specific DNAm changes associated with BMI identified at eight DNAm probes. Employing genome-wide DNAm correlations to evaluate the similarities in the associations of DNAm with complex traits has provided insight into obesity-related traits beyond that provided by genetic correlations.

Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging.

The concept of aging is complex, including many related phenotypes such as healthspan, lifespan, extreme longevity, frailty and epigenetic aging, suggesting shared biological underpinnings; however, aging-related endpoints have been primarily assessed individually. Using data from these traits and multivariate genome-wide association study methods, we modeled their underlying genetic factor ('mvAge'). mvAge (effective n = ~1.9 million participants of European ancestry) identified 52 independent variants in 38 genomic loci. Twenty variants were novel (not reported in input genome-wide association studies). Transcriptomic imputation identified age-relevant genes, including VEGFA and PHB1. Drug-target Mendelian randomization with metformin target genes showed a beneficial impact on mvAge (P value = 8.41 × 10-5). Similarly, genetically proxied thiazolidinediones (P value = 3.50 × 10-10), proprotein convertase subtilisin/kexin 9 inhibition (P value = 1.62 × 10-6), angiopoietin-like protein 4, beta blockers and calcium channel blockers also had beneficial Mendelian randomization estimates. Extending the drug-target Mendelian randomization framework to 3,947 protein-coding genes prioritized 122 targets. Together, these findings will inform future studies aimed at improving healthy aging.

Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles.

Genetic studies have identified numerous regions associated with plasma fibrinogen levels in Europeans, yet missing heritability and limited inclusion of non-Europeans necessitates further studies with improved power and sensitivity. Compared with array-based genotyping, whole genome sequencing (WGS) data provides better coverage of the genome and better representation of non-European variants. To better understand the genetic landscape regulating plasma fibrinogen levels, we meta-analyzed WGS data from the NHLBI's Trans-Omics for Precision Medicine (TOPMed) program (n=32,572), with array-based genotype data from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (n=131,340) imputed to the TOPMed or Haplotype Reference Consortium panel. We identified 18 loci that have not been identified in prior genetic studies of fibrinogen. Of these, four are driven by common variants of small effect with reported MAF at least 10% higher in African populations. Three ( SERPINA1, ZFP36L2 , and TLR10) signals contain predicted deleterious missense variants. Two loci, SOCS3 and HPN , each harbor two conditionally distinct, non-coding variants. The gene region encoding the protein chain subunits ( FGG;FGB;FGA ), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common (MAF=0.180) in African reference panels but extremely rare (MAF=0.008) in Europeans. Through phenome-wide association studies in the VA Million Veteran Program, we found associations between fibrinogen polygenic risk scores and thrombotic and inflammatory disease phenotypes, including an association with gout. Our findings demonstrate the utility of WGS to augment genetic discovery in diverse populations and offer new insights for putative mechanisms of fibrinogen regulation. Key Points: Largest and most diverse genetic study of plasma fibrinogen identifies 54 regions (18 novel), housing 69 conditionally distinct variants (20 novel).Sufficient power achieved to identify signal driven by African population variant.Links to (1) liver enzyme, blood cell and lipid genetic signals, (2) liver regulatory elements, and (3) thrombotic and inflammatory disease.

Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals.

BACKGROUND: DNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at individual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on individual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish individuals. METHODS AND FINDINGS: DNA methylation was assayed at 752,722 CpG sites in whole-blood samples from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation's 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had >20 individuals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in >2 studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to individuals that are not of Scottish and European ancestry. CONCLUSIONS: We discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease.

Unraveling Neuro-Proteogenomic Landscape and Therapeutic Implications for Human Behaviors and Psychiatric Disorders.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.