Predictors of incident stroke among individuals without coronary artery calcification: A pooled cohort analysis from the Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study, and Framingham Heart Study.

INTRODUCTION: The absence of coronary artery calcium (CAC = 0) is associated with low risk of stroke events; however, predictors of incident stroke among those with CAC = 0 are not known. METHODS: Individual participant-level data were pooled from three prospective cohorts (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study, and Framingham Heart Study). Multivariable-adjusted Cox proportional hazards models were used to study the association between cardiovascular risk factors and incident adjudicated stroke among individuals with CAC = 0 who were free of clinical atherosclerotic cardiovascular disease at baseline. RESULTS: Among 6180 participants (mean age 53 [SD 11] years, 62% women, and 44% White, 36% Black, and 20% other individuals), over a median (IQR) follow up of 15 (12-16) years, there were 122 strokes (95 ischemic, 27 hemorrhagic) with an overall unadjusted event rate of 2.0 per 1000 person-years. After multivariable adjustment, risk factors associated with overall stroke included (hazard ratio [95% CI]) systolic blood pressure (SBP): 1.19 (1.05-1.36) per 10-mmHg increase and carotid intima-media thickness (CIMT): 1.21 (1.04-1.42) per 0.1-mm increment. Current cigarette smoking: 2.68 (1.11-6.50), SBP: 1.23 (1.06-1.42) per 10-mmHg increase, and CIMT: 1.25 (1.04-1.49) per 0.1-mm increment were associated with ischemic stroke, whereas C-reactive protein was associated with hemorrhagic stroke risk (0.49, 0.25-0.93). CONCLUSION: In a large cohort of individuals with CAC = 0, the rate for incident stroke was low (2.0 per 1000-person years) and was associated with modifiable risk factors.

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 percentage points higher in African populations. Three signals (SERPINA1, ZFP36L2, and TLR10) contain predicted deleterious missense variants. Two loci, SOCS3 and HPN, each harbor two conditionally distinct, non-coding variants. The gene region encoding the fibrinogen protein chain subunits (FGG;FGB;FGA), contains 7 distinct signals, including one novel signal driven by rs28577061, a variant common in African ancestry populations but extremely rare in Europeans (MAFAFR=0.180; MAFEUR=0.008). 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.

Proteomics of left ventricular structure in the Multi-Ethnic Study of Atherosclerosis.

AIMS: Proteomic profiling offers an expansive approach to biomarker discovery and mechanistic hypothesis generation for LV remodelling, a critical component of heart failure (HF). We sought to identify plasma proteins cross-sectionally associated with left ventricular (LV) size and geometry in a diverse population-based cohort without known cardiovascular disease (CVD). METHODS AND RESULTS: Among participants of the Multi-Ethnic Study of Atherosclerosis (MESA), we quantified plasma abundances of 1305 proteins using an aptamer-based platform at exam 1 (2000-2002) and exam 5 (2010-2011) and assessed LV structure by cardiac magnetic resonance (CMR) at the same time points. We used multivariable linear regression with robust variance to assess cross-sectional associations between plasma protein abundances and LV structural characteristics at exam 1, reproduced findings in later-life at exam 5, and explored relationships of associated proteins using annotated enrichment analysis. We studied 763 participants (mean age 60 ± 10 years at exam 1; 53% female; 19% Black race; 31% Hispanic ethnicity). Following adjustment for renal function and traditional CVD risk factors, plasma levels of 3 proteins were associated with LV mass index at both time points with the same directionality (FDR < 0.05): leptin (LEP), renin (REN), and cathepsin-D (CTSD); 20 with LV end-diastolic volume index: LEP, NT-proBNP, histone-lysine N-methyltransferase (EHMT2), chordin-like protein 1 (CHRDL1), tumour necrosis factor-inducible gene 6 protein (TNFAIP6), NT-3 growth factor receptor (NTRK3), c5a anaphylatoxin (C5), neurogenic locus notch homologue protein 3 (NOTCH3), ephrin-B2 (EFNB2), osteomodulin (OMD), contactin-4 (CNTN4), gelsolin (GSN), stromal cell-derived factor 1 (CXCL12), calcineurin subunit B type 1 (PPP3R1), insulin-like growth factor 1 receptor (IGF1R), bone sialoprotein 2 (IBSP), interleukin-11 (IL-11), follistatin-related protein 1 (FSTL1), periostin (POSTN), and biglycan (BGN); and 4 with LV mass-to-volume ratio: RGM domain family member B (RGMB), transforming growth factor beta receptor type 3 (TGFBR3), ephrin-A2 (EFNA2), and cell adhesion molecule 3 (CADM3). Functional annotation implicated regulation of the PI3K-Akt pathway, bone morphogenic protein signalling, and cGMP-mediated signalling. CONCLUSIONS: We report proteomic profiling of LV size and geometry, which identified novel associations and reinforced previous findings on biomarker candidates for LV remodelling and HF. If validated, these proteins may help refine risk prediction and identify novel therapeutic targets for HF.

Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application.

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α = 0.001. Notably, when 5 % or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31 % of African Ancestry, mean age of 62, with 58 % women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on both pooled samples and within each ancestry group. Our results suggest that mtDNA-encoded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the RNR1 and RNR2 genes (p < 0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations (p < 0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations.

Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1), and one locus associated with a sex-associated mutation pathway (SRGAP2C). We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

Steroid Hormone Biosynthesis and Dietary Related Metabolites Associated with Excessive Daytime Sleepiness.

Background: Excessive daytime sleepiness (EDS) is a complex sleep problem that affects approximately 33% of the United States population. Although EDS usually occurs in conjunction with insufficient sleep, and other sleep and circadian disorders, recent studies have shown unique genetic markers and metabolic pathways underlying EDS. Here, we aimed to further elucidate the biological profile of EDS using large scale single- and pathway-level metabolomics analyses. Methods: Metabolomics data were available for 877 metabolites in 6,071 individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and EDS was assessed using the Epworth Sleepiness Scale (ESS) questionnaire. We performed linear regression for each metabolite on continuous ESS, adjusting for demographic, lifestyle, and physiological confounders, and in sex specific groups. Subsequently, gaussian graphical modelling was performed coupled with pathway and enrichment analyses to generate a holistic interactive network of the metabolomic profile of EDS associations. Findings: We identified seven metabolites belonging to steroids, sphingomyelin, and long chain fatty acids sub-pathways in the primary model associated with EDS, and an additional three metabolites in the male-specific analysis. The identified metabolites particularly played a role in steroid hormone biosynthesis. Interpretation: Our findings indicate that an EDS metabolomic profile is characterized by endogenous and dietary metabolites within the steroid hormone biosynthesis pathway, with some pathways that differ by sex. Our findings identify potential pathways to target for addressing the causes or consequences of EDS and related sleep disorders. Funding: Details regarding funding supporting this work and all studies involved are provided in the acknowledgments section.

Proteogenomic analysis integrated with electronic health records data reveals disease-associated variants in Black Americans.

BACKGROUND: Most genome wide association studies (GWAS) of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry enriched protein quantitative loci (pQTL). METHODS: We conducted a discovery GWAS of ~3,000 plasma proteins measured by the antibody based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS), and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs were further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome wide association study (PheWAS) across two large multi-ethnic electronic health record (EHR) systems in All of Us and BioMe. RESULTS: We identified 1002 pQTLs for 925 proteins. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for Cathepsin L (CTSL) and Siglec-9 that were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, white blood cell count, and multiple sclerosis. CONCLUSIONS: Our findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.

Metagenomic Study of the MESA: Detection of Gemella Morbillorum and Association With Coronary Heart Disease.

BACKGROUND: Inflammation is a feature of coronary heart disease (CHD), but the role of proinflammatory microbial infection in CHD remains understudied. METHODS AND RESULTS: CHD was defined in the MESA (Multi-Ethnic Study of Atherosclerosis) as myocardial infarction (251 participants), resuscitated arrest (2 participants), and CHD death (80 participants). We analyzed sequencing reads from 4421 MESA participants in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program using the PathSeq workflow of the Genome Analysis Tool Kit and a 65-gigabase microbial reference. Paired reads aligning to 840 microbes were detected in >1% of participants. The association of the presence of microbe reads with incident CHD (follow-up, ~18 years) was examined. First, important variables were ascertained using a single regularized Cox proportional hazard model, examining change of risk as a function of presence of microbe with age, sex, education level, Life's Simple 7, and inflammation. For variables of importance, the hazard ratio (HR) was estimated in separate (unregularized) Cox proportional hazard models including the same covariates (significance threshold Bonferroni corrected P<6×10-5, 0.05/840). Reads from 2 microbes were significantly associated with CHD: Gemella morbillorum (HR, 3.14 [95% CI, 1.92-5.12]; P=4.86×10-6) and Pseudomonas species NFACC19-2 (HR, 3.22 [95% CI, 2.03-5.41]; P=1.58×10-6). CONCLUSIONS: Metagenomics of whole-genome sequence reads opens a possible frontier for detection of pathogens for chronic diseases. The association of G morbillorum and Pseudomonas species reads with CHD raises the possibilities that microbes may drive atherosclerotic inflammation and that treatments for specific pathogens may provide clinical utility for CHD reduction.

Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations.

AIMS/HYPOTHESIS: Several studies have reported associations between specific proteins and type 2 diabetes risk in European populations. To better understand the role played by proteins in type 2 diabetes aetiology across diverse populations, we conducted a large proteome-wide association study using genetic instruments across four racial and ethnic groups: African; Asian; Hispanic/Latino; and European. METHODS: Genome and plasma proteome data from the Multi-Ethnic Study of Atherosclerosis (MESA) study involving 182 African, 69 Asian, 284 Hispanic/Latino and 409 European individuals residing in the USA were used to establish protein prediction models by using potentially associated cis- and trans-SNPs. The models were applied to genome-wide association study summary statistics of 250,127 type 2 diabetes cases and 1,222,941 controls from different racial and ethnic populations. RESULTS: We identified three, 44 and one protein associated with type 2 diabetes risk in Asian, European and Hispanic/Latino populations, respectively. Meta-analysis identified 40 proteins associated with type 2 diabetes risk across the populations, including well-established as well as novel proteins not yet implicated in type 2 diabetes development. CONCLUSIONS/INTERPRETATION: Our study improves our understanding of the aetiology of type 2 diabetes in diverse populations. DATA AVAILABILITY: The summary statistics of multi-ethnic type 2 diabetes GWAS of MVP, DIAMANTE, Biobank Japan and other studies are available from The database of Genotypes and Phenotypes (dbGaP) under accession number phs001672.v3.p1. MESA genetic, proteome and covariate data can be accessed through dbGaP under phs000209.v13.p3. All code is available on GitHub ( https://github.com/Arthur1021/MESA-1K-PWAS ).

Analysis of C-reactive protein omics-measures associates methylation risk score with sleep health and related health outcomes.

Introduction: DNA methylation (DNAm) predictors of high sensitivity C-reactive protein (CRP) offer a stable and accurate means of assessing chronic inflammation, bypassing the CRP protein fluctuations secondary to acute illness. Poor sleep health is associated with elevated inflammation (including elevated blood CRP levels) which may explain associations of sleep insufficiency with metabolic, cardiovascular and neurological diseases. Our study aims to characterize the relationships among sleep health phenotypes and CRP markers -blood, genetic, and epigenetic indicators-within the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: In HCHS/SOL, methylation risk scores (MRS)-CRP and polygenetic risk score (PRS)-CRP were constructed separately as weighted sums of methylation beta values or allele counts, respectively, for each individual. Sleep health phenotypes were measured using self-reported questionnaires and objective measurements. Survey-weighted linear regression established the association between the multiple sleep phenotypes (obstructive sleep apnea (OSA), sleep duration, insomnia and excessive sleepiness symptom), cognitive assessments, diabetes and hypertension with CRP markers while adjusting for age, sex, BMI, study center, and the first five principal components of genetic ancestry in HCHS/SOL. Results: We included 2221 HCHS/SOL participants (age range 37-76 yrs, 65.7% female) in the analysis. Both the MRS-CRP (95% confidence interval (CI): 0.32-0.42, p = 3.3 × 10-38) and the PRS-CRP (95% CI: 0.15-0.25, p = 1 × 10-14) were associated with blood CRP level. Moreover, MRS-CRP was associated with sleep health phenotypes (OSA, long sleep duration) and related conditions (diabetes and hypertension), while PRS-CRP markers were not associated with these traits. Circulating CRP level was associated with sleep duration and diabetes. Associations between OSA traits and metabolic comorbidities weakened after adjusting for MRS-CRP, most strongly for diabetes, and least for hypertension. Conclusions: MRS-CRP is a promising estimate for systemic and chronic inflammation as reflected by circulating CRP levels, which either mediates or serves as a common cause of the association between sleep phenotypes and related comorbidities, especially in the presence of diabetes.

A protein risk score for all-cause and respiratory-specific mortality in non-Hispanic white and African American individuals who smoke.

Protein biomarkers are associated with mortality in cardiovascular disease, but their effect on predicting respiratory and all-cause mortality is not clear. We tested whether a protein risk score (protRS) can improve prediction of all-cause mortality over clinical risk factors in smokers. We utilized smoking-enriched (COPDGene, LSC, SPIROMICS) and general population-based (MESA) cohorts with SomaScan proteomic and mortality data. We split COPDGene into training and testing sets (50:50) and developed a protRS based on respiratory mortality effect size and parsimony. We tested multivariable associations of the protRS with all-cause, respiratory, and cardiovascular mortality, and performed meta-analysis, area-under-the-curve (AUC), and network analyses. We included 2232 participants. In COPDGene, a penalized regression-based protRS was most highly associated with respiratory mortality (OR 9.2) and parsimonious (15 proteins). This protRS was associated with all-cause mortality (random effects HR 1.79 [95% CI 1.31-2.43]). Adding the protRS to clinical covariates improved all-cause mortality prediction in COPDGene (AUC 0.87 vs 0.82) and SPIROMICS (0.74 vs 0.6), but not in LSC and MESA. Protein-protein interaction network analyses implicate cytokine signaling, innate immune responses, and extracellular matrix turnover. A blood-based protein risk score predicts all-cause and respiratory mortality, identifies potential drivers of mortality, and demonstrates heterogeneity in effects amongst cohorts.

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts.

BACKGROUND: Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in white individuals, and they used proteomic measures from only one-time point. In this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black). MEDTHODS AND FINDINGS: A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 standard deviation were 1.65 and 1.38 (both p < 0.001) for all-cause mortality, 1.37 and 1.20 (both p < 0.001) for CVD mortality, 1.21 (p = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.68 and 1.36 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals. CONCLUSIONS: In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.

Genetic Risk and Coronary Artery Calcium in Personalizing Antihypertensive Treatment: A Pooled Cohort Analysis.

OBJECTIVE: To assess the role of the systolic blood pressure polygenic risk score (SBP-PRS) in antihypertensive treatment initiation and its comparative efficacy with coronary artery calcium (CAC) scores. PATIENTS AND METHODS: This retrospective cohort study included participants with whole genome sequencing data who underwent CAC scanning between 1971 and 2008, were free of prevalent cardiovascular disease (CVD), and were not taking antihypertensive medications. The cohort was stratified by blood pressure (BP) treatment group and SBP-PRS (low/intermediate, first and second tertiles; high, third tertile) and CAC score (0 vs >0) subgroups. The primary outcome was the first occurence of adjudicated coronary heart disease, heart failure, or stroke during 10-year follow-up. The 10-year number needed to treat (NNT) to prevent 1 event of the primary outcome was estimated. A relative risk reduction of 25% for the primary outcome based on the treatment effect of intensive control (SBP <120 mm Hg) of hypertension in SPRINT (Systolic Blood Pressure Intervention Trial) was used for estimating the NNT. RESULTS: Among the 5267 study participants, the median age was 59 years (interquartile range, 51-68 years); 2817 (53.5%) were women and 2880 (54.7%) were non-White individuals. Among 1317 individuals with elevated BP/low-risk stage 1 hypertension not recommended treatment, the 10-year incidence rate of the primary outcome was 5.6% for low/intermediate SBP-PRS and 6.3% for high SBP-PRS with NNTs of 63 and 59, respectively. Similarly, the 10-year incidence rate of the primary outcome was 2.9% for CAC score 0 and 9.7% for CAC score greater than 0, with NNTs of 117 and 37, respectively. CONCLUSION: Including genetic information in risk estimation of individuals with elevated BP/low-risk stage 1 hypertension has modest value in the initiation of antihypertensive therapy. Genetic risk and CAC both have efficacy in personalizing antihypertensive therapy.

Polygenic scores and Mendelian randomization identify plasma proteins causally implicated in Alzheimer's disease.

Background: An increasing body of evidence suggests that neuroinflammation is one of the key drivers of late-onset Alzheimer's disease (LOAD) pathology. Due to the increased permeability of the blood-brain barrier (BBB) in older adults, peripheral plasma proteins can infiltrate the central nervous system (CNS) and drive neuroinflammation through interactions with neurons and glial cells. Because these inflammatory factors are heritable, a greater understanding of their genetic relationship with LOAD could identify new biomarkers that contribute to LOAD pathology or offer protection against it. Methods: We used a genome-wide association study (GWAS) of 90 different plasma proteins (n = 17,747) to create polygenic scores (PGSs) in an independent discovery (cases = 1,852 and controls = 1,990) and replication (cases = 799 and controls = 778) cohort. Multivariate logistic regression was used to associate the plasma protein PGSs with LOAD diagnosis while controlling for age, sex, principal components 1-2, and the number of APOE-e4 alleles as covariates. After meta-analyzing the PGS-LOAD associations between the two cohorts, we then performed a two-sample Mendelian randomization (MR) analysis using the summary statistics of significant plasma protein level PGSs in the meta-analysis as an exposure, and a GWAS of clinically diagnosed LOAD (cases = 21,982, controls = 41,944) as an outcome to explore possible causal relationships between the two. Results: We identified four plasma protein level PGSs that were significantly associated (FDR-adjusted p < 0.05) with LOAD in a meta-analysis of the discovery and replication cohorts: CX3CL1, hepatocyte growth factor (HGF), TIE2, and matrix metalloproteinase-3 (MMP-3). When these four plasma proteins were used as exposures in MR with LOAD liability as the outcome, plasma levels of HGF were inferred to have a negative causal relationship with the disease when single-nucleotide polymorphisms (SNPs) used as instrumental variables were not restricted to cis-variants (OR/95%CI = 0.945/0.906-0.984, p = 0.005). Conclusion: Our results show that plasma HGF has a negative causal relationship with LOAD liability that is driven by pleiotropic SNPs possibly involved in other pathways. These findings suggest a low transferability between PGS and MR approaches, and future research should explore ways in which LOAD and the plasma proteome may interact.

Genetic Architecture and Analysis Practices of Circulating Metabolites in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program.

Circulating metabolite levels partly reflect the state of human health and diseases, and can be impacted by genetic determinants. Hundreds of loci associated with circulating metabolites have been identified; however, most findings focus on predominantly European ancestry or single study analyses. Leveraging the rich metabolomics resources generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, we harmonized and accessibly cataloged 1,729 circulating metabolites among 25,058 ancestrally-diverse samples. We provided recommendations for outlier and imputation handling to process metabolite data, as well as a general analytical framework. We further performed a pooled analysis following our practical recommendations and discovered 1,778 independent loci associated with 667 metabolites. Among 108 novel locus - metabolite pairs, we detected not only novel loci within previously implicated metabolite associated genes, but also novel genes (such as GAB3 and VSIG4 located in the X chromosome) that have putative roles in metabolic regulation. In the sex-stratified analysis, we revealed 85 independent locus-metabolite pairs with evidence of sexual dimorphism, including well-known metabolic genes such as FADS2 , D2HGDH , SUGP1 , UTG2B17 , strongly supporting the importance of exploring sex difference in the human metabolome. Taken together, our study depicted the genetic contribution to circulating metabolite levels, providing additional insight into the understanding of human health.

Sexually dimorphic DNA methylation and gene expression patterns in human first trimester placenta.

BACKGROUND: Fetal sex and placental development impact pregnancy outcomes and fetal-maternal health, but the critical timepoint of placenta establishment in first trimester is understudied in human pregnancies. METHODS: Pregnant subjects were recruited in late first trimester (weeks 10-14) at time of chorionic villus sampling, a prenatal diagnostic test. Leftover placenta tissue was collected and stored until birth outcomes were known, then DNA and RNA were isolated from singleton, normal karyotype pregnancies resulting in live births. DNA methylation was measured with the Illumina Infinium MethylationEPIC BeadChip array (n = 56). Differential methylation analysis compared 25 females versus 31 males using a generalized linear model on 743,461 autosomal probes. Gene expression sex differences were analyzed with RNA-sequencing (n = 74). An integrated analysis was performed using linear regression to correlate gene expression and DNA methylation in 51 overlapping placentas. RESULTS: Methylation analysis identified 151 differentially methylated probes (DMPs) significant at false discovery rate < 0.05, including 89 (59%) hypermethylated in females. Probe cg17612569 (GABPA, ATP5J) was the most significant CpG site, hypermethylated in males. There were 11 differentially methylated regions affected by fetal sex, with transcription factors ZNF300 and ZNF311 most significantly hypermethylated in males and females, respectively. RNA-sequencing identified 152 genes significantly sexually dimorphic at false discovery rate < 0.05. The 151 DMPs were associated with 18 genes with gene downregulation (P < 0.05) in the direction of hypermethylation, including 2 genes significant at false discovery rate < 0.05 (ZNF300 and CUB and Sushi multiple domains 1, CSMD1). Both genes, as well as Family With Sequence Similarity 228 Member A (FAM228A), showed significant correlation between DNA methylation and sexually dimorphic gene expression, though FAM228A DNA methylation was less sexually dimorphic. Comparison with other sex differences studies found that cg17612569 is male-hypermethylated across gestation in placenta and in human blood up to adulthood. CONCLUSIONS: Overall, sex dimorphic differential methylation with associated differential gene expression in the first trimester placenta is small, but there remain significant genes that may be regulated through methylation leading to differences in the first trimester placenta.

Fetal sex and placenta development affect pregnancy outcomes for both the fetus and mother throughout pregnancy, including risk of miscarriages, preterm birth, preeclampsia, and other outcomes. Epigenetics, the "overlay" of regulatory signals on DNA which affects how DNA is read, is not well understood in early pregnancy when critical placenta developments are happening that affect the rest of pregnancy. Here, we use leftover placenta biopsy samples (n = 56) donated by Cedars-Sinai patients with informed consent to learn about first trimester human placenta DNA methylation differences due to fetal sex. Out of the total 743,461 sites analyzed, we identified 151 sites significantly affected by fetal sex after correcting p-values to reduce false positives (false discovery rate < 0.05). We also performed an analysis to look at multiple sites and identified 11 regions across the genome with significant DNA methylation changes due to fetal sex. Furthermore, because DNA methylation is a regulatory mark on DNA which typically dampens gene expression, we also compared the DNA methylation sex differences to placental RNA-sequencing gene expression analysis using the same tissue from a mostly overlapping patient group (n = 74 total sequenced, n = 51 overlap). We identify 18 genes which show both significant DNA methylation differences and gene expression changes. The most significant gene was transcription factor ZNF300 with higher DNA methylation in males and reduced gene expression in males (and thus higher gene expression in females). This study identifies some sex differences that continue until later pregnancy and others that are unique to first trimester.

Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease.

Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific mechanism-based treatment. We used Mendelian randomization to combine a unique cerebrospinal fluid (CSF) and plasma pQTL resource with the latest European-ancestry GWAS of MRI-markers of cSVD (white matter hyperintensities, perivascular spaces). We describe a new biological fingerprint of 49 protein-cSVD associations, predominantly in the CSF. We implemented a multipronged follow-up, across fluids, platforms, and ancestries (Europeans and East-Asian), including testing associations of direct plasma protein measurements with MRI-cSVD. We highlight 16 proteins robustly associated in both CSF and plasma, with 24/4 proteins identified in CSF/plasma only. cSVD-proteins were enriched in extracellular matrix and immune response pathways, and in genes enriched in microglia and specific microglial states (integration with single-nucleus RNA sequencing). Immune-related proteins were associated with MRI-cSVD already at age twenty. Half of cSVD-proteins were associated with stroke, dementia, or both, and seven cSVD-proteins are targets for known drugs (used for other indications in directions compatible with beneficial therapeutic effects. This first cSVD proteogenomic signature opens new avenues for biomarker and therapeutic developments.

Multi-omics characterization of type 2 diabetes associated genetic variation.

Discerning the mechanisms driving type 2 diabetes (T2D) pathophysiology from genome-wide association studies (GWAS) remains a challenge. To this end, we integrated omics information from 16 multi-tissue and multi-ancestry expression, protein, and metabolite quantitative trait loci (QTL) studies and 46 multi-ancestry GWAS for T2D-related traits with the largest, most ancestrally diverse T2D GWAS to date. Of the 1,289 T2D GWAS index variants, 716 (56%) demonstrated strong evidence of colocalization with a molecular or T2D-related trait, implicating 657 cis-effector genes, 1,691 distal-effector genes, 731 metabolites, and 43 T2D-related traits. We identified 773 of these cis- and distal-effector genes using either expression QTL data from understudied ancestry groups or inclusion of T2D index variants enriched in underrepresented populations, emphasizing the value of increasing population diversity in functional mapping. Linking these variants, genes, metabolites, and traits into a network, we elucidated mechanisms through which T2D-associated variation may impact disease risk. Finally, we showed that drugs targeting effector proteins were enriched in those approved to treat T2D, highlighting the potential of these results to prioritize drug targets for T2D. These results represent a leap in the molecular characterization of T2D-associated genetic variation and will aid in translating genetic findings into novel therapeutic strategies.