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Abstract Whole genome sequences (WGS) enable discovery of rare variants which may contribute to missing heritability of coronary artery disease (CAD). To measure their contribution, we apply the GREML-LDMS-I approach to WGS of 4949 cases and 17,494 controls of European ancestry from the NHLBI TOPMed program. We estimate CAD heritability at 34.3% assuming a prevalence of 8.2%. Ultra-rare (minor allele frequency ≤ 0.1%) variants with low linkage disequilibrium (LD) score contribute ~50% of the heritability. We also investigate CAD heritability enrichment using a diverse set of functional annotations: i) constraint; ii) predicted protein-altering impact; iii) cis-regulatory elements from a cell-specific chromatin atlas of the human coronary; and iv) annotation principal components representing a wide range of functional processes. We observe marked enrichment of CAD heritability for most functional annotations. These results reveal the predominant role of ultra-rare variants in low LD on the heritability of CAD. Moreover, they highlight several functional processes including cell type-specific regulatory mechanisms as key drivers of CAD genetic risk.

Abstract The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N = 200,003), TOPMed (N = 87,652) and All of Us (N = 45,445). We performed rare (

Abstract We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model’s performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Abstract Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Abstract Aims Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk‐factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi‐racial populations. Methods and results Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow‐up. PRSs of AF, BMI, and CHD were associated with incident HF (P

Abstract Background Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality. Methods We searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies—of Exposure tool. Results Out of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies. Conclusions In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Abstract To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Abstract Background Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. Methods We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. Results The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. Conclusions VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

Abstract X-chromosomal genetic variants are understudied but can yield valuable insights into sexually dimorphic human traits and diseases. We performed a sex-stratified cross-ancestry X-chromosome-wide association meta-analysis of seven kidney-related traits (n = 908,697), identifying 23 loci genome-wide significantly associated with two of the traits: 7 for uric acid and 16 for estimated glomerular filtration rate (eGFR), including four novel eGFR loci containing the functionally plausible prioritized genes ACSL4, CLDN2, TSPAN6 and the female-specific DRP2. Further, we identified five novel sex-interactions, comprising male-specific effects at FAM9B and AR/EDA2R, and three sex-differential findings with larger genetic effect sizes in males at DCAF12L1 and MST4 and larger effect sizes in females at HPRT1. All prioritized genes in loci showing significant sex-interactions were located next to androgen response elements (ARE). Five ARE genes showed sex-differential expressions. This study contributes new insights into sex-dimorphisms of kidney traits along with new prioritized gene targets for further molecular research.

BackgroundCirculating immune cells have gained interest as biomarkers of hepatic steatosis. Data on the relationships between immune cell subsets and early-stage steatosis in population-based cohorts are limited.MethodsThis study included 1,944 asymptomatic participants of the Multi-Ethnic Study of Atherosclerosis (MESA) with immune cell phenotyping and computed tomography measures of liver fat. Participants with heavy alcohol use were excluded. A liver-to-spleen ratio Hounsfield units (HU) 30% liver fat content, respectively. Logistic regression estimated cross-sectional associations of immune cell subsets with liver fat parameters adjusted for risk factors. We hypothesized that higher proportions of non-classical monocytes, Th1, Th17, and memory CD4+ T cells, and lower proportions of classical monocytes and naive CD4+ T cells, were associated with liver fat. Exploratory analyses evaluated additional immune cell phenotypes (n = 19).ResultsNone of the hypothesized cells were associated with presence of liver fat. Higher memory CD4+ T cells were associated with >30% liver fat content, but this was not significant after correction for multiple hypothesis testing (odds ratio (OR): 1.31, 95% confidence interval (CI): 1.03, 1.66). In exploratory analyses unadjusted for multiple testing, higher proportions of CD8+CD57+ T cells were associated with liver fat presence (OR: 1.21, 95% CI: 1.02, 1.44) and >30% liver fat content (OR: 1.34, 95% CI: 1.07, 1.69).ConclusionsHigher circulating memory CD4+ T cells may reflect liver fat severity. CD8+CD57+ cells were associated with liver fat presence and severity, but replication of findings is required.