An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates.

DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t-test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.

DNA methylation (DNAm)-based deconvolution provides highly accurate estimates of the proportion of each cell type in a mixed-cell type biological sample (e.g., whole-blood). These estimates can be used for examining the association between cell type proportions and biological or clinical end points; for example, comparing the estimated neutrophil proportion in whole blood between smokers and non-smokers. Cell proportion data has unique features which present challenges for traditional and widely used statistical methods. In response to this issue, our work presents two simulation studies and a real-world analysis that benchmark the performance of current standard statistical methods against an alternative method called analysis composition of microbes (ANCOM), which was originally developed for the analysis of microbiome data. In our real-world analysis we used DNAm data collected from Women's Health Initiative Long Life Study I and compared the results of each method against a gold-standard that is typically not available for these analyses. In each of our simulation studies, ANCOM was able to detect true differences in cell proportions between the groups being compared but had a much lower rate of false discovery compared with the standard statistical methods. Our real-world analysis demonstrated similar findings. Overall, our study highlights the potential of ANCOM as a powerful and robust method for analyzing DNAm-derived deconvolution estimates when the interest is comparisons of cell type proportions and biological or clinical end points. ANCOM's ability to minimize false discovery while maintaining robust statistical power positions it as a valuable addition to the epigenomic analysis toolkit.

Gene expression and splicing QTL analysis of blood cells in African American participants from the Jackson Heart Study.

Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analyses using TOPMed whole-genome sequencing-derived genotype data and RNA-sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.

Association of clonal hematopoiesis and mosaic chromosomal alterations with solid malignancy incidence and mortality.

BACKGROUND: Understanding the impact of clonal hematopoiesis of indeterminate potential (CHIP) and mosaic chromosomal alterations (mCAs) on solid tumor risk and mortality can shed light on novel cancer pathways. METHODS: The authors analyzed whole genome sequencing data from the Trans-Omics for Precision Medicine Women's Health Initiative study (n = 10,866). They investigated the presence of CHIP and mCA and their association with the development and mortality of breast, lung, and colorectal cancers. RESULTS: CHIP was associated with higher risk of breast (hazard ratio [HR], 1.30; 95% confidence interval [CI], 1.03-1.64; p = .02) but not colorectal (p = .77) or lung cancer (p = .32). CHIP carriers who developed colorectal cancer also had a greater risk for advanced-stage (p = .01), but this was not seen in breast or lung cancer. CHIP was associated with increased colorectal cancer mortality both with (HR, 3.99; 95% CI, 2.41-6.62; p < .001) and without adjustment (HR, 2.50; 95% CI, 1.32-4.72; p = .004) for advanced-stage and a borderline higher breast cancer mortality (HR, 1.53; 95% CI, 0.98-2.41; p = .06). Conversely, mCA (cell fraction [CF] >3%) did not correlate with cancer risk. With higher CFs (mCA >5%), autosomal mCA was associated with increased breast cancer risk (HR, 1.39; 95% CI, 1.06-1.83; p = .01). There was no association of mCA (>3%) with breast, colorectal, or lung mortality except higher colon cancer mortality (HR, 2.19; 95% CI, 1.11-4.3; p = .02) with mCA >5%. CONCLUSIONS: CHIP and mCA (CF >5%) were associated with higher breast cancer risk and colorectal cancer mortality individually. These data could inform on novel pathways that impact cancer risk and lead to better risk stratification.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints.

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

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

Multi-ancestry polygenic risk scores for venous thromboembolism.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx_combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx_combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE.

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

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

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations.

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Radon Exposure and Incident Stroke Risk in the Women's Health Initiative.

BACKGROUND AND OBJECTIVES: Little is known about the role of radon in the epidemiology of stroke among women. We therefore examined the association between home radon exposure and risk of stroke among middle-aged and older women in the United States. METHODS: We conducted a prospective cohort study of postmenopausal women aged 50-79 years at baseline (1993-1998) in the Women's Health Initiative. We measured exposures as 2-day, indoor, lowest living-level average radon concentrations in picocuries per liter (pCi/L) as estimated in 1993 by the US Geological Survey and reviewed by the Association of American State Geologists under the Indoor Radon Abatement Act. We used Cox proportional hazards models to estimate risk of incident, neurologist-adjudicated stroke during follow-up through 2020 as a hazard ratio and 95% CI, adjusting for study design and participant demographic, social, behavioral, and clinical characteristics. RESULTS: Among 158,910 women without stroke at baseline (mean age 63.2 years; 83% white), 6,979 incident strokes were identified over follow-up (mean 13.4 years). Incidence rates were 333, 343, and 349 strokes per 100,000 woman-years at radon concentrations of <2, 2-4, and >4 pCi/L, respectively. Compared with women living at concentrations <2 pCi/L, those at 2-4 and >4 pCi/L had higher covariate-adjusted risks of incident stroke: hazard ratio (95% CI) 1.06 (0.99-1.13) and 1.14 (1.05-1.22). Using nonlinear spline functions to model radon, stroke risk was significantly elevated at concentrations ranging from 2 to 4 pCi/L (p = 0.0004), that is, below the United States Environmental Protection Agency Radon Action Level for mitigation (4 pCi/L). Associations were slightly stronger for ischemic (especially cardioembolic, small vessel occlusive, and large artery atherosclerotic) than hemorrhagic stroke, but otherwise robust in sensitivity analyses. DISCUSSION: Radon exposure is associated with moderately increased stroke risk among middle-aged and older women in the United States, suggesting that promulgation of a lower Radon Action Level may help reduce the domestic impact of cerebrovascular disease on public health.

Mortality After Major Cardiovascular Events in Survivors of Childhood Cancer.

BACKGROUND: Adult survivors of childhood cancer are at risk for cardiovascular events. OBJECTIVES: In this study, we sought to determine the risk for mortality after a major cardiovascular event among childhood cancer survivors compared with noncancer populations. METHODS: All-cause and cardiovascular cause-specific mortality risks after heart failure (HF), coronary artery disease (CAD), or stroke were compared among survivors and siblings in the Childhood Cancer Survivor Study (CCSS) and participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. Cox proportional hazard regression models were used to estimate HRs and 95% CIs between groups, adjusted for demographic and clinical factors. RESULTS: Among 25,658 childhood cancer survivors (median age at diagnosis 7 years, median age at follow-up or death 38 years) and 5,051 siblings, 1,780 survivors and 91 siblings had a cardiovascular event. After HF, CAD, and stroke, 10-year all-cause mortalities were 30% (95% CI: 26%-33%), 36% (95% CI: 31%-40%), and 29% (95% CI: 24%-33%), respectively, among survivors vs 14% (95% CI: 0%-25%), 14% (95% CI: 2%-25%), and 4% (95% CI: 0%-11%) among siblings. All-cause mortality risks among childhood cancer survivors were increased after HF (HR: 7.32; 95% CI: 2.56-20.89), CAD (HR: 5.54; 95% CI: 2.37-12.93), and stroke (HR: 3.57; 95% CI: 1.12-11.37). CAD-specific mortality risk was increased (HR: 3.70; 95% CI: 1.05-13.02). Among 5,114 CARDIA participants, 345 had a major event. Although CARDIA participants were on average decades older at events (median age 57 years vs 31 years), mortality risks were similar, except that all-cause mortality after CAD was significantly increased among childhood cancer survivors (HR: 1.85; 95% CI: 1.16-2.95). CONCLUSIONS: Survivors of childhood cancer represent a population at high risk for mortality after major cardiovascular events.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

Multi-ancestry polygenic risk scores for venous thromboembolism.

Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium GWAS meta-analyses of European- (71,771 cases and 1,059,740 controls) and African-ancestry samples (7,482 cases and 129,975 controls). We used LDpred2 and PRSCSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6,261 cases and 88,238 controls) and African-ancestry sample (1,385 cases and 12,569 controls). Multi-ancestry PRSs with weights tuned in European- and African-ancestry samples, respectively, outperformed ancestry-specific PRSs in European- (PRSCSXEUR: AUC=0.61 (0.60, 0.61), PRSCSX_combinedEUR: AUC=0.61 (0.60, 0.62)) and African-ancestry test samples (PRSCSXAFR: AUC=0.58 (0.57, 0.6), PRSCSX_combined AFR: AUC=0.59 (0.57, 0.60)). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS may be used to identify individuals at highest risk for VTE and provide guidance for the most effective treatment strategy across diverse populations.

Clonal Hematopoiesis and Incident Heart Failure With Preserved Ejection Fraction.

Importance: Clonal hematopoiesis of indeterminate potential (CHIP), the age-related clonal expansion of hematopoietic stem cells with leukemogenic acquired genetic variants, is associated with incident heart failure (HF). Objective: To evaluate the associations of CHIP and key gene-specific CHIP subtypes with incident HF with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF). Design, Setting, and Participants: This population-based cohort study included participants from 2 racially diverse prospective cohort studies with uniform HF subtype adjudication: the Jackson Heart Study (JHS) and Women's Health Initiative (WHI). JHS participants were enrolled during 2000 to 2004 and followed up through 2016. WHI participants were enrolled during 1993 to 1998 and followed up through 2022. Participants who underwent whole-genome sequencing, lacked prevalent HF at baseline, and were followed up for HF adjudication were included. Follow-up occurred over a median (IQR) of 12.0 (11.0-12.0) years in the JHS and 15.3 (9.0-22.0) years in the WHI. Statistical analysis was performed from June to December 2023. Exposures: Any CHIP and the most common gene-specific CHIP subtypes (DNMT3A and TET2 CHIP). Main Outcomes and Measures: First incident hospitalized HF events were adjudicated from hospital records and classified as HFpEF (left ventricular ejection fraction ≥50%) or HFrEF (ejection fraction <50%). Results: A total of 8090 participants were included; 2927 from the JHS (median [IQR] age, 56 [46-65] years; 1846 [63.1%] female; 2927 [100.0%] Black or African American) and 5163 from the WHI (median [IQR] age, 67 [62-72] years; 5163 [100.0%] female; 29 [0.6%] American Indian or Alaska Native, 37 [0.7%] Asian or Pacific Islander, 1383 [26.8%] Black or African American, 293 [5.7%] Hispanic or Latinx, 3407 [66.0%] non-Hispanic White, and 14 [0.3%] with other race and ethnicity). The multivariable-adjusted hazard ratio (HR) for composite CHIP and HFpEF was 1.28 (95% CI, 0.93-1.76; P = .13), and for CHIP and HFrEF it was 0.79 (95% CI, 0.49-1.25; P = .31). TET2 CHIP was associated with HFpEF in both cohorts (meta-analyzed HR, 2.35 [95% CI, 1.34 to 4.11]; P = .003) independent of cardiovascular risk factors and coronary artery disease. Analyses stratified by C-reactive protein (CRP) in the WHI found an increased risk of incident HFpEF in individuals with CHIP and CRP greater than or equal to 2 mg/L (HR, 1.94 [95% CI, 1.20-3.15]; P = .007), but not in those with CHIP and CRP less than 2 mg/L or those with CRP greater than or equal to 2 mg/L without CHIP, when compared with participants without CHIP and CRP less than 2 mg/L. Conclusions and Relevance: In this cohort study, TET2 CHIP was an independent risk factor associated with incident HFpEF. This finding may have implications for the prevention and management of HFpEF, including development of targeted therapies.

Radon Exposure, Clonal Hematopoiesis, and Stroke Susceptibility in the Women's Health Initiative.

BACKGROUND AND OBJECTIVES: Studies suggest that clonal hematopoiesis of indeterminate potential (CHIP) may increase risk of hematologic malignancy and cardiovascular disease, including stroke. However, few studies have investigated plausible environmental risk factors for CHIP such as radon, despite the climate-related increases in and documented infrequency of testing for this common indoor air pollutant.The purpose of this study was to estimate the risk of CHIP related to radon, an established environmental mutagen. METHODS: We linked geocoded addresses of 10,799 Women's Health Initiative Trans-Omics for Precision Medicine (WHI TOPMed) participants to US Environmental Protection Agency-predicted, county-level, indoor average screening radon concentrations, categorized as follows: Zone 1 (>4 pCi/L), Zone 2 (2-4 pCi/L), and Zone 3 (<2 pCi/L). We defined CHIP as the presence of one or more leukemogenic driver mutations with variant allele frequency >0.02. We identified prevalent and incident ischemic and hemorrhagic strokes; subtyped ischemic stroke using Trial of ORG 10172 in Acute Stroke Treatment (TOAST) criteria; and then estimated radon-related risk of CHIP as an odds ratio (OR) and 95% CI using multivariable-adjusted, design-weighted logistic regression stratified by age, race/ethnicity, smoking status, and stroke type/subtype. RESULTS: The percentages of participants with CHIP in Zones 1, 2, and 3 were 9.0%, 8.4%, and 7.7%, respectively (ptrend = 0.06). Among participants with ischemic stroke, Zones 2 and 1 were associated with higher estimated risks of CHIP relative to Zone 3: 1.39 (1.15-1.68) and 1.46 (1.15-1.87), but not among participants with hemorrhagic stroke: 0.98 (0.68-1.40) and 1.03 (0.70-1.52), or without stroke: 1.04 (0.74-1.46) and 0.95 (0.63-1.42), respectively (pinteraction = 0.03). Corresponding estimates were particularly high among TOAST-subtyped cardioembolism: 1.78 (1.30-2.47) and 1.88 (1.31-2.72), or other ischemic etiologies: 1.37 (1.06-1.78) and 1.50 (1.11-2.04), but not small vessel occlusion: 1.05 (0.74-1.49) and 1.00 (0.68-1.47), respectively (pinteraction = 0.10). Observed patterns of association among strata were insensitive to attrition weighting, ancestry adjustment, prevalent stroke exclusion, separate analysis of DNMT3A driver mutations, and substitution with 3 alternative estimates of radon exposure. DISCUSSION: The robust elevation of radon-related risk of CHIP among postmenopausal women who develop incident cardioembolic stroke is consistent with a potential role of somatic genomic mutation in this societally burdensome form of cerebrovascular disease, although the mechanism has yet to be confirmed.

Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

Association Between Whole Blood-Derived Mitochondrial DNA Copy Number, Low-Density Lipoprotein Cholesterol, and Cardiovascular Disease Risk.

Background The relationship between mitochondrial DNA copy number (mtDNA CN) and cardiovascular disease remains elusive. Methods and Results We performed cross-sectional and prospective association analyses of blood-derived mtDNA CN and cardiovascular disease outcomes in 27 316 participants in 8 cohorts of multiple racial and ethnic groups with whole-genome sequencing. We also performed Mendelian randomization to explore causal relationships of mtDNA CN with coronary heart disease (CHD) and cardiometabolic risk factors (obesity, diabetes, hypertension, and hyperlipidemia). P<0.01 was used for significance. We validated most of the previously reported associations between mtDNA CN and cardiovascular disease outcomes. For example, 1-SD unit lower level of mtDNA CN was associated with 1.08 (95% CI, 1.04-1.12; P<0.001) times the hazard for developing incident CHD, adjusting for covariates. Mendelian randomization analyses showed no causal effect from a lower level of mtDNA CN to a higher CHD risk (ß=0.091; P=0.11) or in the reverse direction (ß=-0.012; P=0.076). Additional bidirectional Mendelian randomization analyses revealed that low-density lipoprotein cholesterol had a causal effect on mtDNA CN (ß=-0.084; P<0.001), but the reverse direction was not significant (P=0.059). No causal associations were observed between mtDNA CN and obesity, diabetes, and hypertension, in either direction. Multivariable Mendelian randomization analyses showed no causal effect of CHD on mtDNA CN, controlling for low-density lipoprotein cholesterol level (P=0.52), whereas there was a strong direct causal effect of higher low-density lipoprotein cholesterol on lower mtDNA CN, adjusting for CHD status (ß=-0.092; P<0.001). Conclusions Our findings indicate that high low-density lipoprotein cholesterol may underlie the complex relationships between mtDNA CN and vascular atherosclerosis.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.