Background: Recent studies have shown that hematopoietic stems cells can undergo clonal expansion secondary to somatic mutations in leukocytes-related genes, termed clonal hematopeisis of indeterminate potential (CHIP). The epigenetic regulator TET2 is frequently mutated in individuals with CHIP and has been associated with coronary heart disease in humans and Heart Failure in mice models through a mechanism of IL1B/NLRP3 Inflammasome. We investigated whether any of the top three somatic mutations ( DNMT3A,TET2, ASXL1) associated with CHIP were prospectively associated with heart failure (HF), heart failure with preserved ejection fraction (HFPEF)or heart failure with reduced ejection fraction (HFrEF) in post menopausal women.
Methods: A subcohort of 5214 post menopausal women in the Women’s Health Intiative were evaluated for CHIP and heart failure. CHIP was determined at the Broad Institute via whole genome sequencing using the GATK4 MuTect2 somatic variant caller through the NHLBI TOPMed project. Genotypes DNMT3A, TET2, ASXL1 associated with CHIP were evaluated for a prospective association with HF, HFpEF and HFrEF. Acute decompensated hospitalized heart failure was based upon trained physician record review over 15 years of follow-up. HFpEF was defined as an EF >50% and HFrEF as EF<50% . Cox proportional hazards model were evaluate adjusting for age, race, income, education, cigarette smoking, body mass index, coronary heart disease, atrial fibrillation, diabetes mellitus, hypertension, systolic blood pressure, and stroke. Inverse probability weighting was used to account for selection bias associated with the sub cohort selection.
Results: Of the 5214 post menopausal women, 597 developed HF, 283 HFpEF and 204 HFrEF. N=408 had CHIP. The top 3 gene mutations associated with CHIP (N=364) were DNMT3A (4.8%), TET2 (1.7%) and ASXL1 (0.5%). Women with CIP associated with any of the top 3 mutations and with TET2 were associated with HF, HFpEF but not HFrEF. DNMT3A and ASXL1 CHP mutations were not associated HF, HFpEF or HFrEF (See Table ).
HR and 95% CI HF HFpEF HFrEF
CHIP associated with Any of the Top 3 Gene mutations 1.36 (1.08,1.70) 1.42 (1.03,1.94) 0.84 (0.52,1.37)
DNMT3A 1.07 (0.80,1.44) 1.11 (0.74,1.67) 0.62(0.32,1.21)
TET2 2.07 (1.44,2.97) 2.50 (1.54, 4.06) 0.95 (0.38,2.36)
ASXL1 1.60 (0.80,3.23) 1.04 (0.31,3.56) 2.01 (0.70,5.79)
Conclusion: CHIP associated with the top 3 somatic mutations and TET2 were prospectively associated with HF and HFpEF consistent with animal models and the concept of HFpEF being associated with pathologic aging. Replication of these findings in other cohorts is warranted.

Epigenetic clocks have shown that patterns of DNA methylation from blood cells are strongly correlated with chronological age. Those with accelerated methylation age (methylation age that is greater than expected for chronological age) are at higher risk for several diseases of aging and death, but the biological processes underlying such advanced epigenetic age are incompletely understood. Clonal hematopoiesis of indeterminate potential (CHIP) results from somatic mutations in blood stem cells and may be found in ~20% of the elderly. CHIP most commonly arises due to mutations in the DNA methylation altering enzymes, TET2 and DNMT3A, and also associates with increased risk of death, cancer, and cardiovascular disease. Whether CHIP associates with accelerated methylation age is unknown. We used methylation and whole genome sequencing data from several cohorts in TOPMed together comprising thousands of persons to show that CHIP is strongly associated with increased epigenetic age acceleration. The most consistent association is observed for intrinsic aging (2.8 ± 0.36 years, p < 2.5 x 10-14), which measures epigenetic aging that is independent of changes in cell composition, while a more variable association was seen with extrinsic aging (2.5 ± 0.46 years, p < 4.6 x 10-7), which captures epigenetic aging that is driven by changes in cell composition. We also analyzed the gene-specific effects of CHIP mutations on epigenetic aging, dividing our CHIP carriers into DNMT3A, TET2, and all other CHIP mutations. We found that the increase in intrinsic age acceleration seen in CHIP was very consistent across different genes, while the increase in extrinsic aging was lower with DNMT3A mutations and higher in TET2 mutations. The epigenetic clock software we used can also predict cell type composition and leukocyte telomere length (LTL) from methylation. We observed an increased predicted proportion of CD8+/CD28-/CD45RA- T-cells and decreased predicted LTL. Future experiments should seek to determine whether there is a causal relationship between CHIP and epigenetic age acceleration and whether intrinsic or extrinsic age acceleration is predictive of health outcomes in those with CHIP.

GWAS studies have successfully identified thousands of SNPs associated with complex traits, however
identifying the functional elements through which these genetic variants exert their effects remains a critical challenge. Recently, there is increasing evidence that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTLs). Thus, incorporating eQTL information can potentially improve power in highlighting causal genes. Our goal was to investigate the potential to detect novel risk loci among eQTLs only. Our data was comprised of nine platelet aggregation traits from the GeneSTAR family-based study, with whole genome sequencing (WGS) data in European Americans (EA) and African Americans (AA). RNA-seq data were generated from extracted non-ribosomal RNA from 185 (84 AA, 101 EA) iPSC derived megakaryocyte cell lines and 290 (110 AA, 180 EA) platelet samples. We fit a linear mixed model for genetic association within each racial group, including age, sex, principal components for ancestry and random effects from genetic relatedness. We conducted fixed effects meta-analysis using summary statistics in the EA and AA groups. Since eQTLs typically exhibit very strong patterns of linkage disequilibrium, we performed permutation analysis using 1000 permutations for all nine traits to obtain family-wise error rates for eQTL SNPs, substantially lowering the genome-wide significance threshold compared to standard Bonferroni threshold. Our analyses confirmed known platelet aggregation loci such as PEAR1, ADRA2A and ARHGEF3. A number of novel genetic loci were also identified: ADAM22, APIP, ARAP2, BANF2, C6orf195, CBLN2, CEP68, CTNNA1, GPR98, GTF2IRD1, HIVEP2, IMPG2, LOC642236, MACROD2, NT5C1B-RDH14, PI4KAP1, RAB1A, RPTOR, SENP7, SLC1A4 and TMEM120B. Our future work entails exploring Bayesian GWAS by incorporating eQTL information to detect novel risk loci. Our study has the potential of identifying associated SNPs that underlie biological control of gene expression and genes involved in platelet aggregation.