Background DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. Methods and Results To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15,907 blood derived DNA samples from participants in 16 cohorts (including 2,433 current, 6,518 former, and 6,956 never smokers). Comparing current versus never smokers, 2,623 CpG sites (CpGs), annotated to 1,405 genes, were statistically significantly differentially methylated at Bonferroni threshold of p<1×10−7 (18,760 CpGs at False Discovery Rate (FDR)<0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant p<1×10−7 (2,623 CpGs at FDR<0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. Conclusions Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biologic effects of smoking, and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.

Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown. 1 Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations leading to clonal expansion was associated with both hematologic cancer 2 – 4 and coronary heart disease 5 , a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP). 6 Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico -informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer resulting in increased hematopoietic stem cell self-renewal. Overall, we observe that germline genetic variation shapes hematopoietic stem cell function leading to CHIP through mechanisms that are both specific to clonal hematopoiesis and shared mechanisms leading to somatic mutations across tissues.

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The lack of reliable measures of alcohol intake is a major obstacle to the diagnosis and treatment of alcohol-related diseases. Epigenetic modifications such as DNA methylation may provide novel biomarkers of alcohol use. To examine this possibility, we performed an epigenome-wide association study of methylation of cytosine-phosphate-guanine dinucleotide (CpG) sites in relation to alcohol intake in 13 population-based cohorts (ntotal=13 317; 54% women; mean age across cohorts 42–76 years) using whole blood (9643 European and 2423 African ancestries) or monocyte-derived DNA (588 European, 263 African and 400 Hispanic ancestry) samples. We performed meta-analysis and variable selection in whole-blood samples of people of European ancestry (n=6926) and identified 144 CpGs that provided substantial discrimination (area under the curve=0.90–0.99) for current heavy alcohol intake (⩾42 g per day in men and ⩾28 g per day in women) in four replication cohorts. The ancestry-stratified meta-analysis in whole blood identified 328 (9643 European ancestry samples) and 165 (2423 African ancestry samples) alcohol-related CpGs at Bonferroni-adjusted P<1 × 10−7. Analysis of the monocyte-derived DNA (n=1251) identified 62 alcohol-related CpGs at P<1 × 10-7. In whole-blood samples of people of European ancestry, we detected differential methylation in two neurotransmitter receptor genes, the γ-Aminobutyric acid-A receptor delta and γ-aminobutyric acid B receptor subunit 1; their differential methylation was associated with expression levels of a number of genes involved in immune function. In conclusion, we have identified a robust alcohol-related DNA methylation signature and shown the potential utility of DNA methylation as a clinically useful diagnostic test to detect current heavy alcohol consumption.