Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association studies (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of 185 thousand CAD cases and controls, interrogating 6.7 million common (MAF>0.05) as well as 2.7 million low frequency (0.005

Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10−73). Two 10q25 SNPs (rs1329650[G], β = 0.367, s.e. = 0.059, P = 5.7 × 10−10; and rs1028936[A], β = 0.446, s.e. = 0.074, P = 1.3 × 10−9) and one 9q13 SNP in EGLN2 (rs3733829[G], β = 0.333, s.e. = 0.058, P = 1.0 × 10−8) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04–1.08, P = 1.8 × 10−8). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08–1.18, P = 3.6 × 10−8) was significantly associated with smoking cessation.

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

Pulmonary function is an easily measurable and reliable index of the physiological state of the lungs and airways 1 . Pulmonary function also predicts mortality in the general population, even among people who have never smoked (never-smokers) who have only modestly reduced pulmonary function and no respiratory symptoms 2,3 . The peak level of pulmonary function attained in early adulthood and its subsequent decline with age are likely influenced by genetic and environmental factors. Tobacco smoking is a major environmental cause of accelerated decline in pulmonary function with age. Other inhaled pollutants also appear to contribute. Familial aggregation studies suggest a genetic contribution to lung function, with heritability estimates exceeding 40% 4,5 , but little is known about the specific genetic factors involved. A relatively uncommon deficiency of α1-antitrypsin is the only established genetic risk factor for accelerated decline in pulmonary function and for development of chronic obstructive pulmonary disease (COPD), especially in smokers 4,6 . However, α1-antitrypsin accounts for little of the population variability in pulmonary function 4 . Candidate gene studies suggest that other genetic variants may influence the time course of pulmonary function and its decline in relation to smoking, but these putative genetic risk factors remain unknown 4 .Forced expiratory volume in the first second (FEV 1 ) and its ratio to forced vital capacity (FEV 1 /FVC) are two clinically relevant pulmonary function measures. Although both FEV 1 and FVC are influenced by lung size and can be reduced by restrictive lung diseases, obstructive lung disease leads to proportionately greater reduction in FEV 1 than FVC. Therefore, reduced FEV 1 /FVC, an indicator of airflow obstruction that is independent of lung size, is the primary criterion for defining an obstructive ventilatory defect 1 . Whereas low FEV 1 /FVC indicates the presence of airflow obstruction, FEV 1 is used to classify the severity and follow the progression of obstructive lung disease over time 5,7,8 .The first genome-wide association study (GWAS) for pulmonary function evaluating 70,987 SNPs in about 1,220 Framingham Heart Study (FHS) participants revealed no genome-wide significant loci 9 . Recently, a GWAS of FEV 1 /FVC using 2,540,223 SNPs in 7,691 FHS participants identified several SNPs on chromosome 4q31 near HHIP with genome-wide significance 10 . A GWAS of COPD 11 also implicated the HHIP region along with CHRNA3-CHRNA5 on chromosome 15, a region previously associated with nicotine dependence 12,13 .We conducted meta-analyses of GWAS results for a cross-sectional analysis of pulmonary function (FEV 1 /FVC and FEV 1 ) in 20,890 individuals of European ancestry from four Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium 14 studies: Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), FHS and Rotterdam Study (RS-I and RS-II). Given that cigarette smoking is a major risk factor for pulmonary fun...

The timing of puberty is a highly polygenic childhood trait that is epidemiologically associated with various adult diseases. Using 1000 Genomes Project–imputed genotype data in up to ~370,000 women, we identify 389 independent signals (P < 5 × 10−8) for age at menarche, a milestone in female pubertal development. In Icelandic data, these signals explain ~7.4% of the population variance in age at menarche, corresponding to ~25% of the estimated heritability. We implicate ~250 genes via coding variation or associated expression, demonstrating significant enrichment in neural tissues. Rare variants near the imprinted genes MKRN3 and DLK1 were identified, exhibiting large effects when paternally inherited. Mendelian randomization analyses suggest causal inverse associations, independent of body mass index (BMI), between puberty timing and risks for breast and endometrial cancers in women and prostate cancer in men. In aggregate, our findings highlight the complexity of the genetic regulation of puberty timing and support causal links with cancer susceptibility.