Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1–4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.

No abstract

Background: The epithelial Na + channel (ENaC) is intrinsically linked to fluid volume homeostasis and blood pressure. Specific rare mutations in SCNN1A , SCNN1B , and SCNN1G , genes encoding the α, β, and γ subunits of ENaC, respectively, are associated with extreme blood pressure phenotypes. No associations between blood pressure and SCNN1D , which encodes the δ subunit of ENaC, have been reported. A small number of sequence variants in ENaC subunits have been reported to affect functional transport in vitro or blood pressure. The effects of the vast majority of rare and low-frequency ENaC variants on blood pressure are not known. Methods: We explored the association of low frequency and rare variants in the genes encoding ENaC subunits, with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure. Using whole-genome sequencing data from 14 studies participating in the Trans-Omics in Precision Medicine Whole-Genome Sequencing Program, and sequence kernel association tests. Results: We found that variants in SCNN1A and SCNN1B were associated with diastolic blood pressure and mean arterial pressure ( P <0.00625). Although SCNN1D is poorly expressed in human kidney tissue, SCNN1D variants were associated with systolic blood pressure, diastolic blood pressure, mean arterial pressure, and pulse pressure ( P <0.00625). ENaC variants in 2 of the 4 subunits ( SCNN1B and SCNN1D ) were also associated with estimated glomerular filtration rate ( P <0.00625), but not with stroke. Conclusions: Our results suggest that variants in extrarenal ENaCs, in addition to ENaCs expressed in kidneys, influence blood pressure and kidney function.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Arterial stiffness measured by aortic pulse wave velocity (PWV) and augmentation index (AI) is a marker of subclinical organ damage and an independent risk factor for cardiovascular disease events and mortality. 1-5 Although many factors, including aging, diabetes, hypertension, dyslipidemia, and chronic kidney disease, contribute to arterial stiffening, the pathophysiological mechanisms of this process are still not fully understood. Circulating low-weight metabolites represent the intermediate and end products of metabolic pathways and may reflect initial stages of arterial stiffness. Recent advances in metabolomics technology have allowed for global characterization of a large panel of metabolites from biological samples, which provides a unique opportunity for investigating arterial stiffness mechanisms. Previous metabolomics studies have identified important metabolites associated with arterial stiffness, 6-8 revealing novel mechanisms of arterial stiffening. 6-8 However, these studies either had small sample sizes or only assayed a limited number of metabolites, and omitted metabolite networks. In this study, we performed untargeted metabolomics profiling using the most up-to-date metabolites panel in 1,239 participants of the Bogalusa Heart Study (BHS) to identify