Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2 , known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

IMPORTANCE Chronic lung diseases are a leading cause of morbidity and mortality. Unlike chronic obstructive pulmonary disease, clinical outcomes associated with proportional reductions in expiratory lung volumes without obstruction, otherwise known as preserved ratio impaired spirometry (PRISm), are poorly understood. OBJECTIVE To examine the prevalence, correlates, and clinical outcomes associated with PRISm in US adults. DESIGN, SETTING, AND PARTICIPANTS The National Heart, Lung, and Blood Institute (NHLBI) Pooled Cohorts Study was a retrospective study with harmonized pooled data from 9 US general populationbased cohorts (enrollment, 65 251 participants aged 18 to 102 years of whom 53 701 participants had valid baseline lung function) conducted from 1971-2011 (final follow-up, December 2018).EXPOSURES Participants were categorized into mutually exclusive groups by baseline lung function. PRISm was defined as the ratio of forced expiratory volume in the first second to forced vital capacity (FEV 1 :FVC) greater than or equal to 0.70 and FEV 1 less than 80% predicted; obstructive spirometry FEV 1 :FVC ratio of less than 0.70; and normal spirometry FEV 1 :FVC ratio greater than or equal to 0.7 and FEV 1 greater than or equal to 80% predicted. MAIN OUTCOMES AND MEASURESMain outcomes were all-cause mortality, respiratory-related mortality, coronary heart disease (CHD)-related mortality, respiratory-related events (hospitalizations and mortality), and CHD-related events (hospitalizations and mortality) classified by adjudication or validated administrative criteria. Absolute risks were adjusted for age and smoking status. Poisson and Cox proportional hazards models comparing PRISm vs normal spirometry were adjusted for age, sex, race and ethnicity, education, body mass index, smoking status, cohort, and comorbidities.

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.