Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

BackgroundUrinary biomarkers are associated with hypertension and cardiovascular disease (CVD), but the nature of these associations is incompletely understood. MethodsWe performed multivariable-adjusted regression models to assess associations of urinary sodium-potassium ratio (UNa/UK), and urinary albumin adjusted for creatinine (UAlb/UCr) with cardiovascular risk factors, CVD and type 2 diabetes (T2D) in 478,311 participants of the UK Biobank. Further, we studied above associations separately in men and women, and assessed the causal relationships of these kidney biomarkers with cardiovascular outcomes using the two-sample Mendelian randomization (MR) approach. ResultsIn observational analyses, UNa/UK showed significant inverse associations with atrial fibrillation (AF), coronary artery disease (CAD), ischemic stroke, lipid-lowering medication and T2D. In contrast, UAlb/UCr showed significant positive associations with AF, CAD, heart failure, hemorrhagic stroke, lipid-lowering medication and T2D. We found a positive association between UNa/UK and albumin with blood pressure (BP), as well as with adiposity-related measures. Generally, we detected consistent directionality in sex-stratified analyses, with some evidence for sex differences in the associations of urinary biomarkers with T2D and obesity. After correcting for potential horizontal pleiotropy, we found evidence of causal associations of UNa/UK and albumin with systolic BP (betaSBP≥2.63; betaDBP≥0.85 SD increase in systolic BP per SD change UNa/UK and UAlb/UCr; P≤0.038), and of albumin with T2D (odds ratio=1.33 per SD change in albumin, P=0.023). ConclusionOur Mendelian randomization analyses mirror and extend findings from randomized interventional trials which have established sodium intake as a risk factor for hypertension. In addition, we detect a feed-back causal loop between albumin and hypertension, and our finding of a bidirectional causal association between albumin and T2D reflects the well-known nephropathy in T2D.

Background: Mendelian randomization studies are susceptible to meta-data errors (e.g. incorrect specification of the effect allele column) and other analytical issues that can introduce substantial bias into analyses. We developed a quality control pipeline for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC) that can be used to identify and correct for such errors. Methods: We invited cancer GWAS to share summary association statistics with the FAMRC and subjected the collated data to a comprehensive QC pipeline. We identified meta data errors through comparison of study-specific statistics to external reference datasets (the NHGRI-EBI GWAS catalog and 1000 genome super populations) and other analytical issues through comparison of reported to expected genetic effect sizes. Comparisons were based on three sets of genetic variants: 1) GWAS hits for fatty acids, 2) GWAS hits for cancer and 3) a 1000 genomes reference set. Results: We collated summary data from six fatty acid and 49 cancer GWAS. Meta data errors and analytical issues with the potential to introduce substantial bias were identified in seven studies (13%). After resolving analytical issues and excluding unreliable data, we created a dataset of 219,842 genetic associations with 87 cancer types. Conclusion: In this large MR collaboration, 13% of included studies were affected by a substantial meta data error or other analytical issue. By increasing the integrity of collated summary data prior to their analysis, our protocol can be used to increase the reliability of post-GWAS analyses. Our pipeline is available to other researchers via the CheckSumStats package (https://github.com/MRCIEU/CheckSumStats).

Background: A robust method for Mendelian randomization does not require all genetic