Josephine A. Robertson

Understanding the genetic architecture of the circulating proteome can help determine if a protein is causally linked to disease. Previous large-scale genome-wide association studies of proteins have mostly been conducted on pre-defined, targeted subsets of the proteome, and have often concentrated on low-abundance proteins, many of which don’t exert their main function in serum. Mass spectrometry-based proteomics facilitates the study of high-abundance proteins and their isoforms, focusing on proteins active in blood. In 15035 individuals from Generation Scotland, we perform genome-wide association studies of 439 highly abundant serum protein groups as identified and quantified by liquid chromatography tandem mass spectrometry. We identify 1553 independent SNP signals for 398 proteins ( P < 1.2×10 ⁻¹⁰ ). Two-sample Mendelian Randomisation analyses are applied to test if the 398 proteins with significant SNP signals are causally associated with 79 common causes of morbidity and mortality. We report putative causal associations between 13 proteins and 17 outcomes, including neuropsychiatric and cardiovascular conditions. Large-scale genome-wide analyses of the high-abundance proteome complement targeted approaches for the discovery of causal pathways of disease.

Ambient air pollution has been associated with increased incidence of chronic disease and is estimated to contribute towards 4.2 million early deaths annually. Whilst the health impacts are well described, less is understood about the underlying biological mechanisms, particularly when considering the co-occurrence of multiple pollutants. Using an atmospheric chemistry transportation model (EMEP4UK), we generate pre-baseline sampling pollution exposure estimates for eight pollutants in Generation Scotland (N = 22,071, recruited between 2006 - 2011). Cox-proportional hazard models reveal associations between pollution exposure and all-cause dementia (PM _2.5 ) and myocardial infarction (NO _{3_Coarse} ) over 18 years of follow-up. We perform Bayesian multivariate epigenome-wide (N = 18,512, Illumina EPIC v.1) and proteomic (N = 15,314, 133 mass-spectrometry proteins) association studies, revealing 11 pollutant-methylation associations and 140 pollutant-protein associations. We identify positive associations between exposure (PM _2.5 and NO _{3_Fine} ) and epigenetic age-acceleration (PhenoAge epigenetic clock). Furthermore, we explore the development of pollutant EpiScores, assessing these in holdout and independent test sets. Our results enhance knowledge of molecular correlates of air pollution exposure, whilst providing further evidence of contributions of air pollutants to chronic disease.