Inflammatory biomarker genomics:From discovery to causality

Inflammatory biomarkers are a group of proteins circulating in the blood that play key roles in inflammation. The blood levels of these are partially genetically determined, and elevated levels are hallmarks for various types of diseases and sometimes even directly implicated in pathogenesis. In this thesis I aimed to identify previously unknown genetic regions (‘loci’) for well-known inflammatory biomarkers, to understand how they influence molecular levels, and whether they play a causal role in various diseases. After the preface (Chapter 1), I present three large-scale genome-wide association studies (GWASs) that led to the identification of new genetic loci for levels of four inflammatory biomarkers: TNF-α, Interleukin-6, serum albumin and total protein (Chapter 3,4 and 5, respectively). These analyses were supported by a software pipeline that automated data quality checks for these studies (Chapter 2). In Chapter 6, we focussed on disentangling the molecular mechanisms through which genetic determinants influence levels of one of the most widely clinically used inflammatory biomarkers, C-Reactive Protein, followed by an investigation of its involvement in multiple disease classes (Chapter 7). I conclude with a review on GWAS for Coronary Artery Disease (Chapter 8), and argue that we can improve our understanding of the mechanisms by which genetic loci influence traits of interest through the integration with other layers of molecular data, an approach known as systems genetics. This research has increased our biological understanding of genetic determinants of inflammatory biomarkers and provides further leads for investigation of their direct involvement in the pathogenesis of disease

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Publisher Correction:Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals (Nature Genetics, (2020), 52, 12, (1314-1332), 10.1038/s41588-020-00713-x)

In the version of this article originally published, the e-mail address of corresponding author Patricia B. Munroe was incorrect. The error has been corrected in the HTML and PDF versions of the article

Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P&lt;5×10 - 8 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care. </p

Evidence for large-scale gene-by-smoking interaction effects on pulmonary function

Background: Smoking is the strongest environmental risk factor for reduced pulmonary function. The genetic component of various pulmonary traits has also been demonstrated, and at least 26 loci have been reproducibly associated with either FEV1 (forced expiratory volume in 1 second) or FEV1/FVC (FEV1/forced vital capacity). Although the main effects of smoking and genetic loci are well established, the question of potential gene-by-smoking interaction effect remains unanswered. The aim of the present study was to assess, using a genetic risk score approach, whether the effect of these 26 loci on pulmonary function is influenced by smoking. Methods: We evaluated the interaction between smoking exposure, considered as either ever vs never or pack-years, and a 26-single nucleotide polymorphisms (SNPs) genetic risk score in relation to FEV1 or FEV1/FVC in 50 047 participants of European ancestry from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and SpiroMeta consortia. Results: We identified an interaction (beta(int) = -0.036, 95% confidence interval, -0.040 to -0.032, P = 0.00057) between an unweighted 26 SNP genetic risk score and smoking status (ever/never) on the FEV1/FVC ratio. In interpreting this interaction, we showed that the genetic risk of falling below the FEV1/FVC threshold used to diagnose chronic obstructive pulmonary disease is higher among ever smokers than among never smokers. A replication analysis in two independent datasets, although not statistically significant, showed a similar trend in the interaction effect. Conclusions: This study highlights the benefit of using genetic risk scores for identifying interactions missed when studying individual SNPs and shows, for the first time, that persons with the highest genetic risk for low FEV1/FVC may be more susceptible to the deleterious effects of smoking.Peer reviewe

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals.

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health