Identifying the downstream effects of disease-associated single nucleotide polymorphisms (SNPs) is challenging: the causal gene is often unknown or it is unclear how the SNP affects the causal gene, making it difficult to design experiments that reveal functional consequences. To help overcome this problem, we performed the largest expression quantitative trait locus (eQTL) meta-analysis so far reported in non-transformed peripheral blood samples of 5,311 individuals, with replication in 2,775 individuals. We identified and replicated trans-eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Although we did not study specific patient cohorts, we identified trait-associated SNPs that affect multiple trans-genes that are known to be markedly altered in patients: for example, systemic lupus erythematosus (SLE) SNP rs49170141 altered C1QB and five type 1 interferon response genes, both hallmarks of SLE2-4. Subsequent ChIP-seq data analysis on these trans-genes implicated transcription factor IKZF1 as the causal gene at this locus, with DeepSAGE RNA-sequencing revealing that rs4917014 strongly alters 3’ UTR levels of IKZF1. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the ‘transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Biomechanical forces are emerging as critical regulators of embryogenesis, particularly in the developing cardiovascular system 1,2 . After initiation of the heartbeat in vertebrates, cells lining the ventral aspect of the dorsal aorta, the placental vessels, and the umbilical and vitelline arteries initiate expression of the transcription factor Runx1 (refs 3-5), a master regulator of haematopoiesis, and give rise to haematopoietic cells 4 . It remains unknown whether the biomechanical forces imposed on the vascular wall at this developmental stage act as a determinant of haematopoietic potential 6 . Here, using mouse embryonic stem cells differentiated in vitro, we show that fluid shear stress increases the expression of Runx1 in CD41 + c-Kit + haematopoietic progenitor cells 7 ,concomitantly augmenting their haematopoietic colony-forming potential. Moreover, we find that shear stress increases haematopoietic colony-forming potential and expression of haematopoietic markers in the paraaortic splanchnopleura/aorta-gonads-mesonephros of mouse embryos and that abrogation of nitric oxide, a mediator of shear-stress-induced signalling 8 , compromises haematopoietic potential in vitro and in vivo. Collectively, these data reveal a critical role for biomechanical forces in haematopoietic development.In the mouse, the first haemogenic areas appear in the yolk sac starting at day 7.5 of development (E7.5) 9 . After the establishment of circulation and the onset of vascular flow at day 8.5, additional haemogenic sites appear between day 9 and 10.5 as Runx1 + regions within