A genome wide meta-analysis by the CORtisol NETwork (CORNET) consortium1 has identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with changes in morning plasma cortisol and predictive of cardiovascular disease (Crawford et al, Unpublished). SERPINA6 encodes Corticosteroid Binding Globin (CBG) which binds most cortisol in blood and influences delivery of cortisol to target tissues. We hypothesised that genetic variants in SERPINA6 influence CBG expression and cortisol delivery to tissues which promote cardiovascular disease, reflected in tissue-specific variation in cortisol-regulated gene expression.
The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task study (STARNET)2 provides genome wide DNA and RNAseq data in 7 vascular and metabolic tissues from patients undergoing coronary artery bypass grafting. We used STARNET to link SNPs identified in CORNET to SERPINA6 transcript levels and the expression of other trans-associated genes. Causal inference(3) was employed to reconstruct interactions between these genetic factors and their downstream targets.
We identified 21 SNPs that were significant in CORNET (P ≤ 5×10−8) and cis-eQTLs for SERPINA6 expression in liver (q ≤ 0.05). Tissue-specific trans-genes in liver, subcutaneous and visceral abdominal adipose tissue were associated with these SNPs, with over-representation of glucocorticoid-regulated genes. The interferon regulatory trans-gene, IRF2, controls a putative glucocorticoid-regulated network with targets including LDB2 and LIP1, both associated with coronary artery disease.
We conclude that variants in the SERPNIA6/A1 locus mediate their effect on plasma cortisol through variation in CBG expression in liver, and that variation in CBG influences gene expression in extrahepatic tissues through modulating cortisol delivery, notably in adipose tissue. The cortisol-responsive gene networks identified here represent candidate pathways to mediate cardiovascular risk associated with elevated cortisol.
1. Franzén et al. (2016). Science 353:827.
2. Bolton, et al. (2014) PLOS Genet. 10:e1004474.
3. Wang and Michoel. (2017). PLOS Comput. Biol. 13:e1005703.