Wei, Peng

AbstractIt is of great scientific interest to identify interactions between genetic variants and environmental exposures that may modify the risk of complex diseases. However, larger sample sizes are usually required to detect gene‐by‐environment interaction (G × E) than required to detect genetic main association effects. To boost the statistical power and improve the understanding of the underlying molecular mechanisms, we incorporate functional genomics information, specifically, expression quantitative trait loci (eQTLs), into a data‐adaptive G × E test, called aGEw. This test adaptively chooses the best eQTL weights from multiple tissues and provides an extra layer of weighting at the genetic variant level. Extensive simulations show that the aGEw test can control the Type 1 error rate, and the power is resilient to the inclusion of neutral variants and noninformative external weights. We applied the proposed aGEw test to the Pancreatic Cancer Case–Control Consortium (discovery cohort of 3,585 cases and 3,482 controls) and the PanScan II genome‐wide association study data (replication cohort of 2,021 cases and 2,105 controls) with smoking as the exposure of interest. Two novel putative smoking‐related pancreatic cancer susceptibility genes, TRIP10 and KDM3A, were identified. The aGEw test is implemented in an R package aGE.

Background We examined feasibility of a unique approach towards gaining insight into heritable risk for early atherosclerosis: surveying gene expression by endothelial cells from living subjects. Methods and Results Subjects aged <50 years (mean age, 37; range, 22–49) without obstructive coronary artery disease underwent coronary reactivity testing that identified them as having normal or abnormal coronary endothelial function. Cultures of Blood Outgrowth Endothelial Cells (BOEC) from 6 normal and 13 abnormal subjects passed rigorous quality control and were used for microarray assessment of gene expression. Of 9 genes differentially expressed at false discovery rate <0.1%, we here focus upon abnormal subjects having elevated expression of HMGB1 (high mobility group box 1) which we unexpectedly found to be linked to low LAMC1 (laminin gamma 1) expression. This linkage was corroborated by 3 of our past studies and confirmed bio‐functionally. Compared with normal BOEC, abnormal BOEC released 13±3‐fold more HMGB1 in response to lipopolysaccharide; and they deposited one tenth as much LAMC1 into collagen subendothelial matrix during culture. Clinical follow‐up data are provided for 4 normal subjects (followed 13.4±0.1 year) and for 12 abnormal subjects (followed 9.1±4.5 years). Conclusions The known pathogenic effects of high‐ HMGB1 and low‐ LAMC1 predict that the combination would biologically converge upon the focal adhesion complex, to the detriment of endothelial shear responsiveness. This gene expression pattern may comprise a heritable risk state that promotes early coronary atherosclerosis. If so, the testing could be applied even in childhood, enabling early intervention. This approach offers a way to bridge the information gap between genetics and clinical phenotype.