Gene-by-environment (GxE) interactions determine common disease risk factors and biomedically relevant complex traits. However, quantifying how the environment modulates genetic effects on human quantitative phenotypes presents unique challenges. Environmental covariates are complex and difficult to measure and control at the organismal level, as found in GWAS and epidemiological studies. An alternative approach focuses on the cellular environment using in vitro treatments as a proxy for the organismal environment. These cellular environments simplify the organism-level environmental exposures to provide a tractable influence on subcellular phenotypes, such as gene expression. Expression quantitative trait loci (eQTL) mapping studies identified GxE interactions in response to drug treatment and pathogen exposure. However, eQTL mapping approaches are infeasible for large-scale analysis of multiple cellular environments. Recently, allele-specific expression (ASE) analysis emerged as a powerful tool to identify GxE interactions in gene expression patterns by exploiting naturally occurring environmental exposures. Here we characterized genetic effects on the transcriptional response to 50 treatments in five cell types. We discovered 1455 genes with ASE (FDR < 10%) and 215 genes with GxE interactions. We demonstrated a major role for GxE interactions in complex traits. Genes with a transcriptional response to environmental perturbations showed sevenfold higher odds of being found in GWAS. Additionally, 105 genes that indicated GxE interactions (49%) were identified by GWAS as associated with complex traits. Examples include GIPR-caffeine interaction and obesity and include LAMP3-selenium interaction and Parkinson disease. Our results demonstrate that comprehensive catalogs of GxE interactions are indispensable to thoroughly annotate genes and bridge epidemiological and genomewide association studies.

Photosynthetic euglenids acquired chloroplasts by secondary endosymbiosis, which resulted in changes to their mode of nutrition and affected the evolution of their morphological characters. Mapping morphological characters onto a reliable molecular tree could elucidate major trends of those changes. We analyzed nucleotide sequence data from regions of three nuclear-encoded genes (nSSU, nLSU, hsp90), one chloroplast-encoded gene (cpSSU) and one nuclear-encoded chloroplast gene (psbO) to estimate phylogenetic relationships among 59 photosynthetic euglenid species. Our results were consistent with previous works; most genera were monophyletic, except for the polyphyletic genus Euglena, and the paraphyletic genus Phacus. We also analyzed character evolution in photosynthetic euglenids using our phylogenetic tree and eight morphological traits commonly used for generic and species diagnoses, including: characters corresponding to well-defined clades, apomorphies like presence of lorica and mucilaginous stalks, and homoplastic characters like rigid cells and presence of large paramylon grains. This research indicated that pyrenoids were lost twice during the evolution of phototrophic euglenids, and that mucocysts, which only occur in the genus Euglena, evolved independently at least twice. In contrast, the evolution of cell shape and chloroplast morphology was difficult to elucidate, and could not be unambiguously reconstructed in our analyses.

Purpose: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. Methods: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). Results: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. Conclusions: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Background Chronic obstructive pulmonary disease (COPD) is a risk factor for lung cancer. This study evaluates alternative measures of COPD based on spirometry and quantitative image analysis to better define a phenotype that predicts lung cancer risk. Methods Three-hundred-forty-one lung cancer cases and 752 volunteer controls age 21–89 years participated in a structured interview, standardized CT scan and spirometry. Logistic regression, adjusted for age, race, gender, pack-years, and inspiratory and expiratory total lung volume, was used to estimate the odds of lung cancer associated with FEV1/FVC, percent voxels less than -950 Hounsfield Units on the inspiratory scan (HUI) and percent voxels less than -856 HU on expiratory scan (HUE). Results The odds of lung cancer were increased 1.4- to 3.1-fold among those with COPD compared to those without, regardless of assessment method, however, in multivariable modeling, only percent voxels < -856 HUE as a continuous measure of air trapping (OR=1.04; 95% CI (1.03, 1.06)) and FEV1/FVC < 0.70 (OR=1.71; 95% CI (1.21, 2.41)) were independent predictors of lung cancer risk. Nearly 10% of lung cancer cases were negative on all objective measures of COPD. Conclusion Measures of air trapping using quantitative imaging, in addition to FEV1/FVC, can identify individuals at high risk of lung cancer and should be considered as supplemental measures at the time of screening for lung cancer. Impact Quantitative measures of air trapping based on imaging provide additional information for the identification of high risk groups who might benefit the most from lung cancer screening.