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.

Large experimental efforts are characterizing the regulatory genome, yet we are still missing a systematic definition of functional and silent genetic variants in non-coding regions. Here, we integrated DNaseI footprinting data with sequence-based transcription factor (TF) motif models to predict the impact of a genetic variant on TF binding across 153 tissues and 1,372 TF motifs. Each annotation we derived is specific for a cell-type condition or assay and is locally motif-driven. We found 5.8 million genetic variants in footprints, 66% of which are predicted by our model to affect TF binding. Comprehensive examination using allele-specific hypersensitivity (ASH) reveals that only the latter group consistently shows evidence for ASH (3,217 SNPs at 20% FDR), suggesting that most (97%) genetic variants in footprinted regulatory regions are indeed silent. Combining this information with GWAS data reveals that our annotation helps in computationally fine-mapping 86 SNPs in GWAS hit regions with at least a 2-fold increase in the posterior odds of picking the causal SNP. The rich meta information provided by the tissue-specificity and the identity of the putative TF binding site being affected also helps in identifying the underlying mechanism supporting the association. As an example, the enrichment for LDL level-associated SNPs is 9.1-fold higher among SNPs predicted to affect HNF4 binding sites than in a background model already including tissue-specific annotation.

Background Despite many potential effects of the oral microbiome on oral and systemic health, scant information is available regarding the associations between diet and the oral microbiome. Methods Oral rinse DNA samples from 182 participants in a population-based case–control study for colorectal cancer were used to amplify a V3–V4 region of bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq paired end chemistry on 2 runs, yielding approximately 33 million filtered reads that were assigned to bacterial classes. Relative abundances of each class and family as well microbial diversity/richness indices were correlated with selected dietary intakes from a food frequency questionnaire. Results Saturated fatty acids (SFAs) and vitamin C intakes were consistently correlated with alpha (within-subjects) diversity indexes in both richness and diversity. SFA intake was positively correlated with relative abundance of betaproteobacteria and fusobacteria. Vitamin C and other vitamins with correlated intakes—for example, the B vitamins and vitamin E—exhibited positive correlations with fusobacteria class, its family Leptotrichiaceae and a clostridia family Lachnospiraceae. In addition, glycemic load was positively correlated with Lactobacillaceae abundance. Conclusion The observed associations in this study were modest. However, the results suggest that the effects of diets are likely to be habitat specific, and observations from the gut microbiome are not transferrable to the oral microbiome. Further studies are warranted, incorporating a range of host biomarkers, such as cytohistological, molecular, or biochemical measurements, in order to address biological consequences of these dietary intakes in human oral health.

Biology of the twenty-first century is an increasingly quantitative science. Undergraduate biology education therefore needs to provide opportunities for students to develop fluency in the tools and language of quantitative disciplines. Quantitative literacy (QL) is important for future scientists as well as for citizens, who need to interpret numeric information and data-based claims regarding nearly every aspect of daily life. To address the need for QL in biology education, we incorporated quantitative concepts throughout a semester-long introductory biology course at a large research university. Early in the course, we assessed the quantitative skills that students bring to the introductory biology classroom and found that students had difficulties in performing simple calculations, representing data graphically, and articulating data-driven arguments. In response to students' learning needs, we infused the course with quantitative concepts aligned with the existing course content and learning objectives. The effectiveness of this approach is demonstrated by significant improvement in the quality of students' graphical representations of biological data. Infusing QL in introductory biology presents challenges. Our study, however, supports the conclusion that it is feasible in the context of an existing course, consistent with the goals of college biology education, and promotes students' development of important quantitative skills.

Background The equilibrium of oral microbiome may be altered by environmental factors, including cigarette smoking. Several recent studies also suggest that oral pathogens causing periodontal disease, such as Fusobacterium nucleatum, are involved in pathogenesis of colorectal cancer. Methods For this study oral rinse DNA samples from 190 participants in a population-based case-control study for colorectal cancer were used to amplify a V3-V4 region of bacterial 16S rRNA gene. The amplicons were sequenced using Illumina MiSeq paired end chemistry on two runs, yielding approximately 35 million filtered reads which were assigned to bacterial phyla. Results No association was found between Fusobacterium abundance or presence and colorectal cancer. However, adjusted for age and experimental batch, colorectal cancer history was associated with increased presence of genus Lactobacillus and increased relative abundance of Rothia by 28% and current smoking was associated with a 33% decrease in relative counts of Betaproteobacteria (primarily Neisseria) and 23% increase in relative abundance of Veillonellaceae family. We also found that smoking had significant effects on the 2nd component scores and 2nd coordinate distances in principal component and coordinate analyses. Conclusions It remains to be elucidated whether the observed differences can be translated into biochemical changes in oral environment, thus potentially affecting oral health.