While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and geneenvironment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10-12 , Replication Bonferroni p: 2.70x10-9) and iron (Discovery Bonferroni p: 1.09x10-8 , Replication Bonferroni p: 1.73x10-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action.

Environmental exposure pathophysiology related to smoking can yield metabolic changes that are difficult to describe in a biologically informative fashion with manual proprietary software. Nuclear magnetic resonance (NMR) spectroscopy detects compounds found in biofluids yielding a metabolic snapshot. We applied our semi-automated NMR pipeline for a secondary analysis of a smoking study (MTBLS374 from the MetaboLights repository) (n = 112). This involved quality control (in the form of data preprocessing), automated metabolite quantification, and analysis. With our approach we putatively identified 79 metabolites that were previously unreported in the dataset. Quantified metabolites were used for metabolic pathway enrichment analysis that replicated 1 enriched pathway with the original study as well as 3 previously unreported pathways. Our pipeline generated a new random forest (RF) classifier between smoking classes that revealed several combinations of compounds. This study broadens our metabolomic understanding of smoking exposure by 1) notably increasing the number of quantified metabolites with our analytic pipeline, 2) suggesting smoking exposure may lead to heterogenous metabolic responses according to random forest modeling, and 3) modeling how newly quantified individual metabolites can determine smoking status. Our approach can be applied to other NMR studies to characterize environmental risk factors, allowing for the discovery of new biomarkers of disease and exposure status.

Inflammation skews bone marrow hematopoiesis increasing the production of myeloid effector cells at the expense of steady-state erythropoiesis. A compensatory stress erythropoiesis response is induced to maintain homeostasis until inflammation is resolved. In contrast to steady-state erythroid progenitors, stress erythroid progenitors (SEPs) utilize signals induced by inflammatory stimuli. However, the mechanistic basis for this is not clear. Here we reveal a nitric oxide (NO)-dependent regulatory network underlying two stages of stress erythropoiesis, namely proliferation, and the transition to differentiation. In the proliferative stage, immature SEPs and cells in the niche increased expression of inducible nitric oxide synthase (Nos2 or iNOS) to generate NO. Increased NO rewires SEP metabolism to increase anabolic pathways, which drive the biosynthesis of nucleotides, amino acids and other intermediates needed for cell division. This NO-dependent metabolism promotes cell proliferation while also inhibiting erythroid differentiation leading to the amplification of a large population of non-committed progenitors. The transition of these progenitors to differentiation is mediated by the activation of nuclear factor erythroid 2-related factor 2 (Nfe2l2 or Nrf2). Nrf2 acts as an anti-inflammatory regulator that decreases NO production, which removes the NO-dependent erythroid inhibition and allows for differentiation. These data provide a paradigm for how alterations in metabolism allow inflammatory signals to amplify immature progenitors prior to differentiation.

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Background: Anemia is a global health problem that can lead to chronic illness in adults and may be fatal in children and the elderly. While some dietary factors and heavy metals are known risk factors for anemia, there are no environment-wide studies of anemia. Objectives: Our goal was to identify environment-wide risk factors for anemia. Methods: We evaluated general anemia in children and adults and further classified anemia as a) iron, vitamin B12, or folate deficiency anemia; b) anemia in general chronic diseases; and c) anemia in chronic kidney disease. As well as quantitative measures including level of hemoglobin, serum vitamin B12, red blood cell (RBC) folate, and serum iron. Environment-wide association studies (EWAS) were performed to identify novel environmental risk factors of anemia in discovery and replication subsets of the National Health and Nutrition Examination Surveys (NHANES). Results: We identified and replicated 106 potential environmental risk factors for anemia. As expected, serum iron was the top exposure associated with general anemia for adults. Cadmium was associated with adult hemoglobin levels, as were vitamin Bs, micronutrients, smoking, and alcohol consumption. Further, decreased levels of multiple vitamins, including vitamin A, vitamin Es and multiple vitamin Bs, were associated with general anemia in adults. Use of tobacco and alcohol was also found to be associated with red blood cell folate and serum iron levels. In children, serum iron level was associated with folic acid supplements and vitamin A supplements. Discussion: This is the first EWAS of anemia, providing insights into the environmental etiology of anemia risk in children and adults. These results may lead to the development of public health recommendations to mitigate anemia risk factors.

Steady state erythropoiesis produces new erythrocytes at a constant rate to replace senescent erythrocytes removed in the spleen and liver. Inflammation caused by infection or tissue damage skews bone marrow hematopoiesis, increasing myelopoiesis at the expense of steady state erythropoiesis. To compensate for the loss of production, stress erythropoiesis is induced. Stress erythropoiesis is highly conserved between mouse and human but utilizes a different strategy than steady state erythropoiesis. Inflammatory signals promote the proliferation of immature stress erythroid progenitors (SEPS), which in response to erythropoietin and other signals transition to stress erythroid progenitors committed to differentiation. Here we show that TNFα dependent signaling increases ROS in SEPs during the proliferation stage, however, blocking ROS production impairs their later differentiation. In addition to TNFα, nitric oxide dependent signaling drives the proliferation of stress erythroid progenitors and production of nitric oxide must be decreased so that the progenitor cells can differentiate. As progenitor cells transition to differentiation, increased production of the anti-inflammatory metabolite itaconate activates Nfe2l2 or Nrf2, which inhibits Nos2 expression, leading to decreased nitric oxide production. Mutation of Irg1, the enzyme that catalyzes the production of itaconate, causes a delayed recovery from inflammatory anemia induced by heat killed Brucella abortus. Loss of itaconate-dependent activation of Nrf2 is rescued in vivo by IL-10, which leads to activation of Nrf2 and differentiation. These data show that the differentiation of stress erythroid progenitors relies on a switch to an anti-inflammatory metabolism and increased expression of pro-resolving cytokines.

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