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.

Epistasis analysis elucidates the effects of gene-gene interactions (G×G) between multiple loci for complex traits. However, the large computational demands and the high multiple testing burden impede their discoveries. Here, we illustrate the utilization of two methods, main effect filtering based on individual GWAS results and biological knowledge-based modeling through Biofilter software, to reduce the number of interactions tested among single nucleotide polymorphisms (SNPs) for 15 cardiac-related traits and 14 fatty acids. We performed interaction analyses using the two filtering methods, adjusting for age, sex, body mass index (BMI), waist-hip ratio, and the first three principal components from genetic data, among 2,824 samples from the Ludwigshafen Risk and Cardiovascular (LURIC) Health Study. Using Biofilter, one interaction nearly met Bonferroni significance: an interaction between rs7735781 in XRCC4 and rs10804247 in XRCC5 was identified for venous thrombosis with a Bonferroni-adjusted likelihood ratio test (LRT) p: 0.0627. A total of 57 interactions were identified from main effect filtering for the cardiac traits G×G (10) and fatty acids G×G (47) at Bonferroni-adjusted LRT p < 0.05. For cardiac traits, the top interaction involved SNPs rs1383819 in SNTG1 and rs1493939 (138kb from 5' of SAMD12) with Bonferroni-adjusted LRT p: 0.0228 which was significantly associated with history of arterial hypertension. For fatty acids, the top interaction between rs4839193 in KCND3 and rs10829717 in LOC107984002 with Bonferroni-adjusted LRT p: 2.28×10 −5 was associated with 9-trans 12-trans octadecanoic acid, an omega-6 trans fatty acid. The model inflation factor for the interactions under different filtering methods was evaluated from the standard median and the linear regression approach. Here, we applied filtering approaches to identify numerous genetic interactions related to cardiac-related outcomes as potential targets for therapy. The

Most genome-wide association studies (GWASs) assume an additive inheritance model, where heterozygous genotypes (HET) are coded with half the risk of homozygous alternate genotypes (HA), leading to less explained nonadditive genetic effects for complex diseases. Yet, growing evidence indicates that with flexible modeling, many single-nucleotide polymorphisms (SNPs) show nonadditive effects, including dominant and recessive, which will be missed using only the additive model. We developed Elastic Data-Driven Encoding (EDGE) to determine the HET to HA ratio of risk. Simulation results demonstrated that EDGE outperformed traditional methods across all simulated models for power while maintaining a conserved false positive rate. This research lays the necessary groundwork for integrating nonadditive genetic effects into GWAS workflows to identify novel disease-risk SNPs, which may ultimately improve polygenic risk prediction in diverse populations and springboard future applications to thousands of disease phenotypes and other omic domains to improve disease-prediction capability.

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.

Objectives The relapse of acute myeloid leukemia (AML) remains a significant concern due to persistent leukemia stem cells (LSCs) that are not targeted by existing therapies. LSCs show sensitivity to endogenous cyclopentenone prostaglandin J (CyPG) metabolites that are increased by dietary trace element selenium (Se), which is significantly decreased in AML patients. We investigated the anti-leukemic effect of Se supplementation in AML via mechanisms involving the activation of the membrane-bound G-protein coupled receptor 44 (Gpr44) and the intracellular receptor, peroxisome proliferator-activated receptor gamma (PPARγ), by endogenous CyPGs. Methods A murine model of AML generated by transplantation of hematopoietic stem cells (HSCs- WT or Gpr44−/−) expressing human MLL-AF9 fusion oncoprotein, in the following experiments: To investigate the effect of Se supplementation on the outcome of AML, donor mice were maintained on either Se-adequate (Se-A; 0.08–0.1 ppm Se) or Se-supplemented (Se-S; 0.4 ppm Se) diets. Complete cell counts in peripheral blood were analyzed by hemavet. LSCs in bone marrow and spleen were analyzed by flow cytometry. To determine the role of Gpr44 activation in AML, mice were treated with Gpr44 agonists, CyPGs. LSCs in bone marrow and spleen were analyzed. Mice transplanted with Gpr44−/- AML cells were compared with mice transplanted with wild type AML cells and the progression of the disease was followed as above. To determine the role of PPARγ activation in AML, PPARγ agonist (Rosiglitazone, 6 mg/kg, i.p, 14 d) and antagonist (GW9662, 1 mg/kg, i.p. once every other day, 7 injections) were applied to Se-S mice transplanted with Gpr44−/- AML cells and disease progression was followed. Results Se supplementation at supraphysiological levels alleviated the disease via the elimination of LSCs in a murine model of AML. CyPGs induced by Se supplementation mediate the apoptosis in LSCs via the activation of Gpr44 and PPARγ. Conclusions Endogenous CyPGs produced upon supplementation with Se at supraphysiological levels improved the outcome of AML by targeting LSCs to apoptosis via the activation of two receptors, Gpr44 and PPARg. Funding Sources NIH DK 07,7152; CA 175,576; CA 162,665. Office of Dietary Supplements, USDA Hatch funds PEN04605, Accession # 1,010,021 (KSP, RFP).