Adapting Data Adaptive Methods for Small, but High Dimensional Omic Data: Applications to GWAS/EWAS and More

Exploratory analysis of high dimensional omics data has received much attention since the explosion of high-throughput technology allows simultaneous screening of tens of thousands of characteristics (genomics, metabolomics, proteomics, adducts, etc., etc.). Part of this trend has been an increase in the dimension of exposure data in studies of environmental exposure and associated biomarkers. Though some of the general approaches, such as GWAS, are transferable, what has received less focus is 1) how to derive estimation of independent associations in the context of many competing causes, without resorting to a misspecified model, and 2) how to derive accurate small-sample inference when data adaptive techniques are used in this context. This paper focuses on semi-parametric variable importance analysis of high dimensional data sets of modest sample size (e.g., gene expression, mRNA, etc). Though the methodology we propose is generally applicable to similar situations, we present the method in the context of a study of miRNA expression for an environmental exposure. Specifically, the analysis is faced with not just a large number of comparisons, but also trying to tease out of association of the expression of miRNA with an exposure apart from confounds such as age, race, smoking conditions, BMI, etc. Our goal is to propose a method that is reasonably robust in small samples, but does not rely on misspecified (arbitrary) parametric assumptions, and thus will be based on data-adaptive methods. The methodology proposed is we believe a powerful combination of existing semi-parametric statistical methods and theory, as well as a simple framework for use of commonly used empirical Bayes approaches to aid in small sample inference. Specifically, We propose using targeted maximum likelihood estimation (TMLE) for estimating variable importance measures along with a general adaptation of the commonly used Limma approach, which relies on specification of the so-called influence curve of the proposed estimator. The result is a machine-based approach that can estimate independent associations in high dimensional data, but protects against the unreliability of small-sample inference that can result when using data adaptive estimation in relatively small samples

A State-of-the-Science Review of Arsenic's Effects on Glucose Homeostasis in Experimental Models.

BackgroundThe prevalence of type 2 diabetes (T2D) has more than doubled since 1980. Poor nutrition, sedentary lifestyle, and obesity are among the primary risk factors. While an estimated 70% of cases are attributed to excess adiposity, there is an increased interest in understanding the contribution of environmental agents to diabetes causation and severity. Arsenic is one of these environmental chemicals, with multiple epidemiology studies supporting its association with T2D. Despite extensive research, the molecular mechanism by which arsenic exerts its diabetogenic effects remains unclear.ObjectivesWe conducted a literature search focused on arsenite exposure in vivo and in vitro, using relevant end points to elucidate potential mechanisms of oral arsenic exposure and diabetes development.MethodsWe explored experimental results for potential mechanisms and elucidated the distinct effects that occur at high vs. low exposure. We also performed network analyses relying on publicly available data, which supported our key findings.ResultsWhile several mechanisms may be involved, our findings support that arsenite has effects on whole-body glucose homeostasis, insulin-stimulated glucose uptake, glucose-stimulated insulin secretion, hepatic glucose metabolism, and both adipose and pancreatic β-cell dysfunction.DiscussionThis review applies state-of-the-science approaches to identify the current knowledge gaps in our understanding of arsenite on diabetes development. https://doi.org/10.1289/EHP4517

Estrogenic activity, race/ethnicity, and Indigenous American ancestry among San Francisco Bay Area women.

Estrogens play a significant role in breast cancer development and are not only produced endogenously, but are also mimicked by estrogen-like compounds from environmental exposures. We evaluated associations between estrogenic (E) activity, demographic factors and breast cancer risk factors in Non-Latina Black (NLB), Non-Latina White (NLW), and Latina women. We examined the association between E activity and Indigenous American (IA) ancestry in Latina women. Total E activity was measured with a bioassay in plasma samples of 503 women who served as controls in the San Francisco Bay Area Breast Cancer Study. In the univariate model that included all women with race/ethnicity as the independent predictor, Latinas had 13% lower E activity (p = 0.239) and NLBs had 35% higher activity (p = 0.04) compared to NLWs. In the multivariable model that adjusted for demographic factors, Latinas continued to show lower E activity levels (26%, p = 0.026), but the difference between NLBs and NLWs was no longer statistically significant (p = 0.431). An inverse association was observed between E activity and IA ancestry among Latina women (50% lower in 0% vs. 100% European ancestry, p = 0.027) consistent with our previously reported association between IA ancestry and breast cancer risk. These findings suggest that endogenous estrogens and exogenous estrogen-like compounds that act on the estrogen receptor and modulate E activity may partially explain racial/ethnic differences in breast cancer risk

Involvement of N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) in arsenic biomethylation and its role in arsenic-induced toxicity.

BackgroundIn humans, inorganic arsenic (iAs) is metabolized to methylated arsenical species in a multistep process mainly mediated by arsenic (+3 oxidation state) methyltransferase (AS3MT). Among these metabolites is monomethylarsonous acid (MMAIII), the most toxic arsenic species. A recent study in As3mt-knockout mice suggests that unidentified methyltransferases could be involved in alternative iAs methylation pathways. We found that yeast deletion mutants lacking MTQ2 were highly resistant to iAs exposure. The human ortholog of the yeast MTQ2 is N-6 adenine-specific DNA methyltransferase 1 (N6AMT1), encoding a putative methyltransferase.ObjectiveWe investigated the potential role of N6AMT1 in arsenic-induced toxicity.MethodsWe measured and compared the cytotoxicity induced by arsenicals and their metabolic profiles using inductively coupled plasma-mass spectrometry in UROtsa human urothelial cells with enhanced N6AMT1 expression and UROtsa vector control cells treated with different concentrations of either iAsIII or MMAIII.ResultsN6AMT1 was able to convert MMAIII to the less toxic dimethylarsonic acid (DMA) when overexpressed in UROtsa cells. The enhanced expression of N6AMT1 in UROtsa cells decreased cytotoxicity of both iAsIII and MMAIII. Moreover, N6AMT1 is expressed in many human tissues at variable levels, although at levels lower than those of AS3MT, supporting a potential participation in arsenic metabolism in vivo.ConclusionsConsidering that MMAIII is the most toxic arsenical, our data suggest that N6AMT1 has a significant role in determining susceptibility to arsenic toxicity and carcinogenicity because of its specific activity in methylating MMAIII to DMA and other unknown mechanisms

The role of inflammation in age-related disease.

The National Institutes of Health (NIH) Geroscience Interest Group (GSIG) sponsored workshop, The Role of Inflammation inAge-Related Disease, was held September 6th-7th, 2012 in Bethesda, MD. It is now recognized that a mild pro-inflammatory state is correlated with the major degenerative diseases of the elderly. The focus of the workshop was to better understand the origins and consequences of this low level chronic inflammation in order to design appropriate interventional studies aimed at improving healthspan. Four sessions explored the intrinsic, environmental exposures and immune pathways by which chronic inflammation are generated, sustained, and lead to age-associated diseases. At the conclusion of the workshop recommendations to accelerate progress toward understanding the mechanistic bases of chronic disease were identified

Leukemia-related chromosomal loss detected in hematopoietic progenitor cells of benzene-exposed workers.

Benzene exposure causes acute myeloid leukemia and hematotoxicity, shown as suppression of mature blood and myeloid progenitor cell numbers. As the leukemia-related aneuploidies monosomy 7 and trisomy 8 previously had been detected in the mature peripheral blood cells of exposed workers, we hypothesized that benzene could cause leukemia through the induction of these aneuploidies in hematopoietic stem and progenitor cells. We measured loss and gain of chromosomes 7 and 8 by fluorescence in situ hybridization in interphase colony-forming unit-granulocyte-macrophage (CFU-GM) cells cultured from otherwise healthy benzene-exposed (n=28) and unexposed (n=14) workers. CFU-GM monosomy 7 and 8 levels (but not trisomy) were significantly increased in subjects exposed to benzene overall, compared with levels in the control subjects (P=0.0055 and P=0.0034, respectively). Levels of monosomy 7 and 8 were significantly increased in subjects exposed to <10 p.p.m. (20%, P=0.0419 and 28%, P=0.0056, respectively) and ≥ 10 p.p.m. (48%, P=0.0045 and 32%, 0.0354) benzene, compared with controls, and significant exposure-response trends were detected (P(trend)=0.0033 and 0.0057). These data show that monosomies 7 and 8 are produced in a dose-dependent manner in the blood progenitor cells of workers exposed to benzene, and may be mechanistically relevant biomarkers of early effect for benzene and other leukemogens

Critical windows of exposure to household pesticides and risk of childhood leukemia.

The potential etiologic role of household pesticide exposures was examined in the Northern California Childhood Leukemia Study. A total of 162 patients (0-14 years old) with newly diagnosed leukemia were rapidly ascertained during 1995-1999, and 162 matched control subjects were randomly selected from the birth registry. The use of professional pest control services at any time from 1 year before birth to 3 years after was associated with a significantly increased risk of childhood leukemia [odds ratio (OR) = 2.8; 95% confidence interval (CI), 1.4-5.7], and the exposure during year 2 was associated with the highest risk (OR = 3.6; 95% CI, 1.6-8.3). The ORs for exposure to insecticides during the 3 months before pregnancy, pregnancy, and years 1, 2, and 3 were 1.8 (95% CI, 1.1-3.1), 2.1 (95% CI, 1.3-3.5), 1.7 (95% CI, 1.0-2.9), 1.6 (95% CI, 1.0-2.7), and 1.2 (95% CI, 0.7-2.1), respectively. Insecticide exposures early in life appear to be more significant than later exposures, and the highest risk was observed for exposure during pregnancy. Additionally, more frequent exposure to insecticides was associated with a higher risk. In contrast to insecticides, the association between herbicides and leukemia was weak and nonsignificant. Pesticides were also grouped based on where they were applied. Exposure to indoor pesticides was associated with an increased risk, whereas no significant association was observed for exposure to outdoor pesticides. The findings suggest that exposure to household pesticides is associated with an elevated risk of childhood leukemia and further indicate the importance of the timing and location of exposure

Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data

A new data filtering method for SELDI-TOF MS proteomic spectra data is described. We examined technical repeats (2 per subject) of intensity versus m/z (mass/charge) of bone marrow cell lysate for two groups of childhood leukemia patients: acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). As others have noted, the type of data processing as well as experimental variability can have a disproportionate impact on the list of interesting proteins (see Baggerly et al. (2004)). We propose a list of processing and multiple testing techniques to correct for 1) background drift; 2) filtering using smooth regression and cross-validated bandwidth selection; 3) peak finding; and 4) methods to correct for multiple testing (van der Laan et al. (2005)). The result is a list of proteins (indexed by m/z) where average expression is significantly different among disease (or treatment, etc.) groups. The procedures are intended to provide a sensible and statistically driven algorithm, which we argue provides a list of proteins that have a significant difference in expression. Given no sources of unmeasured bias (such as confounding of experimental conditions with disease status), proteins found to be statistically significant using this technique have a low probability of being false positives