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Residents of Wilmington, North, Carolina, were exposed to drinking water contaminated by fluoroethers and legacy per- and polyfluoroalkyl substances (PFAS), such as perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), with fluoroether exposure occurring from 1980 to 2017. PFOA and PFOS have previously been associated with metabolic dysfunction; however, few prior studies have examined associations between other PFAS and lipid levels. We measured the association between serum fluoroether and legacy PFAS levels and various cholesterol outcomes. Participants in the GenX Exposure Study contributed nonfasting blood samples in November 2017 and May 2018 that were analyzed for 20 PFAS (10 legacy, 10 fluoroethers) and serum lipids [total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides] and calculated non-HDL cholesterol. We estimated covariate-adjusted associations between quartiles of exposure to each of the PFAS measures (as well as the summed concentrations of legacy PFAS, fluoroethers, and all 10 targeted PFAS) and lipid outcomes by fitting inverse probability of treatment weighted linear regressions. In this cross-sectional study of 326 participants (age range 6-86 y), eight PFAS were detected in of the population. For PFOS and perfluorononanoic acid (PFNA), non-HDL cholesterol was approximately higher per exposure quartile increase: [PFOS: 4.89; 95% confidence interval (CI): 0.10, 9.68 and PFNA: 5.25 (95% CI: 0.39, 10.1)], whereas total cholesterol was approximately higher per quartile [PFOS: 5.71 (95% CI: 0.38, 11.0), PFNA: 5.92 (95% CI: 0.19, 11.7)]. In age-stratified analyses, associations were strongest among the oldest participants. Two fluoroethers were associated with higher HDL, whereas other fluoroether compounds were not associated with serum lipid levels. PFNA and PFOS were associated with higher levels of total and non-HDL cholesterol, with associations larger in magnitude among older adults. In the presence of these legacy PFAS, fluoroethers appeared to be associated with HDL but not non-HDL lipid measures. https://doi.org/10.1289/EHP11033.

\"David A. Freedman presents here a definitive synthesis of his approach to causal inference in the social sciences. He explores the foundations and limitations of statistical modeling, illustrating basic arguments with examples from political science, public policy, law, and epidemiology. Freedman maintains that many new technical approaches to statistical modeling constitute not progress, but regress. Instead, he advocates a 'shoe leather' methodology, which exploits natural variation to mitigate confounding and relies on intimate knowledge of the subject matter to develop meticulous research designs and eliminate rival explanations. When Freedman first enunciated this position, he was met with scepticism, in part because it was hard to believe that a mathematical statistician of his stature would favor 'low-tech' approaches. But the tide is turning. Many social scientists now agree that statistical technique cannot substitute for good research design and subject matter knowledge. This book offers an integrated presentation of Freedman's views\"--Provided by publisher.

Copy number variants have emerged as an important genomic cause of common, complex neurodevelopmental disorders. These usually change copy number of multiple genes, but deletions at 2p16.3, which have been associated with autism, schizophrenia and mental retardation, affect only the neurexin 1 gene, usually the alpha isoform. Previous analyses of neurexin 1α (Nrxn1α) knockout (KO) mouse as a model of these disorders have revealed impairments in synaptic transmission but failed to reveal defects in social behaviour, one of the core symptoms of autism. We performed a detailed investigation of the behavioural effects of Nrxn1α deletion in mice bred onto a pure genetic background (C57BL/6J) to gain a better understanding of its role in neurodevelopmental disorders. Wildtype, heterozygote and homozygote Nrxn1α KO male and female mice were tested in a battery of behavioural tests (n = 9-16 per genotype, per sex). In homozygous Nrxn1α KO mice, we observed altered social approach, reduced social investigation, and reduced locomotor activity in novel environments. In addition, male Nrxn1α KO mice demonstrated an increase in aggressive behaviours. These are the first experimental data that associate a deletion of Nrxn1α with alterations of social behaviour in mice. Since this represents one of the core symptom domains affected in autism spectrum disorders and schizophrenia in humans, our findings suggest that deletions within NRXN1 found in patients may be responsible for the impairments seen in social behaviours, and that the Nrxn1α KO mice are a useful model of human neurodevelopmental disorder.

Background Schizophrenia is a highly heritable, neuropsychiatric disorder characterized by episodic psychosis and altered cognitive function. Despite success in identifying genetic variants associated with schizophrenia, there remains uncertainty about the causal genes involved in disease pathogenesis and how their function is regulated. Results We performed a multi-stage epigenome-wide association study, quantifying genome-wide patterns of DNA methylation in a total of 1714 individuals from three independent sample cohorts. We have identified multiple differentially methylated positions and regions consistently associated with schizophrenia across the three cohorts; these effects are independent of important confounders such as smoking. We also show that epigenetic variation at multiple loci across the genome contributes to the polygenic nature of schizophrenia. Finally, we show how DNA methylation quantitative trait loci in combination with Bayesian co-localization analyses can be used to annotate extended genomic regions nominated by studies of schizophrenia, and to identify potential regulatory variation causally involved in disease. Conclusions This study represents the first systematic integrated analysis of genetic and epigenetic variation in schizophrenia, introducing a methodological approach that can be used to inform epigenome-wide association study analyses of other complex traits and diseases. We demonstrate the utility of using a polygenic risk score to identify molecular variation associated with etiological variation, and of using DNA methylation quantitative trait loci to refine the functional and regulatory variation associated with schizophrenia risk variants. Finally, we present strong evidence for the co-localization of genetic associations for schizophrenia and differential DNA methylation.

Musculoskeletal chronic pain is prevalent in individuals with Alzheimer’s disease (AD); however, it remains largely untreated in these patients, raising the possibility that pain mechanisms are perturbed. Here, we utilise the TASTPM transgenic mouse model of AD with the K/BxN serum transfer model of inflammatory arthritis. We show that in male and female WT mice, inflammatory allodynia is associated with a distinct spinal cord microglial response characterised by TLR4-driven transcriptional profile and upregulation of P2Y12. Dorsal horn nociceptive afferent terminals release the TLR4 ligand galectin-3 (Gal-3), and intrathecal injection of a Gal-3 inhibitor attenuates allodynia. In contrast, TASTPM mice show reduced inflammatory allodynia, which is not affected by the Gal-3 inhibitor and correlates with the emergence of a P2Y12 − TLR4 − microglia subset in the dorsal horn. We suggest that sensory neuron-derived Gal-3 promotes allodynia through the TLR4-regulated release of pro-nociceptive mediators by microglia, a process that is defective in TASTPM due to the absence of TLR4 in a microglia subset. In inflammatory arthritis, pain neurons communicate with spinal cord microglia to establish nociception. Here, the authors show that this communication is mediated by pain neurons releasing galectin-3, which activates microglia through TLR4. In a mouse model of Alzheimer’s disease, pain is attenuated because microglia lack expression of TLR4.

Soil influences human health in a variety of ways, with human health being linked to the health of the soil. Historically, emphasis has been placed on the negative impacts that soils have on human health, including exposures to toxins and pathogenic organisms or the problems created by growing crops in nutrient-deficient soils. However, there are a number of positive ways that soils enhance human health, from food production and nutrient supply to the supply of medications and enhancement of the immune system. It is increasingly recognized that the soil is an ecosystem with a myriad of interconnected parts, each influencing the other, and when all necessary parts are present and functioning (ie, the soil is healthy), human health also benefits. Despite the advances that have been made, there are still many areas that need additional investigation. We do not have a good understanding of how chemical mixtures in the environment influence human health, and chemical mixtures in soil are the rule, not the exception. We also have sparse information on how most chemicals react within the chemically and biologically active soil ecosystem, and what those reactions mean for human health. There is a need to better integrate soil ecology and agronomic crop production with human health, food/nutrition science, and genetics to enhance bacterial and fungal sequencing capabilities, metagenomics, and the subsequent analysis and interpretation. While considerable work has focused on soil microbiology, the macroorganisms have received much less attention regarding links to human health and need considerable attention. Finally, there is a pressing need to effectively communicate soil and human health connections to our broader society, as people cannot act on information they do not have. Multidisciplinary teams of researchers, including scientists, social scientists, and others, will be essential to move all these issues forward.

Background Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks. Results According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets . With respect to the recovery of signalling pathways , CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 ‘landmark’ genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets. Conclusions Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.

Process tracing is a fundamental tool of qualitative analysis. This method is often invoked by scholars who carry out within-case analysis based on qualitative data, yet frequently it is neither adequately understood nor rigorously applied. This deficit motivates this article, which offers a new framework for carrying out process tracing. The reformulation integrates discussions of process tracing and causal-process observations, gives greater attention to description as a key contribution, and emphasizes the causal sequence in which process-tracing observations can be situated. In the current period of major innovation in quantitative tools for causal inference, this reformulation is part of a wider, parallel effort to achieve greater systematization of qualitative methods. A key point here is that these methods can add inferential leverage that is often lacking in quantitative analysis. This article is accompanied by online teaching exercises, focused on four examples from American politics, two from comparative politics, three from international relations, and one from public health/epidemiology.

Copy number variants (CNVs) at chromosome 16p13.11 have been associated with a range of neurodevelopmental disorders including autism, ADHD, intellectual disability and schizophrenia. Significant sex differences in prevalence, course and severity have been described for a number of these conditions but the biological and environmental factors underlying such sex-specific features remain unclear. We tested the burden and the possible sex-biased effect of CNVs at 16p13.11 in a sample of 10,397 individuals with a range of neurodevelopmental conditions, clinically referred for array comparative genomic hybridisation (aCGH); cases were compared with 11,277 controls. In order to identify candidate phenotype-associated genes, we performed an interval-based analysis and investigated the presence of ohnologs at 16p13.11; finally, we searched the DECIPHER database for previously identified 16p13.11 copy number variants. In the clinical referral series, we identified 46 cases with CNVs of variable size at 16p13.11, including 28 duplications and 18 deletions. Patients were referred for various phenotypes, including developmental delay, autism, speech delay, learning difficulties, behavioural problems, epilepsy, microcephaly and physical dysmorphisms. CNVs at 16p13.11 were also present in 17 controls. Association analysis revealed an excess of CNVs in cases compared with controls (OR = 2.59; p = 0.0005), and a sex-biased effect, with a significant enrichment of CNVs only in the male subgroup of cases (OR = 5.62; p = 0.0002), but not in females (OR = 1.19, p = 0.673). The same pattern of results was also observed in the DECIPHER sample. Interval-based analysis showed a significant enrichment of case CNVs containing interval II (OR = 2.59; p = 0.0005), located in the 0.83 Mb genomic region between 15.49-16.32 Mb, and encompassing the four ohnologs NDE1, MYH11, ABCC1 and ABCC6. Our data confirm that duplications and deletions at 16p13.11 represent incompletely penetrant pathogenic mutations that predispose to a range of neurodevelopmental disorders, and suggest a sex-limited effect on the penetrance of the pathological phenotypes at the 16p13.11 locus.