Evidence suggests many neurological disorders emerge when normal neurodevelopmental trajectories are disrupted, i.e. when circuits or cells do not reach their fully mature state. Microglia play a critical role in normal neurodevelopment and are hypothesized to contribute to brain disease. We used whole transcriptome profiling with Next Generation sequencing of purified developing microglia to identify a microglial developmental gene expression program involving thousands of genes whose expression levels change monotonically (up or down) across development. Importantly, the gene expression program was delayed in males relative to females and exposure of adult male mice to LPS, a potent immune activator, accelerated microglial development in males. Next, a microglial developmental index (MDI) generated from gene expression patterns obtained from purified mouse microglia, was applied to human brain transcriptome datasets to test the hypothesis that variability in microglial development is associated with human diseases such as Alzheimer’s and autism where microglia have been suggested to play a role. MDI was significantly increased in both Alzheimer’s Disease and in autism, suggesting that accelerated microglial development may contribute to neuropathology. In conclusion, we identified a microglia-specific gene expression program in mice that was used to create a microglia developmental index, which was applied to human datasets containing heterogeneous cell types to reveal differences between healthy and diseased brain samples, and between males and females. This powerful tool has wide ranging applicability to examine microglial development within the context of disease and in response to other variables such as stress and pharmacological treatments.

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Much new global genetic research employs whole genome sequencing, which provides researchers with large amounts of data. The quantity of data has led to the generation and discovery of more incidental or secondary findings and to vigorous theoretical discussions about the ethical obligations that follow from these incidental findings. After a decade of debate in the genetic research community, there is a growing consensus that researchers should, at the very least, offer to return incidental findings that provide high-impact, medically relevant information, when it is not unduly burdensome to the research enterprise to do so. Much as genetic research has been limited to U.S. and European settings, the incidental findings debate has primarily focused on research conducted in high-income countries. In a 2015 paper, Alberto Ortiz-Osorno, Linda Ehler, and Judith Brooks note salient differences between the circumstances of research participants in low- and high-resource settings that alter the analysis of when and why incidental findings should be offered to research participants. In this article, we expand on their analysis and present a framework for thinking about how investigators' obligations to return genomic data might change in low-resource settings, particularly in settings where participants do not have access to the medical care needed to treat, assess, or monitor incidental findings that are actionable in settings with plentiful resources.

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SAS 9.1 calculates Akaike's Information Criteria (AIC), Sawa's Bayesian Information Criteria (BIC), and Schwarz Bayesian Criteria (SBC) for every possible 2 p-1 models for p ≤ 10 independent variables. AIC, BIC, and SBC estimate a measure of the difference between a given model and the "true" underlying model. The model with the smallest AIC, BIC, or SBC among all competing models is deemed the best model. This paper provides the SAS code that can be used to simultaneously evaluate up to 1023 models to determine the best subset of variables that minimizes the information criteria among all possible subsets. Simulated multivariate data are used to compare the performance of AIC, BIC, and SBC with model diagnostics root mean square error (RMSE), Mallows' Cp, and adjusted R 2 , and the three heuristic methods forward selection, backward elimination, and stepwise regression. This paper is for intermediate SAS users of SAS/STAT who understand multivariate data analysis.