A fundamental challenge in the post-genome era is to understand and annotate the consequences of genetic variation, particularly within the context of human tissues. We present a set of integrated experiments that investigate the effects of common genetic variability on DNA methylation and mRNA expression in four human brain regions each from 150 individuals (600 samples total). We find an abundance of genetic cis regulation of mRNA expression and show for the first time abundant quantitative trait loci for DNA CpG methylation across the genome. We show peak enrichment for cis expression QTLs to be approximately 68,000 bp away from individual transcription start sites; however, the peak enrichment for cis CpG methylation QTLs is located much closer, only 45 bp from the CpG site in question. We observe that the largest magnitude quantitative trait loci occur across distinct brain tissues. Our analyses reveal that CpG methylation quantitative trait loci are more likely to occur for CpG sites outside of islands. Lastly, we show that while we can observe individual QTLs that appear to affect both the level of a transcript and a physically close CpG methylation site, these are quite rare. We believe these data, which we have made publicly available, will provide a critical step toward understanding the biological effects of genetic variation.

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Microglia, the CNS-resident immune cells, play important roles in disease, but the spectrum of their possible activation states is not well understood. We derived co-regulated gene modules from transcriptional profiles of CNS myeloid cells of diverse mouse models, including new tauopathy model datasets. Using these modules to interpret single-cell data from an Alzheimer's disease (AD) model, we identified microglial subsets-distinct from previously reported "disease-associated microglia"-expressing interferon-related or proliferation modules. We then analyzed whole-tissue RNA profiles from human neurodegenerative diseases, including a new AD dataset. Correcting for altered cellular composition of AD tissue, we observed elevated expression of the neurodegeneration-related modules, but also modules not implicated using expression profiles from mouse models alone. We provide a searchable, interactive database for exploring gene expression in all these datasets (http://research-pub.gene.com/BrainMyeloidLandscape). Understanding the dimensions of CNS myeloid cell activation in human disease may reveal opportunities for therapeutic intervention.