Previous studies have prioritized trait-relevant cell types by looking for an enrichment of genome-wide association study (GWAS) signal within functional regions. However, these studies are limited in cell resolution by the lack of functional annotations from difficult-to-characterize or rare cell populations. Measurement of single-cell gene expression has become a popular method for characterizing novel cell types, and yet limited work has linked single-cell RNA sequencing (RNA-seq) to phenotypes of interest. To address this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize trait-relevant cell types and genes from GWAS summary statistics and gene expression data. RolyPoly is designed to use expression data from either bulk tissue or single-cell RNA-seq. In this study, we demonstrated RolyPoly's accuracy through simulation and validated previously known tissue-trait associations. We discovered a significant association between microglia and late-onset Alzheimer disease and an association between schizophrenia and oligodendrocytes and replicating fetal cortical cells. Additionally, RolyPoly computes a trait-relevance score for each gene to reflect the importance of expression specific to a cell type. We found that differentially expressed genes in the prefrontal cortex of individuals with Alzheimer disease were significantly enriched with genes ranked highly by RolyPoly gene scores. Overall, our method represents a powerful framework for understanding the effect of common variants on cell types contributing to complex traits.

The "housekeeping" sodium/hydrogen exchanger, NHE1, mediates the electroneutral 1:1 exchange of Na+ and H+ across the plasma membrane. NHE1 is ubiquitous and is studied extensively for regulation of pHi, cell volume, and response to growth factors. We describe a spontaneous mouse mutant, slow-wave epilepsy, (swe), with a neurological syndrome including ataxia and a unique epilepsy phenotype consisting of 3/sec absence and tonic-clonic seizures. swe was fine-mapped on Chromosome 4 and identified as a null allele of Nhe1. Mutants show selective neuronal death in the cerebellum and brainstem but otherwise are healthy. This first example of a disease-causing mutation in an Nhe gene provides a new tool for studying the delicate balance of neuroexcitability and cell survival within the CNS.

Dynamic activity of signaling pathways, such as Notch, is vital to achieve correct development and homeostasis. However, most studies assess output many hours or days after initiation of signaling, once the outcome has been consolidated. Here we analyze genome-wide changes in transcript levels, binding of the Notch pathway transcription factor, CSL [Suppressor of Hairless, Su(H), in Drosophila], and RNA Polymerase II (Pol II) immediately following a short pulse of Notch stimulation. A total of 154 genes showed significant differential expression (DE) over time, and their expression profiles stratified into 14 clusters based on the timing, magnitude, and direction of DE. E(spl) genes were the most rapidly upregulated, with Su(H), Pol II, and transcript levels increasing within 5–10 minutes. Other genes had a more delayed response, the timing of which was largely unaffected by more prolonged Notch activation. Neither Su(H) binding nor poised Pol II could fully explain the differences between profiles. Instead, our data indicate that regulatory interactions, driven by the early-responding E(spl)bHLH genes, are required. Proposed cross-regulatory relationships were validated in vivo and in cell culture, supporting the view that feed-forward repression by E(spl)bHLH/Hes shapes the response of late-responding genes. Based on these data, we propose a model in which Hes genes are responsible for co-ordinating the Notch response of a wide spectrum of other targets, explaining the critical functions these key regulators play in many developmental and disease contexts.

Quantitative genetic epistasis has been hypothesized to be an important factor in the development and progression of complex diseases. Cancers in particular are driven by the accumulation of mutations that may act epistatically during the course of the disease. However, as cancer mutations are uncovered at an unprecedented rate, determining which combinations of genetic alterations interact to produce cancer phenotypes remains a challenge. Here we show that by using combinatorial RNAi screening in cell culture, dense and often previously undetermined interactions among cancer genes were revealed by assessing gene pairs that are frequently co-altered in primary breast cancers. These interacting gene pairs are significantly associated with survival time when co-altered in patients, indicating that genetic interaction mapping may be leveraged to improve risk assessment. As many of these interacting gene pairs involve known drug targets, personalized treatment regimens may be improved by overlaying genetic interactions with mutational profiling.