Addressing dispersion in mis-measured multivariate binomial outcomes: A novel statistical approach for detecting differentially methylated regions in bisulfite sequencing data.

Motivated by a DNA methylation application, this article addresses the problem of fitting and inferring a multivariate binomial regression model for outcomes that are contaminated by errors and exhibit extra-parametric variations, also known as dispersion. While dispersion in univariate binomial regression has been extensively studied, addressing dispersion in the context of multivariate outcomes remains a complex and relatively unexplored task. The complexity arises from a noteworthy data characteristic observed in our motivating dataset: non-constant yet correlated dispersion across outcomes. To address this challenge and account for possible measurement error, we propose a novel hierarchical quasi-binomial varying coefficient mixed model, which enables flexible dispersion patterns through a combination of additive and multiplicative dispersion components. To maximize the Laplace-approximated quasi-likelihood of our model, we further develop a specialized two-stage expectation-maximization (EM) algorithm, where a plug-in estimate for the multiplicative scale parameter enhances the speed and stability of the EM iterations. Simulations demonstrated that our approach yields accurate inference for smooth covariate effects and exhibits excellent power in detecting non-zero effects. Additionally, we applied our proposed method to investigate the association between DNA methylation, measured across the genome through targeted custom capture sequencing of whole blood, and levels of anti-citrullinated protein antibodies (ACPA), a preclinical marker for rheumatoid arthritis (RA) risk. Our analysis revealed 23 significant genes that potentially contribute to ACPA-related differential methylation, highlighting the relevance of cell signaling and collagen metabolism in RA. We implemented our method in the R Bioconductor package called "SOMNiBUS."

Parvalbumin interneuron loss mediates repeated anesthesia-induced memory deficits in mice.

Repeated or prolonged, but not short-term, general anesthesia during the early postnatal period causes long-lasting impairments in memory formation in various species. The mechanisms underlying long-lasting impairment in cognitive function are poorly understood. Here, we show that repeated general anesthesia in postnatal mice induces preferential apoptosis and subsequent loss of parvalbumin-positive inhibitory interneurons in the hippocampus. Each parvalbumin interneuron controls the activity of multiple pyramidal excitatory neurons, thereby regulating neuronal circuits and memory consolidation. Preventing the loss of parvalbumin neurons by deleting a proapoptotic protein, mitochondrial anchored protein ligase (MAPL), selectively in parvalbumin neurons rescued anesthesia-induced deficits in pyramidal cell inhibition and hippocampus-dependent long-term memory. Conversely, partial depletion of parvalbumin neurons in neonates was sufficient to engender long-lasting memory impairment. Thus, loss of parvalbumin interneurons in postnatal mice following repeated general anesthesia critically contributes to memory deficits in adulthood.