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Knickmeyer, R. C.

Publications and source records attributed to Knickmeyer, R. C..

2 recordsLinked to original sources

Effect of Human Infant Gut Microbiota on Mouse Behavior, Dendritic Complexity, and Myelination

The mammalian gut microbiome influences numerous developmental processes. In human infants it has been linked with cognition, social skills, hormonal responses to stress, and brain connectivity. Yet, these associations are not necessarily causal. The present study tested whether two microbial stool communities, common in human infants, affected behavior, myelination, dendritic morphology, and spine density when used to colonize mouse models. Humanized animals were more like specific-pathogen free mice than germ-free mice for most phenotypes, although in males, both humanized groups were less social. Both humanized groups had thinner myelin sheaths in the hippocampus, than did germ-free animals. Humanized animals were similar to each other except for dendritic morphology and spine density where one group had greater dendritic length in the prefrontal cortex, greater dendritic volume in the nucleus accumbens, and greater spine density in both regions, compared to the other. Results add to a body of literature suggesting the gut microbiome impacts brain development. TeaserFecal transplants from human infants with highly abundant Bifidobacterium, an important inhabitant of the intestinal tract of breastfed newborns, may promote brain connectivity in mice.

neuroscience↗

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

genetics↗