Exploration of shared genetic architecture between subcortical brain volumes and anorexia nervosa

In MRI scans of patientswith anorexia nervosa (AN), reductions in brain volume are often apparent. However, it is unknownwhether such brain abnormalities are influenced by genetic determinants that partially overlap with those underlyingAN. Here, we used a battery of methods (LD score regression, genetic risk scores, sign test, SNP effect concordance analysis, and Mendelian randomization) to investigate the genetic covariation between subcortical brain volumes and risk for AN based on summary measures retrieved from genome-wide association studies of regional brain volumes (ENIGMA consortium, n = 13,170) and genetic risk for AN (PGC-ED consortium, n = 14,477). Genetic correlationsrangedfrom-0.10to0.23(allp > 0.05). Thereweresomesigns ofaninverseconcordance between greater thalamus volume and risk for AN (permuted p = 0.009, 95% CI: [ 0.005, 0.017]). A genetic variant in the vicinity of ZW10, a gene involved in cell division, and neurotransmitter and immune systemrelevant genes, in particularDRD2, was significantly associated with AN only after conditioning on its association with caudate volume (pFDR = 0.025). Another genetic variant linked to LRRC4C, important in axonal and synaptic development, reached significance after conditioning on hippocampal volume (pFDR = 0.021). In this comprehensive set of analyses and based on the largest available sample sizes to date, there was weak evidence for associations between risk for AN and risk for abnormal subcortical brain volumes at a global level (that is, common variant genetic architecture), but suggestive evidence for effects of single genetic markers. Highly powered multimodal brain-and disorder-related genome-wide studies are needed to further dissect the shared genetic influences on brain structure and risk for AN.Stress-related psychiatric disorders across the life spa

Using settlement vegetation data for paleoclimate and carbon modeling: Preliminary results of the PalEON project

<p>Ecosystem modeling can be improved by testing assumptions about pre-settlement vegetation. PalEON combines historical data with modeling tools, developing robust benchmarks for community structure in the northeastern United States. By building vegetation models to be used with paleo-datasets we help understand drivers of ecosystem change over the last 2000 years in the northeastern United States, improving ecosystem models.</p> <p>Building a detailed map of pre-settlement forest composition from Public Lands Office (PLO) survey data is an initial step. We present a workflow and preliminary results showing pre-settlement stem density and basal area for hard and softwoods in Minnesota and Wisconsin.</p> <p>Preliminary results suggest differences between Ramankutty and Foley (1999) and the PLO data ands provide baseline data for regional biomass estimates.  </p