Relic Neutralino Densities and Detection Rates with Nonuniversal Gaugino Masses

We extend previous analyses on the interplay between nonuniversalities in the gaugino mass sector and the thermal relic densities of LSP neutralinos, in particular to the case of moderate to large tan beta. We introduce a set of parameters that generalizes the standard unified scenario to cover the complete allowed parameter space in the gaugino mass sector. We discuss the physical significance of the cosmologically preferred degree of degeneracy between charginos and the LSP and study the effect this degree of degeneracy has on the prospects for direct detection of relic neutralinos in the next round of dark matter detection experiments. Lastly, we compare the fine tuning required to achieve a satisfactory relic density with the case of universal gaugino masses, as in minimal supergravity, and find it to be of a similar magnitude. The sensitivity of quantifiable measures of fine-tuning on such factors as the gluino mass and top and bottom masses is also examined.Comment: Uses RevTeX; 14 pages, 16 figure

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (r b ). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (r b = 0.70 for cis-eQTLs and r ^ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes. © 2018 The Author(s).Peer reviewe