In PLoS Biology this week, investigators at the Broad Institute and their colleagues describe "the role of nucelosome positioning in the evolution of gene regulation." In examining the relationship between chromatin organization and gene expression in 12 Hemiascomycota yeast species, by determining mRNA abundance and nucleosome positions genome-wide, the team found "substantial conservation of global and functional chromatin organization in all species," and that chromatin organization has "diverged in both global quantitative features, such as spacing between adjacent nucleosomes, and in functional groups of genes."
Vanessa Marrero and Lorraine Symington at Columbia University show in PLoS Genetics this week that Exo1 and Sgs1 are involved in "extensive DNA end processing," which inhibits break-induced replication. Using a novel BIR assay in Saccharomyces cerevisiae, the pair found that "in the exo1Δ sgs1Δ mutant, which is defective in the 5'-3' resection of DSBs, the frequency of BIR was increased by 39-fold." The authors say that BIR template switching causes rare chromosome rearrangements in the exo1Δ sgs1Δ mutant, "suggesting that reduced resection can decrease the fidelity of homologous recombination," they write.
In PLoS One this week, an international research team reports their genome-wide association study for neuroticism, a "moderately heritable personality trait considered to be a risk factor for developing major depression, anxiety disorders, and dementia," in a population-based sample. More than 2,000 participants were evaluated for neuroticism using the Eysenck Personality Questionnaire; the team investigated 430,000 autosomal SNPs, along with 1.2 million SNPs imputed from HapMap samples, and found that "NKAIN2 showed suggestive evidence of association with neuroticism as a main effect." They also "found support for one previously-reported association," PDE4D, but did not replicate other recently reported associations.
Researchers at McGill University and Illumina report in PLoS Computational Biology this week their evaluations of approaches for the "computational analysis of whole-genome differential allelic expression data in human[s]." The authors propose two approaches — first, they suggest "a statistical approach based on z-score computations," and then "a family of machine learning approaches based on Hidden Markov Models." They evaluate each with published data sets and permutation testing and found that allelic imbalance is widespread. "The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases," the team says.