In PLoS Genetics this week, a large, international research consortium reports data from its two-stage genome-wide association study to determine whether common genetic variants modify penetrance for BRCA2 mutation carriers. First, the team genotyped nearly 600,000 SNPs from more than 800 young affected and unaffected carriers, and stratified the individuals based on their BRCA2*6174delT status. Next, the investigators genotyped the top 85 loci in more than 1,200 individuals in both case and control groups; the team found that "FGFR2 rs2981575 had the strongest association with breast cancer risk," the authors write.
In a PLoS One paper that appeared online this week, researchers at the University of California, San Francisco, and their colleagues at Stanford describe the mouse blood-brain barrier transcriptome, which they suggest is a valuable tool "for understanding CNS [central nervous system] endothelial cells and their interaction with neural and hematogenous cells." The UCSF-Stanford team reports its "comprehensive resource of transcripts that are enriched in the BBB forming endothelial cells of the brain," through which they "identified novel tight junction proteins, transporters, metabolic enzymes, signaling components, and unknown transcripts whose expression is enriched in central nervous system endothelial cells," the team writes.
In their examination of 120 paired maternal-umbilical cord blood samples from a prospective birth cohort, a team led by investigators at the Harvard School of Public Health has found that "DNA methylation in maternal blood was correlated with her offspring at LINE-1, Alu, and p16, but not p53." Specifically, by pyrosequencing CpG positions at the promoter regions for each, the team was able to quantify methylation and found that "maternal methylation of p16 at position 4 significantly predicted methylation at the same position in umbilical cord blood," the authors write.
And in PLoS Computational Biology this week, a trio of researchers at the University of Wisconsin-Madison describes GeneForce, "an automated, phenotype-driven approach for refining metabolic and regulatory models." GeneForce, the team writes, works to "identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models." When applied to an integrated model of E. coli, GeneForce "showed improved accuracy for predicting growth phenotypes for 50,557 cases" and identified "native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments," the authors write.