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This Week in PLoS: Oct 18, 2010

In a paper published in PLoS One this week, a team led by researchers at the University of Pittsburgh shows that human embryonic and induced pluripotent stem cells show similar responses to DNA damage. Specifically, the team writes, "iPS cells [temporarily] arrest cell cycle progression in the G2 phase of the cell cycle, displaying a lack of the G1/S cell cycle arrest similar to human ES cells" in response γ-irradiation-induced apoptosis. The authors add that "both cell types remove [double-strand breaks] within six hours of γ-irradiation, form RAD51 foci, and exhibit sister chromatid exchanges suggesting homologous recombination repair."

Over in PLoS Genetics, the Wellcome Trust Sanger Institute's Ni Huang and colleagues characterize the extent of haploinsufficiency in the human genome. Huang et al. mapped 1,079 haplosufficient genes among 8,458 healthy individuals and "contrasted the genomic, evolutionary, functional, and network properties between these HS genes and known HI genes." The team found that "HI genes exhibit higher levels of expression during early development and greater tissue specificity." In addition, the team proposes a model to predict the probability of gene haploinsufficiency.

Investigators at the Baylor College of Medicine and Rice University describe "a novel adaptive method for the analysis of next-generation sequencing data to detect complex trait associations with rare variants due to gene main effects and interactions" in PLoS Genetics. Dajiang Liu and Suzanne Leal propose that their kernel-based adaptive cluster — or KBAC — method, which "combines variant classification and association testing in a coherent framework" and is "implemented in a user-friendly R package," is superior to alternative rare variant analysis methods.

And in a methods paper published in PLoS Computational Biology, researchers at the University of California, San Diego, describe a novel approach for detecting associations between rare variations and common phenotypes. The RareCover algorithm "combines a disparate collection of RVs with low effect and modest penetrance" and "does not require the rare variants be adjacent in location." When the team applied RareCover to re-sequencing data from 289 individuals at the extremes of body mass index distribution, they identified one region significantly associated with endocannabinoid metabolism in each gene.