Mutation rates within the human genome are known to vary based on the site's context as well as the influence of CpG dinucelotides, but University of Sussex researchers report that there are variations in the mutation rate that are not due to those two effects. They tested that idea by looking at SNPs at orthologous sites in the chimpanzee and human genomes and if the mutation rate differed, they expected to see more SNP sites and conclude that there had been cryptic effects on the mutation rate that are complex. In a related Primer article also in PLoS Biology, Laurent Duret says that "the discovery of cryptic mutational hotspots in the human genome illustrates how limited our knowledge of the determinants of mutation rates remains."
In PLoS Medicine, the Strengthening the Reporting of Genetic Association Studies initiative adds to the Strengthening the Reporting of Observational Studies in Epidemiology Statement to encourage transparency in the reporting of GWAS studies. These extensions to the STROBE guidelines include stating if the study is a first report or a replication, reporting how population subsets were chosen, giving the number of people for which genotyping was attempted and the number in which it was successful, among other recommendations. The recommendations are also available at their website and the authors say they welcome comments.
Baylor researchers propose a model for the origin of copy-number variants in the latest issue of PLoS Genetics. They suggest that when replication forks break in stressed cells that lack homologous recombination, it results in an "aberrant repair process with features of break-induced replication." When that occurs, they say that the single-stranded 3' tails from the broken replication forks will anneal with microhomology on any close single-stranded DNA. Their model predicts "that complex genomic rearrangements will often be accompanied by extensive loss of heterozygosity and, in some cases, by loss of imprinting."
University of California San Francisco researchers look into how well sequence similarity networks can be used to analyze diverse protein superfamilies. They say that "because they provide access to these relationships in an intuitively accessible manner and are easy to create and manipulate, these networks fill a need that is not currently well-addressed by other tools."