In this week’s issue of Science, geneticists discuss the genetic control of hotspots ― the preferred chromosomal locations for meiotic recombination events. Vivian Cheung, a Howard Hughes Medical Institute investigator, and her colleagues discuss three papers appearing in the issue that identify a mammalian gene, PRDM9, which appears to control the extent to which crossovers occur at hotspots. PRDM9 codes for a zinc-finger protein of the same name, which has shown to be highly polymorphic. In each of the three studies, researchers identified PRDM9 through mouse models, human studies, and computational analyses, the authors write, adding that “an important lesson from this discovery is that results from genetic studies can direct mechanistic analyses.”
Also in Science this week, Australian researchers, along with their colleague in the UK, describe their finding that the LMO2 oncogene initiates leukemia. The team used cell fate mapping techniques to study the cellular origin of LMO2-induced leukemia in mice. They found the LMO2-induced self-renewal of committed T cells more than eight months before the development of overt T cell acute lymphoblastic leukemia. With further analysis, the researchers deduced that LMO2 promotes the self-renewal of preleukemic thymocytes, a possible explanation for the leukemic transformation process observed in committed T cells.
Fatima Soliman of the Weill Cornell Medical College and her colleagues identify parallel phenotypes in mice and humans, products of a common SNP in the brain-derived neurotrophic factor gene, known for its involvement in anxiety-related behavior. Using an inbred genetic knock-in variant BDNF mouse model, the team found that the individuals were impaired in abating a conditioned fear response. This is paralleled by atypical frontoamygdala activity in humans, they write. Soliman and her team suggest that the BDNF variant allele may provide a useful tool to study anxiety disorders.
In another Science paper, researchers describe their method, dubbed composite of multiple signals, that examines the human genome for multiple signals of selection and increased resolution. By applying CMS to candidate genes from HapMap, the team localized populations-specific signals and was able to indentify known and novel causal variants. The team writes that CMS implicates the precise variants selected by evolution.