Researchers in China and the US provide details on the "Pan-cancer methylation quantitative trait loci" (Pancan-meQTL) database, which currently includes information on the ways that genetic variants affect methylation in more than 7,200 tumors from 23 cancer types profiled for the Cancer Genome Atlas project. When the team brought together genotypes and DNA methylation profiles for the TCGA tumors, it uncovered more than 8 million apparent QTLs influencing nearby methylation marks and almost 965,050 meQTLs acting in trans. At least some of the meQTLs show potential ties to cancer outcomes or overlap with variants found in genome-wide association studies, the authors say, calling Pancan-meQTL "a valuable resource for investigating the roles of genetics and epigenetics in cancer."
A Czech Republic-led team introduces a database for compiling sequences and other information on ancient mitochondrial genomes. The current version of the database, known as AmtDB, currently contains sequences, geographical origins, radiocarbon dated-aging, and other meta-data for more than 1,100 ancient mitochondrial DNA samples going back to the Paleolithic Period, including almost 900 full mitochondrial genomes. The researchers say that the resource offers a "consistent way of mapping the published [ancient DNA] samples from different sources," in conjunction with available meta-data that "itself can be easily used in ancient genomic, archeological, or anthropological studies."
Finally, researchers from the University of Cambridge, Pfizer Worldwide Research and Development, and Weill Cornell Medicine-Qatar describe a computational strategy for finding promising causal genes from risk variants falling at quantitative trait loci. The researchers applied "Prioritization of candidate causal gene(s) at molecular QTLs," or ProGeM, to a set of known metabolite QTLs and to cis-QTLs suspected of influencing blood plasma protein levels based on data for 3,301 healthy individuals. The ProGeM approach compared favorably with expert curation, the authors report, noting that their results "suggest that many of the same bottom-up criteria can be used to effectively prioritize the most likely true positive causal genes for both mQTLs and pQTLs, and that ProGeM may be applicable to other molecular QTL datasets beyond those tested here."