In Genome Biology, an Iowa State University-led team explores trait-related expression using an "expression read depth genome-wide association study," or eRD-GWAS, approach in diverse maize lines. After using RNA sequencing to tease out transcription factor-expression variation ties in five tissue types from 27 sequenced, genetically distinct, inbred maize lines, the researchers established an eRD-GWAS framework for assessing expression variation ties to phenotypic traits in a larger set of 369 inbred maize lines. In the process, they identified genes associated with more than a dozen maize traits, the authors note, "consistent with the hypothesis that genetic variation in transcription factor expression contributes substantially to phenotypic diversity."
Researchers from the Chinese Academy of Medical Sciences, the University of Chicago, and elsewhere consider cancer risk-related regulatory variation using an enhancer quantification method based on the STARR-seq (self-transcribing active regulatory region sequencing) method. Starting with nearly 1,000 SNPs found in prior cancer susceptibility or drug response GWAS, the team came up with capture probes targeting almost 10,700 surrounding SNPs for a STARR-seq-based analysis of human cell lines — a search that led to hundreds of with positive or negative regulatory effects on gene expression.
A team from the US and Israel describe a single-cell RNA sequencing strategy for simultaneously profiling intracellular bacteria and infected host cells. For their study, the researchers used this so-called scDual-Seq approach to study Salmonella typhimurium pathogenicity and gene regulation in infected mouse macrophage cells, uncovering three infected macrophage sub-populations related to the stage of S. typhimurium infection. More broadly, the authors argue that "the ability to capture both the pathogen and host transcription programs at the level of individual cells will be important for understanding the relationships among the different states of infection."