In Nature Communications, researchers from the US, UK, and the Netherlands outline the transcriptome-wide structural equation modeling (T-SEM) method they used to computationally tease out tissue-specific gene expression consequences in relation to several cognitive traits. The team turned to T-SEM to explore the biology behind seven cognitive traits, bringing in data for between 11,200 and nearly 331,700 individuals to focus in on tissue-specific expression patterns that contribute to cross-trait genetic sharing. Across a set of 184 genes with such tissue-specific expression, meanwhile, the authors defined more than two dozen over-represented functional groups with a stratified genome SEM strategy. "We applied T-SEM to distinguish genes whose inferred expression operates across seven diverse cognitive functions as indexed by a genetic g-factor, from those whose inferred expression operates more specifically on individual cognitive traits," the authors write, adding that "implementation of the multivariate, functional methods described here can begin to elucidate the biological mechanisms that are shared and unique across genetically correlated quantitative traits and disease phenotypes."
Cognitive Trait Pathways Tracked Down With Tissue-Specific Expression Effect Analysis
Oct 24, 2022