Biology subjects

Jo, B.

Publications and source records attributed to Jo, B..

3 recordsLinked to original sources

bioRxiv2019

The GTEx Consortium atlas of genetic regulatory effects across human tissues

The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues, and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the v8 data, based on 17,382 RNA-sequencing samples from 54 tissues of 948 post-mortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue-specificity of genetic effects, and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.

genetics↗

bioRxiv2019

Severity of early life stress moderates the effect of fine particle air pollution on adolescent brain development

Air pollution is currently the greatest environmental threat to public health, but we know little about its effects on adolescent brain development. In this context, exposure to air pollution co-occurs, and could interact, with social factors that also affect brain development, such as early life stress (ELS). Here, we show that severity of ELS moderates the association between fine particle air pollution (particulate matter 2.5; PM2.5) and structural brain development. We interviewed adolescents about ELS, used census-tract data to characterize PM2.5 concentrations, and conducted longitudinal tensor-based morphometry to assess regional changes in brain volume over a two-year period. Across various cortical, thalamic, and white matter tract regions, there was a remarkably consistent effect of PM2.5 on volumetric change for adolescents who had experienced less, rather than more, severe ELS. Furthermore, exposure to higher levels of PM2.5 and experiencing moderate to severe ELS were associated with comparable volumetric changes in the brain in adolescence.

neuroscience↗

bioRxiv2019

Causal Network Inference from Gene Transcriptional Time Series Response to Glucocorticoids

Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately paving the way for regulatory network re-engineering. Network inference from transcriptional time series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance but additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time series data of 2, 768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2, 768 genes and 31, 945 directed edges (FDR [≤] 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is freely available as an open source software package at https://github.com/lujonathanh/BETS.

bioinformatics↗