bioRxiv ScienceSearch

Biology subjects

Sebastiani, P.

Publications and source records attributed to Sebastiani, P..

2 recordsLinked to original sources

Cis- and Trans-Acting Expression Quantitative Trait Loci Differentially Regulate Gamma-Globin Gene Expression

Genetic association studies have detected two trans-acting quantitative trait loci (QTL) on chromosomes 2, 6 and one cis-acting QTL on chromosome 11 that were associated with fetal hemoglobin (HbF) levels. In these studies, HbF was expressed as a percentage of total hemoglobin or the number of erythrocytes that contain HbF (F-cells). As the{gamma} -globin chains of HbF are encoded by two non-allelic genes (HBG2, HBG1) that are expressed at different levels we used normalized gene expression and genotype data from The Genotype-Tissue Expression (GTEx)-project to study the effects of cis- and trans-acting HbF expression or eQTL. This allowed us to examine mRNA expression of HBG2 and HBG1individually. In addition to studying eQTL for globin genes we examined genes co-expressed with HBG1, studied upstream regulators of HBG1 co-expressed genes and performed a correlation analysis between HBG2 and HBG1 and known HbF regulators. Our results suggest differential effect of cis and trans-acting QTL on HBG and HBG1 expression. Trans-acting eQTLs have the same magnitude of effect on the expression of both HBG2 and HBG1 while the sole cis-acting eQTL affected only HBG2. Furthermore, the analysis of upstream regulators and the correlation analysis suggested that BCL2L1 might be a new potential trans-acting HbF activator. HbF is the major modulator of the phenotype of sickle cell anemia and {beta} thalassemia. Depending on the effect size, modification of trans-acting elements might have a greater impact on HbF levels than cis-acting elements alone.

genetics

CaDrA: A computational framework for performing candidate driver analyses using binary genomic features

Identifying complementary genetic drivers of a given phenotypic outcome is a challenging task that is important to gaining new biological insight and discovering targets for disease therapy. Existing methods aimed at achieving this task lack analytical flexibility. We developed Candidate Driver Analysis or CaDrA, a framework to identify functionally-relevant subsets of binary genomic features that, together, are associated with a specific outcome of interest. We evaluate CaDrAs sensitivity and specificity for typically-sized multi-omic datasets, and demonstrate CaDrAs ability to identify both known and novel drivers of oncogenic activity in cancer cell lines and primary tumors.

bioinformatics