Identification of cis- and trans-expression quantitative trait loci using Bayesian framework

Abstract

The detection and identification of expression quantitative trait loci (eQTLs) for biological characteristics like gene expression is an important focus of genomics. The existence of cis- and trans-eQTLs is crucial for establishing their cumulative significance to the desired traits. A crucial aspect of genomics is identifying the cis- and trans-eQTLs that capture substantial changes in the expression of distant genes. The goal of the present study was to use an integrated hierarchical Bayesian model to identify the cis- and trans-eQTLS. Molecular approaches are utilized to categorize just the candidate genes when quantitative trait loci or eQTLs are identified. Variations inside or near the gene are hypothesized to determine the genetic variances that reflect transcript levels. The identification of eQTLs has helped us better understand gene regulation and complex trait analysis. The present study focused on barley crops, and only cis-eQTLs were identified; no additional eQTL hotspots were determined. Mouse gene expressions were used to study trans-eQTLs and substantial cis- and trans-eQTLs, as well as four eQTL hotspots were identified.