The effect of developmental variation on expression QTLs in a multi parentalC. eleganspopulation

Abstract

Regulation of gene expression plays a crucial role in the development and adaptation of organisms to changing environments. A population-based procedure used to investigate the genetic regulation of gene expression is eQTL mapping. Typically, the age of the population used for eQTL mapping at the time of sampling is strictly controlled. This is necessary because the developmental process causes changes in gene expression, complicating the interpretation of eQTL mapping experiments. However, organisms can differ in their "developmental age", even if they are of the same chronological age. As a result, eQTL patterns are affected by uncontrolled developmental variation in gene expression. The model organism C. elegans is particularly suited for studying the effect of developmental variation on eQTL mapping patterns. In a span of days C. elegans transitions from embryo through four larval stages to adult while undergoing massive changes to its transcriptome. Here we use C. elegans to investigate the effect of developmental age variation on eQTL patterns and an available normalization procedure. We used dynamical eQTL mapping, which includes developmental age as a cofactor, to separate the variation in development from genotypic variation and explain variation in gene expression levels. We compare classical single marker eQTL mapping and dynamical eQTL mapping using RNA-seq data of ~200 multi-parental recombinant inbred lines of C. elegans. The results show that many eQTLs are actually caused by developmental variation, that most trans-bands are associated with developmental age and that dynamical eQTL mapping detects additional eQTLs not found with classical eQTL mapping.

Linking genes to traits in Arabidopsis using systems genetics

“…The identification of ERECTA as the most likely causal gene for a hotspot on chromosome 2 in use case 2 shows the power of prior knowledge-based prioritization of master regulators. However, it was recently shown that eQTL hotspots could arise due to variation in developmental age within the tested population (van Eijnatten et al 2023;Francesconi and Lehner 2014), batch effect (Michaelson et al 2009), or be attributed to the formation of specific plant tissues (Vosman et al 2019). In such cases, it might be more fruitful to look for commonalities between the target genes since the connection to the actual regulator can involve many regulatory steps, while analysis of the target genes could point to an underlying phenomenon.…”

“…In such cases, it might be more fruitful to look for commonalities between the target genes since the connection to the actual regulator can involve many regulatory steps, while analysis of the target genes could point to an underlying phenomenon. The simplest way is to analyse the GO term or pathway enrichment to identify gene expression co-founder (as in (van Eijnatten et al 2023)), which at the same time can help identify the characteristics of the causal genes based on functional annotations. This feature should be the focus of future development.…”

“…Besides limitations in the method, false positives can also be caused by the experimental design. For example, eQTL hotspots could arise due to confounding factors such as batch effects (Michaelson et al 2009) or variation in developmental age within the tested population (van Eijnatten et al 2023).…”