Mapping Temporally Varying Quantitative Trait Loci in Time-to-Failure Experiments

Abstract

Existing methods for mapping quantitative trait loci (QTL) in time-to-failure experiments assume that the QTL effect is constant over the course of the study. This assumption may be violated when the gene(s) underlying the QTL are up-or downregulated on a biologically meaningful timescale. In such situations, models that assume a constant effect can fail to detect QTL in a whole-genome scan. To investigate this possibility, we utilize an extension of the Cox model (EC model) within an interval-mapping framework. In its simplest form, this model assumes that the QTL effect changes at some time point t 0 and follows a linear function before and after this change point. The approximate time point at which this change occurs is estimated. Using simulated and real data, we compare the mapping performance of the EC model to the Cox proportional hazards (CPH) model, which explicitly assumes a constant effect. The results show that the EC model detects time-dependent QTL, which the CPH model fails to detect. At the same time, the EC model recovers all of the QTL the CPH model detects. We conclude that potentially important QTL may be missed if their time-dependent effects are not accounted for.

A complex network of additive and epistatic quantitative trait loci underlies natural variation of Arabidopsis thaliana quantitative disease resistance to Ralstonia solanacearum under heat stress

“…Theoretical predictions suggest that phenotypic changes in ontogenetic time (typically time‐to‐event or time‐to‐failure traits such as flowering time or death time) are often driven by the temporal regulation of QTLs (Johannes, 2007). In this study, all the 14 top QTLs control QDR to R. solanacearum GMI1000 in a playful manner at both 27 and 30°C, suggesting that disease progression to R. solanacearum highly depends on the time specificity of the genetic effects.…”

Epigenome dynamics: a quantitative genetics perspective

“…when chromatin changes interact with DNA variants through the silencing or activation of genes. Intriguing, although indirect, evidence for time-dependent genetic effects has been reported in several developmental QTL studies [42][43][44][45] . For instance, in an early experiment Cheverud 45 examined body weight in mice over the course of 10 weeks, and discovered different sets of QTLs for the early and later growth stages.…”

Modifier Genes for Mouse Phosphatidylinositol Transfer Protein α (vibrator) That Bypass Juvenile Lethality

“…Linkage analysis for survival beyond 42 days as a dichotomous trait was carried out in R/ QTL using a nonparametric statistic derived from the KruskalWallace test (Broman et al 2003;Broman and Sen 2009). Linkage analysis for QTL with time dependence was carried out using an enhanced Cox proportional hazard model (EC) as described ( Johannes 2007). ANOVA and post-hoc Tukey's honest significant difference (HSD) test were performed in R (v. 2.8.1).…”