Abstract
The study of expression quantitative trait loci (eQTL) is a powerful way of detecting transcriptional regulators at a genomic scale and for elucidating how natural genetic variation impacts gene expression. Power and genetic resolution are heavily affected by the study population: whereas recombinant inbred (RI) strains yield greater statistical power with low genetic resolution, using diverse inbred or outbred strains improves genetic resolution at the cost of lower power. In order to overcome the limitations of both individual approaches, we combine data from RI strains with genetically more diverse strains and analyze hippocampus eQTL data obtained from mouse RI strains (BXD) and from a panel of diverse inbred strains (Mouse Diversity Panel, MDP). We perform a systematic analysis of the consistency of eQTL independently obtained from these two populations and demonstrate that a significant fraction of eQTL can be replicated. Based on existing knowledge from pathway databases we assess different approaches for using the high-resolution MDP data for fine mapping BXD eQTL. Finally, we apply this framework to an eQTL hotspot on chromosome 1 (Qrr1), which has been implicated in a range of neurological traits. Here we present the first systematic examination of the consistency between eQTL obtained independently from the BXD and MDP populations. Our analysis of fine-mapping approaches is based on ‘real life’ data as opposed to simulated data and it allows us to propose a strategy for using MDP data to fine map BXD eQTL. Application of this framework to Qrr1 reveals that this eQTL hotspot is not caused by just one (or few) ‘master regulators’, but actually by a set of polymorphic genes specific to the central nervous system.
Highlights
Understanding how genetic variability affects molecular phenotypes is important for revealing the molecular mechanisms underlying physiological phenotypes such as complex diseases [1]
In order to investigate the reproducibility of expression quantitative trait loci (eQTL) between independent mouse populations, we compared the number of eQTL common between the Mouse Diversity Panel (MDP) and BXD datasets to the number of common eQTL obtained with randomized data
All 25,173 probesets with unique genomic locations shared between the BXD and MDP datasets were included in this comparison
Summary
Understanding how genetic variability affects molecular phenotypes is important for revealing the molecular mechanisms underlying physiological phenotypes such as complex diseases [1]. Using genetically more diverse populations has the advantage that a higher density of informative genetic markers can be achieved, which potentially increases the mapping resolution Those studies often suffer from poor statistical power [21]. In order to address these open questions, we investigated the reproducibility and fine mapping of eQTL based on BXD RI lines [23] and inbred strains of the MDP [21]. We systematically show consistency between the two datasets for a wide range of eQTL thresholds and discuss various strategies to integrate high- and low-resolution eQTL data for fine mapping purposes We assessed those data integration schemes for fine mapping by quantifying the ability of each method to reproduce known functional associations between genes. Note that the majority of BXD markers are part of the MDP markers (3055 out of 3791, 80%)
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