Hypertriglyceridaemia is associated with an increased risk of cardiovascular disease and insulin resistance. There are few effective treatments to lower triglyceride levels, thus the necessity to identify novel regulators of triglyceride metabolism. Genetic screens are a productive avenue for identifying novel targets. However, they have had limited success due to vast diversity in environmental and genetic backgrounds. This can be improved by using mouse GWAS platforms such as the hybrid mouse diversity panel(HMDP), which consists of ~100 genetically distinct inbred mouse lines. There is large variation in plasma triglyceride levels across HMDP strains(40-fold) which we have exploited to identify modifiers of triglyceride metabolism. Genetic screens can also be limiting as they do not allow for differences in mRNA splicing, stability or post-translational modification. Therefore, we performed quantitative proteomic analysis of HMDP mouse liver using isobaric labelling and tandem mass spectrometry. Briefly, proteins were with trypsin-digested and peptides labelled with 10-plex tandem mass tags. 10-plex experiments were performed including a reference sample of pooled C57Bl6/J. Peptides were analysed by multidimensional liquid chromatography coupled to tandem mass spectrometry on an Orbitrap Fusion using MS3-based quantification. Data was analysed using Proteome Discoverer and MaxQuant followed by statistical analysis in Perseus. More than 22,800 peptides were identified on 4,082 proteins at a 1% FDR with 3,095 proteins quantified in 2 of 3 biological replicates. The MS3 approach resulted in excellent correlation between the replicates and ~1,000 proteins showed differences between the strains using ANOVA. Interestingly, one of the top candidates identified was recently described by Auwerx and colleagues in Cell, highlighting the relevance and potential of this targeted proteomics approach¹. Correlation of proteomic data with plasma triglyceride levels identified 85 proteins with significant association including hint2 and acot13, which have recently been shown to play a role in triglyceride metabolism, as well as a number of novel proteins not previously linked to triglyceride metabolism. In conclusion, we have validated this HMDP-proteomic approach as a unique method to identify novel proteins associated with a given phenotype. Furthermore, to our knowledge, this is the highest resolution proteomic screen performed on a genome wide platform.