Updated project metadata. Targeted proteomics plays a specialized role in hypothesis-driven research where the expression of cohorts of dozens of proteins related by function, disease, co-expression, localization, or class are measured after perturbing a pathway. Moreover, a major advance in proteomics is the ability to combine many samples (up to 16) for simultaneous quantification using tandem mass tag (TMT) reagents. Here we present a pathway-centric approach for targeting protein lists selected from up to 10,000 expressed proteins to directly measure their abundances, exploiting sample multiplexing to increase throughput. The strategy, termed GoDig, requires only a single-shot LC-MS analysis, ~1 µg combined peptide material, and real-time analytics to trigger simultaneous quantification of up to 16 samples for hundreds of analytes. We applied GoDig to investigate the impact of genetic variation on protein expression in mice fed a Western-style diet high in fat and sucrose. For selected sets of proteins of interest (e.g., kinases, lipid metabolism- and lipid droplet-associated proteins), protein abundances from mouse livers from 480 fully genotyped Diversity Outbred mice were profiled. The results revealed previously unknown protein quantitative trait loci (QTL) and established potential linkages between specific proteins and lipid homeostasis. In all, GoDig provides an integrated solution for next-generation targeted pathway proteomics.