Unfortunately, we have been unable to globally compare mRNA and protein levels with genetic variation across human populations primarily because of the nonoverlapping gene sets typically collected with current mRNA and protein analysis platforms. Although mass spectrometers (MSs) and MS-based protein analysis methods continue to improve and can quantify thousands of proteins per sample, they currently lack the sensitivity required to consistently observe more than a fraction of the human proteome without depleting highly abundant proteins.13 A major problem for most population-level proteome-by-transcriptome comparisons employing mass spectrometry is the biased sampling of proteins across samples; typically, subsets of proteins are detected and quantified in some samples but undetected in others.10,11,14,15 This biased detection issue coupled with bias to observe and quantify the most abundant proteins within a sample16 results in reduced power to assess the relative contributions of genome influences to the proteome. To better relate genomes to transcriptomes and proteomes, we and others have developed and applied complementary antibody-based “protein-omic” approaches to more reproducibly quantify targeted sets of protein families across individuals provided the availability of validated antibodies directed against the proteins of interest.17 We previously coined the term protein-omic to refer to studies that collect information on targeted subsets of functionally related proteins, by contrast to proteomic that refers to larger, more random sampling-based analyses of the proteome, typically by mass spectrometry.