Although it has become affordable to sequence the entire human genome, measuring all proteins in a sample as complex as human plasma remains currently out of reach. The plasma proteome has an enormous dynamic range and variability, including splice variants, cleavage products, and posttranslational modifications. Antibody-based techniques have predominated, but other affinity-based techniques such as the SOMAscan aptamer assays from SomaLogic also promise the multiplexed measurement of proteins in a scalable manner. Aptamers are short oligonucleotides that have binding affinity to a single protein. They have been developed by introducing a pool of random sequence oligomers to a target protein, applying affinity selection to separate target-oligomer pairs, and then amplifying the surviving sequences. Iterations of this cycle, called SELEX (Systematic Evolution of Ligands by Exponential Enrichment), increase the specificity and avidity of the surviving oligomers to a target protein, eventually identifying a single oligomer, an aptamer, with high affinity for a protein epitope. Additions of side chains improve the stability of aptamers in biological matrices such as plasma. These modifications also change the binding characteristics of the aptamer, giving the same sequence many different properties. The resulting SOMAmers (Slow Off-Rate Modified Aptamers) can be bound to fluorophores and multiplexed, potentially allowing simultaneous quantification of hundreds to thousands of proteins. The most recent iteration of the platform offers≈ 5000 aptamers. The SOMAscan v1. 3 platform consists of 1129 aptamers. 1 In the current edition of Circulation, Mosley and colleagues2 use the SOMAscan v1. 3 platform in the Framingham Offspring Cohort and report plasma levels of 268 of the measured 1129 proteins to be regulated by single nucleotide polymorphisms (SNPs). Robustness of the findings was demonstrated in the Cooperative Health Research in the Region of Augsburg (KORA) F4 data set, using the same platform. When tested in a third cohort (MDCS [Malmö Diet and Cancer Study]) the strongest gene-protein instruments predicted 60% of a protein’s variance, although the median prediction was 8% of the total variance. These gene-protein instruments were used to impute the protein levels of 41 288 previously genotyped individuals in the eMERGE cohort (Electronic Medical Records and Genomics Network), generating the virtual proteome, and their associations with 1128 clinical phecodes derived from electronic health records (EHRs) were assessed. Fifty-five virtual proteins were found to associate with 89 clinical diagnoses in a complex manner; some proteins were associated with more than 1 diagnosis (for example, factor XI was associated with 4 different diagnoses related to venous thrombosis). Some conditions had many associated proteins (for example, thrombosis was predicted by 16 different protein levels). Finally, proteins that were virtually associated