Protein
is a major component of all biological evidence. Proteomic
genotyping is the use of genetically variant peptides (GVPs) that
contain single-amino-acid polymorphisms to infer the genotype of matching
nonsynonymous single-nucleotide polymorphisms for the individual from
whom the protein sample originated. This can be used to statistically
associate an individual to evidence found at a crime scene. The utility
of the inferred genotype increases as the detection of GVPs increases,
which is the direct result of technology transfer to mass spectrometry
platforms typically available. Digests of single (2 cm) human hair
shafts from three European and two African subjects were analyzed
using data-dependent acquisition on a Q-Exactive Plus Hybrid Quadrupole-Orbitrap
system, data-independent acquisition and a variant of parallel reaction
monitoring (PRM) on an Orbitrap Fusion Lumos Tribrid system, and multiple
reaction monitoring (MRM) on an Agilent 6495 triple quadrupole system.
In our hands, average GVP detection from a selected panel of 24 GVPs
increased from 6.5 ± 1.1 and 3.1 ± 0.8 using data-dependent
and -independent acquisition to 9.5 ± 0.7 and 11.7 ± 1.7
using PRM and MRM (p < 0.05), respectively. PRM
resulted in a 1.3-fold increase in detection sensitivity, and MRM
resulted in a 1.6-fold increase in detection sensitivity. This increase
in biomarker detection has a functional impact on the statistical
association of a protein sample and an individual. Increased biomarker
sensitivity, using Markov Chain Monte Carlo modeling, produced a median-estimated
random match probability of over 1 in 10 trillion from a single hair
using targeted proteomics. For PRM and MRM, detected GVPs were validated
by the inclusion of stable isotope-labeled peptides in each sample,
which served also as a detection trigger. This research accomplishes
two aims: the demonstration of utility for alternative analytical
platforms in proteomic genotyping and the establishment of validation
methods for the evaluation of inferred genotypes.