Dataset Information

Proteomic Genotyping: Using Mass Spectrometry to Infer SNP Genotypes in Pigmented and Non-Pigmented Hair

ABSTRACT: Proteomic genotyping is the use of genetically variant peptides (GVPs), detected in a forensic protein sample, to infer the genotype of corresponding non-synonymous SNP alleles in the donor’s genome. This process does not depend on the presence of accessible or useable DNA in a sample. This makes proteomic genotyping an attractive alternative for analysis of problematic forensic samples, such as hair shafts, degraded bones or teeth, fingermarks, or sexual assault evidence. To demonstrate the concept in hair shafts, we developed an optimized sample processing protocol that could be used with high effectiveness on single hairs. This allows us to determine if the detected profiles of genetically variant peptides are robust and result in a consistent profile of inferred SNP alleles regardless of the chemical or biological history of the sample. Several real world scenarios have been evaluated. Here we include a study of four European subjects that had both pigmented and non-pigmented (or gray and non-gray) hair shafts. We tested whether (a) protein profiles change as a result of the loss of pigmentation and (b) these changes were reflected in the inferred genotype derived from detection of genetically variant peptides. Using this information, we can determine whether the resulting GVP profiles are more dependent on the biological context of pigmentation status or the underlying genotype.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hair Shaft

DISEASE(S): Abnormality Of Hair Pigmentation

SUBMITTER: Glendon Parker

LAB HEAD: Glendon John Parker

PROVIDER: PXD016156 | Pride | 2020-05-27

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	QEPlus2_12222017_33_RF17.raw	Raw
	QEPlus2_12222017_35_RF18.raw	Raw
	QEPlus2_12222017_37_RF4.raw	Raw
	QEPlus2_12222017_39_RF11.raw	Raw
	QEPlus2_12222017_41_RF15.raw	Raw

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Proteomic genotyping: Using mass spectrometry to infer SNP genotypes in pigmented and non-pigmented hair.

Franklin Rachel N RN Karim Noreen N Goecker Zachary C ZC Durbin-Johnson Blythe P BP Rice Robert H RH Parker Glendon J GJ

Forensic science international 20200225

Proteomic genotyping uses genetically variant peptides that contain single amino acid polymorphisms to infer the genotype of corresponding non-synonymous SNP alleles. We have focused on hair proteins as a source of protein-based genetic information in a forensic context. An optimized sample processing protocol for hair shafts has been developed for use on a single hair that allows us to conduct validation protocols on real world samples. This includes whether the inferred SNP genotypes are robus ...[more]

PMID: 32182563

Similar Datasets

Project description:Protein is a major component of all biological evidence. Proteomic genotyping is the use of genetically variant peptides that contain single amino acid polymorphisms to infer the genotype of matching non-synonymous single nucleotide polymorphisms for the individual who originated the protein sample. This can be used to statistically associate an individual to forensic evidence. The utility of the inferred genotype increases as the detection of genetically variant peptides increases and as the technology is transferred to mass spectrometry platforms available to forensic practitioners. 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 on a Orbitrap Fusion™ Lumos™ Tribrid™ system, and multiple reaction monitoring on an Agilent 6495 triple quadrupole system. In our hands, average genetically variant peptide detection from a selected 24 genetically variant peptide panel 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 parallel reaction and multiple reaction monitoring (p < 0.05). Targeted methods of analysis resulted in a 1.3-fold to 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 billion to 1 in 10 trillion from a single hair using targeted proteomics. Detected genetically variant peptides 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 by forensic practitioners.