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Techniques for estimating genetically variable peptides and semi-continuous likelihoods from massively parallel

August E Woerner1, Benjamin Crysup2, F Curtis Hewitt3

  • 1Center for Human Identification, University of North Texas Health Science Center, Fort Worth, TX, USA; Department of Microbiology, Immunology and Genetics, University of North Texas Health Science Center, Fort Worth, TX, USA.

Forensic Science International. Genetics
|May 8, 2022
PubMed
Summary
This summary is machine-generated.

Forensic proteomics offers a novel approach for sample analysis when DNA is limited. This study introduces a method using protein polymorphisms to overcome DNA limitations in forensic investigations, enhancing sample attribution.

Keywords:
Genetically variable peptidesMassively parallel sequencingMixturesProbabilistic genotypingProteomics

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Area of Science:

  • Forensic Science
  • Genetics
  • Proteomics

Background:

  • Forensic investigations commonly use DNA analysis for sample attribution.
  • Limited DNA quantity or quality can hinder accurate source identification.
  • Protein polymorphisms, arising from DNA single nucleotide polymorphisms (SNPs), offer an alternative genetic signature.

Purpose of the Study:

  • To address complexities in interpreting protein polymorphisms for forensic use.
  • To develop a novel method for analyzing protein data in forensic contexts.
  • To assess the significance of evidence using a haplotype-based algorithm with proteome data.

Main Methods:

  • Generated expected protein alleles from whole genome/exome sequence data.
  • Employed a haplotype-based semi-continuous likelihood algorithm leveraging whole proteome data.
  • Tested the approach on simulated single-source and mixture samples, comparing likelihood formulations.

Main Results:

  • The novel approach effectively converts genomic information to proteomic information, abstracting SNP-to-protein relationships.
  • Viewing proteins as haplotypes simplifies interpretation, equivalent to managing SNPs in perfect linkage disequilibrium (LD).
  • The adapted likelihood formulation, accounting for LD and potential dropout, showed utility in analyzing protein data, including mixtures.

Conclusions:

  • Forensic proteomics provides a viable alternative when DNA is degraded or scarce.
  • The developed haplotype-based algorithm and analysis framework enhance the interpretation of protein polymorphisms in forensic science.
  • Summary statistics and decision guidelines were introduced to aid in identifying mixtures from protein data.