Highlights
- •A bioinformatic pipeline is described to estimate genetically variable peptide profiles from whole genome sequencing data.
- •The pipeline is designed to consider either short- or long-read massively parallel sequencing data.
- •A semicontinuous likelihood that considers linkage and codon degeneracy is introduced.
- •The likelihood formulation is applied to single-source samples and mixtures.
Abstract
Forensic genetic investigations typically rely on analysis of DNA for attribution
purposes. There are times, however, when the amount and/or the quality of the DNA
is limited, and thus little or no information can be obtained regarding the source
of the sample. An alternative biochemical target that also contains genetic signatures
is protein. One class of genetic signatures is protein polymorphisms that are a direct
consequence of simple/single/short nucleotide polymorphisms (SNPs) in DNA. However,
to interpret protein polymorphisms in a forensic context, certain complexities must
be understood and addressed. These complexities include: 1) SNPs can generate 0, 1,
or arbitrarily many polymorphisms in a polypeptide; and 2) as an object of expression
that is modulated by alleles, genes and interactions with the environment, proteins
may be present or absent in a given sample. To address these issues, a novel approach
was taken to generate the expected protein alleles in a reference sample based on
whole genome (or exome) sequence data and assess the significance of the evidence
using a haplotype-based semi-continuous likelihood algorithm that leverages whole
proteome data. Converting the genomic information into the proteomic information allows
for the zero-to-many relationship between SNPs and GVPs to be abstracted away. When
viewed as a haplotype, many GVPs that correspond to the same SNP is equivalent to
many SNPs in perfect linkage disequilibrium (LD). As long as the likelihood formulation
correctly accounts for LD, the correspondence between the SNP and the proteome can
be safely neglected. Tests were performed on simulated samples, including single-source
and two-person mixtures, and the power of using a classical semi-continuous likelihood
versus one that has been adapted to neglect drop-out was compared. Additionally, summary
statistics and a rudimentary set of decision guidelines were introduced to help identify
mixtures from protein data.
Keywords
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Article info
Publication history
Published online: May 04, 2022
Accepted:
May 1,
2022
Received in revised form:
April 25,
2022
Received:
January 20,
2022
Identification
Copyright
© 2022 Elsevier B.V. All rights reserved.
