Highlights
- •Haplotype data of 21 Y-STRs (from PPY23) were analyzed in six populations.
- •Entropy was used as a measure of haplotype diversity.
- •The seven most rapidly mutating Y-STRs explain >94% of the overall entropy.
- •No decomposition of PPY23 into sets of independent Y-STRs is feasible.
- •Match probabilities cannot be transformed into products of less intricate factors.
Abstract
Match probability calculation is deemed much more intricate for lineage genetic markers,
including Y-chromosomal short tandem repeats (Y-STRs), than for autosomal markers.
This is because, owing to the lack of recombination, strong interdependence between
markers is likely, which implies that haplotype frequency estimates cannot simply
be obtained through the multiplication of allele frequency estimates. As yet, however,
the practical relevance of this problem has not been studied in much detail using
real data. In fact, such scrutiny appears well warranted because the high mutation
rates of Y-STRs and the possibility of backward mutation should have worked against
the statistical association of Y-STRs. We examined haplotype data of 21 markers included
in the PowerPlex®Y23 set (PPY23, Promega Corporation, Madison, WI) originating from six different populations
(four European and two Asian). Assessing the conditional entropies of the markers,
given different subsets of markers from the same panel, we demonstrate that the PowerPlex®Y23 set cannot be decomposed into smaller marker subsets that would be (conditionally)
independent. Nevertheless, in all six populations, >94% of the joint entropy of the
21 markers is explained by the seven most rapidly mutating markers. Although this
result might render a reduction in marker number a sensible option for practical casework,
the partial haplotypes would still be almost as diverse as the full haplotypes. Therefore,
match probability calculation remains difficult and calls for the improvement of currently
available methods of haplotype frequency estimation.
Keywords
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References
- A new method for the evaluation of matches in non-recombining genomes: application to Y-chromosomal short tandem repeat (STR) haplotypes in European males.Forensic Sci. Int. 2000; 114: 31-43
- Fundamental problem of forensic mathematics – the evidential value of a rare haplotype.Forensic Sci. Int. Genet. 2010; 4: 281-291
- Estimating trace-suspect match probabilities for singleton Y-STR haplotypes using coalescent theory.Forensic Sci. Int. Genet. 2013; 7: 264-271
- Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method.Forensic Sci. Int. Genet. 2014; 11: 182-194
- The discrete Laplace exponential family and estimation of Y-STR haplotype frequencies.J. Theor. Biol. 2013; 329: 39-51
- Y-STR frequency surveying method: a critical reappraisal.Forensic Sci. Int. Genet. 2011; 5: 84-90
- Human Y chromosome base-substitution mutation rate measured by direct sequencing in a deep-rooting pedigree.Curr. Biol. 2009; 19: 1453-1457
- Mutability of Y-chromosomal microsatellites: rates, characteristics, molecular bases, and forensic implications.Am. J. Hum. Genet. 2010; 87: 341-353
- Y chromosome haplotype reference database (YHRD): update.Forensic Sci. Int. Genet. 2007; 1: 83-87
- A global analysis of Y-chromosomal haplotype diversity for 23 STR loci.Forensic Sci. Int. Genet. 2014; 12C: 12-23
- A mathematical theory of communication.At&T Tech. J. 1948; 27: 623-656
- Entropy-based SNP selection for genetic association studies.Hum. Genet. 2003; 114: 36-43
- R: A Language and Environment for Statistical Computing.R Foundation for Statistical Computing, Vienna, Austria2009
- Modern Applied Statistics with S.Springer, 2010
Article info
Publication history
Published online: October 24, 2014
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© 2014 Elsevier Ireland Ltd. Published by Elsevier Inc. All rights reserved.