Evaluation of low-template DNA profiles using peak heights

Steele Christopher D. (), Greenhalgh Matthew and Balding David J.
Additional contact information
Steele Christopher D.: University College London – UGI, Darwin Building Gower Street, London WC1E 6BT, United Kingdom of Great Britain and Northern Ireland
Greenhalgh Matthew: Orchid Cellmark Ltd., Abingdon Business Park, Blacklands Way, Abingdon OX14 1YX, United Kingdom of Great Britain and Northern Ireland
Balding David J.: University of Melbourne – Centre for Systems Genomics, School of BioSciences and School of Mathematics and Statistics, Melbourne, Victoria, Australia

Statistical Applications in Genetics and Molecular Biology, 2016, vol. 15, issue 5, 431-445

Abstract: In recent years statistical models for the analysis of complex (low-template and/or mixed) DNA profiles have moved from using only presence/absence information about allelic peaks in an electropherogram, to quantitative use of peak heights. This is challenging because peak heights are very variable and affected by a number of factors. We present a new peak-height model with important novel features, including over- and double-stutter, and a new approach to dropin. Our model is incorporated in open-source R code likeLTD. We apply it to 108 laboratory-generated crime-scene profiles and demonstrate techniques of model validation that are novel in the field. We use the results to explore the benefits of modeling peak heights, finding that it is not always advantageous, and to assess the merits of pre-extraction replication. We also introduce an approximation that can reduce computational complexity when there are multiple low-level contributors who are not of interest to the investigation, and we present a simple approximate adjustment for linkage between loci, making it possible to accommodate linkage when evaluating complex DNA profiles.

Keywords: DNA mixtures; forensic; likelihood ratio; likeLTD; low-template DNA; peak heights (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2016-0038 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:15:y:2016:i:5:p:431-445:n:5

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2016-0038

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().