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The discriminatory power of STRmix illustrated by ROC curves

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posted on 18.02.2020 by Maarten Kruijver, Jo-Anne Bright, Hannah Kelly, Catherine McGovern, James Curran, Rebecca Richards, Sibéal Waldron, Andrew McWhorter, Anne Ciecko, Brian Peck, Chase Baumgartner, Christina Buettner, Scott McWilliams, Claire McKenna, Colin Gallacher, Ben Mallinder, Darren Wright, Deven Johnson, Dorothy Catella, Eugene Lien, Craig O’Connor, Arlene Petrosky, Jason Bundy, Jillian Echard, John Lowe, Joshua Stewart, Kathleen Corrado, Sheila Gentile, Marla Kaplan, Michelle Hassler, Naomi McDonald, Paul Stafford Allen, Rachel H. Oefelein, Shawn Montpetit, Melissa Strong, Sarah Noël, Simon Malsom, Kyle Duke, Jessica Skillman, Tamyra Moretti, Teresa McMahon, Thomas Grill, Tim Kalafut, MaryMargaret Greer-Ritzheimer, Vickie Beamer, Duncan A. Taylor, John S. Buckleton

The use of probabilistic genotyping methods has seen a significant uptake in recent years throughout the world. There is a continuing need for empirical validation of such methods in contexts involving different wet chemistry conditions and various types of (mixed) samples. We have published a large scale empirical validation study in response to the 2016 PCAST report addressing, specifically, the issue that some sample categories were perceived to have received little, to no, attention in the empirical validation literature. More recently, the use of receiver operating characteristic (ROC) analysis was suggested as an important part of such validation exercises. We present the results of ROC analysis for a previously published study. The ROC curves demonstrate the great discriminatory power of DNA demonstrated using the probabilistic genotyping software STRmix™.

Funding

US National Institute of Justice: Grant No. 2017-DN-BX-K541

History

STRmix™

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