The discriminatory power of STRmix illustrated by ROC curves
report
posted on 2020-02-18, 02:53 authored by Maarten Kruijver, Jo-Anne Bright, Hannah Kelly, Catherine McGovern, James CurranJames 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<p>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™.</p>
