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Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters

Caroline Bouakaze 1 Franklin Delehelle 2 Nancy Sáenz-Oyhéréguy 2 Andreia Moreira Stéphanie Schiavinato 2 Myriam Croze Solène Delon Cesar Fortes-Lima 3 Morgane Gibert 4 Louis Bujan 5 Eric Huyghe 6 Gil Bellis 7 Rosario Calderon 8 Candela Lucia Hernández Efren Avendaño-Tamayo Gabriel Bedoya 9 Antonio Salas 10 Stéphane Mazières 11 Jacques Charioni Florence Migot-Nabias 12 Andrés Ruiz-Linares 13 Jean-Michel Dugoujon 2 Catherine Thèves 2 Catherine Mollereau-Manaute Camille Noûs 14 Nicolas Poulet 15 Turi King 16 Maria Eugenia D’amato Patricia Balaresque 3
1 IML - Laboratoire d'Anthropologie Moléculaire, Institut de Médecine Légale, Strasbourg, France
2 AMIS - Anthropologie Moléculaire et Imagerie de Synthèse
3 EA - Éco-Anthropologie
4 UAABC - UMR 6578 : Adaptabilité Biologique et Culturelle
5 CECOS Midi-Pyrénées, centre de sterilité masculine et équipe d'accueil Fertilité Humaine
6 Service d'Urologie - Transplantation Rénale - Andrologie
7 INED - Institut national d'études démographiques
8 Departamento de Zoologia y Antropologia Fisica
9 Laboratorio de Genética Molecular
10 Archaeogenetics Laboratory
11 ADES - Anthropologie bio-culturelle, Droit, Ethique et Santé
12 MERIT - UMR_D 216 - Mère et enfant face aux infections tropicales
13 UCL-GEE - Dept of Genetics, Evolution and Environment [London]
14 Laboratoire Cogitamus
15 ONEMA - Office national de l'eau et des milieux aquatiques
16 Department of Oncology
Abstract : We developed a new mutationally well-balanced 32 Y-STR multiplex ( CombYplex) together with a machine learning (ML) program PredYMa Le to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, Pred YMa Le was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %). We observe heterogeneity in haplogroup assignation accuracy among classes, with most haplogroups having high prediction scores (99–100 %) and two (E1b1b and G) having lower scores (67 %). The small sample sizes of these classes explain the high tendency to misclassify the Y-profiles of these haplogroups; results were measurably improved as soon as more training data were added. We provide evidence that our ML approach is a robust method to accurately predict haplogroups when it is combined with a sufficient number of markers, well-balanced mutation rate Y-STR panels, and large ML training sets. Further research on confounding factors (such as CNV-STR or gene conversion) and ideal STR panels in regard to the branches analysed can be developed to help classifiers further optimize prediction scores.
Keywords : Y-STR Incremental mutation rates Assignation accuracy and haplogroup prediction (Hg prediction) Machine learning
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UP-SCIENCES | UP-SANTE | UNIV-PARIS | CAMPUS-CONDORCET | SITE-ALSACE | ANTHROPO-MARSEILLE | INED | UNIV-AMU | CNRS | UNIV-TLSE3 | MNHN | IRD

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Caroline Bouakaze, Franklin Delehelle, Nancy Sáenz-Oyhéréguy, Andreia Moreira, Stéphanie Schiavinato, et al.. Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters. Forensic Science International: Genetics , Elsevier, 2020, 48, pp.102342. ⟨10.1016/j.fsigen.2020.102342⟩. ⟨hal-02906055⟩

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