Luísa PereiraFarida AlshamaliRune AndreassenRuth BallardWasun ChantratitaNam Soo ChoClotilde CoudrayJean Michel DugoujonMarta EspinozaFabricio González-AndradeSibte HadiUta Dorothee ImmelCatalin MarianAntonio Gonzalez-MartinGerhard MertensWalther ParsonCarlos PeroneLourdes PrietoHaruo TakeshitaHéctor Rangel VillalobosZhaoshu ZengLev ZhivotovskyRui CamachoNuno A. FonsecaUniversidade do PortoGeneral Department of Forensic Sciences and CriminologyOsloMet – storbyuniversitetetCalifornia State University SacramentoMahidol UniversityNational Institute of Scientific InvestigationUniversite Paul Sabatier Toulouse IIIPoder JudicialHospital MetropolitanoUniversity of Central LancashireMartin-Universitat Halle-WittenbergLombardi Comprehensive Cancer CenterUniversidad Complutense de MadridUniversitair Ziekenhuis AntwerpenMedizinische Universitat InnsbruckUniversidade Federal de Minas GeraisUniversity Institute of Research Police Sciences (IUICP)Shimane UniversityUniversidad de GuadalajaraZhengzhou UniversityRussian Academy of SciencesInstituto de Engenharia de Sistemas e Computadores Porto2018-05-032018-05-032011-09-01International Journal of Legal Medicine. Vol.125, No.5 (2011), 629-63614371596093798272-s2.0-80052320295https://repository.li.mahidol.ac.th/handle/20.500.14594/12372Because of their sensitivity and high level of discrimination, short tandem repeat (STR) maker systems are currently the method of choice in routine forensic casework and data banking, usually in multiplexes up to 15-17 loci. Constraints related to sample amount and quality, frequently encountered in forensic casework, will not allow to change this picture in the near future, notwithstanding the technological developments. In this study, we present a free online calculator named PopAffiliator ( http://cracs.fc.up.pt/popaffiliator ) for individual population affiliation in the three main population groups, Eurasian, East Asian and sub-Saharan African, based on genotype profiles for the common set of STRs used in forensics. This calculator performs affiliation based on a model constructed using machine learning techniques. The model was constructed using a data set of approximately fifteen thousand individuals collected for this work. The accuracy of individual population affiliation is approximately 86%, showing that the common set of STRs routinely used in forensics provide a considerable amount of information for population assignment, in addition to being excellent for individual identification. © 2010 Springer-Verlag.Mahidol UniversityMedicineArticleSCOPUS10.1007/s00414-010-0472-2