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An assessment of Bayesian and multinomial logistic regression classification systems to analyse admixed individuals
Institution:1. National Centre for Forensic Studies, University of Canberra, Australia;2. Forensic Genetics Unit, Institute of Legal Medicine, University of Santiago de Compostela, Spain;3. Faculty of Mathematics, University of Santiago de Compostela, Spain;4. Office of the Chief Forensic Scientist, Victoria Police Forensic Services Department, Victoria, Australia;1. Laboratorio de Genética Molecular de Cruz Vital – Cruz Roja Ecuatoriana Quito, Ecuador;2. Laboratorio GENES Ltda, Medellin, Colombia;1. Armed Forces DNA Identification Laboratory, Armed Forces Medical Examiner System, United States;2. American Registry of Pathology, United States;3. Flinders University, Australia;4. National Institute of Standards and Technology, United States;1. Forensic Science Program, Department of Applied Science, Faculty of Science, Prince of Songkla University, Thailand;2. School of Biological Sciences, Flinders University, Adelaide, South Australia, Australia;3. Princess Maha Chakri Sirindhorn Natural History Museum, Prince of Songkla University, Thailand;4. Department of Molecular Biotechnology and Bioinformatics, Faculty of Science, Prince of Songkla University, Thailand;1. Department of Forensic Genetics, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu 610041, Sichuan, PR China;2. Shanghai Key Laboratory of Forensic Medicine, Institute of Forensic Sciences, Ministry of Justice, Shanghai 200063, PR China
Abstract:Multinomial logistic regression (MLR) has been applied to the prediction of hair and eye colour. Here we apply it to the prediction of biogeographical ancestry (BGA) in a test set of 1092 admixed and non-admixed genotypes from the 1000 Genomes Project using a training set of 571 non-admixed genotypes from the HGDP CEPH cell line panel. Predicted BGAs are consistent with those of Structure, a naïve Bayesian classifier.
Keywords:Multinomial logistic regression  Biogeographical ancestry  Phenotype prediction  Structure
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