TY - JOUR T1 - Machine-Learning Approaches for Classifying Haplogroup from Y Chromosome STR Data A1 - Schlecht, Joseph A1 - Kaplan, Matthew E. A1 - Barnard, Kobus A1 - Karafet, Tatiana A1 - Hammer, Michael F. A1 - Merchant, Nirav C. Y1 - 2008/06/13 N2 - Author SummaryThe Y chromosome is passed on from father to son as a nearly identical copy. Occasionally, small random changes occur in the Y DNA sequences that are passed forward to the next generation. There are two kinds of changes that may occur, and they both provide vital information for the study of human ancestry. Of the two kinds, one is a single letter change, and the other is a change in the number of short tandemly repeating sequences. The single-letter changes can be laborious to test, but they provide information on deep ancestry. Measuring the number of sequence repeats at multiple places in the genome simultaneously is efficient, and provides information about recent history at a modest cost. We present the novel approach of training a collection of modern machine-learning algorithms with these sequence repeats to infer the single-letter changes, thus assigning the samples to deep ancestry lineages. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 4 IS - 6 UR - https://doi.org/10.1371/journal.pcbi.1000093 SP - e1000093 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1000093 ER -