Haplogroup prediction from Y-STR data

They really should have compared against Whit Athey's calculator which is freely available or more generally against his published method. Rather they compare their method against a naive nearest neighbor algorithm.

PLoS Comput Biol. 2008 Jun 13;4(6):e1000093.

Machine-learning approaches for classifying haplogroup from Y chromosome STR data.

Schlecht J, Kaplan ME, Barnard K, Karafet T, Hammer MF, Merchant NC.

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.

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