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

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article numbere1000093
JournalPLoS Computational Biology
Volume4
Issue number6
DOIs
StatePublished - Jun 2008

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artificial intelligence
Y Chromosome
Y chromosome
Chromosomes
Microsatellite Repeats
Chromosome
Single nucleotide Polymorphism
Learning systems
chromosome
Machine Learning
Nucleotides
Polymorphism
microsatellite repeats
single nucleotide polymorphism
Single Nucleotide Polymorphism
polymorphism
Genetic Variation
Mutation Rate
Systems Analysis
systems analysis

ASJC Scopus subject areas

  • Cellular and Molecular Neuroscience
  • Ecology
  • Molecular Biology
  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Computational Theory and Mathematics

Cite this

Machine-learning approaches for classifying haplogroup from Y chromosome STR data. / Schlecht, Joseph; Kaplan, Matthew E.; Barnard, Jacobus J; Karafet, Tatiana; Hammer, Michael F; Merchant, Nirav C.

In: PLoS Computational Biology, Vol. 4, No. 6, e1000093, 06.2008.

Research output: Contribution to journalArticle

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