Abstract
Single Nucleotide Polymorphisms (SNPs) can be used to identify the differences among populations. However, for high-level organisms, there are numerous number of SNPs distributed throughout entire of the genomes. Animal breeders can make use of these genetic markers to different subpopulations. For economical purpose, finding a minimum number of SNPs that can accurately identify different breeds is needed. In this paper, given a set of SNP genotyping samples, without knowing what breed a sample belong to (unlabeled samples), we developed a framework to classify these samples into different animal groups (breeds) based on their genotyping profiles. The proposed framework further identifies a small set of SNPs, called ancestry informative markers (AIMs) that can accurately classify these samples to these groups. The proposed framework adopted the Principal Component Analysis (PCA) technique, and Student’s t-test, to cluster unlabeled genotype data and determine AIMs, respectively. This unsupervised approach can avoid potential ascertainment biases due to mistakenly label some samples or having unlabeled data to be classified.
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Rodpan, A., Wangkumhang, P., Assawamakin, A., Prom-on, S., Tongsima, S. (2010). Unsupervised Algorithms for Population Classification and Ancestry Informative Marker Selection. In: Chan, J.H., Ong, YS., Cho, SB. (eds) Computational Systems-Biology and Bioinformatics. CSBio 2010. Communications in Computer and Information Science, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16750-8_18
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DOI: https://doi.org/10.1007/978-3-642-16750-8_18
Publisher Name: Springer, Berlin, Heidelberg
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