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Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset

Author

Listed:
  • Tsukuda Koji

    (Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, 153-8902, Tokyo, Japan)

  • Mano Shuhei

    (The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa-shi, 190-8562, Tokyo, Japan)

  • Yamamoto Toshimichi

    (Department of Legal Medicine and Bioethics, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya-shi, 466-8550, Aichi, Japan)

Abstract

Short Tandem Repeats (STRs) are a type of DNA polymorphism. This study considers discriminant analysis to determine the population of test individuals using an STR database containing the lengths of STRs observed at more than one locus. The discriminant method based on the Bayes factor is discussed and an improved method is proposed. The main issues are to develop a method that is relatively robust to sample size imbalance, identify a procedure to select loci, and treat the parameter in the prior distribution. A previous study achieved a classification accuracy of 0.748 for the g-mean (geometric mean of classification accuracies for two populations) and 0.867 for the AUC (area under the receiver operating characteristic curve). We improve the maximum values for the g-mean to 0.830 and the AUC to 0.935. Computer simulations indicate that the previous method is susceptible to sample size imbalance, whereas the proposed method is more robust while achieving almost identical classification accuracy. Furthermore, the results confirm that threshold adjustment is an effective countermeasure to sample size imbalance.

Suggested Citation

  • Tsukuda Koji & Mano Shuhei & Yamamoto Toshimichi, 2020. "Bayesian approach to discriminant problems for count data with application to multilocus short tandem repeat dataset," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-18, April.
    • Handle: RePEc:bpj:sagmbi:v:19:y:2020:i:2:p:18:n:3
    DOI: 10.1515/sagmb-2018-0044
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