Towards Development of Clustering Applications for Large-Scale Comparative Genotyping and Kinship Analysis Using Y-Short Tandem Repeats

Abstract

Y-chromosome short tandem repeats (Y-STRs) are genetic markers with practical applications in human identification. However, where mass identification is required (e.g., in the aftermath of disasters with significant fatalities), the efficiency of the process could be improved with new statistical approaches. Clustering applications are relatively new tools for large-scale comparative genotyping, and the k-Approximate Modal Haplotype (k-AMH), an efficient algorithm for clustering large-scale Y-STR data, represents a promising method for developing these tools. In this study we improved the k-AMH and produced three new algorithms: the Nk-AMH I (including a new initial cluster center selection), the Nk-AMH II (including a new dominant weighting value), and the Nk-AMH III (combining I and II). The Nk-AMH III was the superior algorithm, with mean clustering accuracy that increased in four out of six datasets and remained at 100% in the other two. Additionally, the Nk-AMH III achieved a 2% higher overall mean clustering accuracy score than the k-AMH, as well as optimal accuracy for all datasets (0.84–1.00). With inclusion of the two new methods, the Nk-AMH III produced an optimal solution for clustering Y-STR data; thus, the algorithm has potential for further development towards fully automatic clustering of any large-scale genotypic data.

Performance Evaluations of κ-Approximate Modal Haplotype Type Algorithms for Clustering Categorical Data

“…Moreover, in a recent attempt to improve the clustering results of Y-STR data, three new κ-AMH algorithms were developed as extended κ-AMH algorithms. These include the new κ-AMH I (Nκ-AMH I), the new κ-AMH II (Nκ-AMH II) and the new κ-AMH III (Nκ-AMH III) (Seman et al, 2015). These κ-AMH-type algorithms are derived from the κ-AMH algorithm by maintaining the same clustering procedure with two newly introduced methods: (1) New initial center selection method and (2) New dominant weighting method.…”

“…This improvement is, in fact, contributed by the two methods above. Detailed results for κ-AMH-type algorithms can be found in Seman et al (2015).…”

“…The descriptions above represent the simplified version of the κ-AMH algorithm reported by Seman et al (2015). However, the original descriptions of the κ-AMH algorithm that are similar to those described above can be found in Seman et al (2012a).…”

“…C New initial center selection method: This method replaces the randomized initial center by selecting any identical or similar center selections. The detailed steps and descriptions of this method can be found in Seman et al (2015) C New dominant weighting method: This method replaces the original dominant weighting method for the κ-AMH algorithm, based on a 50 or 100% probability with a weighting method based on three probabilities, 50, 75 and 100%. An additional value of 0.75 (75%) was proposed to appropriately handle any maximum membership values ranging from 1/κ to 1.0, where κ is the number of clusters.…”

“…The detailed steps and descriptions for this method can be found in Seman et al (2015). For clarity, Table 1 shows the main differences between κ-AMH-type algorithms.…”