Abstract / Synopsis
This paper describes a methodology for analyzing X-chromosome data to establish biogeographical contributions to the author’s X chromosome. We present an exposition of how Hidden Markov Modeling (HMM) can be used as a black box for ancestry analysis and focus on a set of conditions that are not universal but fairly common. The first condition is that the ancestral populations are drawn from regions that have had very little or no contact with each other since prehistoric times. The second condition is that the number of possible ancestral populations is small. In this analysis, we assume that the ancestral populations are Native North American, Northwestern European, and West African. We compare the result of our analysis with the analyses carried out by the companies 23andMe and deCODEme for the same data. Finally, we point to a mechanism for reducing noise by adjusting the data before applying HMM.
Melvin R. Currie, "Using Hidden Markov Modeling for Biogeographical Ancestry Analysis," Journal of Humanistic Mathematics, Volume 9 Issue 2 (July 2019), pages 60-77. DOI: 10.5642/jhummath.201902.06. Available at: https://scholarship.claremont.edu/jhm/vol9/iss2/6