Ancestry inference is a frequently encountered problem and has many applications such as forensic analyses, genetic association studies, and personal genomics. The main goal of ancestry inference is to identify an individual's population of origin based on our knowledge of natural populations. Because both self-reported ancestry in humans or the sampling location of an organism can be inaccurate for this purpose, the use of genetic markers can facilitate accurate and reliable inference of an individual's ancestral origins. At a higher level, there are two different paradigms in ancestry inference: global ancestry inference which tries to compute the genome-wide average of the population contributions and local ancestry inference which tries to identify the regional ancestry of a genomic segment. In this mini review, I describe the numerous approaches that are currently available for both kinds of ancestry inference from population genomic datasets. I first describe the general ideas underlying such inference methods and their relationship to one another. Then, I describe practical applications in which inference of ancestry has proven useful. Lastly, I discuss challenges and directions for future research work in this area.
Keywords: Bayesian inference; global ancestry; hidden Markov models; local ancestry; maximum likelihood estimation.