Issues with the reliability of DNA testing for phylogeographical research
Why is it that the various major DNA testing companies ascribe differing ethnicity results to a single genome and how is this a problem for phylogeography? One explanation is the genomic similarity of people with different ethnicities due to genetic admixture caused by migration (Jobling et al. 2016). This renders it difficult for researchers to isolate the ancient DNA from the modern inhabitants. The diverse results of different testing services can be accounted for by the different algorithms they use, which interpret single nucleotide polymorphisms as representing a particular lineage or population (Jobling et al. 2016). The main problem with these algorithms is that they are developed using interpretive rather than methodological means (Nielson & Beaumont 2009). The consequential unreliability of ethnicity, resulting from diverse and converging practices, impacts on anthropological research due to problems identifying historical demographic groups, their size, the location of refugial areas, the extent of migration and gene flow, the extent of fragmentation, and the sequence of events leading to their genomic influence on the present geographic distribution of genotypes (Emerson & Hewitt 2005). Other issues with the reliability of genetic testing for phylogeography include: genetic ‘noise’, lack of sample groups for some ethnicities (such as Indigenous Australian), and established cultural preconceptions (Jobling et al. 2016). Researching genetic heritage is vital in understanding the roots of humanity and how humans have come to be who we are today. Furthermore, such research aids us to understand our traits, the origins of these traits, and the nature of heredity; which could potentially lead to the elimination of negative traits such as genetic diseases, disorders, and defects. Hence, the need to develop a reliable genetic test, in order to accurately research phylogeography, is important.
All of these issues regarding the interpretive method of genetic testing for phylogeographical purposes can be avoided by using a systematic approach. One such approach is the Nested Clade Phylogeographic Analysis (NCPA) designed by Alan Templeton, based on an earlier cladistics approach called the Nested Clade Analysis (NCA) (Nielson & Beaumont 2009). However, the NCPA was found by Knowles and Maddison (2002) to be inaccurate, due to Templeton’s inference key, which seems to neglect inference patterns that do not fit the key and often give incorrect inferences (Nielson & Beaumont 2009). Another line of research for phylogeography is based on coalescent theory and tools from computational statistics (Nielson & Beaumont 2009). Though, coalescent-based methods often have issues with models being too simple (Nielson & Beaumont 2009). Yet, if models of population histories could be constructed and tested to induce a scientific hypothesis of a single phylogeographic model with reduced interpretive inferences, resulting in a cohesive and shared genetic testing system, then genetic anthropological research would be greatly improved. In order to accomplish this, it is necessary to create a model which tests as much of the genome as possible, as Emerson and Hewitt (2005) state, one that includes “…nuclear, cytoplasmic, sex-linked, autosomal, conserved and neutral sequences, including examples with high and low mutation rates.” Thus, issues with the reliability of DNA testing for the identification and research of genetic heritage would be expelled.
Emerson, B.C & Hewitt, G.M. 2005. Phylogeography. Current Biology, 15(10): 367-371.
Jobling, M.A, Rasteiro, R., & Wetton, J.H. 2016. In the blood: the myth and reality of genetic markers of identity. Ethnic and Racial Studies, 39(2): 142-161.
Nielson, R. & Beaumont, M.A. 2009. Statistical inferences in phylogeography. Molecular Ecology, 18(6): 1034-1047.