The number of SNPs required for distinguishing Japanese from other East Asians

Development of an Okinawa panel for biogeographic inference of Okinawans

BACKGROUND

The Precision ID Ancestry Panel with 165 SNP markers was unable to differentiate between mainland Japanese and Okinawa Japanese or to distinguish either of them from other East Asian populations.

AIM

An Okinawa panel was developed with the aim of further separating Okinawa Japanese individuals from mainland Japanese and other Asian groups. Seventy-five SNPs were selected using the most informative markers from the literature. Further, 22 SNPs were selected to separate Okinawa Japanese at minimum SNPs.

SUBJECTS AND METHODS

Samples were collected from 48 unrelated individuals from mainland Japan and 46 unrelated residents of the Okinawa prefecture. Data were evaluated by STRUCTURE, principal component, and GenoGeographer analyses.

RESULTS

The 22 SNP set had similar levels of differentiation in STRUCTURE and PCA analyses as the 75 SNP set. GenoGeographer analysis showed that, out of the 46 Okinawa Japanese individuals, the 75 SNP and 22 SNP sets correctly assigned the Okinawan population as the most likely population of origin for 32 and 31 individuals, respectively.

CONCLUSION

Neither SNP set could completely differentiate between Okinawa Japanese and other Asian groups, however, these sets should be useful for crime investigation, when the sample, cost and time are limited.

Multivariate statistical approach and machine learning for the evaluation of biogeographical ancestry inference in the forensic field

The biogeographical ancestry (BGA) of a trace or a person/skeleton refers to the component of ethnicity, constituted of biological and cultural elements, that is biologically determined. Nowadays, many individuals are interested in exploring their genealogy, and the capability to distinguish biogeographic information about population groups and subgroups via DNA analysis plays an essential role in several fields such as in forensics. In fact, for investigative and intelligence purposes, it is beneficial to inference the biogeographical origins of perpetrators of crimes or victims of unsolved cold cases when no reference profile from perpetrators or database hits for comparative purposes are available. Current approaches for biogeographical ancestry estimation using SNPs data are usually based on PCA and Structure software. The present study provides an alternative method that involves multivariate data analysis and machine learning strategies to evaluate BGA discriminating power of unknown samples using different commercial panels. Starting from 1000 Genomes project, Simons Genome Diversity Project and Human Genome Diversity Project datasets involving African, American, Asian, European and Oceania individuals, and moving towards further and more geographically restricted populations, powerful multivariate techniques such as Partial Least Squares-Discriminant Analysis (PLS-DA) and machine learning techniques such as XGBoost were employed, and their discriminating power was compared. PLS-DA method provided more robust classifications than XGBoost method, showing that the adopted approach might be an interesting tool for forensic experts to infer BGA information from the DNA profile of unknown individuals, but also highlighting that the commercial forensic panels could be inadequate to discriminate populations at intra-continental level.