Putting RFMix and ADMIXTURE to the test in a complex admixed population

Abstract Global and local ancestry inference in admixed human populations can be performed using computational tools implementing distinct algorithms, such as RFMix and ADMIXTURE. The accuracy of these tools has been tested largely on populations with relatively straightforward admixture histories but little is known about how well they perform in more complex admixture scenarios. Using simulations, we show that RFMix outperforms ADMIXTURE in determining global ancestry proportions in a complex 5-way admixed population. In addition, RFMix correctly assigns local ancestry with an accuracy of 89%. The increase in reported local ancestry inference accuracy in this population (as compared to previous studies) can largely be attributed to the recent availability of large-scale genotyping data for more representative reference populations. The ability of RFMix to determine global and local ancestry to a high degree of accuracy, allows for more reliable population structure analysis, scans for natural selection, admixture mapping and case-control association studies. This study highlights the utility of the extension of computational tools to become more relevant to genetically structured populations, as seen with RFMix. This is particularly noteworthy as modern-day societies are becoming increasingly genetically complex and some genetic tools are therefore less appropriate. We therefore suggest that RFMix be used for both global and local ancestry estimation in complex admixture scenarios.