Large-scale selection of highly informative microhaplotypes for ancestry inference and population specific informativeness

Microhaplotypes (MHs) describe physically close genetic markers that are inherited together and are gaining prominence due to their efficiency in forensic, clinical, and population studies. They excel in kinship analysis, DNA mixture detection, and ancestry inference, offering advantages in precision over individual SNPs and STRs. In this study, a pipeline was developed to efficiently select highly informative MHs from large-scale genomic datasets. Over 120,000 MHs were identified from almost a million markers, which allow this non-independent information to be efficiently used for inference. The MHs were compared to SNPs in terms of their informativeness and performance of their subsets in ancestry inference and all the results consistently favored MHs. A method for ranking markers by specific population informativeness was also introduced, which showed improvement in the accuracy of Native American ancestry estimation, overcoming the challenges of its underrepresentation in datasets. In conclusion, this study presents a comprehensive way for selecting highly informative MHs for accurate ancestry inference. The proposed approach and the subsets selected by specific population informativeness offer valuable tools for improving ancestry inference accuracy, particularly for admixed populations as demonstrated for a Brazilian dataset.