ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction

Machine learning applications in forensic DNA profiling: A critical review

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

Ancestry and admixture of a southernmost Chilean population: The reflection of a migratory history

OBJECTIVES

Punta Arenas is a Chilean city situated on ancestral Aönikenk territory. The city was founded by 19th- and 20th-century colonists from Chile (Chiloé) and Europe (Croatia). This work uses uniparental and ancestry-informative markers (AIMs) to explore the effects of historic migratory and admixture patterns on the current genetic composition of Punta Arenas.

METHODS

We analyzed mitochondrial DNA (mtDNA), Y-chromosome single-nucleotide polymorphisms (SNPs), and 141 AIMs obtained from 129 DNA samples from male residents with regional ancestry. After characterizing uniparental lineages and ancestry proportions, multivariate analysis was used to explore relationships among the various types of data.

RESULTS

Punta Arenas has an admixed population with three main genetic components: European (56.5%), northern Native (11.3%), and south-central Native (28.6%). The Native component is preponderant in the mtDNA (83.76%), while the foreign component predominates in the Y-chromosome (92.25%). Non-Native mtDNA lineages are associated with European genetic ancestry, and Native mtDNA lineages originated mainly in the southern and southernmost regions of Chile. Most non-Native Y-chromosome SNPs originated in Spain, and secondly, in Croatia.

CONCLUSIONS

The population of Punta Arenas is mainly of Chilote origin with south-central Native and Spanish ancestral components, as well as some Croatian components. The persistence of local Native lineages is notable, suggesting continuity with the ancestral populations of the region such as the Kawésqar, Aönikenk, Yámana, or Selknam peoples. This study contributes to our knowledge of local history and its links to national and global developments in genetic ancestry.

Human ancestry indentification under resource constraints -- what can one chromosome tell us about human biogeographical ancestry?

BACKGROUND

While continental level ancestry is relatively simple using genomic information, distinguishing between individuals from closely associated sub-populations (e.g., from the same continent) is still a difficult challenge.

METHODS

We study the problem of predicting human biogeographical ancestry from genomic data under resource constraints. In particular, we focus on the case where the analysis is constrained to using single nucleotide polymorphisms (SNPs) from just one chromosome. We propose methods to construct such ancestry informative SNP panels using correlation-based and outlier-based methods.

RESULTS

We accessed the performance of the proposed SNP panels derived from just one chromosome, using data from the 1000 Genome Project, Phase 3. For continental-level ancestry classification, we achieved an overall classification rate of 96.75% using 206 single nucleotide polymorphisms (SNPs). For sub-population level ancestry prediction, we achieved an average pairwise binary classification rates as follows: subpopulations in Europe: 76.6% (58 SNPs); Africa: 87.02% (87 SNPs); East Asia: 73.30% (68 SNPs); South Asia: 81.14% (75 SNPs); America: 85.85% (68 SNPs).

CONCLUSION

Our results demonstrate that one single chromosome (in particular, Chromosome 1), if carefully analyzed, could hold enough information for accurate prediction of human biogeographical ancestry. This has significant implications in terms of the computational resources required for analysis of ancestry, and in the applications of such analyses, such as in studies of genetic diseases, forensics, and soft biometrics.