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Forensic genomic systems allow simultaneously analyzing identity informative (iiSNPs), ancestry informative (aiSNPs), and phenotype informative (piSNPs) genetic markers. Among these kits, the ForenSeq DNA Signature prep (Verogen) analyzes identity STRs and SNPs as well as 24 piSNPs from the HIrisPlex system to predict the hair and eye color. We report herein these 24 piSNPs in 88 samples from Monterrey City (Northeast, Mexico) based on the ForenSeq DNA Signature prep. Phenotypes were predicted by genotype results with both Universal Analysis Software (UAS) and the web tool of the Erasmus Medical Center (EMC). We observed predominantly brown eyes (96.5%) and black hair (75%) phenotypes, whereas blue eyes, and blond and red hair were not observed. Both UAS and EMC showed high performance in eye color prediction (p ≥ 96.6%), but a lower accuracy was observed for hair color prediction. Overall, UAS hair color predictions showed better performance and robustness than those obtained with the EMC web tool (when hair shade is excluded). Although we employed a threshold (p > 70%), we suggest using the EMC enhanced approach to avoid the exclusion of a high number of samples. Finally, although our results are helpful to employ these genomic tools to predict eye color, caution is suggested for hair color prediction in Latin American (admixed) populations such as those studied herein, principally when no black color is predicted. [ABSTRACT FROM AUTHOR]

Background: STR allele frequency databases from populations are necessary to take full advantage of the increased power of discrimination offered by massively parallel sequencing (MPS) platforms., Material and Methods: For this reason, we sequenced 58 STRs (aSTRs, X-STRs, and Y-STRs) and 94 identity informative SNPs (iiSNPs) on 105 Mestizo (admixed) individuals from Monterrey City (Northeast, Mexico), with the Primer Set-A of the ForenSeq™ DNA Signature Prep Kit., Results: Most of the STR markers were in Hardy Weinberg equilibrium, with a few exceptions. We found 346 different length-based alleles for these 58 STRs; nevertheless, they became 528 alleles when the sequence was assessed. The combined power of discrimination from autosomal STRs (aSTRs) was -virtually- 100% in both length and sequence-based alleles, while the power of exclusion was 99.9999999976065 and 99.9999999999494%, respectively. Haplotypes based on X-STRs and Y-STRs showed 100% of discriminatory capacity., Conclusions: These results provide -for the first time- forensic genomic population data from Mexico necessary for interpretation in kinship and criminal analyses., (© 2022. The Author(s), under exclusive licence to Springer Nature B.V.)

Understanding fractures and fracture networks is essential for the investigation and use of subsurface reservoirs. The aim is to predict the fractures and the fracture network when there is no direct access to subsurface images available. This article presents a universal workflow to numerically compute a discrete fracture network by combining the 1D scanline survey method, processed with the newly written SkaPy script, together with the multiple point statistic method (MPS). This workflow is applied to a potential geothermal site in Mexico called Acoculco. We use Las Minas outcrops and quarries as surface analogues for the Acoculco reservoir, as Las Minas and Acoculco are both formed by the influence of a plutonic intrusion into the Jurassic–Cretaceous carbonate sequence of the Sierra Madre Oriental in the Trans-Mexican volcanic belt (TMVB). The intrusion is associated with contact metamorphism and metasomatic phenomena, providing the basis for the mining activities at Las Minas. The results obtained using this workflow demonstrate the feasibility of the approach, which presents a solution combining the efficiency of data processing and an interpretation-driven approach to build realistic discrete fracture networks. This workflow can be used in the process of estimating the permeability of a fracture controlled reservoir, with using only scanline surveys data as input. This is essential in the process of evaluating the feasibility to develop an enhanced geothermal system. • A workflow for generating Discrete Fracture Networks from linear scanline surveys. • SkaPy processes multiple scanline surveys together to extract statistical values. • SkaPy plots the scanlines solely or together, in a georeferenced system. • Multiple Point Statistics method offers real control on extrapolation. • The workflow combines SkaPy with MPS to offer large scale DFNs. [ABSTRACT FROM AUTHOR]