Ancestry-informative markers (AIMs) show high allele frequency divergence between different ancestral or geographically distant populations. These genetic markers are especially useful in inferring the likely ancestral origin of an individual or estimating the apportionment of ancestry components in admixed individuals or populations. The study of AIMs is of great interest in clinical genetics research, particularly to detect and correct for population substructure effects in case-control association studies, but also in population and forensic genetics studies. This work presents a set of 46 ancestry-informative insertion deletion polymorphisms selected to efficiently measure population admixture proportions of four different origins (African, European, East Asian and Native American). All markers are analyzed in short fragments (under 230 basepairs) through a single PCR followed by capillary electrophoresis (CE) allowing a very simple one tube PCR-to-CE approach. HGDP-CEPH diversity panel samples from the four groups, together with Oceanians, were genotyped to evaluate the efficiency of the assay in clustering populations from different continental origins and to establish reference databases. In addition, other populations from diverse geographic origins were tested using the HGDP-CEPH samples as reference data. The results revealed that the AIM-INDEL set developed is highly efficient at inferring the ancestry of individuals and provides good estimates of ancestry proportions at the population level. In conclusion, we have optimized the multiplexed genotyping of 46 AIM-INDELs in a simple and informative assay, enabling a more straightforward alternative to the commonly available AIM-SNP typing methods dependent on complex, multi-step protocols or implementation of large-scale genotyping technologies.

While traditional forensic genetics has been oriented towards using human DNA in criminal investigation and civil court cases, it currently presents a much wider application range, including not only legal situations sensu stricto but also and, increasingly often, to preemptively avoid judicial processes. Despite some difficulties, current forensic genetics is progressively incorporating the analysis of nonhuman genetic material to a greater extent. The analysis of this material—including other animal species, plants, or microorganisms—is now broadly used, providing ancillary evidence in criminalistics in cases such as animal attacks, trafficking of species, bioterrorism and biocrimes, and identification of fraudulent food composition, among many others. Here, we explore how nonhuman forensic genetics is being revolutionized by the increasing variety of genetic markers, the establishment of faster, less error-burdened and cheaper sequencing technologies, and the emergence and improvement of models, methods, and bioinformatics facilities.

Objective . The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional connectivity and to characterize the EEG coupling alterations at the different stages of dementia due to Alzheimer’s disease (AD). Approach . Magnitude squared coherence ( MSCOH ), imaginary part of coherence ( iCOH ), lagged coherence ( lagCOH ), amplitude envelope correlation ( AEC ), synchronization likelihood ( SL ), phase lag index ( PLI ), phase locking value ( PLV ), and corrected imaginary PLV ( ciPLV ) were applied to: (i) synthetic signals generated with a Kuramoto-based model of several coupled oscillators; and (ii) a resting-state EEG database of real recordings from 51 cognitively healthy controls, 51 mild cognitive impairment (MCI) subjects, 51 mild AD (AD _mil ) patients, 50 moderate AD (AD _mod ) patients, and 50 severe AD (AD _sev ) patients. Main results . Our results using synthetic signals showed that PLI was the least affected parameter by spurious influences in a simulated volume conduction environment. Results using real EEG recordings showed that spontaneous activity of MCI patients is characterized by a significant coupling increase in the band. As dementia progresses, this increase in the band became more pronounced, and a significant widespread decrease in band appeared at the last stage of dementia. Significance . Our results revealed that the estimation of functional EEG connectivity using PLI could reduce the bias introduced by the spurious influence of volume conduction, and it could increase the insight into the underlying brain dynamics at different stages of the AD continuum.

Alzheimer’s disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (AD_MIL), 50 moderate AD patients (AD_MOD), and 50 severe AD patients (AD_SEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal–Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. AD_MIL and AD_MOD vs. AD_SEV comparisons (p-values < 0.05, FDR-corrected Mann–Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.