Plex Latino

[Muredac Ford]

LosGatos, Calif., March 17, 2021 (GLOBE NEWSWIRE) -- Plex, the popular streaming media platform for all your favorite free movies, TV, and entertainment, today announced the addition of 16 new free-to-watch Spanish-language channels to Live TV on Plex, available in countries across Latin America, including Mexico, Argentina, and Colombia. As of today, nearly 100 channels of myriad genres are available to be enjoyed in the region for free, anytime, with no registration, credit card, or subscription required.

Live TV on Plex now includes 130+ channels from the top movie, TV, and entertainment brands in the world, all free-to-stream on most any device. More information about streaming Live TV on Plex, including regional channel availability, can be found here. Watch now here.

About Plex

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A number of biogeographically informative SNP marker panels have been developed [9,10], and with the adoption of Massively Parallel Sequencing (MPS) as a resource in forensic genetics, commercial MPS forensic panels are available that amplify both traditional STR, Y-STR phenotypic and AIM markers in a single reaction run [11-13]. However, despite the promise of MPS and the development of AIM panels, the forensic genetics community continues to use traditional multiplex-PCR kits for the amplification and size separation of STRs through capillary electrophoresis (CE). Reasons for the slow adoption of MPS technologies include high per sample cost, increased processing time and uncertainty around data handling and ethics [13,14]. As such, laboratories continue to use CE approaches and have seen the number of loci included in commercial panels virtually double in the last five years. For example, the European standard set (ESS) now comprises 17 STR loci and the American CODIS system comprises 20 core loci [15-18], while both the commercially available GloablFiler and PowerPlex Fusion kits boast an impressive 22+ STR loci [19,20]. Consequently, the increasing number of STR loci may now enable better resolution between populations, thus making expanded and mega-plex STR kits suitable for genetic differentiation between populations [21] and the inference of ethnic origin. Such an approach would be useful if labs continue to use CE methods for the foreseeable future.

Population assignment requires the use of a mathematical model that groups an unknown individual to a putative population and can be used to detect dispersal, hybridization, genetic mixture, origin of specific individuals, population delineation and structure [22]. Common population assignment models include Bayesian assignment, frequency-based, and Bayesian clustering approaches. The Bayesian assignment approach developed by Rannala and Mountain [23] calculates the posterior probabilities that a genotype is observed at a locus when the individual belongs to each putative population. The probability is then determined for each locus (assuming no linkage) and multiplied, and results are provided as the posterior probability with lower values indicating rarer events. This approach has been used in the detection of poaching hot-spots [24], differentiation between closely related species [25] and the identification of illegally translocated deer [26]. An alternative, frequency-based method developed by Paetkau et al., [27] calculates genotype likelihood ratios and determines the probability that the genotype groups with each population using Monte Carlo resampling. This approach has been used to assign individual dogs to their population of origin [28], identify livestock predators [29], and to detect fishing competition fraud [30]. These two approaches, popular in molecular ecology, have seen little application in human population assignment, where research has concentrated on the development of bespoke models [7,10,31]. Perhaps one of the most common approaches to investigate human population genetic differentiation is the Bayesian clustering method developed by Pritchard et al., [32] which uses multi-locus genotype data to infer the number of distinct genetic clusters (populations) based on the allele frequencies observed in each population. Individuals across the dataset are assigned to single populations, or to multiple populations if admixture is detected. This approach has been successfully used to map clines in human population genetic structure with geography [33-35].

The Bayesian clustering method STRUCTURE [32] was first used to identify the likely number of distinct genetic clusters (populations; K) existing in the data for each of the STR profiling kits. Two different analysis parameters (1 and 2) were initially tested to explore population structuring with and without the inclusion of known sample population data (Table 1). Each parameter set underwent five analysis iterations at each possible K (1-5). The optimal K was identified using three different approaches, avoiding the use of a single ad-hoc approach [32,39]: first, the highest mean log-likelihood value (lnPD) method outlined in ref. [32] was used; second, the ΔK method detailed in ref. [40] was calculated using the web-based STRUCTURE HARVESTER programme [41]; and third, the point at which the lnPD values begin to plateau as outlined in ref. [33]. CLUMPAK [42] was used to visualise the data. The use of the LOCPRIOR setting in parameter set two was shown to identify fine scale population differences more effectively and was selected for use when assessing population assignment.

There are slight differences in the returned K value for analysis parameter sets 1 and 2, with a higher K obtained with less loci for parameter set 2 using both the plateau and highest lnPD. Parameter set 2 used the LOCPRIOR setting in STRUCTURE which allows the software to use information associated with the samples such as phenotype, in this instance the sample population of African American, Caucasian, Hispanic or Asian, to support the resolution of fine scale clusters [32]. Analysis under these parameters provided greater resolution to the inferred ancestry scores leading to more confidence in the population clusters (Figure 1), while not having a substantial impact on the number of clusters observed. For application in a forensic setting, the samples can be considered as a database containing samples of known origin allowing the use of the LOCPRIOR setting for population assignment.

Genetic differentiation begins to be observed when K=2 with the African American population showing a distinct cluster while all other populations group as a single cluster (Figure 1). The next cluster to appear when K=3 is the separation of the Asian population from the Caucasian and Hispanic populations. This pattern is expected as human populations have their geographic origins in Africa, with dispersal first east through the Asian continent and then again later west through the European continent [46]. In addition, both the Asian and European populations are thought to have undergone population bottlenecks in their life histories [47] that will have led to variation in their allele frequencies. The grouping of Caucasian and Hispanic populations when K=3 can also be explained due to the admixed nature of the Hispanic population in America, which is derived from the influx of Europeans into the native population, and so shares recent common ancestry with the Caucasian population [48]. Differences between the Hispanic and Caucasian populations begin to emerge when K=4 and 5 and is apparent for the 30-locus panel shown in figure 1.

Calculating the predictive accuracy of population assignment was performed on the 984 individuals analysed under parameter 3 using data for K=4 to enable assignment to the four known populations. While the STRUCTURE results provide most support for three clusters (although K=5 for highest lnPD), the distribution of inferred ancestry scores for the 30-locus panel shows there is some clear pattern of differentiation between the four populations over four clusters (Figure 2). Threshold values, as shown for the African American population in Figure 2, were determined from these distributions and formed the four criteria for assignment to a population.

Figure 2. Box and Whisker plots showing distribution of Inferred Ancestry Scores (IAS) to each of the four genetic clusters identified by STRUCTURE for the 30-locus panel. Where there is overlap in one distribution there is usually clear separation at another which allows the setting of a contingent threshold for each population for each cluster. The thresholds and examples values for the AA population are shown in red.

Thresholds set for each population for each kit were used to assess the predicative accuracy of the assignment test. Individuals satisfying all criteria were considered positive (true or false) and any single criteria not satisfied led to the individual being considered negative (true or false). The sensitivity and specificity for the test was calculated based on the number of true/false positive and true/false negative assignments across the 984 individuals (Table 2). For the 30-locus data set the thresholding mechanism described was able to correctly assign 99% of all individuals to the correct population. A high degree of accuracy (>96%) was also shown for the two commercial mega-plex panels and those with expanded core loci, suggesting that the approach detailed here is robust and repeatable across panels, with only small fluctuations in accuracy.

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