TLR2 coding region, as assessed by nucleotide diversity, and that genetic diversity was high. This indicates that it is easy to select and improve the available genetic tools to improve health and production in swamp buffalo when the populations are diverse. The authors suggest that TLR2 gene variations may have the potential for marker-based breeding programs aimed at breeding better disease resistance in swamp buffalo. This study will assist in the conservation of genetic resources and breeding planning for the sustainable utilization of Thai swamp buffalo. It is essential to clearly understand the diversity of genetic resources for breeding planning and genetic conservation. High genetic variation has the potential to exist, and vice versa; the species may easily become extinct when low genetic variation exists. The study revealed that the indicators of the genetic diversity of the swamp buffalo population were also genetically diverse in the TLR2 to enhance available genetic tools for improving health and production in swamp buffalo. A total of 50 buffaloes were randomly sampled from the northeastern part of Thailand for genomic DNA extraction and sequencing. Nucleotide sequences were aligned and compared with cattle and river buffalo based on the database. The results showed, there were 29 SNP locations in swamp buffalo and 14 different locations in both cattle and buffaloes. Haplotype analysis revealed that 27 haplotypes occurred. Swamp buffalo were identified from 13 SNPs based on biallelic analysis, which found eight synonymous and five nonsynonymous SNPs. Nucleotide diversity (π) was 0.16, indicating genetic diversity. Genetic diversity  was high at 0.99 ± 0.04. This indicates a high probability that the two sample haplotypes are different. The π and HD values are important indicators of the genetic diversity of the swamp buffalo population. In summary, the Thai swamp buffalo population detected a polymorphism of the coding region of the TRL2 gene. Therefore, further, in-depth study of the relationship between these genes in the immune system and disease resistance should be recommended.Toll-like receptors (TLRs) are transmembrane proteins important for directing immune responses. Their primary role is to recognize pathogens based on single-nucleotide polymorphism (SNP) characteristics. TLR2 is categorized as a pattern recognition receptor (PRR) that is important for the recognition of pathogens. Nucleotide variation in the coding region determines the conformation of the TLR protein, affecting its protein domain efficiency. This study aimed to identify SNPs in the coding region of  The difference in chromosome numbers between swamp buffalo and river buffalo is one reason we are interested in performing molecular studies on them. Swamp buffaloes have 48 chromosomes, and river buffaloes have 50 chromosomes. Genetically diverse populations are easy to select and improve through breeding. Improvement through breeding is essential to clearly understand the diversity of genetic resources to formulate breeding plans and for genetic conservation . GeneralBubalus bubalis) can be divided into two subspecies, swamp buffaloes  and river buffaloes , and these two species have a different number of chromosomes. Swamp buffaloes have 48 chromosomes, and river buffaloes have 50 chromosomes. The worldwide buffalo population reported by the FAO in 2021 was estimated at 203.9 million head, a 1.37% increase from 2020 [n = 48). They have adapted well to rural conditions where crop residues or local feed resources such as rice straw are used as the main feed. Moreover, meat from buffalo is relatively high in protein. Consequently, increasing production efficiency might enhance food security [Water buffaloes (security .TLR) gene are related to disease resistance and susceptibility in livestock [Single-nucleotide polymorphisms (SNPs) can be used for diversity studies. Moreover, SNPs of functional genes can also be developed for markers in a selection approach. Disease resistance traits are one of many characteristics that must be genetically improved in livestock. Several reports have mentioned that polymorphisms of the Toll-like receptor (ivestock . The Toll-like receptor (TLR) family is a group of receptors that play a role in innate immune activation. TLRs are linked together in forming heterodimeric complexes that induce lipid conjugates, i.e., TLR2 and TLR1 or TlR6 . VariatiTLR2 is a gene that plays a significant role in pathogen recognition based on the characteristics of an extracellular N-terminal domain, which consists of 16 to 28 leucine-rich repeats (LRRs) and the characteristics of the intracellular C-terminal domain or TIR domain needed to activate the downstream signaling pathway [TLR2 is classified as a pattern recognition receptor (PRR), which has been previously described to be involved in recognizing bacteria, including mycobacteria, which cause the pathogenesis of tuberculosis. In addition, SNPs of TLR2 have previously been identified, which have also been detected in buffaloes associated with disease resistance. In TLR2 exon 2 region CDs, a region of genes reported to be found, 76% of SNPs were in the LRR protein domain, the region responsible for ligand recognition , which were reported by Mossallam et al. [TLR2 nucleotide polymorphisms have been reported in swamp buffaloes. Therefore, the present study aimed to identify SNPs in the coding sequences (CDs) of TLR2 to enhance available genetic tools for improving health and production in swamp buffalo. Among the TLRs reported in mammals,  pathway ,16. A pr pathway . TLR2 ism et al. . Eight Sm et al. , whereasA total of 50 buffalos reared by farmers in five provinces of northeast Thailand   were rang for 5 min, the supernatant was aspirated into a new 1.5 mL microtube. A total of 600 µL of absolute cold isopropanol was added, and then the tube was flipped up/down gently, causing precipitation of DNA, and then centrifuged at 6440× g for 5 min. The supernatant was discarded, and the pellet was washed 2× with 1000 µL 75% ethanol. The DNA pellet was air-dried at room temperature and dissolved in 30–50 µL of TE buffer. The solution was incubated in a thermos box to dissolve DNA at 37 °C for 1–2 h. One microliter was aspirated to check DNA quality by Nanodrop, and the DNA solution was maintained at −20 °C.Ten milliliters of blood were collected from each buffalo and inserted into a microtube containing 10% 0.5 M EDTA as an anticoagulant. DNA was then extracted by using guanidine HCl as an adaptation of the method of Goodwin et al.  as folloThe forward primer (F: TTTGTAGGTCAAATCACTGGACA) and reverse primer (R: TCCTGGCCACCGACA) were designed using the riverine buffalo database previously reported by Alfano et al. . The PCRBubalus bubalis using BLAST tools . Additionally, the predicted peptide sequence was aligned against nonredundant protein sequences by BLASTx (nucleotide-protein BLAST) to verify homology with other species and identify the predicted gene. Multiple alignments based on nucleotide sequences were performed using ClustalW via BioEdit 7.2 software [Sixty microliters of the pure PCR product were used for Sanger and nucleotide sequencing by sequencing analysis software version 7.0 (SqeqA7). The process was performed by ATGC Co. Ltd., Thailand Science Park (TSP) . The predicted gene sequence was aligned against NCBI reference mRNA sequences of software .TLR2 gene were analyzed and constructed by unweighted pair group method with arithmetic mean (UPGMA) on NTSYS version 2.10.Haplotype and genetic diversity were calculated with ARLEQUIN version 3.5.1.2 . GeneticTLR2 (exon2) gene partial nucleotide sequence of approximately 1131 base pairs was amplified from swamp buffalo and compared with reported sequences of other species. The swamp buffalo sequence exhibited 98.37% homology with the riverine buffalo TLR2 gene (GenBank acc no. HM756161.1), 99.00% homology with the goat TLR2 gene (GenBank acc no. NM-001285603.1), and only 97.12% homology with the bovine TLR2 sequence (GenBank acc no. EU413951.1). The swamp buffalo TLR2 gene has been characterized and found to code for a 784 amino acid protein similar to that of riverine buffalo.The allele frequency of swamp buffalo ranged from 0.06 to 0.94. The TLR2 gene compared with reference nucleotide sequences of Bubalus bubalis were found for 29 SNP locations  and swamp buffaloes compared to cattle, and 13 amino acids were altered because of the nucleotide sequence changes. Genetic polymorphisms by mutation site of the ng T785C .TLR2 exon 2. The most frequent cases were 14% of Hap7 (TCACAGAGTAGTATTTAACGCTACGACGT), 12% of Hap5 (TCACAGAGTAGTATCTAACGCTATGAGGT), 8% of Hap1 (TCACAGAGCAGTATCTAACGCTATGAGGT), Hap4 (TCACAGAGCAGTATCTAACGCTATGAGAT) and Hap14 (TCACAGAGTAGTATCTAACGCTATGAGAT), and 6% of Hap8 (TCACAGAGCAGTATTTAACGCTACGACGT), and the remaining haplotypes were less than 5%. This study found no haplotype patterns that appeared in all groups. The NP group buffaloes did not share haplotypes with any other group. However, six haplotypes were shared between groups. Hap5 and Hap7 shared haplotypes between 3 buffalo groups , and Hap1 was shared between the MK, SN, and KK groups. In addition, a shared haplotype was found between the two buffalo groups: Hap14 between the KK and Np groups and Hap4 and Hap8 between the SN and NP groups.The haplotype analysis revealed that 27 haplotypes occurred based on 29 SNP locations of The heterozygosity values of a population can indicate its genetic diversity. The current study found that the total heterozygosity of the buffalo divided into the northeastern part of Thailand was 0.16 . The bufp > 0.05). The parameters of a neutrality test were investigated by the method of Tajima’s D [p > 0.05) and Fu’s FS of −2.52 (p > 0.05), indicating that the buffalo population had not deviated from neutral equilibrium.Molecular diversity indices are shown in jima’s D  and Fu’sjima’s D . A deviaThe study of the genetic structure of the population is presented in TLR2 gene between swamp and river buffaloes was 96.6%, cattle 92.7%, and the highest 97.9% in sheep [TLR2 gene between swamp buffaloes and goats was also high (99%). While the amino acid identity (%) between swamp buffaloes and river buffaloes was the highest at 98.9%, cattle at 97.1%, and goats at 96.9%, reported by Shi et al. [TLR sequence with that of the goat was observed to be in concordance with previous reports on TLR8.In a previous study, the % identity in the coding region of the in sheep , which ii et al. . Dubey ei et al.  reportedTLR2 exon 2 sequence exhibited 98.30% homology with that of the riverine buffalo, initiated from the sequence 1171–3525 referenced by GenBank Acc. No HM756161. Genetic polymorphisms by mutation site of the TLR2 gene were found for 29 SNP locations. The number of SNP locations (only in the part we studied) was more than that in the previous report in swamp buffaloes, whereas only ten mutations were found in riverine buffaloes [The swamp buffalo TLR2 (exon 2) in swamp buffalo. In this study, the positions were found to be different from those reported in the water buffalo of [TLR2, TLR4, and TLR9 [TLR2 polymorphisms performed by DnaSPv6.12.03 software. This generated 27 possible haplotypes. We found six haplotypes with a frequency greater than 6%, which displayed a frequency of approximately 2% in the remaining haplotypes. Previously, the haplotype CTTACCAGCGGGCCAGTCCCCC was reported to be associated with disease resistance by Alfano et al. [Only one part was used to translate into the first 438 amino acids. This study is the first report finding 13 SNPs in ffalo of , which rffalo of , who repand TLR9 . Synonymand TLR9 . Only thTLR family gene (TLR3 and TLR8) in five cattle breeds native to Yunnan. The average HD was 0.99 (range 1.0 to 0.93), with high diversity similarity as TLR families reported high HD values (0.88) in native cattle from Yunnan (Dengchuan and Dianzhong). Nucleotide variations show more potential utilization in genetic selection by use as marker genes. In previous studies, nucleotide variations in the bovine and buffalo TLR2 gene were associated with somatic cell counts, mastitis, and tuberculosis [Expected heterozygosity (HE) is a general statistic used to estimate heterozygosity within populations, High HE levels also indicate high genetic diversity. The nucleotide diversity was found to be 0.16, indicating a high level of genetic diversity. As Nei  defined,rculosis ,32,33,34TLR2 gene SNP data from 29 loci. Diversity declines have been reported in 17 populations of Turkey’s river buffaloes, with an estimated low Fst of 0.032 based on a study using 20 microsatellite markers [The fixation index (Fst) has been estimated as a measure of genetic differentiation between populations, with a high value of Fst close to 1) indicating high genetic differentiation between populations, the two populations do not share genetic diversification, or the populations are fixed. On the other hand, a low value of Fst (close to 0) indicates a complete distribution of the genetic material. Criteria for distinguishing the degree of differentiation of Fst values are small genetic difference (Fst < 0.05), moderate genetic difference (Fst = 0.05 to 0.15), great genetic difference (Fst = 0.15 to 0.25) and very great genetic difference (Fst > 0.25) reported by Hartl and Clark . We foun indicati markers .Molecular variance analysis performed by AMOVA revealed that individual diversity within the population represents approximately 77% of the total of all genetic variations and approximately 22% of all genetic variations clarified by differences among the population. The proportion of molecular variance in swamp buffaloes with individuals within the population in our report was less than that reported in Turkey’s river buffaloes, which was approximately 88% based on microsatellites .TLR2 genes can be divided into two groups. Genetic affinity did not arise from neighboring provinces. Genetic diversity in members of the TLR family needs to be shown with clinical data to increase the likelihood of selective utilization from endemic animal genetic sources.The pairwise Fst was shown the genetic distances. The Fst values of pairwise comparisons among the 5 Thai swamp buffalo ranged from 0.000 to 0.657, which is a large extent compared to the previous report by Üral et al.  in the TThis study revealed the accelerating conservation of genetic resources if overall population diversity is reduced, and genetic management is implemented. This finding will help lead to further studies on the relationship between these genes and other important traits in swamp buffalo, such as immune response, disease susceptibility, or resistance.


Humans are voracious imaginers, with internal simulations supporting memory, planning and decision-making. Because the neural mechanisms supporting imagery overlap with those supporting perception, a foundational question is how reality and imagination are kept apart. One possibility is that the intention to imagine is used to identify and discount self-generated signals during imagery. Alternatively, because internally generated signals are generally weaker, sensory strength is used to index reality. Traditional psychology experiments struggle to investigate this issue as subjects can rapidly learn that real stimuli are in play. Here, we combined one-trial-per-participant psychophysics with computational modelling and neuroimaging to show that imagined and perceived signals are in fact intermixed, with judgments of reality being determined by whether this intermixed signal is strong enough to cross a reality threshold. A consequence of this account is that when virtual or imagined signals are strong enough, they become subjectively indistinguishable from reality. How humans distinguish perception from mental imagery is not well understood. Here, the authors show that reality judgements are based on the intensity of a mixture of imagined and real signals. Contemporary generative models of brain function propose that mental imagery relies on similar neural machinery to that engaged by veridical perception4, a hypothesis supported by neuroimaging data8. While allowing for a vast increase in cognitive sophistication, the existence of stimulus-independent processing poses a fundamental challenge for the nervous system: given that internally and externally triggered signals are often similar, there is considerable potential for confusing perception and imagery9. The capacity to resolve such confusion, and distinguish between imagination and reality, is known as perceptual reality monitoring.In order to function in complex environments, humans have evolved to move beyond stimulus-triggered responses to guide behaviour via offline simulations, such as during navigation and planning11. Accordingly, sensory inputs congruent with self-triggered actions are attenuated because they are ‘explained away’ by a top-down intention signal. Hallucinations in disorders of reality monitoring such as schizophrenia are thus explained as reflecting disturbances in the forward model which predicts the sensory consequences of actions12. In this scenario, the sensory consequences of internally triggered speech or thoughts are not explained away and are therefore erroneously attributed to an external source15.One cue that the brain might use for reality monitoring is volition or intention. In the literature on sense of agency, the extent to which incoming sensory activity can be predicted from the intention to move is believed to generate the feeling that such activity is self-caused, rather than reflecting a change in the outside world17. In this study, participants were instructed to imagine various objects at a certain location on the wall while, unbeknownst to the participants, images of the same objects were simultaneously projected to the same location. All participants failed to notice the presence of these real stimuli, reflecting that “if I hadn’t known I was imagining, I would have thought it real” .”Anecdotal evidence that a similar mechanism might underlie perceptual reality monitoring in otherwise healthy observers comes from the Perky effect, which was demonstrated by Mary Cheves West Perky in 1910 and has achieved almost mythical status within imagery research19. Another cue that the brain might use to dissociate imagination and reality is sensory strength. The phenomenological experience of imagery tends to be much weaker and less clear than that of perception. This difference in sensory strength has recently been suggested to be a direct consequence of running the visual system backwards when engaged in mental imagery21. Due to the absence of bottom-up input during imagery, the excitatory neurons within the middle layer of primary visual cortex are not activated23, leading to lower activation levels overall. Furthermore, because imagery signals are likely to originate in higher-level areas which have larger receptive fields, the precision of sensory signals is also lower during imagery25. Accordingly, the sensory consequences of imagery might not need to be explained away because they are generally not strong enough to be mistaken for reality.However, visual imagery is often also triggered automatically, outside of voluntary control and, despite the absence of a clear intention in these instances, such involuntary imagery is generally still not mistaken for reality28 (but see29 for opposite effects) and that it can hamper or facilitate perceptual discrimination33. However, the consequence of these interactions for source judgements about whether signals reflect imagination or reality remain unclear. Moreover, in the context of a perceptual reality monitoring task, as soon as a participant realizes that an external stimulus might be presented, attention on subsequent trials will naturally be directed externally, biasing interpretation of sensory signals back towards reality35. This might explain the steady decline and eventual reversal in reports of the Perky effect over the years as the development of modern technology made multi-trial psychophysics the norm in psychology labs  but that the overlap found in previous studies might for example be an artefact of the poor resolution of neuroimaging methods which look at the average activation over many thousands of neurons37. Accordingly, this null model (H0) predicts that whether imagery is congruent or not with perception is immaterial for reality judgements, and that reality judgements do not influence reported imagery vividness.We combined these large-scale single-trial psychophysical data with computational modelling and neuroimaging to distinguish between three hypothesized mechanisms for perceptual reality monitoring. According to our null model, the subjective experiences of imagery and perception are distinct and they do not get confused Fig. ; H0. In Our second hypothesis (H1) is inspired by Perky’s findings and captures the idea that sensory signals in line with an imagery intention are inferred to reflect imagination. It predicts that if the same content is both imagined and perceived, external input is ‘explained away’ as reflecting imagery Fig. ; H1. Thi38. Furthermore, because imagery and perception are intermixed and reality judgements are based on the sensory strength of the mixed signal, this model also predicts that when stimuli are judged to reflect reality, they should be associated with stronger ratings of imagery vividness compared to when they are judged to reflect imagination.Finally, if imagery and perception are supported by similar computations and imagery signals are not explained away, we might expect the subjective experiences of imagery and perception to only be distinguishable based on their relative strength Fig. ; H2. In 39 (see Methods for details). Critically, as shown in Fig. decrease in the likelihood a stimulus is judged as real  are shown in Fig. β = 0.860, SE = 0.279, p = 0.002, CI = 0.31–1.41; Fig. M = 3.12, SD = 1.17) compared to participants who judged the critical trial to be imagined =0.40, p = 0.000013, CI = 0.22–0.58). The difference in vividness in the incongruent condition was not significant (β(1)=0.40, p = 0.092, CI = −0.03 – 0.40). This qualitative pattern of results is exactly as predicted under the complete source mixing hypothesis  = 15.93, p < 0.001, β = 0.84, CI: 0.39–1.30). Critically, however, there was no difference in d’ between the conditions (F(1) = 2.851, p = 0.092, CI = −0.33– 0.026) nor an interaction between condition and reality judgement (F(1) = .753, p = 0.386), indicating that our effect also cannot be explained by individual differences in discrimination sensitivity.One potential concern is that due to the online nature of these experiments, participants were less engaged with either imagination, perception, or both. To investigate this possibility, we simulated how changes in task engagement influenced the predicted patterns of results for each of our models. We modelled task engagement as influencing either external attention , the strength of the imagery signal, or both. While task engagement did influence the overall predicted proportion of presence responses and vividness ratings in all models, the patterns of predicted condition differences remained unchanged when comparing between models  in which we never presented a stimulus on the critical trial, meaning that participants could not mistakenly interpret the vividness of an external stimulus as imagery. The instructions were identical to Experiment 1. Importantly, the different models again make qualitatively distinct predictions about how vividness covaries with reality judgements on the critical trial – even in the absence of external input  compared to critical trials correctly judged to be imagined  = 0.22, p = 0.013, CI = 0.05–0.40; Fig. An alternative explanation of our results is that participants may have been confused about the task instructions, and mistakenly reported the vividness of the stimulus on the critical trial rather than their imagery. According to this explanation, imagery and perception are in fact subjectively dissociable, but participants confused them when giving their ratings – perhaps due to the surprising appearance of an external stimulus on the final, critical trial. In order to rule out this possibility, we ran another experiment . During perception, the stimuli were presented very briefly followed by a backward mask, leading to variation in visibility. The imagery task was a retro-cue task in which participants imagined one of two previously perceived stimuli, without any physical stimulus being present, and without any detection judgment being required.Together, the results from Experiments 1 and 2 reveal that the overlap between imagery and perception places fundamental constraints on perceptual reality monitoring. A simple signal detection model accounted for these findings by hypothesising that the vividness of imagery and the visibility of perception rely on common latent variables Fig. ; H2. If 19. Furthermore, using cross-decoding multivariate pattern analysis (after mean-centring the activation within imagery and within perception to account for mean differences in strength), we found that the manner in which high versus low visibility and vividness was encoded in each area was also similar, leading to significant cross-decoding within each ROI  = 2.96, p = 0.0003, d = 0.5, CI = 0.51–0.55; anterior insula: M = 0.52, SD = 0.04, t(34) = 2.96, p = 0.0008, d = 0.5, CI = 0.51– 0.54; right dlPFC: M = 0.52, SD = 0.05, t(34) = 2.37, p = 0.0097, d = 0.4, CI = 0.502–0.53). Note that this analysis did not investigate the neural correlates of the content of imagery and perception, but rather their strength or vividness, which might explain the absence of visual cortex involvement. The notion of higher-order, abstract (content-free) neural codes for perceptual strength has attracted much recent debate in both philosophy and psychology41 but direct empirical evidence for such magnitude representations has been lacking. Our results suggest that the strength of visual experience is encoded in similar activity patterns, regardless of whether it reflects imagery or perception. The functional nature of such signals remains to be fully understood. For instance, one possibility is that these areas might reflect changes in (a model of) visuospatial attentional strength which is in turn used to infer both perceptual visibility43 and imagery vividness.In line with our hypothesis, imagery vividness and perceptual visibility both correlated with activation in the same brain areas, despite the different nature of the task: the pre-SMA, anterior insula and right dlPFC Fig. . This co44. Importantly, however, vividness might not be related to overall activation levels, but rather to how precisely stimulus information is encoded in activation patterns. For instance, previous studies have found that imagery vividness is related to the extent of representational overlap between imagery and perception in EVC – i.e. the more vivid the imagery, the more perception-like the stimulus representation in EVC47. Our source mixing model nuances this hypothesis, and predicts that not only should high-vividness imagery be more perception-like than low-vividness imagery, but also that low-visibility perception should be more imagery-like than high-visibility perception. To further investigate the influence of vividness/visibility on sensory activity, we ran a region of interest (ROI) analysis on the activation in EVC  = 1.76, p = 0.18; Fig. F = 0.35, p = 0.78; Fig. M = 0.51, SD = 0.03, t(34) = 1.18, p = 0.13, CI = 0.5–0.52; Fig. M = 0.513, SD = 0.029) than from low vividness imagery trials  = 2.50, p = 0.017, d = 0.5, CI = 0.006–0.02), indicating that perception is more akin to highly vivid imagery. We were also able to significantly decode the identity of imagined stimuli from a classifier trained on low visibility , but not from high visibility perception trials , suggesting that imagery is akin to low visibility perception   = −0.15, p = 0.88).We did not find any direct effects of imagery vividness or perceptual visibility on univariate activation in sensory areas, despite previous studies suggesting a key role for the early visual cortex (EVC) in determining the vividness of visual experienceEVC Fig. . In line20. Such a model predicts that reality monitoring should be worse in people with more vivid imagery. In line with this idea, we found that the frequency of source confusions – mistaking reality for imagination or imagination for reality – was associated with generally higher imagery vividness across subjects  compared to participants who correctly judged those stimuli to be real  = 2.04, p = 0.043, d = 0.35, CI = 0.02–0.72), but not during the incongruent condition (t(124) = 1.19, p = 0.234). Furthermore, in the no-input Experiment 2, mean pre-critical-trial vividness was higher for participants who erroneously thought a real stimulus had been presented  compared to participants who accurately judged their sensory experience to be imagined  = 3.38, p = 0.0008, d = 0.57, CI = 0.23–0.85).Our findings suggest that imagery and perception are subjectively intermixed. How, then, do we ever determine whether something is real? According to our model, this is achieved simply by evaluating whether the total strength of a signal exceeds a reality threshold  distinct mechanisms55. Future studies investigating the influence of visual imagery on simultaneously congruent perception in different scenarios as well as the neural consequences of congruent imagery on sensory processing of external inputs are necessary to test this hypothesis.Our findings are inconsistent with a Perky effect, in which people tend to downweigh or discard incoming sensory information when imagining. More broadly, our results challenge a proposal that the intention or volition associated with imagery is used to classify an experience as imagined rather than real. According to a range of theoretical frameworks, sensory signals that can be predicted from top-down intentions are tagged as self-triggered and external input congruent with these predictions is suppressed56, participants may have been more likely to believe that their sensory experience was a product of their imagination – because what else could have caused it? This might also partly explain the steady decline of the Perky effect over the last century when technological advances made visual presentation of stimuli more and more common, leading to a decrease in reality thresholds .If congruent sensory input is not suppressed during mental imagery, why did Perky then observe such a clear suppressive effect of imagery in her study? One possibility is that Perky’s finding was a false positive, caused by the low statistical power of her sample. Another, more interesting possibility is that the reality threshold is likely to be dynamic and influenced by several factors. Both in the lab and in the world, the current context provides information about the likelihood of an external stimulus being presented. In Perky’s experiment, during which stimuli were presented using a hidden ‘magic lantern’, and in an era when video presentations were very uncommon59. Within our source mixing model, such hallucinations would arise when internally triggered activity is so strong that it crosses a reality threshold. Reality monitoring errors could alternatively also be caused by problems with setting a reality threshold. One factor that might influence this calibration is individual differences in imagery vividness, with more vivid imagery rendering it more difficult to choose a setting of the reality threshold that cleanly separates reality from imagination. This hypothesis is in line with our findings that people who reported higher imagery vividness in general  were more likely to exhibit failures of perceptual reality monitoring. Furthermore, more vivid imagery has been associated with an increased probability of experiencing hallucinations in both clinical61 as well as non-clinical populations62 . Future studies could investigate whether the reality monitoring errors found in the current study are indeed associated with a higher probability of experiencing hallucinations in both clinical and non-clinical samples, as would be predicted by our source mixing model.Our source mixing model for perceptual reality monitoring has intriguing clinical implications. A model in which sensory strength biases source judgements towards reality is in line with a proposal that hallucinations are caused in part by hyperactivation of sensory areas64. Second, while feature-based attention provides a signal-dependent boost in sensory input, leading to an increase in perceptual sensitivity, imagery appears to add sensory evidence irrespective of input strength65. Moreover, an explanation of intermixing in terms of top-down attention only accounts for imagery’s influence on perception but does not explain why perception in turn should also increase imagery vividness. Another possibility is that the intermixed sensory representation is read out by higher-order brain areas to determine both its strength and source. This idea would neatly fit with our neuroimaging results showing that frontal areas code both perceptual and imagery vividness while sensory areas encode stimulus content, and would be in line with both higher-order theories of consciousness and recent models of the mechanisms supporting perceptual reality monitoring68.The exact mechanism through which imagery and perception are intermixed in the brain is an interesting question for future research. One possibility is that imagery amplifies perceptual signals in a similar way as top-down, feature-based attention. However, two recent studies suggest that imagery and feature-based attention might rely on different mechanisms. First, people with aphantasia – an inability to form mental images – perform just as well as controls on a feature-based attention taskTaken together, our results reveal a subjective intermixing of imagery and perception, leading to widespread perceptual reality monitoring failure in large general population samples. The success of a signal detection model in capturing these data patterns indicates that reality monitoring may be implemented simply by comparing sensory signals against a reality threshold. Such a model is parsimonious and powerful, but also has profound implications. In particular, a consequence of this account is that it predicts when virtual or imagined sensory signals are strong or detailed enough, they become indistinguishable from reality. While currently the most common driver of top-down signals is imagination, in near-future scenarios in which brain stimulation and/or virtual reality technology is used to drive sensory signals we might be less able to tell apart reality from unreality than we would like to believe. Our model provides a framework within which to investigate these issues, and offers a route towards ensuring our reality thresholds remain well-calibrated in the face of technological advances.https://osf.io/rdqvm/?view_only=ec5e3e7afd78409cb9b4419c1ae41902) unless otherwise stated. The computational modelling and neuroimaging analyses were not included in the pre-registration document.The experimental design and initial analyses were preregistered within the Open Science Framework  approaches, the perceptual sample is drawn from a bivariate Gaussian with mean The vividness sample is drawn from a bivariate Gaussian with mean Due to the symmetry of left- and right-tilted stimuli, when simulating the model, we assumed imagery was always for left-tilted stimuli , which we assumed to be fixed and equal to mean vividness across subjects (2.5). If In all models the integrated percept www.prolific.co) and completed the study online. Data were collected on a private institutional server managed by the JATOS tool69. Informed consent was obtained from each participant included in the study. The study took approximately 10 min to complete and participants were paid £1.25 for their contribution, equivalent to an hourly rate of £7.50. All procedures were approved by the University College London Research Ethics Committee. Due to the single-critical-trial design of our experiment, we were unable to determine individual threshold contrast for each participant a priori, and instead we estimated participants’ discrimination sensitivity at the presented contrast after the main task (see below for more details). This meant that we had to exclude a high number of participants who would have been unable to detect the stimuli in the main task due to low sensitivity. 4 participants were removed due to technical issues, 4 because they participated in multiple conditions, 73 because of having discrimination performance below 55%, 11 because they indicated in the debrief questions not to have imagined the stimuli as instructed and 36 because they indicated the presence of the incorrect stimulus (see below). The final sample consisted of 272 participants , 146 in the congruent condition and 126 in the incongruent condition. For experiment 2, we collected participants as above until we obtained 40 usable participants who reported the presence of a grating despite none being presented, to provide a sufficient between-subject false alarm rate for analysis. This led to the collection of 461 participants. 4 of these were removed because they participated multiple times, 87 because of having discrimination performance below 55%, 12 because they indicated in the debrief questions not to have imagined the stimuli as instructed and 23 because they indicated the presence of the incorrect stimulus. The final sample for experiment 2 contained 339 participants 400 participants were recruited for experiment 1 (100 per condition: imagery (left vs. right tilt) x perception (left vs. right tilt)) using Prolific (71). The VVIQ measures people’s general imagery vividness and also serves the purpose of clearly explaining the concept of mental imagery and the imagery vividness scale. During the main task, participants were instructed to imagine a left or right tilted grating as vividly as possible while looking at dynamic noise , suggesting that the contrast was too low for participants to be able to perceive the presented grating during the critical trial. Therefore, in the experiments reported here, the visibility level was increased to 7%. With this higher visibility level the proportion of presence responses during the critical imagary trial was still relatively low (44% on average over conditions), but 72% of those were for the correct grating, indicating that participants were able to sometimes perceive the (correct) grating.72. Interesting, despite the low hit rate during the reality judgement, detection sensitivity as estimated using the discrimination task with the same visibility level but without imagery was high and significantly above chance . Finally, there was a significant negative correlation between averaged pre-critical trial imagery vividness and discrimination sensitivity in both experiment 1  and experiment 2 .Differences in viewing distance and monitor brightness between participants likely resulted in considerable variation in grating visibility between participants. Therefore, to ensure that all included participants could detect the stimuli at this contrast level, after the main experiment participants performed a standard forced-choice discrimination task on the left and right-tilted gratings presented at the same contrast in the same dynamic noise schema for 40 trials. We chose a discrimination task instead of a detection task to avoid measurement of perceptual sensitivity being influenced by differences in detection criterion. Furthermore, discrimination and detection performance can be mathematically related under signal detection theory (SDT), allowing us to use discrimination sensitivity to infer detection sensitivityFinally, it is possible that participants were unsure what gratings at threshold were supposed to look like on the critical trial, limiting our ability to characterise reality monitoring failures. Therefore, after participants performed the discrimination task (and had plenty of experience with the stimuli involved) they were asked again whether they saw a grating on the critical imagery trial and asked to again rate their confidence in this response. Of the included participants 150/272 (55%) changed their judgement after the discrimination task in experiment 1 and 204/339 (60%) in experiment 2. These changes of mind consisted mostly of ‘imagined’ responses being changed to ‘real’ responses after the discrimination task (75% of the changes of mind in experiment 1 and 89% in experiment 2), which is consistent with the discrimination task revealing to participants both a) that real near-threshold gratings are commonplace in our task environment and b) that such gratings are often difficult to see. As we already found clear results with the immediate reality monitoring judgement, the data of this second judgement were not analysed further. Importantly, the qualitative pattern of results  was similar when looking at the second response, but given the delay, we considered the immediate response to be a more reliable measure of source confusion.73 and SPSS 2574. To investigate whether external input was mistaken for imagery, we tested whether imagining a congruent stimulus was associated with a different (i.e. two-sided) probability of reporting real stimulus presence compared to imagining an incongruent stimulus. To this end, we used binary logistic regression with response  as the dependent variable and condition (congruent vs. incongruent) as the independent variable. “Imagined” responses were coded as zeros and correct presence  responses as ones. We removed participants who indicated that they perceived a grating orthogonal to the presented grating because these responses are hard to interpret  and they are not essential for answering our main question.Analyses were performed in MATLAB R2018b, JASP 0.14.1.078. Consistent with the possibility of such dissociations, we found no effect of vividness as measured by the VVIQ on reality judgement responses , despite effects on the within-task vividness ratings (see main text). Similarly, while there was a robust correlation between the two vividness measures both in experiment 1  and experiment 2 , a large proportion of the variation in vividness measured by the online ratings was not captured by the VVIQ. We therefore decided to focus on within-task vividness ratings as our primary individual difference measure of mental imagery. Nonetheless, despite the absence of any task effects, the VVIQ was still useful as a validated way to explain the concept of mental imagery to our participants.We also measured participants’ detection sensitivity and imagery vividness. Detection sensitivity was calculated based on performance in the discrimination task as We expected the probability of reporting stimulus presence to be positively correlated with detection sensitivity and negatively with imagery vividness. Furthermore, we hypothesized that these factors might interact with congruency such that source confusion, measured as the difference between congruent and incongruent imagery, would be largest for participants with low detection sensitivity and high imagery vividness. We tested this by adding detection sensitivity and imagery vividness as well as their interactions (both with each other and with congruency) as predictors to the logistic regression model.X2(1) = 28.442, p < 0.001). This model explained 13.8% (Nagelkerke R2) of the variance in reality judgements and correctly classified 70.6% of cases. The main effects of condition are reported in detail in the main text. Detection sensitivity, as measured by the discrimination task, also significantly predicted the likelihood of reporting presence of a real stimulus .We used backwards selection to determine which factors best predicted the presence responses. The model with the best fit, measured as the lowest Bayesian Information Criterion, included only the main effects of condition (congruent vs. incongruent), detection d’ and imagery vividness as predictors (t(1) = 0.020, p = 0.984), response (t(1) = 0.753, p = 0.451) or their interaction (t(1) = 0.178, p = 0.859) on confidence.Finally, we considered that if participants indeed confused external signals as imagery, this might also manifest in subjects’ confidence reports during the critical reality judgement trial. Specifically, stimulus-presence judgements might be held with less confidence in the congruent compared to the incongruent conditions, due to the source confusion inherent to this condition. We tested this using multinomial ordinal regression with confidence as dependent variable and condition as independent variable. There was no significant effect of either condition  followed by an ISI of 66 ms and a mask of 400 ms. After each imagery or perception trial, participants decided whether the perceived or imagined stimulus was animate or inanimate. Stimuli were 4 full-colour objects/animals: a watering can, a football, a fish and a rooster. After the discrimination decision, participants rated their imagery vividness/perceptual visibility on a scale from 1 to 4. Response mappings (whether 1 indicated high or low visibility) were randomized over trials so that visibility modulations cannot reflect motor-related activity.fMRI data from 35 participants were analysed . The imagery task was a retro-cue task in which two stimuli were presented after each other for 500 ms each followed by a retro-cue indicating whether the first or second stimulus was to be imagined. The participant then imagined this retro-cued stimulus for 4st-test and then correcting the p-value for multiple comparisons using FDR correction at a q-value of 0.01.Analyses were performed in MATLAB R2018b. First, trial specific beta maps were obtained using SPM12 within a GLM with one regressor per trial plus nuisance regressors . Next, we determined which brain areas were modulated by imagery vividness and perceptual visibility by running a GLM on the trial-specific beta values with a regressor for the main effects of perception and imagery as well as the parametric modulation of vividness and visibility respectively. Significance was determined by comparing the betas for vividness and visibility at the group level against 0 with a one sample 80 to determine regions of interest (ROIs) for cross-decoding of vividness and visibility, based on which brain areas were revealed as being univariately modulated by vividness and visibility. We identified three ROIs: pre-SMA , anterior insula (containing ‘G_Insulate_Anterior-376’ and ‘G_Insulate_Anterior-477’) and dorsolateral prefrontal cortex . Cross-decoding was performed by training a classifier on low (ratings 1 and 2) versus high (ratings 3 and 4) imagery vividness and then testing it on high versus low perceptual visibility and vice versa. We then averaged over decoding directions to calculate the cross-decoding accuracy81. Decoding was done using a linear discriminant analysis, as in ref. 82. Prior to decoding, data were down-sampled so that there were an equal number of trials in the low and high class, and mean-centred so that overall amplitude differences between imagery and perception were removed and only relative differences between high and low vividness/visibility remained. Participants with fewer than 10 trials per class were removed from this analysis (N = 4).We used the AICHA atlas83. Stimulus identity was decoded in a pairwise fashion and subsequently averaged over all pairs to create a stimulus decoding summary statistic for each classification type. Classifiers were trained on low (ratings 1 and 2) versus high (ratings 3 and 4) imagery vividness/perceptual visibility trials and tested on all perception/imagery trials to investigate generalization of decoding. Participants with fewer than 2 trials per stimulus class per visibility/vividness level were removed from the analysis (N = 2).To explore to what extent activation in visual cortex was modulated by vividness, we performed an ROI analysis on the early visual cortex (EVC). The EVC ROI was created by combining all early visual areas (V1-V4) from the Kastner atlas84. Comparisons between decoding accuracies were done using t-tests.Determining whether decoding accuracy was significantly above-chance was done by comparing mean group decoding accuracy with a null distribution created by permuting the class labels prior to decoding 25 times per participant and then using bootstrapping to create a group null-distributionFurther information on research design is available in the Supplementary infoReporting Summary


Glucose-6-phosphate dehydrogenase deficiency (G6PDd) is an X-linked disorder affecting over 400 million people worldwide. Individuals with molecular variants associated with reduced enzymatic activity are susceptible to oxidative stress in red blood cells, thereby increasing the risk of pathophysiological conditions and toxicity to anti-malarial treatments. Globally, the prevalence of G6PDd varies among populations. Accordingly, this study aims to characterize G6PDd distribution within the Ecuadorian population and to describe the spatial distribution of reported malaria cases.Molecular variants associated with G6PDd were genotyped in 581 individuals from Afro-Ecuadorian, Indigenous, Mestizo, and Montubio ethnic groups. Additionally, spatial analysis was conducted to identify significant malaria clusters with high incidence rates across Ecuador, using data collected from 2010 to 2021.The A- c.202G > A and A- c.968T > C variants underpin the genetic basis of G6PDd in the studied population. The overall prevalence of G6PDd was 4.6% in the entire population. However, this frequency increased to 19.2% among Afro-Ecuadorian people. Spatial analysis revealed 12 malaria clusters, primarily located in the north of the country and its Amazon region, with relative risks of infection of 2.02 to 87.88.The findings of this study hold significant implications for public health interventions, treatment strategies, and targeted efforts to mitigate the burden of malaria in Ecuador. The high prevalence of G6PDd among Afro-Ecuadorian groups in the northern endemic areas necessitates the development of comprehensive malaria eradication strategies tailored to this geographical region.The online version contains supplementary material available at 10.1186/s12936-023-04716-x. Glucose-6-phosphate dehydrogenase (G6PD) is an evolutionary conserved enzyme associated with the pentose phosphate pathway. As an oxidoreductase, it generates reduced nicotinamide adenine dinucleotide phosphate (NADPH) as a byproduct of glucose oxidation . This meG6PD gene to date, with most of them being single nucleotide variants (SNVs) [G6PD into one of five levels, considering the percentage of reduction in enzymatic activity [G6PD deficiency (G6PDd) is an X-linked disorder characterized by reduced enzymatic activity. This reduction primarily stems from molecular variants, which adversely affect the enzyme’s stability. Over 200 mutations have been identified in the s (SNVs) . The Woractivity . While tactivity . Class IReduced activity of G6PD affects NAPDH homeostasis, particularly in red blood cells, leading to varying conditions based on enzyme activity levels . IndividPlasmodium vivax hypnozoites and Plasmodium falciparum gametocytes is the 8-aminoquinoline drug family, including primaquine and tafenoquine. G6PDd individuals generate toxicity upon administration of these drugs, probably due to an imbalance in the redox reactions within blood cells, favouring the formation of highly reactive oxidized metabolites of primaquine [P. vivax malaria are at a higher risk of developing haemolytic anaemia because of the increased dose of primaquine they receive (0.25–0.5 mg over a 14-day treatment period), compared to those with P. falciparum malaria (a single dose of 0.25 mg on the first day of treatment) [P. vivax patients compared to lower doses, there are inherent risks due to factors the unknown G6PD levels in many patients [G6PDd also has significant implications in the context of malaria, particularly in terms of treatment efficacy and the development of toxicity-associated conditions. The recommended treatment drug for eliminating imaquine . Patienteatment) . While hpatients , 14.The diversity of genetic factors contributing to G6PDd is closely linked to ethnogeography. The reduced function Mediterranean c.563T variant is prevalent in the Middle Eastern and Southern Italy. Similarly, the Mediterranean together with the Seattle c.844 C rs137852318) variants are prevalent for European groups, with allelic frequencies for the minor allele (MAFs) of 0.07% and 0.11% respectively , 16. In  variantsP. vivax and P. falciparum are prevalent [P. vivax and P. falciparum) [G6PD associated with reduced enzymatic activity in various ethnic groups across Ecuador through a genetic screening approach employing Sanger sequencing, targeting multiple coding region sequences of the G6PD and with a specific focus on prevalent variants such as African A- c.202 A, A- c.968 C, and Mediterranean c.563T. The aim is also to provide a spatial distribution of reported malaria infections thus far. Describing the allelic and genotypic frequencies of G6PD molecular variants associated with decreased enzymatic activity will not only enrich the genetic characterization of this diverse Latin American population, but will also aid in the development of genetic screening strategies to identify individuals with G6PDd. The findings of this study will have implications for improving anti-malarial drug therapy in the Ecuadorian population, particularly for those groups residing in areas where malaria is endemic.In Latin America, malaria is considered endemic, especially in coastal and Amazonian areas, where both revalent , 23. Everevalent . Althougciparum) , 26. Preciparum) , 28. HowThe sampling process was conducted during the period 2017 to 2018, and a total of 292 healthy men and 289 healthy women of Ecuadorian nationality, unrelated and of legal age, were included in the study. On the contrary, people who did not have Ecuadorian nationality, minors, and people sharing first-generation ancestors, were excluded from the study. To assess genetic diversity in current Ecuadorian populations, participants from the 3 main regions of Ecuador were considered: Coast (CO), Andes (HG), and Amazonia (AZ). In addition, ethnic self-determination was considered, and the sampled individuals were classified into 4 ethnic groups: Afro-Ecuadorian (AFE), Indigenous (IND), Mestizo (MEZ), and Montubio (MON). The MEZ group was sampled in the provinces of: El Oro, Esmeraldas, Guayas, Los Ríos, Manabí, Santa Elena, and Santo Domingo (CO); Azuay, Bolívar, Carchi, Cañar, Chimborazo, Cotopaxi, Imbabura, Loja, Pichincha, and Tungurahua (HG); and Morona Santiago, Napo, Pastaza, and Sucumbíos (AZ). The Montubio group was limited to the provinces of Los Ríos and Manabí (CO region). IND groups were selected from the provinces of: Chimborazo, Cotopaxi, and Imbabura (HG region); Morona Santiago, Napo, Pastaza, Orellana, and Zamora Chinchipe (AZ region); and Santo Domingo (CO region). Finally, the AFE groups were included from the provinces of: Esmeraldas province (CO region); and Imbabura and Carchi (HG region).Each participant signed an informed consent form for population genetic studies. The applied experimental methodology was approved by the Ethical Committee in Human Research from the Universidad de las Américas, with registration number CEISH-UDLA 2017 - 0301.Genomic DNA was extracted from blood samples using the salting-out method . DNA washttps://www.ncbi.nlm.nih.gov/tools/primer-blast/), reference sequence NG_009015.2 (GRCh38.p13). The amplified regions included the class I Harilaou c.648T > G (rs137852319) variant, the class II Mediterranean c.563 C > T (rs5030868) variant, the class III A- c.202G > A (rs1050828) and A- c. 968T > C variants, and the class IV SNV A- c.376 A > G (rs1050829). In addition, the SNVs c.1365-13 C > T (rs2071429) and c.1431 C > T (rs77214077) described as likely benign were screened.For the design of the primers, 4 classes of SNVs related to a decreased enzymatic activity of G6PD were considered, and the Primer-BLAST tool was used  in a final volume of 15 µl, with 0.3 µMol of each primer and ~ 20 ng of genomic DNA. Amplified products were tested by electrophoresis in a 2% agarose gel and purified using exonuclease I (Exo I) and alkaline phosphatase (FastAP™) (Thermo Scientific™) according to the manufacturer’s standard protocol.Sequencing reactions were performed using the BigDye® Terminator v3.1 Cycle Sequencing Kit , following the manufacturer’s instructions. Within the reaction, a final concentration of 1 µM of primer was used in a volume of 10 µl. Next, amplicons were purified using Sephadex G-50 Fine  and run in an ABI 3130 Genetic Analyzer 142 . Finally, the sequences were aligned and analysed in the software Geneious Prime, version 2022.1.1.ST), and analysis of molecular variance (AMOVA), were calculated using Arlequin software v.3.5.2.2. HWE analysis was performed only for females, and LD analysis in males. The significance level of 0.05 was adjusted by applying Bonferroni’s correction for multiple tests.Population genetic parameters, such as allele frequencies, gene diversity, Hardy-Weinberg Equilibrium (HWE), pairwise genetic distances . Spatial analyses were performed using QGIS software (version 3.32.0), while the calculation of incidence rates was performed using the Epitools package, R version 4.1.3.G6PD were amplified to genotype previously described variants using the primers described in Additional file Five regions in G6PD variants in the total population. The MAFs in AFE from the HG (Imbabura and Carchi), and the CO  reach 0.143 and 0.304 respectively. On the other hand, only one AFE heterozygous individual was recorded in the province of Esmeraldas for the SNV A- c.968T > C (rs76723693). Additionally, a high prevalence was observed for the intronic variant c.1365-13 C > T (rs2071429) in the Ecuadorian population (MAF = 0.701). Allele frequencies of 0.358 and 0.021 are recorded for the minor allele of variants class IV c.1116G > A (rs2230036) and c.1431 C > T (rs77214077) respectively . However, these differences disappear when considering ethnic self-determination and when developing the analysis by AFE, IND, MEZ, and MON groups. No statistically significant deviations from HWE expectations were detected for the 6 loci in the studied population when considering the groups by ethnic self-determinations and geographic location , MEZ  and MON . When considering the distribution of ethnic groups by geographic region, there is a certain degree of differentiation between MEZ populations in the HG and those in the CO   than the IND groups (0.014). For MEZ and MON populations, individuals with diminished phenotypes are not recorded . The yearly evolution of hospital records showed a decline in the incidence of malaria through the years , coupled with a single administration of primaquine at a dosage of 0.75 mg per kilogram of body weight. In cases of non-complicated malaria due to P. vivax or Plasmodium ovale, the treatment strategy consists of 3-day administration of chloroquine accompanied by a 14-day regimen of primaquine at a dosage of 0.5 mg. It is important to exercise caution with chloroquine and hydroxychloroquine due to their potential anti-malarial efficacy in inducing oxidative stress through the generation of haem-based reactive oxygen species [It is noteworthy that Ecuador, like many South American countries excluding Venezuela, has witnessed a significant decrease in malaria prevalence in recent years, and most cases are now reported as asymptomatic , 46. The Ecuador , the prifor 2.1% . A similed cases . Consideetocytes –52. Accoetocytes , the rec species , 54. Ind species , 56.In Ecuador, different ethnic groups show a specific spatial distribution due to historical and social events, which places special value on the present study. The effects of G6PDd in malaria-exposed populations of different ethnic origins can be characterized with less bias than in other regions where ethnic groups are mixed, or where there is dynamic variation among them. As shown in Fig. The complex interplay between genetics, ethnicity, and disease susceptibility, shaped by historical and societal influences on malaria distribution and genetic factors such as G6PDd, offers invaluable insights. Understanding these dynamics can guide the development of culturally sensitive strategies for malaria prevention and control, considering the diverse needs and vulnerabilities of different ethnic groups. Patient management requires careful consideration of the individual risk/benefit ratio in determining the most suitable treatment approach. In order to mitigate the occurrence of adverse clinical outcomes associated with anti-malarial drugs, it is essential to know the patient’s G6PD status prior to prescribing any 8-aminoquinoline-based drug . For exaThis study provides for the first time the prevalence and distribution of genetic variants associated with G6PDd within the Ecuadorian population, encompassing its four primary ethnic groups. However, there are inherent limitations. The sample size of 581 individuals might not capture the full genetic diversity of the population, suggesting that a larger sample could offer a more comprehensive insight into G6PDd and its relation to malaria patterns in Ecuador. Similarly, the employed methodology facilitated a genetic screening for prevalent variants in G6PDd that hold predictive value in malaria treatment. While several coding regions of the gene were screened (9 out of 13 exons), the need remains for forthcoming studies to employ high-throughput sequencing techniques and broaden the coverage of genotyping. Lastly, the spatial cluster analysis relied on publicly available data, which might be prone to reporting biases or data gaps, further exacerbated by the COVID-19 pandemic. Although the calculated incidence rate shows a decreasing trend in all the years analysed, the possible biases introduced during this pandemic period highlight the need for a cautious interpretation of the findings.G6PD gene identified the presence of class III A- c.202G > A (rs1050828) and A- C.968T > C (rs76723693) variants, both of which are associated with a reduction in G6PD enzymatic activity. The frequency of these variants differed between the studied Ecuadorian ethnicities, with the AFE groups showing the highest prevalence. Evaluating G6PDd has significant implications for the population in question, especially in light of the spatial distribution of reported malaria cases in Ecuador and the distribution of G6PDd molecular variants. Twelve clusters as endemic malaria areas in Ecuador were identified, mainly distributed along the northern coast , in the Andean region (Carchi), and the Amazon region . Given the high prevalence of G6PDd in AFE groups (19.2%) located in the northern  of the country, coupled with the elevated rates of malaria infection identified in this geographic region (a relative risk ranging from 2.06 to 87.88), there is a pressing need to implement genetic screening strategies within these ethnic communities. This would mitigate toxicity risks when administrating anti-malarial therapies. The integration of genetic screening into patient management enables health professionals to optimize treatment plans, improve patient safety, and enhance therapeutic outcomes.The genotyping analysis of the Additional file 1: Table S1. Primer sequences and amplification conditions for genotyped regions in G6PD. Table S2. Allelic and genotypic frequencies of SNVs in G6PD found in different Ecuadorian ethnic groups. Table S3. Observed and expected values of heterozygotes and significant p-values calculated for the Hardy-Weinberg equilibrium of molecular variants of G6PD in Ecuadorian females. Table S4. Pairwise genetic distances (FST) calculated from the G6PD genetic variants between the Ecuadorian populations grouped by geographic regions. Table S5. G6PDd in different Ecuadorian ethnic groups. Table S6. Spatial analysis of significant malaria clusters across Ecuador.


Prunus domestica) cultivars and hybrids in Lithuania using SSR markers. In total, 107 plum genotypes (including 68 European plum cultivars and 39 hybrids) from the genetic resources collection of the Institute of Horticulture of the Lithuanian Research Centre for Agriculture and Forestry (LRCAF IH) were evaluated using nine microsatellite markers (SSRs) previously published and suggested by the European Cooperative Programme for Plant Genetic Resources (ECPGR). Up to six alleles per locus with each primer pair were generated for some genotypes due to the hexaploidy of plums. The number of alleles in each primer ranged from 18 to 30, with an average of 24.33. The highest number of alleles was generated with the PacA33 primer pair (30). The most informative primer, according to the PIC value, was BPPCT007. Sixty-two unique alleles  have been detected in the plum germplasm developed in Lithuania. According to UPGMA cluster analysis, 58 European plum genotypes were separated into eight groups without any relation to fruit color or shape. By genetic diversity (UPGMA) and structure (Bayesian) analysis, European plum hybrids were grouped into clusters according to their pedigree.This study’s aim was to evaluate the genetic diversity of European plum ( Rosaceae family, genus Prunus, the European plum (Prunus domestica L.) is grown in temperate zones for its fleshy fruit, consumed fresh, dried, or canned worldwide [Prunus germplasm, including Prunus domestica, in Europe [A member of the orldwide . The Eurorldwide . The Eurn Europe .Prunus domestica has been known for only a century. Crane and Lawrence [P. cerasifera and tetraploid P. spinosa. This experiment was confirmed by molecular biology methods [Plum cultivars have different pomological characteristics and significant variability in phenotypic values within the germplasm. The precise origin of Lawrence  performe methods .In plant breeding, it is crucial to know the parental form of the species and the phenotypic and genetic characteristics to ensure that the correct germplasm or gene of interest is included in modern plant breeding programs . Modern Prunus domestica species at the genomic level. Prunus domestica is a less-analyzed specie of the Prunoideae subfamily due to its polyploidy [Prunus domestic molecular markers is complicated due to sequencing, assembling genomes and analyzing polyploid genomic data [Prunus species, such as peach or sweet cherry [It is important to analyze European plum cultivars growing in Lithuania and newly introduced hybrids through morphological, phenological and molecular characterizations . In the lyploidy . Analysimic data . For thit cherry . Howevert cherry .Prunus domestica [The ECPGR confirmed a set of nine SSR markers for SSR analysis of omestica . Since tomestica ,23,25. AA total of 68 European plum cultivars (including six foreign reference cultivars (R-Plum group) and 14 cultivars of Lithuanian-origin (LT origin-Plum group), 48 accessions from Lithuania genetic resources collection of plum (LT-Plum group) and 39 hybrids were analyzed using nine microsatellite loci published by Nybom et al. [According to the evaluation of plum cultivars, the total number of polymorphic alleles in reference cultivars (R-Plum) was smaller  compared to cultivars of Lithuanian origin (LT origin-Plum group)  . The numThe maximum number of alleles in genotypes of both groups was in the range of 3–6. Among all analyzed cultivars, the lowest number of alleles in genotype (3) was observed in locus UDP98-407. In referenced cultivars (R-Plum group), the lowest number of alleles in the genotype was in loci UDP98-407 and CPSCT026 .In both groups of cultivars, the allele size ranged from 96 to 258 bp, with the smallest size in primer UDP96-005 and the highest in primer BPPCT034, respectively . In locio) was high, ranging from 0.71 to 1.00, except for UDP98-407 loci. The average of observed heterozygosity in Lithuanian-origin cultivars (LT origin-Plum group) and referenced cultivars (R-Plum) is 0.88 and 0.91, respectively. In the LT origin-Plum group, the Ho ranged from 0.71–1.00. The highest heterozygosity (1.00) was observed in loci BPPCT040, BPPCT034, BPPCT039, BPPCT007 and CPSCT026. The results are similar in the R-plum group, and two other loci, BPPCT014 and PacA33, show high heterozygosity (1.00). The lowest heterozygosity value (0.33) was observed in the R-Plum group’s locus UDP98-407. However, in the LT origin-Plum group, this locus shows a much higher heterozygosity value (0.71).Observed heterozygosity  and UDP96-005 (0.240). For the R-Plum group, all loci are informative, with an average PIC value of 0.363.To find the minimal set of primers for genetic diversity, a phylogenetic tree with the most informative primer according to PIC value was constructed. This process was continued by adding primers according to their informativeness  until all genotypes were separated. The minimal set for the Lithuanian-origin plum genotype’s genetic diversity analysis consists of two highly informative SSR primers, BPPCT007 and CPSCT026, with PIC values of 0.374 and 0.280, respectively.ip) of the 14 Lithuanian-origin (LT origin-Plum group) and six reference cultivars (R-Plum group) ranged from 5% to 85%  were classified as rare. In three loci: BPPCT040, BPPCT039, and UDP98-407, rare alleles were absent at all. Most unique alleles in Lithuanian-origin cultivars were rare, 80.6%  and observed in all nine loci. All 16 unique alleles in reference cultivars were rare and observed at BPPCT034, BPPCT039, UDP98-407, PacA33, CPSCT026 and UDP96-005 loci.The allele frequencies (% to 85% . Of the In Lithuanian-origin cultivars (LT origin-Plum group), homozygosity was observed at four loci  . The mosTo evaluate the polymorphism of 39 European plum hybrids and six parental forms, 191 and 109 alleles, respectively, were observed . In hybrThe allelic size range in hybrids and their parental forms was the same in three loci: BPPCT007, CPSCT026, and UDP96-005. In hybrids, the allele size range in five loci  was wider than in their parental forms. Only in one locus, BPPCT039, the allelic size range was wider in the parental forms of the hybrids .The average observed heterozygosity in hybrids was higher than in their parental forms . At loci BPPCT040, BPPCT034, BPPCT039, BPPCT00, and CPSCT026, the observed heterozygosity in hybrids and their parental forms was 1.00. In the parental forms of hybrids, heterozygosity was high in locus BPPCT014 (1.00); however, in hybrids, the heterozygosity was lower (0.95) in this locus. The lowest heterozygosity value was observed in UDP98-407 in the hybrid parental forms (0.5).According to the PIC value, primer informativeness for the parental forms of hybrids varies from 0.284 to 0.363, with an average of 0.342 in all analyzed loci. However, for the hybrids, the PIC value was lower .The phylogenetic tree was constructed to evaluate genetic diversity and relations between data from 9 SSR loci and morphological traits of 58 European plum cultivars . EuropeaThe first cluster (I) consists of nine cultivars, including the international reference cultivar ‘Anna spath’ and the Lithuanian cultivar ‘Zalioji renklode’. The second cluster (II) consists of seven European cultivars and one cultivar from the USA. Two Lithuanian cultivars, ‘Vietine geltonoji’ and ‘Alge’, belong to this group. The cultivar ‘Alge’ was grouped with the Italian cultivar ‘Italu vengrine’ with significant bootstrap value. This cluster also includes France’s international reference cultivar ‘Reine Cloude doullins’. The third cluster (III) consists of six cultivars from Russia and Europe, including a Lithuanian cultivar, ‘Orija’. In the fifth cluster (V), four cultivars from Russia, Lithuania and Sweden can be found. The Lithuanian cultivar ‘Katra’ was closest to the Russian cultivars ‘Privet oktiabra’ and ‘Stachanovka’. The sixth cluster (VI) consists of five European cultivars and one Russian cultivar. The Lithuanian cultivar ‘Aleksona’ was the most similar to ‘Julius’ from Estonia.The seventh cluster (VII) was the largest group and consisted of two subgroups. For the first subgroup, four Lithuanian cultivars ‘Altano renklode’, ‘Staro Vengrine’, ‘Jure’, ‘Skalve’, and an international reference cultivar ‘Mirabelle de Nancy’ from Trance, a Latvian cultivar and two cultivars from the United Kingdom belonged. The second subgroup comprises ten cultivars from Russia, Europe, the USA and Canada, including two international reference cultivars, ‘Stenley’ and ‘Valor’, with significant bootstrap value (83%).Cultivars were grouped according to geographical location only in two clusters. The fourth cluster (IV) consisted of cultivars only from Europe, including the international reference cultivar ‘Hanita’. The eighth cluster (VIII) consists of two Lithuanian cultivars, ‘Rype’ and ‘Vilniaus Vengrine’.Morphological data of European plum cultivars fruits is provided in Two cultivars were grouped according to the pedigree. Cultivars ‘Stenley’ and ‘Valor’ are grouped in the same cluster with significant bootstrap. These two cultivars, ‘Stenley’ (‘d‘Agen’ × ‘Grand Duke’) from the USA and ‘Valor’  from Canada, might be grouped together because of the same parents from ‘Grand Duke’.The phylogenetic tree was constructed for hybrids and their parental forms. All genotypes were grouped into seven clusters according to pedigree A. The fiBayesian clustering analysis was performed with STRUCTURE software, based on nine SSR loci for all 45 genotypes of hybrids and parental forms. A bar plot of the results is provided on the right side of the phylogenetic tree B. WithinThe populations of plant species on the border of the spread are more unique than populations in the central part of the areal . Higher For plant species, molecular markers provide tools for genetic diversity analysis and genome structure studies. Microsatellites (SSR) are polymorphic and codominant, meaning they can be beneficial for cultivar identification, phylogenetic studies and species fingerprinting .In this study, the genetic diversity of 68 European plum cultivars grown in Lithuania was analyzed, including six foreign reference cultivars: ‘Anna spath’ (Germany), ‘Hanita’ (Germany), ‘Mirabelle de Nancy’ (France), ‘Reine Claude doullins’ (France), ‘Stenley’ (United States), ‘Valor’ (Canada), 14 cultivars of Lithuanian origin and 48 foreign cultivars. SSR analysis of genotypes showed a high level of genetic diversity in plums.The genotyping data of referenced cultivars analyzed in this research agrees with the results of Nybom et al. . HoweverIn this study, the mean value of alleles per locus in all analyzed genotypes was lower (24.33) compared to the study of genotypes sampled across European countries like Great Britain, Estonia, Latvia, Norway, Sweden, Italy and Greece   were BPPCT040—with the highest number of alleles and BPPCT007 and CPSCT026 with the highest value of polymorphism information content (PIC).A wide range of unique alleles was observed among the analyzed Lithuanian genotypes, which shows the genetic individuality of the population. In Lithuanian-origin cultivars, 62 (39.5%) alleles were observed as unique, indicating genetic diversity, which is essential for adaptation to strict ecological conditions of the northern part of the species’ habitat . This geThe cluster analysis of studied cultivars shows that foreign and domestic cultivars do not form separate groups according to geographical location; the same tendency of results distribution in the phylogenetic tree clusters is shown in other research works ,25. GenoIn the cluster analysis of hybrids, all hybrids clustered with their parental forms according to their pedigree. The same clustering tendency was noted in this study with ‘Stenley’ and ‘Valor’ cultivars and in Makovics-Zxohar et al.  researchIn this research, morphological traits did not have significant implications for SSR results; only the skin color of the plum fruit had an impact on the cultivar’s genetic clustering results. In most research, no significant correlation was observed between morphological traits and molecular data ,18. HowePrunus domestica) cultivars .The leaves were collected from one-year shots in the spring from a single plum tree for each of analyzing plum cultivars at the Lithuanian Research Centre for Agriculture and Forestry institute of horticulture in 2021. Leaves were frozen with nitrogen and kept at −70 °C until further analysis. DNA was extracted using a modified  CetyltriThe observed heterozygosity, frequency of alleles, and polymorphism information content (PIC) were calculated for each of nine SSR primer pairs according to Roldán-Ruiz et al. . The infThe genetic structure of hybrids and their parental cultivars was determined using the Bayesian model-based clustering method with Structure v. 2.2.3 software . Absent The application of microsatellite markers to European plum cultivars growing in Lithuania enabled the characterization and identification of genotypes and highlighted the uniqueness of analyzed cultivars. Lithuanian-origin European plum cultivars have various unique alleles essential for plant breeding under exceptional northern climate conditions. ECPGR recommended primer set for SSR analysis of European plum cultivars is appropriate for genetic diversity analysis of Lithuanian-origin cultivars (LT origin-Plum group). According to the highest number of alleles BPPCT040 primer pair is the most informative, and the highest polymorphism information content was determined with BPPCT007 primer pair. According to the PIC value, the least informative primer pair for Lithuanian cultivars (LT origin-Plum group) fingerprinting was UDP96-005. The minimal set for the plum genotype’s genetic diversity analysis consists of two highly informative SSR primers, BPPCT007 and CPSCT026, with PIC values of 0.374 and 0.280, respectively. However, it would be beneficial to use a complete primer set for molecular fingerprinting and genetic diversity evaluation of European plum. In the phylogenetic analysis, cultivars were grouped according to the pedigree. However, geographical location and morphological traits did not significantly affect SSR results.


Evaluating intraspecific genetic structure and diversity is fundamental to assessing a species’ conservation status, but direct incorporation of such information into legal frameworks such as the EU's Habitats Directive is surprisingly rare. How genetic structure aligns with EU member state boundaries or biogeographic regions may be very important in designing management plans or achieving legislative goals. The Eurasian fish otter experienced a sharp population decline during the 20th century but is currently re‐expanding in several countries. The species is listed under Annex II and IV of the European Habitats Directive, and member states are obliged to assess the species separately across different biogeographic regions. We genotyped 2492 otter spraints across four provinces in Austria, collected between 2017 and 2021. A total of 384 different genotypes were identified, supporting densities along river habitats from 0.1 to 0.47 otters per river km (mean: 0.306), with a resampling‐based simulation supporting limited density overestimation at survey lengths of 20 km or more. Three distinct genetic clusters were revealed, two of them presumably reflecting two relict populations whereas the source of the third cluster is unknown. The geographic extent of the three clusters does not coincide with provincial or biogeographic boundaries, both relevant for assessment and management within existing national or European legislative frameworks. We advocate more consideration of genetic structure in the assessment and conservation management planning of species listed in the European Habitats Directive. In contrast, implementation of genetic data into conservation or management decisions related to the EU Habitats Directive (Council Directive 92/43/EEC) has been minimal. According to Frankham  regularly incorporates genetic data to describe sub‐specific units of endangered or threatened species, particularly in defining distinct population units (DPS) Kelly, . For exaFrankham  and KoskFrankham , the lacFrankham  give recTo date, geographic management units of the EU Habitats Directive are shaped by administrative borders  or biogeographic regions . These assessments must be done separately for each biogeographic region, and within Austria, this relates to the alpine and continental biogeographic regions. Therefore, it would be relevant to evaluate to what extent genetic structure aligns with administrative or biogeographic units, especially if management across these units is not uniform.1.2L. lutra, Linnaeus 1758, hereafter otter) is listed in Annex II and IV of the European Habitats Directive since 1992. It was widespread throughout Europe during the 19th century but experienced a sharp population decline during the 20th century  sampling, have quickly become preferred for data collection  for fragment analysis. Selected reference samples were run on both sequencers and positive controls with known genotypes were run with every sequence run to ensure data consistency. Electropherograms were analyzed using GeneMapper 3.7 or 6 (Applied Biosystems). The multiple tube approach of Navidi et al. (2O) was processed. A sample was classified as “successfully genotyped” if it had confirmed genotypes at 9–11 microsatellite loci. For successfully genotyped samples, two sex‐markers, namely Lut‐SRY  or E.Z.N.A. Stool DNA Kit  with minor modification to the manufacturers’ protocols. For QIAamp Kit extractions, final incubation time was extended to 5 mins and final elution volume was 100 μL. For E.Z.N.A. Kit extractions, during Steps 1–5 of the Human DNA Detection protocol, all volumes were downscaled by the factor 1.6 and then continued as recommended with a final elution volume of 100 μL. For both protocols, 100–200 mg starting material was used if the sample was stored without storage buffer and 300–400 mg from samples preserved in storage buffer. Tissue samples were extracted using the DNeasy Blood & Tissue Kit  according to the manufacturer's protocol. All extracts were store at −20°C until further processing. Each extraction batch consisted of a maximum of 11 samples and with each spraint extraction batch, a negative extraction control was processed. For genotyping, 11 microsatellite loci, originally described by Dallas and Piertney  were usei et al.  was appli et al.  and conti et al.  and eachi et al. . If the 2.3Cervus 3.0.7  were used for this calculation. The presence of potential Null‐Alleles was reviewed with the software Micro‐Checker  an observed (Ho) heterozygosity per locus as well as probability of identity (PID) and probability of identity for siblings (PIDsibs) were calculated with Cervus and overall unbiased probability of identity (PIDunbias) were calculated using Gimlet v.1.3.3  was carried out in adegenet, retaining 30 principal components and two discriminant functions. Selection of the number of principal components to be retained was done interactively, using a threshold of 90% explained cumulative variance. Diversity indices  100 random sample stretches were drawn along the full 30 km, and densities on the drawn sample stretches were calculated. Relative density errors (the percentage of over‐ or underestimation compared to the density estimate using the full 30 km stretch) were calculated across all 15 reference stretches. To evaluate whether overall otter density (using the full 30 km stretch) itself was a factor influencing estimation errors, the relative density errors were also regressed on this overall otter density, across the dataset and their correlation evaluated with the non‐parametric Kendall's tau statistic.33.1Across all five surveys, 2877 spraint samples and 14 tissue samples were collected. Of these, 2492 spraint and all 14 tissue samples were processed in the laboratory. Of the 2492 spraint samples, a total of 1100 was successfully genotyped  was 1.51 × 10−9. Micro‐Checker Analysis suggested presence of potential Null‐Alleles at seven out of the 11 Loci, which might obscure HWE calculations  .A total of 139 otter individuals were identified along the 15 30‐km‐long stretches in Upper Austria and Salzburg. Otter densities varied from 0.1 to 0.47 otter per river‐kilometer  . However, no statistically significant difference could be observed between males and females, neither concerning same‐sex nor other‐sex overlaps , with males  having significantly longer recapture distances than females   Figure . Overall3.3p = 0.017 at 15 km), or borderline significant  value.Across all 15 reference stretches, the relative density error was positive (supporting overestimation) across all sample stretch lengths, though relatively stable at about 20 km or more Figure . The mea4In this study, we juxtapose the genetic structure and diversity of a European Habitats Directive listed species to their current management units. We describe genetically determined individual densities as well as the distribution of individuals along Austrian river courses and estimate the risk of error in such density estimates to aid future survey design.4.1FST: 0.094 for Austrian clusters, mean global FST among British River Basin District regions: 0.125). The genetic structure revealed by this study does not follow administrative  or biogeographic borders and river catchments seem to have only a limited effect on the genetic structure. The lack of such a structure at provincial or biogeographic scales supports our initial hypothesis and highlights a clear difference between genetically identified units and current management units. For example, within Carinthia, one of the two Austrian provinces that ratified a decree for a regulated otter culling, two genetically distinct clusters have been identified. On the other hand, Upper Austria, which falls into both the alpine and the continental region  reveals only one homogeneous population throughout its whole area. Additionally, both Upper Austria and Salzburg share borders with the state of Bavaria in Germany, which is also currently considering culling measures for the otter, based on their own ongoing population estimates. The obvious question to pose, biologically, is whether the fish otter population in Bavaria is shared or distinct with the fish otter population in Salzburg or Upper Austria.Our dataset reveals a clear mismatch between the genetically identified clusters and current management units, which are to date solely defined by administrative borders and biogeographic regions. Three clearly distinct genetic clusters were identified within Austria. Their differentiation among each other is slightly lower but comparable to those among the British populations reported by Thomas et al.  , particularly at lower altitudes encompassing less steep terrain. The main geographical feature that shaped genetic structure in Austria was the main Alpine divide, representing a clear colonization barrier. A similar pattern was also observed in Hungary by Lehoczky et al.  where siTwo of the three clusters identified among Austrian otters could reflect the two refuge areas during the 1990s  and genotyping success rates fall within the range of previous studies  is lower than that of the otter population in Hungary  and Upper Austria . Whether this represents a temporal trend between 2017 and 2021 or is rather a spatial pattern, needs to be evaluated. Two independent sex‐markers as well as a positive control were typed for sex determination, rendering laboratory errors unlikely.Sex ratio  in our dataset was relatively even overall, however we found a female‐biased sex ratio in the two most recent studies, namely in Salzburg . For landscape‐wide extrapolation, a reduction of approximately 10% would be warranted when sample stretches of 20–30 km in length are used. Nonetheless we note that typical 95% CIs of such landscape‐wide abundance estimates were already at plus or minus 20% of mean estimates  is also reflective of what has been described previously Kranz,  but we dIn summary, this work demonstrates how the application of genetic data can support the evaluation of various biological attributes of a species relevant for conservation assessment and management. In particular, we highlight the incompatibility of administrative or biogeographically circumscribed management units with population genetic structure and point out that this is the status quo for conservation management among member states of the European Union. We advocate more focused integration of genetic structure into population assessment and especially management planning. Within the European context, there need not be any change to legislation to facilitate more science‐based decisions, but rather more communication and cooperation across administrative borders be it within or among EU member states.The authors declare no conflicts of interest.Data S1.Click here for additional data file.Data S2.Click here for additional data file.Data S3.Click here for additional data file.Data S4.Click here for additional data file.Data S5.Click here for additional data file.Data S6.Click here for additional data file.


N = 9), Braunvieh (N = 9), Gyr (N = 5), and Simmental (N = 15) breeds. Additionally, samples from a locally adapted creole cattle, the Arequipa Fighting Bull , were incorporated. Female individuals were genotyped with the GGPBovine100K and males with the BovineHD. Quality control, and the proportion of polymorphic SNPs, minor allele frequency, expected heterozygosity, observed heterozygosity, and inbreeding coefficient were estimated for the five breeds. Admixture, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were performed. Also, a dendrogram was constructed using the Neighbor-Joining clustering algorithm. The genetic diversity indices in all breeds showed a high proportion of polymorphic SNPs, varying from 51.42% in Gyr to 97.58% in AFB. Also, AFB showed the highest expected heterozygosity estimate (0.41 ± 0.01), while Brahman the lowest (0.33 ± 0.01). Besides, Braunvieh possessed the highest observed heterozygosity (0.43 ± 0.01), while Brahman the lowest (0.37 ± 0.02), indicating that Brahman was less diverse. According to the molecular variance analysis, 75.71% of the variance occurs within individuals, whereas 24.29% occurs among populations. The pairwise genetic differentiation estimates (FST) between breeds showed values that ranged from 0.08 (Braunvieh vs. AFB) to 0.37 (Brahman vs. Braunvieh). Similarly, pairwise Reynold’s distance ranged from 0.09 (Braunvieh vs. AFB) to 0.46 (Brahman vs. Braunvieh). The dendrogram, similar to the PCA, identified two groups, showing a clear separation between Bos indicus (Brahman and Gyr) and B. taurus breeds . Simmental and Braunvieh grouped closely with the AFB cattle. Similar results were obtained for the population structure analysis with K = 2. The results from this study would contribute to the appropriate management, avoiding loss of genetic variability in these breeds and for future improvements in this nucleus. Additional work is needed to speed up the breeding process in the Peruvian cattle system.New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucleus cattle herd of four commercial breeds from a Peruvian institution were determined. This nucleus comprises Brahman ( Livestock production around the world is a large sector with an important contribution of 40% and 20% to agriculture production in developed and developing countries, respectively . A largeDue to the low productivity, small farmers breeding strategy is to crossbred PCC with other specialized breeds in order to take advantage of the heterosis effect . HoweverIn developing countries, nucleus breeding systems represent a good strategy for animal genetic improvement for ruminants. Concentrating nucleus cattle in one or a few herds to disseminate genetic material to other populations is helpful . In 1993A total of 63 blood samples were collected from four commercial breeds of taurus  and indicus cattle (Brahman and Gyr). According to their pedigree, up to grandfathers, genetic origins for Brahman and Gyr were predominantly from Brazil; for Braunvieh, Switzerland and Colombia; while for Simmental was Germany . Blood sWe extracted genomic DNA from whole blood and hair samples with the Wizard Genomic DNA Purification Kit  following the manufacturer’s instructions. The quality and quantity of genomic DNA were assessed using agarose gel electrophoresis and a Nanodrop spectrophotometer  prior to genotyping. In addition, 40 genotypes from reference breeds were included in the analyses. The reference breeds were sourced from DNA samples were genotyped using Illumina Bovine HD Genotyping BeadChip and Illumina GGP Bovine 100K BeadChip with the help of the commercial genotyping service provider . Female individuals were genotyped with the GGPBovine100K and males with the BovineHD. The Bovine HD and 100K chips possess 777,962 and 95,256 SNPs, respectively, uniformly spanning over the entire bovine genome. A total of 87,669 common markers between both SNP panels were used for the following analysis. From the total of 71 animals sampled, we discarded the ones with a genotype call rate minor to 85% . A totalSNPs quality control was performed using the PLINK v1.9 program . SNPs asIS) were estimated using R package dartR (To assess the genetic diversity within the studied population we used different genetic diversity parameters. The proportion of polymorphic SNPs (Pn), MAF, expected heterozygosity (He), observed heterozygosity (Ho), and inbreeding coefficient (Fge dartR . The disST) fixation indices were calculated using 20,000 permutations and a significance level of 0.05. Also, Reynold’s distance was performed. Second, a principal component analysis (PCA), and a discriminant analysis of principal components (DAPC) were perform with PLINK. The factorextra  was performed with ARLEQUIN v.3.5.2 software , with thtorextra  and adegadegenet  R packag compared to the counts of SNPs in the higher MAF intervals. The count of SNPs for the Braunvieh, AFB, and Simmental cattle breeds was shown to be higher as the MAF interval increased. For the Brahman and Gyr breeds the count of SNPs decreased.ST and Reynold’s distance among all populations were estimated (ST estimates among breeds ranged from 0.08 (AFB-Braunviehpair) to 0.37 (Braunvieh-Brahman pair). The pairs Braunvieh-Brahman and Braunvieh-Gyr showed high pairwise FST values, with 0.37 and 0.36, respectively. Furthermore, the pairs Braunvieh-AFB, and AFB-Simmental showed the lowest pairwise FST values, with 0.08 and 0.09, respectively. The pairwise Reynold’s distance showed a pattern similar to the one obtained with the FST statistics, with values ranging from 0.09 (AFB-Braunvieh pair) to 0.46 (Braunvieh-Brahman pair). The pairs Brahman-Braunvieh and Braunvieh-Gyr showed high pairwise Reynold’s distance values, with 0.46 and 0.44, respectively. Furthermore, the pairs AFB-Braunvieh, and AFB-Simmental showed the lowest pairwise FST values, with 0.09, respectively each.The AMOVA results  showed tstimated . The paiK = 2 was the most optimal number for the inferred genetic structure of the populations (K = 2 a considerable source of variation among cattle breeds was perceptible. Cluster 1 comprised of the Brahman and Gyr breed groups (N = 14 genotypes), whereas cluster 2 consisted of the Braunvieh Simmental, and AFB cattle groups (N = 33 genotypes). The Brahman and Gyr populations displayed a separated cluster, whereas the Braunvieh, and AFB, and Simmental populations presented similar genetic construction.A graphic representation of cluster structure analysis is depicted in ulations . At K = A neighbor-joining tree was constructed from SNPs , displayInvestigating genetic diversity parameters of populations is critical for developing future breeding objectives . Here foB. taurus, particularly Brown Swiss and Simmental in high Andean areas  to −0.07 (Brahman and Braunvieh). So, this negative FIS values could indicate that the population was in outbreeding  and AFB-Simmental pair (0.09).According to the AMOVA results , the proB. indicus. On the contrary, higher genetic distances were observed between Brahman with 1) Braunvieh (0.37), 2) Simmental (0.35), 3) and AFB (0.31), indicating some degree of isolation between these breeds, that is, they are not currently breeding with one another. This could be because the region where the AFB samples were collected is located in the southern parts of the country, where climatic conditions are cold. Hence, European breeds such as Brown Swiss, Braunvieh, Simmental, Overo negro, Jersey, etc., are more commonly used in these regions because of the cold climate, whereas Zebuine breeds, such as the Brahman and Gyr, are preferably used in the Amazon region of the country, in the north. Our phylogenetic reconstruction is in concordance with ADMIXTURE analysis and genetic distances. We identified that the AFB breed is closer to Braunvieh than the Simmental breed and others. B. taurus with an analysis of the mitochondrial genome and validated these relationships, probably influenced by the high introgression and crossing over. Also, a strong relationship between Gyr and Brahman breeds was identified.We analyzed the genetic structure of the AFB cattle by using SNP markers. This information will be valuable to our farmers as well as future studies. The results of this study provide some insight that AFB can become a separate breed in the future. The analysis also provides evidence for two subgroups within the AFB group , with onB. indicus (Brahman and Gyr) and B. taurus breeds . Interestingly, the AFB was placed in a single cluster, providing evidence that this may be considered a breed as farmers from Arequipa breed their animals in favor of fighting traits. Additional work is needed to also characterize other cattle herd of INIA located in San Martin region. We hope this work will pave the way towards developing a modern cattle breeding program in Peru.We here determined for the first time the genetic diversity and population structure of a Peruvian cattle herd using SNP data. Braunvieh breed possessed the highest genetic diversity while Brahman the lowest. Most of the variance occurs within individuals among the five breeds evaluated in this study. A total of two clusters were identified, showing, as expected, a clear separation between