Aphos porosus population, which is a vulnerable species with low dispersal potential. We evaluated the effects of these two major disturbances on the diversity and spatial-temporal genetic structure of Aphos porosus in two neighboring areas that were impacted on different levels . Thirteen microsatellites (from 2008 to 2015) amplified in individuals collected from both locations were used to evaluate the effects of the two disturbances. Results showed that after the strong hypoxic-upwelling event and the mega earthquake-tsunami, Aphos porosus populations exhibited lower genetic diversity and less effective population sizes (Ne < 20), as well as asymmetries in migration and spatial-temporal genetic structure. These findings suggest a rise in extinction-recolonization dynamics in local Aphos porosus populations after the disturbances, which led to a loss of local genetic diversity (mainly in Coliumo Bay area impacted the most), and to greater spatial-temporal genetic structure caused by drift and gene flow. Our results suggest that continuous genetic monitoring is needed in order to assess potential risks for Aphos porosus in light of new natural and anthropogenic disturbances.Natural disturbances can modify extinction-colonization dynamics, driving changes in the genetic diversity and structure of marine populations. Along Chilean coast , a strong hypoxic-upwelling event in 2008, and a mega earthquake-tsunami in 2010 caused mass mortality within the  Mass mortality events provoked by strong natural disturbances in marine ecosystems can modify the extinction-colonization dynamics of species, and drive changes in diversity as well as spatial-temporal genetic structures in populations, thus decreasing population sizes, modifying connectivity and, ultimately, increasing the local effects of genetic drift and gene flow4.Genetic diversity is essential to the survival, adaptability, and evolution of populations6. Several studies suggest that upwelling process, independent from hypoxic stressing conditions generated in short-time periods, can affect connectivity among local marine populations. Upwelling acts as a barrier to dispersal, resulting in growth of in genetic structure9, as observed in the Sebastes thompsoni10 fish, for instance. On the other hand, earthquakes and tsunamis can promote gene flow among populations, increasing local genetic diversity, and reducing genetic population structure. The latter was observed, for example, in the marsh plant Carex rugulosa after the 2011 mega earthquake-tsunami in Japan2. However, responses to the same disturbance can be extremely diverse among species, and will depend on the disturbance’s regime , and the life-history traits of the species affected 11.Marine species can be exposed to different regimes of natural disturbances, especially in the Southeast Pacific coasts where frequent hypoxic-upwelling events, and less frequent, but stronger, mega earthquakes and tsunamis tend to occur2 l−1 in the water column), which occurred on January 3, 2008, and caused massive mortality among pelagic and benthic organisms, mainly fish species in the Coliumo Bay13. The second disturbance was a mega earthquake-tsunami , which occurred on February 27, 2010, impacting the coast along the Central-South of Chile. This event uplifted part of the coastline by more than 3 m, generating a tsunami with waves up to 14 m that devastated extensive areas of the coastal seabed and modified benthic and coastal environments, causing a massive mortality among different species in intertidal and subtidal habitats, especially in the Coliumo Bay16. Both disturbances, presented different degrees of magnitudes throughout the affected area, creating a complex patchy landscape, where the Coliumo Bay was impacted the most, and the surrounding northern area of the Itata shelf to a lesser extent. In the Itata Shelf area, the hypoxic-upwelling event decreased the dissolved oxygen on the surface, but the values remained at normoxia levels (> 2 ml O2 l−1) on the sea floor. In this area, the tsunami during the mega-earthquake was less destructive, with wave heights almost half of those in Coliumo Bay17 , two strong natural disturbances impacted an extension of approximately 500 km of coastline. The first disturbance was a hypoxic-upwelling event  was one of the species severely affected by the 2008 hypoxic-upwelling event, as well as by the 2010 mega earthquake-tsunami18. Aphos is a monotypic genus, and Aphos porosus is the only species of the Batrachoididae family present in Chile19. This benthic-demersal fish is distributed in the Southwest Pacific (3°–53°S) from Puerto Pizarro in Perú to the Strait of Magellan in Chile, and from the coast to the demersal shallow environment (0–100 m)22. Aphos porosus has a generation time frame of 3 years, and a breeding period that occurs in the austral spring–summer season. Females choose a protected site in intertidal pools and subtidal rocky areas, where they deposit up 167 eggs that will later be fertilized by a male. The male assists the female in spawning, and apparently secretes an adhesive substance that helps the eggs stick to the rock. Both parents care for the eggs, and the male protects the site by emitting grunts. Embryonic stages survive attached to the rocks until day 65, after which swimming larvae continue to be associated with the seabed23. The long generation time frame, and lack of pelagic behavior in larvae make these species highly vulnerable to natural and anthropogenic disturbances, given their low dispersal potential18.The Aphos porosus breeding season, in January and February, severely affecting the areas where the species lays eggs, and the larvae develop. Studies indicate that after the strong 2008 hypoxic-upwelling, the average Aphos porosus population density decreased drastically by 75% in the Coliumo Bay, and by 54% on the Itata Shelf, compared to 2007 before this disturbance ocurred18. 2 years later, after the 2010 mega earthquake-tsunami, the average Aphos porosus population density sharply decreased again by 83% and 64% in Coliumo Bay and Itata Shelf, respectively, compared to 2009 before this disturbance.The 2008 hypoxic-upwelling event and the 2010 mega earthquake-tsunami occurred during the Aphos porosus toadfish. To this end, we analyzed microsatellite markers of individuals from local populations after the two disturbances (2008–2015) in the Coliumo Bay and the Itata shelf, which are two neighboring areas with contrasting levels of impact  due to geomorphological characteristics  on the genetic diversity and structure of the Aphos porosus individuals were collected from the sea floor between the years 2008 and 2015 in Coliumo Bay and Itata Shelf areas mentioned above. Benthic sampling was carried out every 3 months using a modified Agassiz trawl . All samplings were carried out using R.V. Kay–Kay I and R.V. Kay–Kay II . The population density  was standardized according to the number of fish collected in each tow, in relation to the swept area24. All individuals collected were genotyped using 18 microsatellite markers, previously developed by Silva et al. (2015)25. All experimental protocols were approved by bioethical committee of the Universidad Católica de la Santísima Concepción, Chile. Project Fondecyt Regular N° 1130868. All methods used in this study were carried out in accordance with Chilean bioethical guidelines and regulations. A physical euthanasian protocol for moribund collected fish was developed according to the “Manual de Normas de Bioseguridad y Riesgos Asociados de FONDECYT-CONICYT”  and its references. No chemical product was used during physical euthanasia, and no laboratory experiments were carried out with collected fishes. Where applicable, all methods were reported in accordance with ARRIVE guidelines 2.0.A total of 565 26. Presence of null alleles and other genotyping errors were evaluated with MICRO-CHECKER v2.2.327, by comparing observed genotypes to the distribution of randomized genotypes. Deviations from Hardy–Weinberg equilibrium (HWE) and Linkage disequilibrium (LD) were evaluated for each locus by area and year using a Markov chain algorithm28 with dememorization = 1000, batches = 100, Iterations per batch = 1,000 in GENEPOP v4.7 + 30. Due to multiple comparisons, we applied a Bonferroni correction to HWE and LD p-values, with a new p-value = 0.004.Microsatellite genotyping and detection of amplification errors were performed locus by locus using the automatic procedure with manual correction in Geneious v7.131, using Number of pilot runs = 20, Burn-in length = 50,000, Number of outputted iterations = 100,000, and Thinning interval size = 10. To correct for multiple testing, the program computes q-values based on the posterior probability for each locus. q-values < 0.05 and α-values significantly > 0 suggest selection diversification, whereas q-values < 0.05 and α-values significantly < 0 suggest balancing or purifying selection.Deviations from neutral expectations were evaluated with the Bayesian method implemented in the BayeScan v2.132 according to Hartl and Clark (1989), as well as Peakall and Smouse (2012)32. Comparisons between areas, as well as before and after disturbances years (when possible) were made with the two-sample t-test, assuming unequal variances in Excel, and applying a Bonferroni correction to solve issues resulting from multiple comparisons. Effective population sizes (Ne) for both areas were estimated with NeEstimator v2.133, using Model II of the temporal method proposed by Nei and Tajima (1981)34, with a Jackknife confidence interval of 95% and PCrit = 0.020, assuming a generation timeframe of tree years according to a previous study18. The individuals collected in 2008 from Coliumo Bay were eliminated from this analysis, so as to allow for comparisons between both areas, given that the number of individuals collected in 2008 on the Itata Shelf were insufficient for genetic analysis. Thus, individuals from 2009 were considered generation 0, and whose from 2015 were deemed generation 2.Intrapopulation genetic diversity estimated over all loci by area and year were evaluated as follows: no. Alleles (Na) = number of different alleles; No. Alleles Private (Nap) = number of alleles unique to a single population; No. Alleles Common (Nac) = number of locally common alleles (frequency >  = 5%) found in 50% or fewer populations, observed heterozygosity (Ho), and unbiased expected heterozygosity (uHe). All indices were estimated in GenAlex v6.532, which provided a view of genetic differentiation between the areas under study  as well as over time (2008–2015). Then, a clustering analysis was carried out with STRUCTURE v2.335, so as to infer differences in population structure between the two areas and years. A mixed model was used, with k = 1 to k = 15, each with 10 replicates, burning = 50,000 steps, repeat = 1,000,000 steps, and LocPrior = 0. k was selected based on the highest Delta K36 value in the Structure Harvester37. Replicas of the cluster analysis were aligned by using CLUMPP v1.1.238, and plotted with DISTRUCT39. The number of genetic clusters or populations (k) inferred with STRUCTURE was then contrasted with an Analysis of Molecular Variance (AMOVA) by using No. of different alleles as a distance method (FST), No. of permutations = 999, considering 14 populations within 3 groups, where 1 = Coliumo Bay (with the years 2008–2011 nested), 2 = Coliumo Bay (with the years 2012–2015 nested), and 3 = Itata Shelf (with the years 2009–2015 nested) with Arlequin v3.540. In addition, paired FST tests were estimated among 14 populations using No. of different alleles as a distance method (FST), No. of permutations = 999, and applying Bonferroni corrections to solve issues resulting from multiple comparisons. FIS was estimated for each area and year, following the method proposed by Weir and Cockerham (1984) with GENEPOP v4.7.541.To evaluate spatial and temporal genetic structures, a Principal Coordinate Analysis (PCoA) was carried out with GenAlex v6.542, setting the following parameters: Generator seeds s = 100, iterations I = 10,000,000–100,000,000, burning = 1,000,000, sampling intervals n = 100, allelic frequencies a = 0.30, inbreeding coefficient f = 0.50 and migration rate m = 0.10. Convergence of the CMCMs were reviewed in Tracer v1.743.Gene flow between Coliumo Bay and the Itata Shelf, as well as recent migration rates per generation (m), and posterior probabilities for individual ancestral distributions of immigrants were estimated with a Bayesian method implemented on BayesASS v3.0Among the 18 loci, 5 presented null alleles and were removed from the analysis, including 118 individuals presenting amplification errors. Therefore, a total of 13 loci and 447 individuals collected between 2008 and 2015 were included in the analyses . Similarly, observed heterozygosity (Ho) and corrected expected heterozygosity (uHe) were significantly lower after 2008 (“the hypoxia year”) and 2010 (“the mega earthquake-tsunami year”), compared to 2013, 2014, and 2015 (p < 0.004). On the Itata Shelf, the number alleles (Na) was significantly lower after 2010 (“the mega earthquake-tsunami year”) compared to 2009 (p < 0.004) (Table IS) were negative for all the years under focus (Table Ne = 17.4 (95% CI 10.0–29.9) and Ne = 19.1 (95% CI 11.0–34.1) for Coliumo Bay and the Itata Shelf, respectively (see Supplementary Table Genetic diversity of k = 3 for (1) Coliumo Bay years 2008–2011, (2) Coliumo Bay years 2012–2015, and (3) Itata Shelf all years    Coliumo Bay years 2008–2012, (2) Coliumo Bay years 2013–2015, and (3) Itata Shelf all years . Among populations within groups = 3.52%; (FSC = 0.036 p < 0.05), and between individuals within populations = − 27.27%  , but extremely small effect sizes (0.011–0.077). Genetic differentiation between Coliumo Bay and the Itata Shelf was higher in 2012 and 2013 . The greatest difference within Coliumo Bay was observed between 2008 (“the hypoxia year”) and samples collected in 2013 and 2015 . On the Itata Shelf, the greatest difference was observed between 2012 and 2013 (FST = 0.039), but in general, inter-annual differences were smaller compared to Coliumo Bay  Coliumo Bay (years 2008–2011), (2) Coliumo Bay (years 2012–2015), and (3) Itata Shelf  (FGene flow between Coliumo Bay and the Itata Shelf during the years after 2008 (“the hypoxia year”), was asymmetric from Coliumo Bay to the Itata Shelf. In Coliumo Bay, the rate of immigrants per generation from the Itata Shelf was nearly zero, except in 2013, where it increased to 0.3. On the Itata Shelf, the rate of immigrants per generation from Coliumo Bay was equal to 0.3, except for 2013 when gene flow decreased to 0  where spatial arrangements of local populations can modulate both demographic and genetic changes45. Considering this, we evaluated the effects of the strong 2008 hypoxic-upwelling event and the 2010 mega earthquake-tsunami on the genetic diversity and genetic structure of the Aphos porosus toadfish in two nearby areas, namely Coliumo Bay and the Itata Shelf, which were affected by the two disturbances to different magnitudes of impact. Results showed that genetic diversity and effective population size of Aphos porosus were lower after the two disturbances, mostly in the Coliumo Bay area, which was impacted the most, and exhibited greater spatial–temporal genetic structure and asymmetric migration during the years analyzed.Natural strong disturbances, including hypoxic-upwelling events, earthquakes, and tsunamis, have resulted in mass mortality among marine populations, increasing drift strength and affecting gene flow, causing the loss of genetic diversity as well as changes in population structureAphos porosus, and particular oceanographic characteristics of the inhabiting areas can increase the probability of genetic isolation and structure, limiting the geographical distribution of this species46. The latter in combination with the impact from the disturbances, could explain the genetic diversity pattern observed. Population differentiation occurred despite the short distance between Coliumo Bay and the Itata Shelf, as suggested by the different approaches used in this study. Thus, a combined effect between ecological life history traits of the species, oceanographic conditions, and environmental stressors could explain the spatial structure pattern observed.Biological and ecological traits of Aphos porosus larvae have limited dispersal potential, and adult mobility is also restricted, similar to what has been observed for Opsanus tau, which is another Batrachoididae48. Additionally, the literature suggests temporal reproductive isolation for Aphos porosus populations on the latitudinal gradient, which is aligned with what has been observed in other regions of Chile23. For instance, previous studies in Chile have shown gaps of 1–2 months in the reproductive season of this species, specifically in populations in Valparaiso , and Coliumo Bay , which are areas separated by approximately 500 km23.Firstly, 49, which is a physical process that occurs regularly in the geographical area of south Pacific, can act as a hydrodynamic force, affecting connectivity among local populations, acting as a physical barrier to dispersal, and promoting population genetic structure, as observed in Merluccius capensis9, Epinephelus andersoni7, and Pomacanthus maculosus8 fishes in other upwelling areas, for instance. Thirdly, the 2008 hypoxic-upwelling, and the 2010 mega earthquake-tsunami caused a drastic decline in the densities of Aphos porosus populations (> 60%)18 both in the Coliumo Bay and Itata Shelf areas, and in a short time period . This led to lower genetic diversity and a greater spatial-temporal genetic structure, similar to the effects of a bottleneck50. Low dispersal from the Itata Shelf to Coliumo Bay, as inferred from our estimates of gene flow, could generate temporal reproductive isolations, and genetic differentiations between both areas. Consequently, compounding factors including a generation timeframe of 3 years, spatial reproductive gaps, and oceanographic barriers could explain the population differentiation observed in these nearby areas, as well as their dissimilar responses to the same major disturbances51.Secondly, from an oceanographic perspective, studies have suggested that upwellingAphos porosus population density in Coliumo Bay was sixty times higher than in the Itata Shelf area before the 2008 hypoxic-upwelling, which suggests that Coliumo Bay could be used as a nursey area for different fish species, including A. porosus18. Population density decreased in Coliumo Bay and the Itata Shelf after both disturbances. Differences between these areas before and after the disturbances could explain the asymmetries observed in migration and spatial genetic structure. Because genetic drift tends to be higher in smaller populations, the Itata Shelf could be more strongly impacted by drift, generating a differentiated population from Coliumo Bay due to rapid fixation of alleles, and the rapid loss of private alleles. Genetic structure has also been observed in the Poricthys notatus toadfish, the sister genus of Aphos, along the Pacific coast the US, as well as in Opsanus tau and O. beta, other Batrachoididae species along the Western Atlantic coast of the US51.Previous studies showed that 34. Studies on species affected by mega earthquakes and tsunamis reveal that populations with high densities and high larval dispersal are key to maintain genetic diversity, as occurred with the snail Batillaria attramentaria and the sea urchin Mesocentrotus nudus after the 2011 Japan’s earthquake53. Other genetic studies suggest that disturbances like earthquakes and tsunamis can enable the colonization of new lineages55 from less affected areas. For example, another toadfish species from the genera Porichthys and Opsanus have been observed to raft on logs for 25 miles during the summer period, and on sponges during stormy times57. Moreover, all samples and years revealed evidence of negative FIS values, which signals outbreeding that could be explained on the Itata Shelf by the high number of immigrants observed from Coliumo Bay. Heterozygosity increased by 10% in Coliumo Bay after 2011, which together with negative FIS values could indicate immigration from an unsampled population, but might also signal the effects of genetic drift resulting from the disturbances. The latter is supported by the high population differentiation observed after 2011.A temporal population substructure was only observed in Coliumo Bay, which may have been related to the stronger intensity of both disturbances in this area. Coliumo Bay exhibited greater genetic differentiation between 2008 (hypoxic-upwelling year) and the years 2013 and 2015 (after the mega earthquake-tsunami). Inter-annual substructures observed in Coliumo Bay may reflect the increase in drift and gene flow after disturbances, probably due to the seasonal (Summer-Autumn) recruitment of new cohorts and immigrants from neighboring localities with new alleles. Unfortunately, this hypothesis could not be corroborated, given that other surrounding areas were not genetically sampled, and the method used in this study did not include migration from unobserved populationsAphos porosus populations, for instance, HWE deviations and evidence of LD, which in general were higher in Coliumo Bay compared to the Itata Shelf. Deviations from HWE and LD can appear to be consequences of immigrants strongly differing genetically from local populations. This could be due to mixing and finite population abundances with higher frequencies of heterozygotes than expected, but it might also be due to substructure62.Drift, immigration and substructure could have also caused another pattern observed in Agarophyton chilense showed a population decline and a loss of 10–40% in allelic diversity, recovering 2 years post disturbance due to migration63. The intertidal crustaceans Emerita analoga and Excirolana hirsuticauda showed low haplotype diversity after the 2010 mega earthquake-tsunami, followed by a recovery after 3 years. As a consequence of this disturbance, E. analoga revealed a lower genetic differentiation and increased gene flow post disturbance, probably due to its high larval dispersal potential. Unlike Agarophyton chilense or E. analoga, Aphos porosus did not show recovery in genetic diversity nor density population after 5 years of the 2010 mega earthquake-tsunami. The low dispersal potential, together with the generational timeframe of 3 years, could limit their ability to recover fast in impacted areas51. E. hirsuticauda showed a slight increase in population differentiation, and few changes in gene flow. In this case recovery was associated with adult survival and rapid colonization, due to the strong swimming capacity of juveniles and adults11. Similar to what was observed for E. hirsuticauda, Aphos porosus showed an increase in genetic structure in Coliumo Bay after the 2010 mega earthquake-tsunami, which could probably be explained by the combined effect of increasing drift, recruitment of new cohorts from survivors, and asymmetric immigration from neighboring areas introducing new alleles.The effects of the 2010 mega earthquake-tsunami on genetic diversity indices have been studied in other organisms, suggesting that not all species were affected similarly. For instance, the red seaweed 64. Hence, the response of each species to the same disturbance could be extremely diverse, as shown previously64. Studies suggest that species with high population abundances, and high dispersal or mobility have higher resilience capabilities51. For Aphos porosus, which is a species with low dispersal potential, there are no previous studies regarding its genetic diversity and structure in its entire geographic distribution range, and there is a lack of information about the mobility capacity of adults and juveniles. Our results suggest that after the two disturbances, local populations inhabiting Coliumo Bay and the Itata Shelf decreased in genetic diversity and increased in spatial–temporal genetic structure caused by drift and gene flow.The effects of disturbances on genetic diversity depend on both the disturbance regime and life history traits of each species, which as a whole determine the resilience capacity of the populations to resist and subsequently recoveryAphos porosus population responded sequentially after the two major natural disturbances considered in this study, deferring along short spatial distances with different intensities of impact. These results gain importance under a global perspective, given that natural disturbance regimes such as hypoxic-upwelling events are expected to increase due to climate change66. Results suggest that monitoring for the conservation and maintenance of genetic diversity should be an essential management priority for ensuring the future resilience and adaptive potential of marine populations worldwide68.Several studies have examined how marine species can be affected by hypoxia and earthquakes separately, but not by the combined effect of both disturbances. Here, we focused on both types of disturbances, and provided evidence on how the Supplementary Information.


Moreover, rES reduced genetic diagnostic costs by 1.5% (€85 per neonate).The introduction of rapid exome sequencing (rES) for critically ill neonates admitted to the neonatal intensive care unit has made it possible to impact clinical decision-making. Unbiased prospective studies to quantify the impact of rES over routine genetic testing are, however, scarce. We performed a clinical utility study to compare rES to conventional genetic diagnostic workup for critically ill neonates with suspected genetic disorders. In a multicenter prospective parallel cohort study involving five Dutch NICUs, we performed rES in parallel to routine genetic testing for 60 neonates with a suspected genetic disorder and monitored diagnostic yield and the time to diagnosis. To assess the economic impact of rES, healthcare resource use was collected for all neonates. rES detected more conclusive genetic diagnoses than routine genetic testing , in a significantly shorter time to diagnosis (15 days (95% CI 10–20) vs. 59 days (95% CI 23–98, Conclusion: Our findings demonstrate the clinical utility of rES for critically ill neonates based on increased diagnostic yield, shorter time to diagnosis, and net healthcare savings. Our observations warrant the widespread implementation of rES as first-tier genetic test in critically ill neonates with disorders of suspected genetic origin.The online version contains supplementary material available at 10.1007/s00431-023-04909-1. Genetic disorders are frequent causes of neonatal morbidity and mortality, and disease presentations are often undifferentiated at birth . Since gApproximately 2.5% of newborns is admitted to neonatal intensive care units (NICU) in the Netherlands . The preBesides clinical implications, implementation of improved diagnostic tests can also have health economic impact. Gonzaludo et al.  have shoBased on several studies showing that rES provides a faster diagnosis, enabling timely precision medicine aiming to decrease morbidity and mortality of infants with genetic disorders , 11–13, We performed a multicenter prospective parallel cohort study, in which we assessed the clinical utility of rES compared to conventional genetic testing, i.e., routine genetic testing/the genetic trajectory based on decisions of the clinicians. In order to compare these two genetic trajectories, all study participants received both conventional genetic testing and rES in parallel Fig. . This stThis study was approved by the Medical Research Ethics Committee Arnhem/Nijmegen under file number 2016–2486/ NL57511.091.16.We studied 60 neonates admitted to a NICU in five out of ten centers in the Netherlands between May 2017 and January 2019. This sample size was calculated based on a two-sided chi-square test, using a power of 80% and a significance level of 0.05. The diagnostic yield of routine genetic testing was estimated to be 5% and 25% for rES. The sample size calculating resulted in a minimum of 55 patients, with a significance level of < 0.001. Taking into account a possible drop out of 10%, during the course of the study, 60 patients were recruited. Criteria for inclusion were a postnatal age less than 3 months at presentation and high suspicion of a genetic disorder . In addition, EDTA blood samples of both biological parents were required for participation. Exclusion criteria were a previously  established genetic diagnosis or a clinically phenotype highly associated with trisomy 13, 18, 21, or monosomy X. Parents were informed about the study by the attending clinician, in consultation with a clinical geneticist, if routine genetic testing was indicated. Written informed consent was obtained for all participating families.Clinical features were scored using the human phenotype ontology (HPO) terms  Supplem. CA wereThe rES procedure was performed in the ISO15189 accredited genetic diagnostic laboratories affiliated to the five NICUs. Whereas minor technical differences exist between centers, such as, for instance, different enrichment kits or sequencing equipment used, the overall procedures were similar –19. ImpoFor each neonate, we monitored the routine genetic diagnostic trajectory . In contrast, the TTD was only determined for neonates who received a conclusive diagnosis. The latter was chosen as this reflects an objective end point of genetic diagnostic care for index patients. The TTD was measured from the moment the first genetic test was requested by the involved clinician until the return of the final conclusive genetic diagnostic report. Of note, in case ES was request in the conventional diagnostic strategy, only the rES was performed, and the TAT of routine ES was censored to overcome the need to perform the ES procedure twice. To determine this censored TAT of ES in conventional diagnostic testing, the TAT of ES was determined from a random, anonymized, set of individuals, unrelated to this project, but equal in size of the number of neonates receiving rES. This resulted in a TAT for ES of 105 days (95% CI 96–113 days).Healthcare consumption was collected from the electronic patient file of 56/60 patients. For four neonates, these data could not be collected, and therefore, these patients were excluded from the cost analyses. Healthcare activities were linked to their unit prices (index year 2020) retrieved from the Dutch Healthcare Authority (Nederlandse Zorgautoriteit (NZA)) and the National Healthcare Institute (Zorginstituut Nederland (ZiN)) , 22, aftFor all healthcare data, average costs per neonate were calculated, including the minimum, maximum, and median. Costs were divided into seven categories based on their type of healthcare activity: diagnostics, hospitalization, consult, surgery, medicines, genetic, and other healthcare costs. Per category, a percentage of total healthcare costs was calculated. For the category “genetic costs,” the types of genetic tests performed were also retrieved to provide more detailed insight to the build-up of costs associated with genetic testing.Next, two cost analyses were performed, firstly the economic impact of implementation of rES compared to the conventional genetic testing, and secondly, the evaluation of the timing of expenditure for genetic testing, differentiating between costs made during the neonatal period (first 28 days starting at birth) and costs during the post-neonatal period with a maximum follow-up period of 2 years after birth.t test was performed to analyze whether the difference in TAT and costs between the two diagnostic trajectories were significant.Data analysis of the clinical variables was performed in Excel (version 2016), and the cost analysis was performed in R (version 4.0.3) . In morep < 0.001), as expected given our study purpose and the relation between genetic disorders and CA [A total of 60 newborns admitted to the NICU with clinical features suggestive for a possible genetic disorder were enrolled in this study. Demographic information is presented in Table s and CA . PatientFor all 60 neonates, the genetic diagnostic trajectory started in the neonatal period, at an average age of 8 days (95% CI 6–10 days). From this start point onwards, the neonates received both routine genetic testing and rES in parallel, allowing to directly compare outcome measures as neonates served as their own controls.In routine genetic testing, a total of 112 genetic tests were performed, resulting in an average of 1.87 tests per patient , providing higher diagnostic yield than the routine genetic tests  reduction in genetic costs by 1.5% . We subsequently aimed on gaining insight into the timing of healthcare utilization and associated costs  were substituted by the costs of trio-based rES Supplementary file2 (XLSX 19 KB)Supplementary file3 (XLSX 16 KB)Supplementary file4 (XLSX 16 KB)Below is the link to the electronic supplementary material.


N = 160,824), problems , and dependence , alcoholic drinks per week , suicide attempt , impulsivity , and extraversion . We used genomic structural equation modeling (Genomic SEM) to, first, estimate a common factor model with alcohol consumption, problems, and dependence, drinks per week, and SA included as indicators. Next, we evaluated the correlations between this common genetic factor and five factors representing genetic liability to negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance. Common genetic liability to ACP and SA was significantly correlated with all five impulsive personality traits examined , and the largest correlation was with lack of premeditation, though supplementary analyses suggested that these findings were potentially more strongly influenced by ACP than SA. These analyses have potential implications for screening and prevention: Impulsivity can be comprehensively assessed in childhood, whereas heavy drinking and suicide attempt are quite rare prior to adolescence. Our findings provide preliminary evidence that features of impulsivity may serve as early indicators of genetic risk for alcohol problems and suicidality.Heavy drinking and diagnosis with alcohol use disorder (AUD) are consistently associated with risk for suicide attempt (SA). Though the shared genetic architecture among alcohol consumption and problems (ACP) and SA remains largely uncharacterized, impulsivity has been proposed as a heritable, intermediate phenotype for both alcohol problems and suicidal behavior. The present study investigated the extent to which shared liability for ACP and SA is genetically related to five dimensions of impulsivity. Analyses incorporated summary statistics from genome-wide association studies of alcohol consumption ( Alcohol use disorder (AUD) and suicide attempt (SA) represent substantial public health concerns. In the United States alone, the annual costs attributable to alcohol consumption and SA have been estimated as $234 billion  and $5.2There are several possible explanations for the association between AUD and suicidal behavior. The relationship of alcohol consumption and problems (ACP) with SA may be causal, such that alcohol use increases risk for suicidal ideation and attempt, SA results in escalation of ACP, or both. Another possible, and non-mutually exclusive, explanation is that associations between ACP and suicidal behavior are non-causal, attributable to shared environmental factors and/or overlapping genetic influences. AUD and SA show substantial heritability –8 and arOnly one study, to date, has evaluated potential explanations for the genetic overlap between substance use disorders (SUDs) and suicide-related outcomes . In thisIdentifying shared endophenotypes is one important step toward understanding the observed genetic correlation between alcohol-related outcomes and SA . To be cThe extant evidence suggests that impulsivity meets at least some of the criteria for an endophenotype. However, when evaluating the relationships among impulsivity, ACP, and suicidal behavior, it is critical to consider what is meant by “impulsivity.” This term has been applied to a heterogeneous amalgamation of constructs related to rash action, behavioral disinhibition, novelty-seeking, and inattention. As a result, studies of “impulsivity” may actually be studying different constructs, which impedes our understanding of the associations among impulsivity, ACP, and suicidality.Efforts to integrate varying conceptualizations of impulsivity –26 have A recent genome-wide association study (GWAS) indicates that negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance are partially distinct at the genetic level, as well. Sanchez-Roige et al.  conducteThe present study built upon genetic analyses of impulsive personality traits , 28 and Analyses incorporated summary statistics from previously published GWAS of alcohol consumption , 34, proN = 160,824) , an = 0.78)  and thos = 0.78) . Conditi = 0.78) , impleme = 0.78) . This me, an = 0.s et al.  GWAS of Neff = 4υ(1 − υ)n, where υ is the sample prevalence and n is the original sample size [Neff = 26,853 for alcohol dependence and Neff = 74,887 for SA.Next, we used the munge function in the GenomicSEM R package  to pre-pple size . EffectiAfter preparing the summary statistics files, we computed liability-scale heritability estimates and pairwise genetic correlations between traits using LD Score regression .Cg). A Comparative Fit Index (CFI) greater than 0.90 and Standardized Root Mean Squared Residual (SRMR) less than 0.10 were considered as evidence for acceptable model fit [We used Genomic SEM  to invesodel fit , 53.Cg) and these five dimensions of impulsivity . P < 0.05 was used as the threshold for statistical significance.Next, we performed a confirmatory factor model to reproduce a prior analysis of the genetic architecture of impulsivity , which prg = 0.95), they were included as indicators of a latent genetic factor for alcohol consumption to avoid issues with multicollinearity. We specified separate models for UPPS-P negative urgency, UPPS-P positive urgency, UPPS-P lack of premeditation, UPPS-P sensation-seeking, UPPS-P lack of perseverance, the BIS total score, and extraversion.To evaluate whether genetic correlations with impulsive personality traits were driven by variance specific to any one phenotype , we also conducted a series of genetic multivariable regression analyses . Univarirg = 0.54–0.95, SE = 0.05–0.08). Among the alcohol-related outcomes, suicide attempt exhibited the highest genetic correlation with alcohol dependence . Genetic correlations among impulsive personality traits ranged from rg = −0.28 (SE = 0.15) for negative urgency and extraversion to rg = 0.79 (SE = 0.35) for lack of premeditation and the BIS total score, underscoring that the dimensions of impulsive personality proposed by the UPPS-P have distinct genetic underpinnings.Liability-scale heritability estimates are presented in Table Cg). A common factor model provided good fit to the data (χ2(5) = 33.82, p < 0.0001, AIC = 53.82, CFI = 0.980, SRMR = 0.095). SA exhibited the lowest loading on the common genetic factor . However, the factor loading for SA was statistically significant, suggesting that a significant proportion of the genetic variance in SA is accounted for by Cg. The factor loadings were estimated as 0.93  for AUDIT-C, 0.84  for AUDIT-P, 0.65  for alcohol dependence, and 0.97  for drinks per week  = 7.55, p = 0.5800, AIC = 45.55, CFI = 1.000, SRMR = 0.072). We then specified the primary model (χ2(44) = 144.58, p < 0.0001, AIC = 212.58, CFI = 0.965, SRMR = 0.105), which estimated genetic correlations between Cg, negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance. Though the SRMR was greater than 0.10, the high CFI value provided evidence to support adequate model fit. In this model, shared genetic liability to ACP and SA was genetically correlated with all five dimensions of impulsivity. Cg was most highly correlated with lack of premeditation, followed by positive urgency, lack of perseverance, sensation-seeking, and negative urgency , with AUDIT-C, drinks per week, AUDIT-P, and alcohol dependence included as indicators, and a suicide-specific latent factor (SUICg), with SA included as a single indicator. This alternative model also provided good fit to the data (χ2(39) = 142.86, p < 0.0001, AIC = 220.86, CFI = 0.964, SRMR = 0.099). Parameter estimates are shown in Fig. g with negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance were very similar, but did differ slightly, from the correlations between Cg and impulsive personality traits in the primary model. The suicide-specific latent factor was most highly correlated with lack of premeditation  and negative urgency , though these associations did not reach statistical significance.We conducted two supplementary analyses to further explore findings from our primary model. First, because SA exhibited a low factor loading on rg = 0.40–0.44) than consumption (rg = 0.21–0.21), we specified a follow-up model with AUDIT-C and drinks per week removed from the common genetic factor. This model provided good fit to the data (χ2(25) = 50.32, p = 0.0002, AIC = 110.32, CFI = 0.940, SRMR = 0.091). Compared to Cg in the primary model, a common genetic factor for alcohol problems and SA was more highly correlated with negative urgency (rg = 0.36 versus 0.24), positive urgency (rg = 0.50 versus 0.39), lack of premeditation (rg = 0.68 versus 0.53), and lack of perseverance (rg = 0.32 versus 0.30). Conversely, the correlation with sensation-seeking was smaller in magnitude (rg = 0.14 versus 0.26) and statistically non-significant , AUDIT-P, alcohol dependence, and SA as predictors, and alternately treated the genetic components of UPPS-P negative urgency, UPPS-P positive urgency, UPPS-P lack of premeditation, BIS total score, UPPS-P sensation-seeking, extraversion, and UPPS-P lack of perseverance as the outcome. As shown in Fig. In the current study, we investigated associations between shared genetic liability for ACP and SA and multiple correlated dimensions of impulsivity. A common genetic factor for alcohol consumption, alcohol problems, and suicidality was positively correlated with all five impulsive personality traits examined. However, the magnitude of these associations varied, such that shared genetic liability for ACP and SA was most strongly correlated with lack of premeditation. Our findings are consistent with prior work supporting shared genetic influences across ACP and suicidality , 10, 54.One question motivating the present work was whether impulsive personality traits may be considered as candidate shared endophenotypes for ACP and SA. Our findings suggest that shared genetic liability for ACP and SA overlaps with all five traits proposed by the UPPS-P Model of Personality: , 26 negaHowever, because the present analyses focused only on the genetic relationships among ACP, SA, and impulsive personality traits, additional work is needed before recommendations for prevention and intervention are warranted. First, studies should confirm that shared genetic liability for ACP and SA is associated with phenotypic levels of impulsivity across development. Second, further research is needed to distinguish whether the associations between ACP, SA, and impulsive personality traits are consistent with an endophenotype mediational model or an endophenotype liability-index model. Endophenotype mediational models propose that genetic influences on a trait or disorder are partially or fully mediated through the endophenotype, whereas endophenotype liability-index models suggest that a common set of genes are associated with the endophenotype and the disorder . ImportaCg) was replaced by an alcohol-specific latent factor (ACPg) and a suicide-specific latent factor (SUICg). As observed for Cg in the primary analysis, ACPg was significantly genetically correlated with all five impulsive personality traits. Conversely, none of the genetic correlations between SUICg and facets of impulsivity were statistically significant. In addition, ACPg and SUICg differed somewhat in their genetic associations with features of impulsivity. For example, genetic liability for ACP was more strongly correlated with lack of perseverance and sensation-seeking, whereas genetic liability for SA showed a larger genetic correlation with negative urgency. This supplementary analysis suggests that results from our primary model are potentially more strongly influenced by ACP than SA.We also specified an alternative model in which common genetic liability for alcohol consumption, alcohol problems, and SA  and an impulsivity-related construct unique of the other predictors in the model.Finally, though our primary aim was to characterize associations of overlapping genetic influences on ACP and SA with negative urgency, positive urgency, lack of premeditation, sensation-seeking, and lack of perseverance, we also performed genetic multivariable regression analyses to contextualize the primary results and further describe the genetic relationships among ACP, SA, and impulsivity. Our primary factor model and the genetic multivariable regression models address complementary research questions: The main analyses assessed the degree to which In these analyses, the conditional standardized associations of ACP and SA with impulsive personality traits were largely non-significant, underscoring that the genetic relationships with impulsive personality traits are driven by genetic factors shared by ACP and SA. Nonetheless, some tentative insights may be drawn based on the magnitude of the associations. For example, the conditional standardized associations of alcohol consumption and dependence with negative urgency were small and negative , which limited our statistical power and contributed to large 95% confidence intervals. Second, because Genomic SEM requires ancestrally homogeneous samples [However, our findings should also be considered in light of several limitations. First, the largest available GWAS of impulsive personality traits had a relatively small sample size ( samples , and the samples , the conFourth, the present study focused on self-report measures of impulsive personality traits, though laboratory tasks are also widely used to assess impulsivity-related constructs. The correlations between self-report and task-based measures of impulsivity are small, and self-report scales and laboratory tasks are uniquely associated with risk behaviors . TherefoFifth, the present analyses focused on genetic liability for alcohol consumption and problems. Use and dependence on tobacco, cannabis, and other illicit substances have also been associated with risk for suicidality –65. ThouIn summary, we found that shared genetic liability among alcohol consumption, alcohol problems, and SA is positively correlated with the genetic components of multiple dimensions of impulsivity. These analyses have potential implications for screening and prevention: Impulsivity can be comprehensively assessed in childhood –71, wherSupplementary Information


RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data. RGStraP is a computational pipeline to estimate RNA-seq-based genetic principal components and assess whether these can be used to control for population structure in gene expression analyses. Its application extends to the clinical setting, allowing us to further elucidate complex diseases and identify prospective biomarkers in both communicable and non-communicable diseases3.RNA sequencing (RNAseq) has revolutionized our understanding of the transcriptome, offering both accurate quantification method for gene expression as well as identification of specific alternative splicing sites and cell-type-specific transcripts7. To overcome this issue, researchers have typically relied on genome-wide array or whole genome sequence (WGS) data matched for the same individuals with RNAseq. This allows researchers to deploy approaches to control for population stratification, such as the calculation of genetic principal components (PCs) and their use as covariates in subsequent statistical association models10. The genetic PCs are taken to represent the latent genetic structure within and between populations, which introduce confounding due to differences in social environment11 or  due to heterogeneity of quantitative trait loci between groups. However, the need for genome-wide array or WGS to match with RNAseq data is potentially unnecessary and indeed may not be possible in settings where resources are limited, such as Low and Low Middle Income Countries (LMICs) with highly diverse and understudied populations.Yet, studies using RNAseq rarely consider the germline genetic variation also contained within RNAseq read sets. Studies which do not leverage this information may be vulnerable to bias and confounding, such as population stratification, which may affect transcription between groups14. The approach of utilizing RNAseq data to capture genetic structure has been applied for livestock and agricultural purposes18, for example to investigate the population structure, history and adaptation of domesticated barley (Hordeum vulgare)17. While proof-of-concept and subsequent utility of RNAseq-based genotypes have been demonstrated such as for tissue-specific variants19, its application to infer human population structure shows promise yet remains relatively underexplored20.It has been demonstrated that genotype calls can be made from RNAseq data using tools such as GATK22. We developed an RNAseq analysis pipeline (RGStraP) to calculate genetic principal components directly from RNAseq data, then validated RGStraP’s performance with genome-wide array genotype data from the same Nepalese individuals. We also tested the pipeline on samples from the Geuvadis consortium, which contains 465 samples with paired genotype-RNAseq data from five of the 1000 Genomes populations23. Finally, we show the validity of adjusting for RG-PCs in an association analysis to identify sex-specific gene expression. Overall, our study establishes that human population structure, particularly from an understudied but diverse population, can be effectively captured and controlled directly using RNAseq data.The aims of this study are to (i) demonstrate that RNAseq-based genotypes can capture the genetic population structure of a diverse yet understudied human population, and (ii) show that use of RNAseq-based genetic principal components (RG-PCs) can effectively control for population structure in association analysis. Here, we recruited and generated whole blood RNAseq data of 376 individuals from Nepal, a landlocked country situated in the Himalayas with over 125 ethnic groupshttps://github.com/fachrulm/RGStraP)24.In this study, we constructed the RGStraP pipeline to calculate RG-PCs from genetic variants called from RNAseq data. RGStraP relies on GATK for its variant calling suite, as well as PLINK and flashPCA to filter the SNPs and calculate genetic principal components from them, respectively (Methods). We make RGStraP available to the community via github . The cohort included individuals with and without confirmed S. Typhi infection; for the purpose of this study, the disease groups were used as a regression covariate to adjust for gene expression during downstream analyses. Self-reported ethnicity showed individuals belonging to 6 broad ethnic / caste groups.ρ = 0.487) with the total genetic variants called per sample , linkage disequilibrium (LD) and the use of a pre-specified set of genetic variants. We found that the selection of a MAF threshold of >0.05 and a pairwise LD threshold of ρ = 0.1926; Supplementary Fig. Among the 280 samples with matched array and RNAseq-based genotypes, we found 7343 overlapping SNPs between the MAF-filtered RNAseq and array SNP sets based on their exact chromosome positions. Genetic concordance was then calculated from the common SNPs based on matching allele genotypes for each position, taking into consideration strand flipping. Most RNAseq samples were found concordant with their respective paired array genotypes, with mean concordance of 0.925 for all samples and 232 sample-pairs (82.8%) having concordance higher than 0.90 Fig. . We foun25 offered higher quality genotype calls and improved correlation between RNAseq and array-based genetic PCs , followed by RG-PC3 and PC2 from array data (|ρ| = 0.61) and RG-PC2 and PC3 from array data (|ρ| = 0.6)  did not represent the genetic structure found in the array-based PCs, and meaningful correlation was only found from RG-PC3 and on . Clustering by each population can be seen in the main PCs from both paired array and RNAseq data: PC1 separates the European (EUR) and African (AFR) samples, while PC2 separates the EUR samples, with more distinct clusters seen for FIN and TSI samples  reaching a cumulative proportion of shared variance of 0.998 , Utah residents with Northern and Western European ancestry (CEU), Finnish in Finland (FIN), Toscani in Italy (TSI), and Yoruba in Ibadan, Nigeria (YRI)998 Fig. .Fig. 4RGTo project the Nepalese samples into PC space with other populations, we performed PCA of the Nepalese and Geuvadis samples together. Akin to the array PCs, results from RGStraP were able to distinguish the broad ancestry groups; RG-PC1 was able to separate between AFR and other samples, whereas RG-PC2 distinguished the EUR and Nepal  samples Fig. . SeparatTo assess the extent to which genetic PCs control population stratification in differential gene expression analysis (DGE) of these individuals, we performed DGE on the 280 Nepal samples to identify sex-specific gene expression, with and without adjustment for genetic PCs using either array PCs or RG-PCs (Methods). Prior to using edgeR for DGE analysis, we filtered out lowly expressed genes based on counts per million (CPM > 0.05) to account for differences in sequencing depths. Only autosomal genes were included in the analyses.p-value < 0.05) and 325 (FDR < 0.05) genes were differentially expressed when adjusting only for age and disease groups, whereas the number declined after adjusting with genetic PCs; inclusion of array PCs reduced the numbers of differentially expressed genes to 2585  and 144 (FDR < 0.05), while the number declined to 2778  and 272 (FDR < 0.05) when including RG-PCs. The majority of the differentially expressed genes identified without considering genetic PCs were still found after including either array PCs or RG-PCs . The majority of DE genes were shared between the results using array PCs and RG-PCs . When taking into consideration log-fold change, 4 genes passed the filter  in the set without considering genetic PCs, and the number decreased to 3 when including either array or RG-PCs. This demonstrates how RG-PCs control for population stratification in downstream RNAseq analysis similar to the genetic PCs calculated from paired array genotypes, reducing significant associations that reflected variations in population structure instead of the biology of interest.A systematic reduction was evident when adjusting for population structure in DGE analysis. A total of 3038 (m); comparing the ratio of medians of the chi-square statistics between DGE results without genetic PCs and with RG-PCs as covariates, we found a slight systematic reduction of test statistics after including RG-PCs (m = 0.935). A similar reduction is also found when using array PCs as covariates   plots, with attenuated test statistics across the transcriptome in the analysis with RG-PCs Fig. . This is85) Fig. .Fig. 5Quhttps://github.com/fachrulm/RGStraP) for the wider research community.Population structure is typically captured via genotype arrays, which are not always done for projects focused on gene expression analyses. Genotyping may not be practical when resources are limited, or when analyzing existing and/or publicly available RNAseq datasets which nearly always do not offer access to original samples. In this study, we demonstrated how SNPs acquired solely from RNAseq variant calling were able to capture population structure comparable to the results from array data. We further showed that RNAseq-based genetic principal components (RG-PCs) were able to control for population structure in differential gene expression analysis, and that mixed linear model analysis using a genetic relatedness matrix based on RNAseq genotype calls was able to achieve similar. To facilitate the use of RG-PCs, we also construct and make freely available the tool RGStraP  with highly diverse and structured populations or for traits where fine-scale genetic population structure may be confounding.25. These rare, non-transcribed variants are mostly linked to increased risk of various diseases and are enriched within expressed quantitative trait loci (eQTLs)28.Our study has several limitations. The main challenge of constructing genetic PCs from RNAseq variant calling results is how to properly curate the SNPs to include, as MAF and LD filtering are not enough to remove uninformative SNPs, resulting in the main PCs not corresponding to self-reported ethnicity. This was addressed by using the overlapping SNPs between the RNAseq variant calling results and HapMap3 variants, allowing us to acquire a set of well-established SNPs; however, this SNP set may still miss important genetic structure, e.g. low-frequency and rare variants. Due to the nature of RNAseq platforms only capturing variants in transcribed regions, rarer variants that fall in crucial functional regions such as promoter and enhancer regions go unaccounted for19. However, the approach heavily relied on genotype imputation using a reference panel that remains predominantly European, rendering the approach still suboptimal for eQTL analysis in other populations, especially those that are understudied. Thus, the method described in our study should not be utilized as a one-to-one replacement for conventional genotyping array or whole genome sequencing.Variants called from RNAseq data using a comparable GATK method have been reported to be reliable for tissue-specific eQTL mapping and allele-specific expression (ASE) analysesIn summary, we have developed an approach and tool for inferring genetic population structure directly from RNAseq data across diverse populations, then demonstrated its use in differential gene expression analysis to control for genetic structure where genotyping data are unavailable. We hope that our results enable better control for confounding in RNAseq analyses and facilitate more rigorous retrospective and meta-analyses of RNAseq data.The STRATAA study was approved by the Nepal Health Research Council  and OxTREC . All participants provided informed consent for human genetic tests. Blood and nucleic acid samples, and associated data, were de-identified by the STRATAA team in Nepal prior to being sent to overseas for analysis.30. Blood was collected from febrile participants recruited into the passive surveillance study, specifically those presenting >38.5 °C temperature or history of fever for >72 h. From the total blood sample volumes , aliquots were subjected to (i) bacteriological culture to identify presence of Salmonella enterica serovars Typhi (S. Typhi); (ii) storage in PAXgene tubes for later RNA extraction; and (iii) DNA extraction and subsequent human genotyping. Blood was also collected from healthy participants in the serosurvey , from which aliquots were also subjected to (i) serological analysis; (ii) PAXgene storage for RNA analysis; and (iii) DNA extraction.This study utilized blood samples collected in Lalitpur, Nepal as part of the Strategic Typhoid Alliance across Africa and Asia (STRATAA) study, which included passive surveillance for enteric fever and a population-based serosurveyS. Typhi culture-positive participants and 275 S. Typhi culture-negative participants from passive surveillance, and 52 healthy controls from the serosurvey. PAXgene tubes were sent to Monash University in Melbourne, Australia, where RNA was extracted using PAXgene Blood RNA Kit. An aliquot of RNA  for each sample was sent to the Wellcome Sanger Institute in Hinxton, England for sequencing. Library preparation was done using NEBNext Ultra II RNA custom kit performed on an Agilent Bravo WS automation platform, pulling down poly(A) tails. After polymerase chain reaction (PCR) cycles (14 standard cycles), plates were purified using Agencourt AMPure XP SPRI beads and libraries were then quantified using Biotium Accuclear Ultra high sensitivity dsDNA Quantitative kit. Pooled libraries were quantified using Agilent bioanalyser and normalised to 2.8 nM. Samples were then subjected to globin depletion using the KAPA RNA HyperPrep with RiboErase (HMR) Globin kit. Libraries were subjected to 2 × 100 bp paired-end sequencing using the Illumina Novaseq platform. Depending on the level of multiplexing, each library was sequenced up to three times to generate an average of up to 80 million reads per sample.We analysed RNA from 49 P value < 0.001; SNPs with a Hardy-Weinberg equilibrium P value < 1e−07 in controls; SNPs with a Hardy-Weinberg equilibrium P value < 1e−11 in cases. Samples with a call rate of <95%, those with a lower call rate sample from each sample pair having first- or second-degree relationship from identity by descent (IBD), and those that were PCA outlier samples were removed.For 299 of the Nepalese participants with RNAseq data, human DNA was successfully extracted  using the QIAamp DNA blood midi kit (QIAGEN) and DNA aliquot was shipped to the Genome Institute of Singapore for SNP genotyping. Genotyping was performed using the Illumina Infinium Global Screening Array-24 Kit (GSAMD-24v2_A1 chip). Stringent quality control filters were used to remove poorly performing SNP markers. SNPs were removed based on the following parameters; SNPs with a call rate of <95%; SNPs with differential call-rate between cases vs control 31. After extraction of total RNA with TRIzol Reagent (Ambion) and assessment of RNA quality with Agilent Bioanalyzer RNA 6000 Nano Kit, library preparation was done using TruSeq RNA Sample Prep Kit v2 for 2 × 75 bp paired-end mRNA sequencing on the Illumina HiSeq2000 platform23. The available paired genotype data was obtained from the 1000 Genomes Project Phase 3 dataset31.We also used a validation dataset from the Geuvadis consortium, which contains 465 lymphoblastoid cell line (LCL) samples from five of the 1000 Genomes populations: British in England and Scotland (GBR), Utah residents with Northern and Western European ancestry (CEU), Finnish in Finland (FIN), Toscani in Italy (TSI), and Yoruba in Ibadan, Nigeria (YRI)Computational analyses were performed on an HPC cluster with 6 nodes, each equipped with 2 × 16 cores (32 threads) CPUs and 512GB memory. It is advised to run RGStraP on a high-performance computing (HC) cluster as the alignment step alone requires a minimum RAM of 32GB. We recommend at least 100GB of memory, a multi-core processor with at least 8 cores, and ample storage depending on the number of samples processed. A workload management system such as SLURM is also recommended for better resource management.FastQC (bioinformatics.babraham.ac.uk/projects/fastqc) readouts. Illumina adapters were then trimmed using Trim Galore! , and optical duplicates were removed using bbmap’s Clumpify. We then followed GATK’s best practices pipeline for RNAseq short variant discovery, which commenced with a 2-pass mapping process to human genome GRCh38 using STAR32, from which the resulting sequencing alignment map (BAM) files from the different runs of each sample were merged using Picard33. Low quality reads (MAPQ < 20) were filtered out from the merged files using SAMtools34, followed by further QC, variant calling and filtering steps with GATK412. The analysis-ready variants were then filtered using PLINK36, removing duplicated and palindromic variants and only keeping autosomal single nucleotide polymorphisms (SNPs). We excluded individuals with more than 20% missing genotypes and further filtered RNAseq-based SNPs, after removing variants in the HLA region, based on minor allele frequency  as well as linkage disequilibrium . RG-PCs were successfully captured for 351 and 463 individuals in the Nepal and Geuvadis cohorts, respectively. The same SNP filters were also applied to the array-based SNPs, and for the SNP-level correlation analyses we converted genome annotations of the array SNPs from GRCh37 to GRCh38 (http://genome.ucsc.edu/cgi-bin/hgLiftOver). Merging of the SNP sets from the Nepal and Geuvadis samples was done using PLINK36.Quality control (QC) was done to the sequencing data according to the To calculate SNP-level concordance, we took overlapping SNPs between MAF-filtered array and RNAseq results based on chromosome position and calculated paired sample concordance based on matching genotype (considering heterozygosity and possible strand flipping) for each SNP in both results. A total of 280 Nepalese samples with RG-PCs and matching array genotype was used for this analysis.37, from which PCA plots were created with ggplot238 and Spearman correlations between the two sets of PCs were calculated using the function from the stats package of R39. We also calculated canonical correlation coefficients42 between the array PCs and RG-PCs to assess how well the latter captured genetic structure information presented by the former, as a single array PC may be represented by multiple RG-PCs.To keep only meaningful SNPs from the RNAseq variant calling results for generating genetic principal components (PCs), we took overlapping SNPs  between MAF-filtered RNAseq SNPs and HapMap3 variants to get a set of well-defined SNPs, after which LD filtering was performed on the set. We then generated genetic PCs for the LD-filtered RNAseq and array SNP sets separately using flashPCAfeatureCounts (http://bioinf.wehi.edu.au/featureCounts/), from which we kept only autosomal genes and genes with CPM > 0.05 in at least 20% of the samples from the analyses. Differential gene expression (DGE) analyses was done contrasting males and females using edgeR44, taking into account age, disease group, and sequencing batches; we ran the analyses with and without populations structure PCs as an additional covariate to then compare how genetic structure may stratify gene expression. From both results, we also plotted the Q-Q plot and calculated the systematic inflation (m), which is the ratio of the median of the empirically observed chi-squared test statistics  to the expected median chi-squared test statistics , to quantify the stratification due to population structure in gene expression data.Sequencing count data of the Nepalese samples were extracted from the aligned sequence files using 45. From the two results, a quantile-quantile (Q-Q) plot was created and systematic inflation (m) was then calculated to quantify the effect of populations structure PCs on the genomic data46.We also created a genetic relationship matrix (GRM) and ran a mixed linear model (MLM) on the RNAseq SNPs with and without population structure PCs as random effects with fastGWAFurther information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary DataReporting SummaryEditorial Policy Checklist


The Dorper sheep breed was created to thrive in harsh environments in South Africa. Two breed variants were developed in the selection process. The breed gained popularity and was exported to several regions of the world where it is reported to be doing better than the purported locally adapted sheep breeds. To enhance the performance of other native sheep breeds, Dorper is widely utilized in crossbreeding with native breeds. There has not been any research done to examine the genomic status of Dorper in South Africa and Dorper in other places outside of South Africa. The genomic architecture of white Dorper and Dorper from South Africa and Hungary are compared in this study. White Dorper, Dorper, and Dorpers from South Africa and Hungary all have significantly distinct genomes. Different environmental factors and variations in coat color could be the cause of the genetic variations.Dorper sheep was developed for meat production in arid and semi-arid regions under extensive production systems in South Africa. Two variants with distinct head and neck colors were bred during their development process. White Dorper have a white coat while Dorper have a black head and neck. Both variants have grown in popularity around the world. Therefore, understanding the genomic architecture between South African Dorpers and Dorper populations adapted to other climatic regions, as well as genomic differences between Dorper and White Dorper variants is vital for their molecular management. Using the ovine 50K SNP chip, this study compared the genetic architecture of Dorper variants between populations from South Africa and Hungary. The Dorper populations in both countries had high genetic diversity levels, although Dorper in Hungary showed high levels of inbreeding. White Dorpers from both countries were genetically closely related, while Dorpers were distantly related according to principal component analysis and neighbor-joining tree. Additionally, whereas all groups displayed unique selection signatures for local adaptation, Dorpers from Hungary had a similar linkage disequilibrium decay. Environmental differences and color may have influenced the genetic differentiation between the Dorpers. For their molecular management and prospective genomic selection, it is crucial to understand the Dorper sheep’s genomic architecture, and the results of this study can be interpreted as a step in this direction. The hardy South African composite breed Dorper was created in the 1930s by mating the Black-headed Persian and the Dorset Horn. The primary breeding goal of the Dorper breed’s founders was to develop a breed of sheep suitable for producing slaughter lambs in arid and semi-arid environments under an extensive production system. Therefore, Dorper’s founders aimed to combine the Dorset-Horn’s capacity to produce mutton with the Black-headed Persian’s hardiness ,3. It isThe breed is used in crossbreeding with native sheep in several countries to improve the performance of the offspring. For instance, Dorper×Red Maasai lambs were observed to grow at higher rates than the average of their pure parents in Kenya ,8, the DThe relationship between genotype and phenotype as well as the interaction between genotype and environment are of great interest at present. According to previous research, the environment has a significant impact on how an organism’s genetic architecture develops. While going through their process of natural selection, the organism goes through “genomic evolution” to adapt to the environment. Following domestication, the interaction between genotype and environment altered the ovine species as they inhabited diverse ecological regions and evolved into numerous breeds with varying phenotypic expressions. Recently, numerous studies reported breed eco-regional genetic variation, e.g., ,14,15. TWe contrasted the genome architecture of the Dorper populations found in South Africa and Hungary because we hypothesize that the latter may have undergone genomic evolution to survive the new temperate climate rather than the tropical climate in which they were originally developed. Therefore, our objectives were to (i) examine the genetic position of Dorpers’ within the world sheep population, (ii) assess the genomic status of both Dorper populations from Hungary and South Africa, and (iii) identify and contrast the selection signatures for each population. Alongside it, we present our well-considered view on how Africa and other developing countries might enhance the management of their domestic animal genetic resources.n = 20 for the Dorper (HUDOR), n = 20 for the White Dorper (HUWDOR), and n = 20 for the Hortobágy Racka (HORAC) breed, which served as an out-group. This study also included South African Dorper  and South African White Dorper , which were retrieved from the WIDDE [To represent breed-based populations, samples from unrelated individuals for each breed were gathered from several farms. Vacutainer tubes were used to collect blood samples for Hungarian populations  from the jugular vein. The total number of samples collected was he WIDDE  databasehe WIDDE . SADOR a−6.DNA extraction and genotyping of the Hungarian samples were done by Neogen Company. Ovine50K Illumina microarray bead chips  were proThe Arlequin software version 3.5  was usedPlink 1.09 software was used to perform principal component (PC) analysis on two separate datasets. The first dataset, dubbed “MergedGlobalOvineDataset,” was created by merging “MergedBeforeQC” with global sheep datasets examined by Kijas et al. . The goaFurther, autosomes of only four Dorper populations were used to perform an Analysis of Molecular Variance (AMOVA) and fixation index (FST) computation using the Arlequin 3.5 software . The fix2) matrix was calculated in Plink 1.09 [The linkage disequilibrium , the populations under study are significantly different  and long (>48 mbps) ROHs. Two copies of a haplotype from a common ancestor combine in an individual to form ROH. Short runs occur from haplotypes inherited from distant ancestors, while long runs are indicative of haplotypes inherited from a recent common ancestor .Since local recombination and mutation rates as well as other evolutionary processes have a significant role, the pattern and distribution of ROHs along the genome are not random or homogeneous. As a result, the development and distribution of ROHs across the genome are the results of the interaction between various factors, including recombination rate and selection pressure . In the p < 0.001), suggesting a non-negligible differentiation. The FST values found in the current study are higher than those found in Tunisian [In comparison with the global sheep dataset analyzed by Kijas et al. , the priTunisian  and MoroTunisian  sheep brTunisian  as well Tunisian .2 values were approximately 0.5, 0.3, 0.3, 0.25, and 0.2, respectively. The r2 values observed in this study are comparable to those obtained in Spanish Churra sheep [2 of at least 0.3 is a significant value for QTL mapping. The rate of LD decay is vital in the implementation of genomic selection, genome-wide association studies, as well as defining the population recombination history.It was found that when marker distance increases, linkage disequilibrium diminishes in all groups F. For thra sheep  and Iranra sheep  which arra sheep . Shifmanra sheep  proposedNotably, the number of genes found in each population varied and were located on several chromosomes. For instance, the HUDOR, HUWDOR, SADOR, SAWDOR, and HORAC, respectively, had around 5, 13, 12, 7, and 22 genes mapped out  which is believed to be responsible for the control of body temperature and regulation of energy balance . Immune Gene names and their proposed functions were retrieved from the GeneCard  databaseAll parties involved must work together to address the issue of inadequate breeding strategies and research in Africa and other developing countries that are impeding the adoption of novel genomic applications. To assist in the application of these technologies, benchmarking from regions such as the European Union, Great Britain, and the USA that has already achieved success in the management of animal genetic resources can be done. It is important to close the information flow gap and bridge the gap between researchers and end users. Through their advisory activities, such as the advisory and other services provided by the chambers of agriculture in the European Union , governmTo inform farmers and breeders of the advantages of genomic applications and how to correctly use and interpret the data, capacity development and training programs might be set up. To ensure the greatest comprehension and uptake, these programs might be customized to the local context and language. Additionally, public-private collaborations might be created to advance the study and creation of regionally tailored genomic technologies and to make them available and affordable. Government agencies could offer financial help and policy measures, while private enterprises could offer technical aid.The findings of the current study show that Dorper populations are very genetically diverse, with a significant amount of variation originating within the population. The genomic differences observed may be due to different environment-based selection pressures that each population experiences. Thus, the genomic differences between Dorper populations in South Africa and Hungary may have been influenced by environmental factors. Even after going through a similar developmental process, the Dorper and White Dorper variants differ significantly in their genomic makeup possibly due to the continued and long-term selection differences. Given the popularity of Dorper populations worldwide, the findings of this study provide a foundation for their molecular management.


Aedes koreicus is a mosquito species native to East Asia which has recently invaded several countries in Europe. In Italy, this mosquito was first detected in the North-East in 2011 and is now widely distributed in the entire northern part of the country. The development of specific genetic markers, such as microsatellites, is necessary to uncover the dispersal routes of this mosquito from its native areas and, eventually, to plan future control interventions.Ae. koreicus were screened in silico using BLASTn to identify possible microsatellite-containing sequences. Specific primer pairs were then designed, and their efficiency was determined through polymerase chain reaction (PCR) on 32 individuals of Ae. koreicus collected in Italy. PCR conditions were optimised in three multiplex reactions. Genotyping of individual mosquitoes was performed on both single and multiplex PCR reactions. Finally, analysis of intra-population variation was performed to assess the level of polymorphism of the markers.Available raw sequences of genomic DNA of Ae. koreicus genome raw sequences, 11 were polymorphic in the examined mosquito samples.Mosquito genotyping provided consistent results in both single and multiplex reactions. Out of the 31 microsatellite markers identified in the Ae. koreicus populations. These markers could thus represent a novel and useful tool to infer the routes of invasion of this mosquito species into Europe and other non-native areas.The results show that the 11 microsatellite markers developed here hold potential for investigating the genetic structure of The online version contains supplementary material available at 10.1186/s13071-023-05823-z. Aedes mosquitoes represent a global concern for public health due to their role as vectors of several pathogens and their ability to colonise new territories [Invasive ritories , 2. Theiritories , 4 that In this context, population genetics is a powerful tool: determining the “genetic makeup” of a species can help, for example, in locating the putative origin(s) of multiple introductions, revealing species expansion phenomena, and providing clues on the potential vectorial capacity of different populations of invasive species –10.Aedes koreicus, the Korean bush mosquito, is a species native to temperate areas of Asia, particularly Korea, China, and Far East Russia [Ae. koreicus mosquitoes were collected in all the Italian North-East regions , iniewed in , in fiveiewed in , 22, andiewed in .Ae. koreicus in Europe, and in particular in Italy, possible entry routes into the continent have not been reconstructed so far, nor it is clear how the mosquito spread after its first introduction(s). Filling this knowledge gap is important, considering that the Korean bush mosquito has the potential to further spread in currently uncolonised European countries [Ae. koreicus and the understanding of the genetic relatedness between individuals from different locations would permit us to hypothesise how this mosquito spread in Northern Italy, and even in other European countries [Despite the large number of reports of ountries . Indeed,ountries .Aedes native and invasive mosquito species, such as Ae. albopictus, Ae. japonicus, Ae. taeniorhynchus, Ae. polynesiensis, Ae. sticticus, Ae. fluviatilis, and Ae. aegypti [Ae. japonicus were indicated as polymorphic also in Ae. koreicus [Ae. koreicus, but the amplification resulted ineffective (data not shown). Since no other nuclear genetic markers have been developed so far for the study of the genetic structure of Ae. koreicus, SSR markers for this mosquito species have been developed in the present study.To achieve this goal, genetic markers are needed, and microsatellites were selected as the markers of choice. Indeed, simple sequence repeats (SSRs) are characterised by high variability level and relative ease of use at low cost ; moreove aegypti , 26–30. koreicus . For thiAe. koreicus thanks to the screening of raw sequencing data available in the Sequence Read Archive  witR7598207 , 35. Thees Table , and difAe. koreicus using the methodology described by Arnoldi et al. [® Reaction Buffer , 0.6 U GoTaq® DNA polymerase , 0.2 mM dNTPs , 0.5 μM of each primer, and ~10 ng of DNA as template. Concerning multiplex PCRs, the final concentrations of the reagents is the same, except for primer pairs. Particularly, in multiplex A and B (both with four primer pairs), primer concentration was 0.125 μM; on the contrary, in multiplex C (three primer pairs), primer concentration was 0.167 μM  and Bergamo (collected in summer 2022). Mosquitoes were identified as i et al. . In partThe amplicons were visualised on 1.5% agarose gel, and only loci that showed clear single bands for all the amplified samples were considered for further testing , number of effective alleles (Ne), observed and expected heterozygosity , unbiased expected heterozygosity (uHe), Shannon diversity index (I), and inbreeding coefficient (F) were computed using GenAlEx 6.5 software ; in addiOur initial in silico screening identified 31 putative SSR-containing sequences, named AK01–AK31 (see Table n − 1 and n + 1 positions), and length was shorter than the expected size . The size range of the AK24 locus was broad, and the presence of spurious peaks made correct peak calling impossible. Although the AK11, AK19, and AK21 loci showed no technical problems, they were not further considered because they were not polymorphic in the 32 samples analysed.Primers designed for SSR amplification were assayed in eight mosquito samples using PCR, and for 16 primers pairs the amplification was successful. After PCR validation, FLA of these 16 loci was performed on the 32 mosquito samples  , four representative DNA samples of Ae. koreicus were amplified with primer pairs designed in this study. a Pictures of amplicons obtained with AK01–AK10. b Pictures of amplicons obtained with AK11–AK20. c. Pictures of amplicons obtained with AK21–AK11. M molecular marker ; A Adult mosquito of Ae. Koreicus, N negative control.Additional file 2: Figure S2 Representative electropherograms of single and multiplex PCR performed to validate the efficiency of the 11 microsatellite markers identified in the genome of Aedes koreicus; electropherograms were visualised with Peak Scanner v.2 . a–k Peaks associated with a single marker (one page for each locus). l–n Multiplex A, B, and C are reported . The legend displaying the dye colours, and the corresponding fluorophores/loci, together with the distribution of the microsatellite markers in the multiplex assays, are reported at the end of the document.


Mitochondrial DNA (mtDNA) is one of the most often used genetic markers in animal population studies. Its relevance as a genetic marker is due to the high mutation rate and variability, the maternal inheritance, and the supposedly nearly neutral mode of evolution. Therefore, mtDNA diversity is typically assumed to reflect demographic effects, i.e., variations in population size between species or populations, which makes it a popular tool for conservation purposes. Thus, mtDNA diversity allows a better understanding of the evolutionary plasticity and provides insights into the molecular and evolutionary mechanisms modeling mitochondrial genomes.p < 0.05), indicating that the abundance of rare alleles was most likely due to population expansion after a recent bottleneck. The Median joining network showed that almost all haplotypes belonging to haplogroup B formed a star-like network, which revealed a weak genetic differentiation and a large gene flow between the Bulgarian native breeds.The geographical, geomorphological, and climatic characteristics of Bulgaria are particularly favorable for animal breeding and, above all, for pastoral farming and sheep breeding. These conditions created prerequisites for the creation of about 30 unique local breeds of sheep. In this study we investigated the genetic diversity of eight of the most popular Bulgarian native breeds, based on the sequence analysis of a part of the mitochondrial D-loop region. An almost entire mitochondrial DNA (mtDNA) D-loop region (1180 bp) was amplified and sequenced. The obtained results showed the presence of a large number of haplotypes–225, belonging to two main haplogroups. The majority of samples showed a high prevalence of the European haplogroup B (95.2%) while the remaining individuals were assigned to haplogroup A (4.8%). None of the other reported mitochondrial haplogroups were observed. The number of polymorphic sites, nucleotide and haplotype diversity was high , which is evidence for multiple maternal origins in all populations. The Tajima D-test value in all the study populations was −1.905 ( Ovis aries) is one of the livestock species whose breeding activity has proceeded over the centuries mainly in several directions—wool, dairy, meat, and hide production. According to archeological and historical evidence and recent molecular genetics data, the sheep (Ovis aries) was domesticated near the Fertile Crescent, approximately 10,500 years ago [Ovis orientalis), also known as O. gmelinii [O. musimon) could be the ancestor of the domestic sheep, based on archeological and genetic evidence as well as the same chromosome number as that of domestic sheep (2n = 54) [The domestic sheep  . It was 2n = 54) ,8, where2n = 54) .The dispersal of early domesticated sheep into Neolithic Europe is inextricably linked to the emergence of different archeological entities  in ancient civilizations ,11. The All these observations suggest that the neolithization of prehistoric Europe, first via the Mediterranean coastal route and after about 500 years via the Danube route, and the formation of the earliest European cultures inevitably had an impact on the genetic diversity of domestic sheep due to human-mediated migrations into Neolithic Europe.cytB gene of modern sheep  has shown the presence of five major mitochondrial haplogroups—A, B, C, D, and E [The sequence analysis of the complete mitochondrial genome, the mtDNA D-loop fragment and the D, and E ,21,22. HD, and E ,24,25. CD, and E ,26,27, wD, and E .Haplogroup C is the third haplogroup with high distribution among modern and ancient sheep, especially in the Middle East ,29,30. HThe last two haplogroups, D and E, have been observed with very low frequencies among both modern and ancient sheep samples ,34,36. HThe diverse ecological and economic conditions in Bulgaria and the various needs and interests of the local people in our country allowed raising a large number of native sheep breeds. According to official statistics, there are currently 18 native sheep breeds in Bulgaria . AccordiTo date, the majority of studies on the genetic diversity among native Bulgarian sheep have been performed only on autosomal short tandem repeat variations  ,42,43,44All experimental procedures were reviewed and approved by the Animal Research Ethics Committee of the Bulgarian Food Safety Agency (BFSA) (Art. 154 of the Law on Veterinary Activity), in accordance with European Union Directive 86/609.2EDTA BD vacutainer®  from 310 unrelated sheep belonging to 8 native Bulgarian breeds: Breznishka , Karakachan , Kotel , Pleven Blackhead , Copper-Red Shumen sheep , Middle Rhodopean Sheep , Local Starozagorska , and Patch-faced Maritza  (Blood samples were collected by using a 6 mL K1 flock) . All samX Sp. z o.o., Gdańsk, Poland) in accordance with the manufacturer’s guidelines. The integrity of the extracted DNA was checked by 1% agarose gel electrophoresis and then visualized on UV transilluminator gel documentation systems after staining with SimpliSafe™ . The isolated DNA samples were preserved at −20 °C until further analysis.DNA was isolated from all blood, using a commercial GeneMATRIX Quick Blood DNA Purification Kit  reference sequence NC_001941)  of the mThe PCR mixtures contained 25 µL of Color Taq PCR Master Mix (2×) , 1 µM of each primer (FOR/REV), and 1 µL of extracted DNA for a total volume of 50 µL. All PCR amplifications were carried out using a LifeExpress Classic Thermal Cycler  under the following conditions: initial denaturation at 94 °C for 5 min; 30 cycles , and final extension at 72 °C for 10 min.The successfully amplified PCR products were visualized on 1% agarose gel stained with SimplySafe™ . The fragment size was determined using DNA Molecular Weight Marker XIV . The PCR products were purified by a PCR/DNA Clean-Up Purification Kit  and sequenced in both directions by a PlateSeq kit .Ovis aries reference sequence of haplogroup B (NC_001941) [All 310 obtained DNA sequences (about 1180 bp) were manually edited and aligned with an _001941)  using th_001941)  as imple_001941)  and haveFor each breed, DnaSP 5.0  was usedPrincipal component analyses (PCA) were performed using Excel software Version 2206 implemented by XLSTAT . Two PCAhttp://www.mitotool.org/ accessed on 22 February 2023) was used [For haplogroup identification, the MitoToolPy software  [The analysis of the molecular variance (AMOVA) , implemest 2023) , was useNeighbour-joining (NJ) tree using MEGA6  was consHd = 0.912, π = 0.009; Hd = 0.953, π = 0.009, respectively). Pleven Blackhead and Copper-Red Shumen sheep were completely similar in terms of haplotype (0.986) and nucleotide (0.011) diversity , using the MitoToolPy program. Haplogroup B was with the highest prevalence among the samples, showing a frequency of 95.2%. This haplogroup was present at an extremely high frequency in all sheep breeds . In compIn order to graphically display the mitochondrial relationships among the analyzed sheep breeds as well as the haplogroup frequencies in different European populations mainly from the Balkans, we performed a principal component analysis (PCA)—a method that considers each haplogroup as a discrete variable and allows a summary of the initial dataset into principal components (PCs) . The PCAWe also examined the genetic distance within and among the studied native Bulgarian breeds, measured in nucleotide substitutions per site. The obtained results clearly showed the very low genetic differentiation between them, thus supporting the evidence from the network and PCA analysis . The lowIn addition, we calculate the FST value between each two breeds . The higThe AMOVA analysis at the breed’s level supported low genetic differentiation among studied breeds . The obtThe low genetic differentiation between the breeds is visualized in Genetic relationship among all sheep breeds was further confirmed by the construction of NJ phylogenetic tree . The resThe intensification of the output of animal production in Bulgaria during the last decades has led to a decrease in the number of breeds used as the basis for production. Breeds having a lower total productivity or not suitable for keeping in the conditions of industrial complexes were quickly replaced by more productive and commercial breeds through crossbreeding ,62. TogeIn this study, we evaluated the mitochondrial diversity (1180 bp of D-loop region) of eight native Bulgarian native breeds. The high prevalence of haplogroup B in about 95% of our samples is not unusual as it is widespread in European sheep breeds ,28. The The Asian specific haplogroup A was present at a very low frequency in our samples (~5.0%). A relatively high frequency of this haplogroup was observed in Kotel and Patch-faced Maritza sheep (~14%) . These bSurprisingly, we did not detect haplogroup C in our set, although this haplogroup has been found in some sheep breeds from the Balkan Peninsula—Bosnia and Herzegovina and Albania ,56. The In order to compare the haplogroup frequencies in the Bulgarian sheep populations with those of other European populations, we performed a PCA analysis . The PCAWe constructed the NJ phylogenetic tree to demonstrate the phylogenetic relationship among studied sheep breeds . The phyTo analyze the genetic variation among and within breeds, we performed AMOVA and evolutionary divergence genetic distance analysis . The AMOThe present study analyzed the polymorphism of mtDNA (D-loop region) in eight native Bulgarian sheep breeds. The obtained results indicated significant genetic diversity in the studied populations, with 225 distinct haplotypes. The majority of samples showed a high prevalence of the European haplogroup B (95.2%) while the remaining individuals were assigned to haplogroup A (4.8%). None of the other known mitochondrial haplogroups were detected. The constructed median-joining network showed that almost all haplotypes formed joined clusters among the populations, which indicated very low genetic differentiation and possibly a gene flow between breeds. The obtained results from this study will be useful for improving strategies for the conservation of native Bulgarian sheep genetic resources. Continuing research on large samples from other native sheep breeds will provide a more complete pattern of the genetic diversity and demographic history of native Bulgarian sheep populations.