Hd = 0.915 ± 0.011) across Southeast Asia and Oceania. The phylogenetic analysis and median-joining (MJ) network revealed predominant maternal lineage haplogroup D classified throughout the population, while support for Philippine–Pacific subclade was evident, suggesting a Philippine origin of Pacific chickens. Here, we observed Philippine red junglefowls (RJFs) at the basal position of the tree within haplogroup D indicating an earlier introduction into the Philippines potentially via mainland Southeast Asia (MSEA). Another observation was the significantly low genetic differentiation and high rate of gene flow of Philippine chickens into Pacific chicken population. The negative Tajima’s D and Fu’s Fs neutrality tests revealed that Philippine chickens exhibited an expansion signal. The analyses of mismatch distribution and neutrality tests were consistent with the presence of weak phylogeographic structuring and evident population growth of Philippine chickens (haplogroup D) in the islands of Southeast Asia (ISEA). Furthermore, the Bayesian skyline plot (BSP) analysis showed an increase in the effective population size of Philippine chickens, relating with human settlement, and expansion events. The high level of genetic variability of Philippine chickens demonstrates conservation significance, thus, must be explored in the future.The Philippines is considered one of the biodiversity hotspots for animal genetic resources. In spite of this, population genetic structure, genetic diversity, and past population history of Philippine chickens are not well studied. In this study, phylogeny reconstruction and estimation of population genetic structure were based on 107 newly generated mitochondrial DNA (mtDNA) complete D-loop sequences and 37 previously published sequences of Philippine chickens, consisting of 34 haplotypes. Philippine chickens showed high haplotypic diversity ( BP, then widespread to the south and west into the ISEA toward Indonesia and east into the Pacific Islands at ca. 3,300–3,150 cal. BP (The rich history of human migrations and settlements in the islands of Southeast Asia (ISEA) provides interesting records of earlier agricultural populations in the Malay Archipelago . The two cal. BP . Recentl cal. BP . Evidenc cal. BP . These hcal. BP can be found. Most likely, the multiple wave of human translocations exerted a huge influence on the earlier lineages of domestic chickens introduced in the Philippines  from MSEA served as the progenitor of all present-day chickens , 1996. HTherefore, in the present study, complete mitochondrial DNA (mtDNA) D-loop sequences from RJFs and native chickens (NCs) in the Philippines were investigated to assess their matrilineal phylogeny, genetic diversity, and population genetic structure of Philippine chickens across ISEA and the Pacific. In addition, this study attempted to reconstruct the population history and probable dispersal of Philippine chickens throughout the Pacific.n = 7) and NCs (n = 100) from selected areas of Samar and Leyte Provinces, Philippines. RJFs were captured in the wild by the locals. All samples were collected following the Experimental Animal Care Guidelines established by the Laboratory of Animal Genetics, Hiroshima University (015A170426). Animal owners consented the inclusion of their animals in the study.Blood samples were collected from the brachial vein of the wing of RJFs . The DNA fragments obtained from the segmental amplification were cleaned and purified using Exonuclease I (ExoI) and Shrimp Alkaline Phosphatase (SAP) to degrade the residual PCR primers and dephosphorylate the remaining dNTPs, respectively. Subsequently, the mtDNA D-loop fragments were directly sequenced using 3130/3130xl Genetic Analyzers .About 5.0-kbp mtDNA D-loop fragments were amplified using a long and accurate PCR (LA-PCR) kit  with chicken DNA as a template and LA-PCR primer sets: 1 and were aligned together with 495 complete mtDNA D-loop sequences across Asia using ClustalW (n = 3) (MK085033–MK085035) and Samar NCs (n = 17) (MK085038–MK085054) (n = 17) were also included in the analysis (NC_040970) were edited and viewed using the BioEdit sequence alignment editor ClustalW . PreviouK085054)  and otheanalysis . Alignedt editor . All comMN986403 .Hd), nucleotide diversity (π), and mean number of pairwise difference, were estimated using the DnaSP v. 6.0 software  sites (S), haplotype diversity (software .Phylogenetic tree was inferred by IQ-TREE using the maximum likelihood (ML) method  to estimTruncated partial sequences (764-bp fragment) were also analyzed for more fine-grained phylogeographic analysis of chicken population in the ISEA and Pacific region together with other partial sequences  from IndFST  and Slatkin’s linearized FST was estimated using the Arlequin v. 3.5.2.2 software  (The population pairwise net genetic distance based on population pairwise utation) . The levr  using the program DnaSP v. 6.0 software . Expecter , was user . To furtr  to the likelihood stationary distribution was evaluated using the Tracer v. 1.7.1 software   was estiear rate . The piesoftware .A total of 144 complete mtDNA D-loop sequences  of Philippine chickens were analyzed in this study of which 107 were newly generated. There were 34 haplotypes (18 parsimony-informative sites) identified, 29 of which were possessed by NCs with 1 haplotype (Hap_68) shared with a RJF, while 5 haplotypes are unique in the RJF samples. The overall haplotypes of Philippine chickens (RJFs and NCs) had 32 polymorphic sites . The distribution of the nucleotide positions and sequence variations of each haplotype are presented in Hd) was relatively high ranging from 0.884 ± 0.103 in RJFs to 0.904 ± 0.012 in NCs . The overall gene diversity concurred with the previously described intrapopulation genetic diversity of chicken populations in Samar, Philippines (The haplotype (gene) diversity  consistently showed distinct subclades of these chicken populations in the ISEA and Pacific region  of Philippine chickens . Clearlyc region .FST), Slatkin’s linearized FST, and pairwise differences among populations of Philippine chickens (RJFs and NCs), Pacific chickens, Indonesian chickens, and MSEA chickens. Low genetic differentiation was observed between Philippine and Indonesian chickens  and between Philippine and Pacific chickens , which suggest that chicken populations in these regions were not isolated from each other  were lower than Philippine and Pacific chickens , which suggest a genetic closeness in the former populations due to geographical proximity and closer maritime ranges. However, interesting findings documented high genetic divergence (FST = 0.4788) between Pacific and Indonesian chickens, while chickens from the MSEA were the most remotely related to the Pacific chickens (FST = 0.5916). The PCoA analysis support clustering of closely related Philippine and Pacific chickens within haplogroup D, while distant to Indonesian chickens  than within the Philippine–Pacific group  at haplogroup D  and Philippine–Pacific  showed lower among-group variances with 9.43 and 20.97%, respectively, while higher among-group variance is observed in the Philippine–MSEA  with 34.17% . Howeverogroup D .r) values. Both Tajima’s D and Fu’s Fs neutrality tests further indicated that chickens from the ISEA and Pacific region deviated from neutrality except chickens from MSEA, which support a model of demographic expansion. The negative and significant Fs statistical values in Philippine chickens (haplogroup D) and Pacific chickens provided strong evidence of population growth signatures of these chicken populations in the region (The mismatch distributions of Philippine chickens (haplogroup D), Philippine RJFs, and Pacific chickens were unimodal , characte region . Evidence region .Ne) at different points along the genealogical timescale. The BSP showed evidence of Philippine chickens experiencing a long period of relatively constant Ne during the early Holocene period, followed with a gradual increase, which started approximately 3,500 BP, while an episode of eminent population growth commenced about 3,000 BP  (Philippine chickens have been shown to have high haplotypic diversity with a relatively large proportion of low-frequency haplotypes in the predominant haplogroup D (gene diversity = 0.915 ± 0.011). They show higher genetic diversity than Indonesian crowing chickens , Thai in0.00591) . The hig0.00591) , high po0.00591) ; thus, dPrevious fine-grained mtDNA phylogeographic study of chickens across the world revealed 13 divergent haplogroups (A–I and W–Z)  with then = 31) of Philippine NCs assigned to haplogroup E are believed to be the result of interbreeding between present-day chickens and commercial or show breeds. This haplogroup is widely represented in European domestic chickens and commercial lines with distinct haplotypes dispersed in the Middle Eastern and Indian subcontinents (n = 5) and four haplotypes in haplogroup B (n = 7), which are believed to have been introduced from neighboring countries including South and East China, Japan, and some countries in the MSEA  of Philippine chickens, with a predominant haplogroup D throughout the population. This confirms previous genetic evidence that haplogroup D is the maternal lineage largely concentrated in the ISEA–Pacific region  and distntinents . This stthe MSEA .Although Neolithic archeological records of Philippine chickens are still enigmatic , mtDNA eFST < 0.2) was observed between Philippine and Pacific chickens (p = 0.001) than between Indonesian and Pacific chickens, which suggests little or no genetic difference among the former populations. This weak genetic structure is confirmed by the AMOVA analysis with 69.61% within-population variance (p = 0.000) for the Philippine–Pacific group, rejecting geographical-based isolation. This potentially reflects regional translocation from the Philippines going eastward to the Pacific by Austronesian speakers around <3,000 years ago , which validated our previous analysis inferring the contributions of Philippine chickens to the genetic characteristics of the Pacific chickens. The negative and significant Fu’s Fs statistical test  can be found in the article/The animal study was reviewed and approved by the Laboratory of Animal Genetics, Hiroshima University (015A170426). Animal owners personally consented the inclusion of their animals (native chickens) in the study.CG, PD, DE, and MM conceptualized the study. CG and MM were in charge of the data curation. CG, MM, and MN developed the methodology and were in charge of the software. CG and MN performed the formal analysis. CG wrote and prepared the original draft. CG, MN, DE, and PD wrote, reviewed, and edited the manuscript. MN acquired the funding. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Warm-adapted species survived the cold stages of the past glacial-interglacial cycles in southern European peninsulas and recolonized major parts of Central and Northern Europe in the wake of postglacial warming. However, many of the genetic lineages which differentiated within these refugia predate even the Pleistocene. One of these refugia is the Italian Peninsula with its surrounding islands. In this meta-analysis, we compiled phylogeographic patterns of multiple species across this region. We transformed existing geographic information on 78 animal and plant species  within 17 predefined areas into presence/absence matrices. We elaborated three matrices: (i) only old lineages exclusively distinguished by deep splits, (ii) only young lineages distinguished by shallow (i.e. recent) splits, and (iii) presence/absence of the respective species. To infer biogeographic relationships between the predefined areas, we performed bootstrapped neighbour joining cluster analyses on these three matrices. In addition, we reviewed the geological history of Italy to identify causes of the observed biogeographic patterns. We found Sardinia and Corsica to be biogeographically closely linked with each other, and that they diverge strongly from all other regions. Sicily also diverges strongly from all other regions, while the intra-island differentiation was comparatively low. On the Italian mainland, Calabria exhibited the most pronounced biogeographic differentiation, often with several lineages present, resulting from old vicariance events within the region. Furthermore, southern Apulia and the Po Plain with adjoining areas of northern peninsular Italy displayed considerable distinctiveness. Admixture prevailed in the areas between these three regions. The ancient isolation of Sicily, as well as Sardinia plus Corsica, resulted in endemic lineages with only moderate recent exchange with adjacent mainland regions. Pronounced diversification occurs within the Italian Peninsula. The complex tectonic activities, such as shifting (micro)plates, submergence of major parts of peninsular Italy with the genesis of numerous Pliocene islands, in combination with the climatic cycles during the Pleistocene have most likely generated the current biogeographic pattern of species. Immigrations from the Balkan Peninsula into northern Italy partly accounted for the distinctiveness of this region.The online version contains supplementary material available at 10.1186/s12983-021-00418-9. SinWe evaluated 127 articles (published up to the end of February 2021) addressing biogeography and population genetics across major parts of peninsular Italy (including the Po Plain) and/or the three major adjacent islands . Ninety of these articles contained data suitable for our analysis, i.e., data for several of the predefined areas, based on different molecular marker systems , nuclear (10) and plastid (11) DNA sequences, microsatellites (8), allozyme polymorphisms (6), AFLPs (1), ISSR (1), chromosome number polymorphisms (1); note that one genetic data set has sometimes been derived from more than one article, and that more than one data set may have been presented within one article. An overview of all studies is provided in Appendix SFor each species, we assessed whether each genetic lineage is present or absent in each of our 17 predefined areas. In many cases, genetic information was not available for the entire distribution range in Italy and Corsica. If so, for an individual species, areas without genetic data were either considered ambiguous or assigned to the lineage occurring in the surrounding areas, if this was unequivocally possible . Ambiguity was assigned to a lineage within an area without a record (1) if the adjoining areas had different genetic lineages (or even ambiguities) and a decision could not be made whether the respective lineage is present or not, or (2) the area without record is at the margin of the species distribution in Italy so that either the lineage of an adjoining area is present, or another still unknown lineage. This, in particular, affected the islands Sardinia and Sicily: if genetic information was not available for each of their, respectively, two and three areas, the area(s) without data was/were considered ambiguous, although the presence of this lineage on the entire island is likely. Areas with unsupported presence of a genetic lineage entered the statistical analyses as absence to avoid artificially increased biogeographic distributions and decreased discrimination power. Note that two or more lineages can co-occur within one single area. In cases in which one such lineages only marginally entered an area, this lineage was also recorded as present.In addition, we assigned lineages to two age categories in cases where genetic structuring within a species showed a clearly hierarchical age structure and thus enabled an unequivocal distinction:(i)Deeply divergent lineages , resulting in 213 units, and.(ii)younger genetic lineages , resulting in 258 units.a posteriori assignments of molecular clocks to all of them, this differentiation into old and young lineages merely reflects the hierarchical genetic structuring within the respective taxon. Therefore, these two sets of genetic lineages cannot be unequivocally assigned to two absolute time windows. It is only a relative ranking. Nevertheless, the biogeographic structuring revealed by analysis of the set of old lineages, separated by deep genetic splits, should be more ancient than the structures revealed by the analyses of the geographic distribution of the young lineages, distinguished only by shallow splits. Consequently, no clear dating of the structures was possible in our analyses. However, in cases where phylogeographic analyses have utilised molecular clocks, diversification events separating old lineages have mostly been assigned to the Pliocene, whereas Pleistocene age was often assigned to the diversification events involving young lineages. As a third matrix, we also used a presence/absence data set at the level of species. Additionally, we performed our analyses separately for vertebrates and plants; the amount of data for invertebrates was insufficient for such analyses.If no such hierarchical structure was detected, no distinction into old and young lineages was performed. In these cases, the derived lineages were included in the analysis of the old genetic lineages. As molecular clocks are missing for many of the analysed datasets and the heterogeneity of the existing ones did not allow the implementation of For the two divergence levels  and the presence/absence data of species, we constructed three lineage × area presence-absence matrices. Therefore, our study is basically a phenetic approach, similar to a co-occurrence analysis in community ecology . ConsequJ using the Turnover software [a priori reason to expect that certain lineages cannot occur in any one of the Italian areas. From these null models, we calculated for each species i standardised effect sizes i is the difference between observed and expected Ji, and σi is the standard deviation of the null model distribution. Under the assumption that this distribution is approximately normal, SES values > |2.00| indicate statistical significance at the two-sided 5 % error level.We further calculated the pattern of spatial co-occurrence of each species at each level based on Sørensen dissimilarities software . As the Neighbour joining cluster analyses Figs.  and 4 deIn turn, the younger lineages return consistent links of Sardinia and Corsica with Sicily Fig. d-f. NeveAnalyses restricted to vertebrates yield a pattern similar to that obtained for all taxa, but the Po Plain is more isolated in the former  and a weaker correlation for the younger lineages .The biogeographic pattern revealed for species occurrences resembles that of the younger genetic lineages Fig. . An unroIn our species-specific approach, we look at the pattern of co-occurrence of all species Fig. . Fifty-tAs an answer to our first research question about the biogeographic importance of the three large islands adjoining peninsular Italy, our meta-analysis strongly suggests that Sardinia plus Corsica as well as Sicily represent the two most divergent biogeographic units. Both have some similarity when only shallow genetic divergences are considered but not for the lineages distinguished by deep divergences . Mainland Italy also shows a strong biogeographic structuring. Southern Calabria , Apulia  as well as the northern half of peninsular Italy  consistently cluster together and correspond to three distinct biogeographic units. Northern Calabria (I) is part of the Calabrian region but is distinguished remarkably from southern Calabria  in all analyses. The remaining areas in southern central and southern Italy  constitute a transition zone between the two southern Italian regions on the one hand and northern Italy on the other. Consequently, we confirm the hypotheses implicit in our second initial question, that the biogeographic structures correlate with the geological history of Italy and its surrounding islands in the Neogene. Below, we discuss in detail this relationship between genetic structures and the geological, orographic and environmental history.Podarcis sicula [Erinaceus europaeus [Emys orbicularis [Podarcis sicula [The biogeographic uniqueness of Sardinia and Corsica is easily explained by their geological history . Both iss sicula , 44 and uropaeus . Howevericularis , 47. Alss sicula , 44. Furs sicula . Howevers sicula , 49–56, Emys trinacris [Emys trinacris [The strong phylogeographic isolation of Sicily appears at first glance unexpected when its present geographic location is considered, separated from Calabria only by the 3-km-wide Strait of Messina Fig. . Howeverrinacris ), and Sirinacris . The simrinacris ).Lissotriton italicus [Podarcis sicula [Answering our third starting question, we found that the genetic structure of the biota of peninsular Italy is highly complex and in accordance with the refugia-within-refugia model of Gómez & Lunt , i.e., sitalicus  and the s sicula . During s sicula ), explaiLissotriton italicus [Calabria comprised three separate islands during the Pliocene Fig. c and befitalicus . Consequitalicus , 12, 14.italicus ).Pelobates syriacus [Erannis ankeraria [Charanyca apfelbecki, as well as the mountain butterfly Coenonympha rhodopensis [The history and role of the Salentine Peninsula (H) is considerably different. This region was a single island during the Pliocene and before Fig. a–c. Latesyriacus , while osyriacus , the motnkeraria  and Chardopensis .Our results indicate that the diversification of much of the extant flora and fauna of peninsular Italy probably predates the Pleistocene and that main centres of differentiation were southern Calabria and southern Apulia. However, a number of younger differentiation centres might have been located north of that, especially in the Gargano area (E), the southern Tyrrhenian area (F) and northern Calabria (I). It is likely that these more northern centres shaped the range expansions and retractions during the Pleistocene climatic cycles, simultaneously reinforcing the genetic uniqueness of the more southern Italian areas.in situ. Repeated immigration from the Balkan Peninsula also contributed to the observed distinctiveness.The populations from the Po Plain and the two adjoining peninsular areas B and C form a single faunal unit. This applies to older and younger genetic lineages as well as to species composition. Importantly, these areas (i.e. A–C) are more strongly distinguished from southern Italy than would be expected from their location and the young geological age of the Po Plain, that originated in the Pleistocene  are always nearest neighbours in all cladograms based on the geographic distribution of older and younger lineages. Most of the few cases of differentiated lineages east and west of the Apennines refer to semi-aquatic species, such as the European pond turtle icularis  or the belvetica . For suc siculus , 44. In in situ, is most likely responsible for the unexpectedly high biogeographic distinctiveness of this region.The present study confirms on the meta-scale what had emerged in individual studies of smaller species´ assemblages: The Italian Peninsula and the neighbouring large islands Corsica, Sardinia, and Sicily display pronounced phylogeographic diversification patterns. For many species, the geographic density of different lineages, and sometimes the age of the splits separating lineages, are remarkably high in our study region. These characteristics are often more pronounced than the geographic complexity and ages observed for the Iberian and the Balkan peninsulas, and hence contradict the previous assumption that a relatively simple structure and shallow level of differentiation exists in Italy, due to its small size . On the Additional file 1: Appendix S1. Overview of all studies analysed with main phylogeographic patterns.Additional file 2: Appendix S2. (a) Number of genetic lineages within 17 Italian biogeographic areas used for the present study. (b) Geographic distribution of all lineages over the 17 Italian biogeographic areas. (c) Sørensen dissimilarity matrix between 17 Italian biogeographic regions based on 213 and 258 genetic lineages, respectively.Additional file 3: Appendix S3. (A1) Unrooted neighbour joining cluster analyses (Sørensen similarities) of vertebrate  and plant species .


K = 3) assigned all populations into three clusters with a degree of genetic admixture in the Murrah and Nili-Ravi buffalo populations. Admixture analysis reveals introgression among Murrah and Nili-Ravi breeds while identified the Gojri as unique buffalo germplasm, indicating that there might be a common origin of Murrah and Nili-Ravi buffaloes. The study provides important insights on buffalo breeds of North India that could be utilized in designing an effective breeding strategy, with an appropriate choice of breeds for upgrading local non-descript buffaloes along with conservation of unique germplasm.To understand the similarities and dissimilarities of a breed structure among different buffalo breeds of North India, it is essential to capture their morphometric variation, genetic diversity, and effective population size. In the present study, diversity among three important breeds, namely, Murrah, Nili-Ravi and Gojri were studied using a parallel approach of morphometric characterization and molecular diversity. Morphology was characterized using 13 biometric traits, and molecular diversity through a panel of 22 microsatellite DNA markers recommended by FAO, Advisory Group on Animal Genetic Diversity, for diversity studies in buffaloes. Canonical discriminate analysis of biometric traits revealed different clusters suggesting distinct genetic entities among the three studied populations. Analysis of molecular variance revealed 81.8% of genetic variance was found within breeds, while 18.2% of the genetic variation was found between breeds. Effective population sizes estimated based on linkage disequilibrium were 142, 75 and 556 in Gojri, Nili-Ravi and Murrah populations, respectively, indicated the presence of sufficient genetic variation and absence of intense selection among three breeds. The Bayesian approach of STRUCTURE analysis (at  Bubalus bubalis in India. Many well-defined breeds have been formed for various purposes and the high-performance breeds are intensely selected worldwide. This has led to the replacement of native low performance breeds with high performance ones causing erosion of genetic resources  among the esources . Howeveresources . The genesources .Bubalus Bubalis) that most probably domesticated in Indus Valley region for multiple utility creates a rich Bubaline diversity in Northern regions of India, comprising states of Punjab, Haryana, Himachal Pradesh, Delhi and Western Uttar Pradesh. The predominant bubaline genetic resources documented from the region include Murrah, Nili Ravi and Gojri buffaloes (http://www.nbagr.res.in/regbuf.html). Murrah being dominating buffalo germplasm with superior milk-producing ability has suppressed the need for identification and characterization of other breeds. On the other hand, Gojri is one of the little-known buffalo population of the region, with a good milch potential on low to zero input system of dairying and is maintained on a semi-migratory extensive system of management  require ample knowledge of the geographical distribution of the breeds, identification of unique characteristics, population size and structure, production environment, and genetic diversity. It is customary to perform a detailed molecular study along with physical and phenotypic assessment to check within and between population diversity in order to characterize a population . Vohra eThe neutrality, co-dominant inheritance and high polymorphic information content of microsatellite markers have rendered them as the markers of choice for diversity studies . SeveralThe genetic diversity within Murrah, Nili-Ravi and Gojri breeds have been studied independently that share the common breeding tract in North India. However, in India, buffalo breeding is largely restricted to natural mating that subsequently may have led to admixture of these populations. Hence, there is a need to assess the between-breed genetic diversity among these breeds. The present study was performed to assess the levels of genetic diversity, and population structure among three buffalo breeds of North India. The results will help in formulating an effective breeding, management policy, shaping future conservation plans for maintaining breed purity and reducing the possible admixture due to introgression among purebreds. Thus, it is imperative to compare the region-specific diversity and breed status of bubaline germplasm.Sampling was done from their respective native tracts, to compare the genetic diversity between three different breeds. Gojri buffalo samples were collected during 2017–18 from areas of Punjab and Himachal Pradesh (30° 9′ to 32° 3′ N and 75° to 77° E) states of India, and samples for Nili-Ravi buffaloes were collected from Punjab state ( 28° 17′ to 32° 17′ N and 74° to 76° 41′ E). The Nili-Ravi has a comparatively smaller geographical distribution compared to Murrah and Gojri. In India, Murrah buffaloes are found in almost all regions but its native area is Haryana state (28° 02′ to 30° 21′ N and 75° to 77° E) hence, sampling was performed from Haryana and Punjab. The data of Murrah and Nili-Ravi was taken for comparative analysis from Buffalo Genomics Lab of National Bureau of Animal Genetic Resources, Karnal. The breeding and sampling tract had a herd size of 2–6 buffaloes per households. To ensure that selected animals are unrelated, in the absence of detailed pedigree accounts, buffalo breeders were interviewed in detail and their records were checked. Only those animals who were not having common parents for at least 3–4 generations were included in the study. Buffaloes were selected for this study following guidelines of measurement of domestic animal diversity program  those reMorphometric traits were measured on a total of 242 adult female buffaloes, comprising of 113 Murrah, 37 Nili-Ravi and 92 Gojri buffaloes, to avoid the sex and age differences. Thirteen (13) different traits were measured on all three breeds Na), theta estimate (θH), expected heterozygosity (He), FIT , FST (measurement of population differentiation) and FIS (within- population-inbreeding estimate) were calculated using Arlequin v3.5  was checked for the three population. Ne was estimated using linkage disequilibrium method using NeEstimator v2.01  was set to 0.05, below which all the alleles were rejected. Jackknife confidence intervals (CI) were calculated for each estimate, Ne, of different population. Discrimination between populations was elucidated graphically through principal coordinate analysis (PCoA) using Darwin v6.0.021  across all populations ranged from 0.14 ± 0.02 at ILSTS19 to 0.81 ± 0.04 at ILSTS58. The mean He estimated over all loci was lowest in Murrah (0.58 ± 0.25) while it was highest in Gojri population (0.70 ± 0.15) (θH) that is obtained using observed homozygosity values was estimated under infinite allele model. Mean θH ranged from 1.36 in Murrah to 2.33 in Gojri buffaloes (H ranged from 0.17 ± 0.03 (ILSTS19) to 4.44 ± 1.16 (ILSTS58). Marker wise number of alleles, He, and θH in each breed given in Among 25 microsatellite loci genotyped for this study, only 22 loci that were polymorphic for all three populations were used for further downstream analysis. A total of 145, 138 and 173 alleles were found across 22 loci in the 128 individuals sampled from the Murrah, Nili-Ravi, and Gojri buffaloes, respectively. ILSTS60 was highly polymorphic in Gojri buffaloes, ILSTS95 in both Murrah and Nili-Ravi and ILSTS61 in Murrah . Mean nu ± 0.15) . Estimatuffaloes . Across FIS) whereas the total population had a 27.8% deficit of heterozygotes (FIT). The average genetic differentiation (FST) between the breeds was 18.2% (p = 0.00001) indicating significantly higher discrimination between breeds  values were used to illustrate the genetic distance between breeds  using 19 polymorphic loci was accomplished. Wright’s F-statistics values obtained from the results of global AMOVA revealed 11.7% deficit of heterozygotes for each of the analyzed breeds  was estimated excluding rare alleles with an allele frequency below 0.05. The estimated effective population size of Gojri, Nili-Ravi and, Murrah was found to be 142, 75 and 556, respectively. The Jack-knife CIs for the Ne estimates were 83–396, 48–141 and 136 to infinity for Gojri, Nili-Ravi and Murrah, respectively at 0.05 P-critical value of rare alleles.Effective population size  along with average proportion of each individuals classified into respective pre-assigned breeds  is reported . Wilks’ Lambda value close to zero represents a greater number of variables contribute to the discriminant function  in population over a range of loci is considered a fair indicator of allelic variation. The mean Na ranged from 0–10, 0–13 and 3–14 in Nili-Ravi, Murrah and Gojri buffaloes, respectively 2 − 1]  ranged from 0.12 to 0.82 that was comparable with the results reported by He were reported in Pandharpuri 2 − 1] , where Hrefer to  is indicrefer to . In Gojrdharpuri , Mehsanadharpuri , Egyptiadharpuri  and Purndharpuri  buffaloeFIS = 0.11744, FST = 0.18252 and FIT = 0.27852  indicating low to moderate amount of inbreeding in the population. This could be attributed to assortative mating in small herds owned by farmers, genetic hitchhiking, or the null alleles . Between Murrah and Gojri it was found to be 0.33  value was calculated as previously described  per locus among the Nili-Ravi, Murrah and Gojri buffalo populations (b) Expected heterozygosity (He) per locus among the Nili-Ravi, Murrah and Gojri buffalo populations(a) Number of alleles  obtained per locus among the Nili-Ravi, Murrah and Gojri buffalo populations(c) Theta H (Click here for additional data file.


Ancestral inbreeding had a strong increasing trend, whereas trends in individual inbreeding and rate of individual inbreeding were slightly negative.Deutsch Drahthaar (DD) is the most popular hunting dog in Germany, fulfilling all aspects of hunting including searching for trails. This breed was newly created at the beginning of the 20th century from a large number existing versatile hunting dog breeds. The aim of the breed was, and still is, to achieve the best performance in all aspects of hunting. We analyzed pedigrees of DD using demographic measures to quantify genetic diversity such as probabilities of gene origin and degrees of ancestral and individual inbreeding. A large number of genetically diverse founder dogs should open up the opportunity of creating a breed with a high genetic diversity and a low increase of inbreeding per generation. On the other hand, intense use of top sires and dams from a limited number of breeding lines may accelerate breeding progress in hunting abilities but reduce genetic diversity. Monitoring genetic diversity should help to maintain a high diversity of breeding populations. Our analysis of pedigree data from 101,887 DD dogs revealed inbreeding measures  were 0.042 and 0.00551, respectively, with a slightly decreasing trend in ΔFi. Exposure of ancestors to identical-by-descent alleles explored through ancestral coefficients of inbreeding showed a strong increasing trend. Comparisons between new and ancestral inbreeding coefficients according to Kalinowski et al. showed an average relative contribution of 62% of new inbreeding to individual inbreeding. Comparisons among average coancestry within the parental population and average inbreeding in the reference population were not indicative of genetic substructures. In conclusion, the creation of the DD dog breed about 120 years ago resulted in a popular breed with considerable genetic diversity without substructuring into lines or subpopulations. The trend of new inbreeding was declining, while ancestral inbreeding through ancestors who were autozygous at least once in previous generations was increasing. Loss of genetic diversity and high inbreeding rates confer an increased risk of congenital anomalies and diseases and thus impacting dog breeding. In this study, we analyzed recent and ancestral inbreeding as well as other measures of genetic variability in the Deutsch Drahthaar (DD) dog population. Analyses included pedigree data from 101,887 animals and a reference population with 65,927 dogs born between 2000 and 2020. The mean equivalent complete generations was 8.6 with 69% known ancestors in generation 8. The mean realized effective population size was 92 with an increasing trend from 83 to 108 over birth years. The numbers of founders, effective founders and effective ancestors, as well as founder genomes, were 814, 66, 38 and 16.15, respectively. Thirteen ancestors explained 50% of the genetic diversity. The mean coefficient of inbreeding and individual rate of inbreeding (ΔF Hunting is of great importance for humans as well as for the environment. Although hunting is no longer the basis of the food supply, it is necessary for the promotion of wildlife species and thus for nature and animal conservation and it serves to represent the status quo ,2. AccorIntensive selection for specific traits such as hunting performance and morphological phenotypes can lead to bottlenecks and thus, increased inbreeding rates. Consequently, the probability of homozygosity for recessive genes can lead to a higher prevalence of hereditary diseases and reduced fitness traits ,11. To cPedigree-based inbreeding coefficients (F) vary widely among different dog breeds ,13,14 T. Czech SThe objective of this study was to evaluate the genetic diversity of the DD population born from 2000 to 2020. Therefore, we analyzed the probabilities of gene origin, classical and ancestral inbreeding coefficients and effective population size based on pedigree data containing 101,887 DDs.Pedigree data were provided by the VDD. Data included all available pedigree records of the VDD population. The pedigree file employed for analysis contained 101,887 animals with 7881 dams and 3782 sires. For the analysis of demographic measures, only animals with both known parents were included. To reduce the risk of underestimation of the parameters, only birth years with at least 6.5 GE were included in the reference population. Therefore, dogs with both parents known and born between 2000 and 2020 were defined as the reference population. On average 3139 animals were born per birth year. The reference population comprised 65,927 animals descending from 4501 dams and 2124 sires. The founders in the pedigrees available were born in the 1960s.Pedigree analyses were carried out using the PEDIG software . Data edThe number of equivalent complete generations (GE) was calculated to allow an assessment of the pedigree completeness. GE was defined as the sum of the proportion of known ancestors over all generations .f) and were expected to be unrelated with an inbreeding coefficient of zero. As a measure of genetic diversity in the breed and the number of effective founders (ef) was estimated as: f = number of founders, kq = probability of gene origin of the individual ancestor (k) [Animals with unknown ancestors, to which all other individuals of the population could be traced back, formed the founders (stor (k) . This vaaf) was calculated to assess how balanced the use of reproductive animals was. We calculated af as the marginal genetic contribution (q) of an individual ancestor (iq) and thus the contribution of an ancestor (a) that could not be explained by any other ancestor before [The effective number of ancestors (r before :(2)fa=1∑gf) was chosen to describe the balanced contribution of founders and random losses of founder alleles in descendants. It was estimated in the same way as the effective number of founders, but the individual contribution of founders was replaced by the contribution of founder genes [f = number of founders, kq = proportion of genes of the founder k, which can be found in the reference population and kr = expected proportion of founder alleles that have been kept within the descendant population.The effective number of founder genomes (er genes :(3)fg=1∑af/ef and gf/ef were used to indicate the loss of genetic diversity during the development since the founder generation. aF/ef-values < 1 refer to bottlenecks in the population. The impact of drift on the population is shown by the gf/ef-ratio.Ratios GD), which accounts for the loss of genetic diversity resulting from genetic drift and unequal contribution of founders, was calculated using the following formula [The amount of genetic diversity ( formula :(4)GD=1−GD*) accounting for loss of genetic diversity due to an unequal contribution of founders was estimated as follows [Correspondingly, the amount of genetic diversity ( follows :(5)GD*=1GD* and GD.The loss of genetic diversity as a consequence of genetic drift can be expressed as the difference between inbred).The inbreeding coefficient according to Meuwissen and Luo  was estigd), measures of ancestral inbreeding according to Ballou [a_Bal), ancestral  and new (FNew) inbreeding coefficient according to Kalinowski et al. [Individual inbreeding using the genedrop method i et al.  and an ai et al.  (AHC) wei et al. ,33. a_Bal is defined as the cumulative proportion of an individual’s genome which was previously exposed to ancestral inbreeding [a_Bal considers inbreeding from each ancestor and increases with each inbred ancestor in the pedigree and is independent of the individual inbreeding coefficient [a_Bal > 0. A gradual increase of Fa_Bal compared to F over the years implies an increase of inbred ancestors in the pedigrees, but not necessarily an increase of individual inbreeding. The ancestral inbreeding coefficient Fbreeding . Fa_Bal fficient . Thus, ia_Kal refers to the currently homozygous alleles that were already homozygous in at least one ancestor of the individual. FNew represents those alleles that are currently homozygous for the first time. Because ancestors in common have to occur on both sides of the pedigree in inbred individuals, Fa_Kal is zero if the individual inbreeding coefficient F is zero [According to Kalinowski et al. , inbreed is zero . The ancestral history coefficient AHC according to Baumung et al. measures how often a randomly taken allele was in IBD status during pedigree segregation .Trends of the different inbreeding coefficients were quantified by an analysis by birth years. Pearson correlation coefficients among the different coefficients of inbreeding were calculated using SAS, version 9.4 . In addition, we calculated Pearson correlation coefficients for individual and ancestral inbreeding coefficients between offspring and parents as well as between both parents using SAS. In order to assess the genetic substructure of the reference population we compared the average coancestry within the parental population (Φ) with the average coefficient of inbreeding according to Meuwissen and Luo  of the ri) was calculated dependent on the GE according to Gutiérrez et al. [The individual rate of inbreeding  of the DD reference population was estimated [Based on the mean of ΔFstimated :(8)Ne=12eN) represents the number of reproducing animals in an idealized population that would produce the same genetic diversity as the population under study. The expected effective population size under random mating (ecN) was calculated accordingly using Φ instead of F and mean GE of both parents [The effective population size ( parents . The expNeffS) and dams (NeffD):is (id) is the relative frequency of use of the sire or dam i among all sires (dams) of the reference population [The unbalanced use of reproducers can be estimated through the effective number of sires (pulation .f) and the effective number of founders (ef) and founder genomes (gf) were 814, 66, and 16.2  was 0.031 and losses due to genetic drift were 0.0233 (GD* − GD). Losses through unequal contributions of founders (1 − GD*) reached 0.008. The relative effects due to genetic drift on genetic diversity were 75.9% and thus were much larger than the influences due to unequal use of founders with 24.1%. The amount of genetic diversity lost in the reference population since founder generation due to bottlenecks and genetic drift  was lower and the eN of SHPs (eN = 92) was similar to the DD population studied [eN is an important parameter for the evaluation of the genetic diversity and the endangerment status of populations. eN in the range of 50 to 100 is considered critical for the endangerment of populations. Bijma [i of 0.5 to 1.0% per generation, which corresponds to an eN of 50 to 100. The DD population exceeded this threshold. Even if the mean eN for the complete reference population was below 100, eN for the birth years from 2017 to 2020 was above this critical value. The European Association for Animal Production set a threshold of 5% inbreeding in 50 years for a non-endangered population [eN in the recent birth years, it can be assumed that this threshold will be reached. pedigree . The Ne g breeds . Higher  and 123 . Similarabout 90 . For Novabout 90 ,18,22. Ts. Bijma  suggestepulation . With anNew of the animal had a very low correlation to Fa_Kal and other measures of ancestral inbreeding of the dam and sire indicative that new inbreeding in the offspring was not associated with ancestral inbreeding of parents. Analysis of probabilities of gene origin indicated only a moderate influence on loss of genetic diversity through unequal use of founder animals even if a large proportion of genetic diversity was lost through genetic drift in consequence of a severe decrease of an effective number of founder genomes compared to an effective number of founders. Parameters derived from the probabilities of gene origin help to describe the development of the genetic variability in a population. Dividing the reference population into four birth year classes showed a decreasing trend for gf and gf/ef due to an increasing genetic drift. Founders ef, af and af/ef slightly increased in 2016–2020, and inbreeding measures decreased due to a more balanced representation of founders in the reference population of these birth years. The founders of the 2016–2020 reference population did not lead to an increase of gf, as these dogs were not introgressed from other dog breeds into DD, but came from lines that were not used as frequently or regularly in breeding during 2005–2015. Thus, the original gene pool of DD was not increased. A slight increase of ef and af in 2016–2020 indicated a more balanced representation of founders than in the 5–10 birth years before. The reason for the great genetic diversity of this relatively new hunting dog breed may be seen in the broad use of a large number of genetically diverse founders, the increasing popularity resulting in a large population size and the breeding management aiming at a restricted use of sires and dams. The average and median number of progeny per sire were 32 and 14, respectively, with 8 to 38 progeny and 4 to 120 in the 25% to 75% and 5% to 95% quantiles, respectively. There were only 26 popular sires with more than 200 progeny. Effective sires and effective dams in the reference population were 702 and 2763, respectively, as well as per birth year at 132 ± 10 and 359 ± 32, respectively . Ratios ef/f-ratio of 0.08. A total of 32 Australian breeds had an averaged ef/f-ratio of 0.18 with populations with very large registries (>80.000) or a larger GE (>8) comparable to the DD dogs having smaller mean ratios of 0.14 and 0.17 [ef/f-ratio of 0.52, Nova Scotia Duck Tolling Retrievers showed large values [ef/f-ratio indicates a strong unequal contribution of founders and the presence of genetic drift in the early development of the population. The af/ef-ratio of 0.58 in DD suggests the presence of bottlenecks since the founder generation due to an unbalanced use of breeding animals. Despite some populations such as the Bavarian Mountain dogs (af/ef = 0.78) [af/ef = 0.89) [af/ef-ratio of 0.17 [gf/ef-ratios (0.20) than the DD population studied here. An Australian population of 44,396 Labrador Retrievers had a very similar gf/ef-ratio [The number of founders was reasonably high in the DD population. Cavalier King Charles and English Cocker Spaniels achieved similar levels with 836 and 837 founders, respectively . Nova Scand 0.17 . An Austand 0.17 ,45. Withe values . The sma = 0.78)  or the L = 0.89)  having o of 0.17 . The impfe-ratio . Lower lfe-ratio  and Finife-ratio , while Hfe-ratio  and sevefe-ratio  among oti estimate of 0.00551, which is in the lower range compared with many other dog breeds (i across breeds exceeded the estimate for the DD population. The large population size of the DD population with a large number of genetically diverse founders may have contributed for the low estimates of F and ΔFi. Correlations of individual, new and ancestral inbreeding measures between both parents were low, indicating that matings among highly inbred partners and partners with inbred ancestors in their pedigrees were uncommon. In addition, a comparison of average coancestry within parents with average coefficients of inbreeding in the reference population revealed that clustering of the population was largely avoided and thus, the increase of inbreeding was kept at a low rate. The restriction of matings for sires with a maximum of 6 litters per year seemed to prevent genetic substructures and line breeding. In the 2016–2020 birth year cohort, α decreased to 0.00624 reflecting a slightly increasing trend of average coancestry among parents but a stronger opposite trend in the individual coefficient of inbreeding. Therefore, the decrease in α may be explained by fewer matings among close relatives and thus, by the avoidance of new inbreeding, as shown by lower estimates for FNew and the individual rate of inbreeding per generation (eN and ecN were smallest for the 2016–2020 birth year cohort. Further indicators for breeding management are coefficients of inbreeding. In the DD, F was at the same level as in many other dog breeds . SHPs hag breeds . All meaneration . Accordia_Bal and AHC showed a strong increasing trend, indicating an increasing number of ancestors in the pedigree who were autozygous at least once in previous generations. The correlation of 1 between Fa_Bal and AHC indicated that these coefficients have similar assumptions, despite the different estimation methods. Therefore, estimation of only one of the two coefficients may be sufficient. The inbreeding coefficients by Kalinowski et al. [To assess the risk of negative effects of high inbreeding coefficients, it is important to distinguish between ancestral and recent inbreeding. Recent inbreeding is assumed to be more harmful than ancient inbreeding, because the frequency of deleterious alleles is expected to decrease over time through selection . To our i et al.  showed ti et al.  should bThe results of this study indicate a loss of genetic diversity mainly due to genetic drift, as shown by the loss of effective founder genomes in comparison to the effective number of founders. The effective number of founder genomes continued to decrease and the number of ancestors that were autozygous in previous generations increased in the pedigrees. The ancestral inbreeding coefficients indicate a loss of genetic diversity in the past due to the use of breeding animals with common ancestors. Despite unequal employment of breeding animals and genetic drift in the last 60 years, a large genetic diversity has been maintained and the individual rate of inbreeding has been kept at a low level in the last 20 years.


Harpagifer, in the area are described two nominal species. Nevertheless, this genus recently colonized South America from Antarctica, providing a short time for speciation processes. Combining DNA sequences and genotyping-by-sequencing SNPs, we evaluated the role of Quaternary glaciations over the patterns of genetic structure in Harpagifer across its distribution in the Magellan Province. DNA sequences showed low phylogeographic structure, with shared and dominant haplotypes between nominal species, suggesting a single evolutionary unit. SNPs identified contrastingly two groups in Patagonia and a third well-differentiated group in the Falkland/Malvinas Islands with limited and asymmetric gene flow. Linking the information of different markers allowed us to infer the relevance of postglacial colonization mediated by the general oceanographic circulation patterns. Contrasting rough- and fine-scale genetic patterns highlights the relevance of combined methodologies for species delimitation, which, depending on the question to be addressed, allows discrimination among phylogeographic structure, discarding incipient speciation, and contemporary spatial differentiation processes.Molecular-based analysis has become a fundamental tool to understand the role of Quaternary glacial episodes. In the Magellan Province in southern South America, ice covering during the last glacial maximum (20 ka) radically altered the landscape/seascape, speciation rates and distribution of species. For the notothenioid fishes of the genus  Quaternary glacial cycles are considered main drivers of current distribution patterns in cold-temperate and polar near-shore biotas, as continental ice sheet expansions during glacial maxima led to the eradication of most marine benthic organisms in large ice-covered areas –5. Thus,2 with a volume of around 500 000 km3 ) . Using pc signal .Table 2a,b). H. bispinis and H. palliolatus shared several haplotypes, including the dominant ones, and showed low frequencies of private haplotypes. For the nuclear marker rhodopsin, derived haplotypes were linked by no more than three mutational steps to the dominant and broadly distributed haplotype (c).Genealogical reconstructions of haplotypes using mtDNA markers (COI and D-loop) showed a star-like pattern, with a single broadly distributed dominant haplotype a,b. H. baplotype c.FigureHarpagifer in South America. Bayescan determined that 68 SNPs showed strong or very strong evidence of being putatively under diversifying selection and in consequence were removed from the dataset. Finally, 2993 putatively neutral non-targeted SNPs were used to evaluate the spatial genetic structure and contemporary gene flow of Patagonia and Falkland/Malvinas in Harpagifer populations.We obtained a total of 3061 SNPs for a,b). Independently of whether analyses were based on individuals (Structure), a spatial model incorporating isolation by distance (conStruct) or sampled localities (DAPC), they showed the same pattern. With clustering approaches, Falkland/Malvinas appears as an isolated group with slight signals of admixture with Patagonia with conStruct, Structure and BayesAss (figure 3a–c). For Structure, and in agreement with DAPC, three main groups were detected as optimal clustering using Evanno's method: two in Patagonia (P1 and P2) and one in Falkland/Malvinas. Using HPRare, no private alleles were detected in Falkland/Malvinas, while 58 were detected in Patagonia. Furthermore, no diagnostic allele was found comparing Patagonia with Falkland/Malvinas.In contrast with results with DNA sequence data, there was a significant geographic structure found in the study area . With sifigure 3c) showed asymmetrical migration rates between the two groups in Patagonia following a northern migration pattern, with a migration rate of 18.8% from P1 to P2. A lower migration rate of 2.03% was recorded from P2 to P1 (c). The same analysis showed limited and asymmetrical migration rates from Patagonia to Falkland/Malvinas Islands, with an estimated proportion of 3.69% migrants in total from both groups in Patagonia (P1 and P2) (c), with 96.3% of the individuals of Falkland/Malvinas with ancestry in the same island (self-recruitment), results that agreed with admixture proportion pies estimated with conStruct (c).Contemporary gene flow determined with BayesAss c showed P2 to P1 c. The sa and P2) c, with 9onStruct c.. 4Harpagifer, which exhibited very low (and even absence of) genetic differentiation. Neither of the clustering analyses using traditional DNA sequences was able to recognize significant groups associated with the nominal species H. bispinis and H. palliolatus. In fact, each of the analysed markers consistently showed the presence of shared dominant haplotypes in both nominal species. Moreover, levels of genetic distance between H. bispinis and H. palliolatus (0.213 in D-loop) are lower than those recorded for intraspecific population studies of fishes (0.343 ± 0.05%) [Harpagifer populations across the Magellan Province represent a single evolutionary unit, supporting a recent biogeographic ichthyological revision of sub-Antarctic fish fauna suggesting a single Magellan ecoregion [Harpagifer bispinis (Forster 1801), is currently distributed along the Pacific and the Atlantic margins of South America and in the Falkland/Malvinas Islands.The biogeography of the Southern Ocean largely reflects the historical and contemporary interaction among plate tectonics, oceanography, climate and the biota through the Cenozoic, and particularly during the last 50 Ma –76. Our ± 0.05%) . Hence, coregion . FinallySiphonaria fuegiensis [S. lessonii [Harpagifer populations in South America exhibited middle to low levels of mtDNA and nuclear DNA genetic diversity compared to other Magellan fishes including the notothenioid Eleginops maclovinus [Galaxias maculatus [Lepidonotothen spp. [Trematomus [Harpagifer may be explained by the Quaternary glacial history of the study area, as well as the ecology of the species. The colonization of areas that were formerly glaciated would have involved a series of genetic bottlenecks and therefore recolonized areas should exhibit low genetic diversity dominated by few haplotypes and high frequency of sequences from the founding population. Accordingly, the main phylogeographic patterns in Harpagifer provide evidence for the E–C model of Quaternary biogeography. Alternatively, the absence of genetic structure found in Harpagifer using DNA sequences could also be a consequence of the recent diversification estimated for the genus that may have colonized the Magellan Province less than approximately 1 Ma [Low levels of phylogeographic structure in the study area have been reported in the pulmonates egiensis  and S. llessonii . Also, Hclovinus  and the aculatus , being mhen spp. ,81 and Tematomus . Low levely 1 Ma .H. bispinis, two of them restricted to Pacific Patagonia, one located north of the Strait of Magellan (P1) another south of it (P2), as well as a third differentiated group from the Falkland/Malvinas Islands (M1). Our analyses showed a marked separation between Pacific Patagonia and the Falkland/Malvinas Islands, a pattern that could be explained mainly by the geographic distance between populations. Despite these areas being highly differentiated, we found evidence of low but significant asymmetrical gene flow where at least 3% of the individuals collected in the Falkland/Malvinas Islands are likely to be derived from Pacific Patagonia. In the opposite direction, a negligible percentage (less than 0.1%) of the individuals from Patagonia could have been from the Falkland/Malvinas Islands. Contemporary asymmetrical gene flow from Pacific Patagonia towards the Falkland/Malvinas Islands in H. bispinis is expected under the general oceanographic circulation pattern in this region and has been also found in patellogastropods [Complementing the results obtained with traditional DNA sequences, SNP-GBS identified a contrasting pattern; three genetic groups in stropods ,33 and pstropods  using trH. bispinis is the presence of two groups in Pacific Patagonia separated by the Strait of Magellan. After Quaternary glacial cycles, the Strait of Magellan is mainly a long waterway that separates Patagonia from Tierra del Fuego, with a minimum width of 2 km and a maximum depth of approximately 1800 m. Although this area has been considered as a transition zone by some authors [H. bispinis exhibit major restrictions of movement. Hence, individuals are mainly benthic inhabitants of coastal rocky shores and intertidal pools, and have the lowest natant ability among sub-Antarctic nototheniids [H. antarcticus [Harpagifer larvae tend to be retained close to the coast [Nacella magallanica [N. mytilina [An interesting spatial pattern that emerged from the SNP-based structuring analyses in  authors ,84, to d authors  and the theniids . Accordiarcticus ). Howevehe coast ,88, limihe coast . The Strallanica  and N. mmytilina .Harpagifer bispinis represent the first evidence of a genetic discontinuity across this area and show the importance of the use of fine-scale molecular markers in genetic differentiation studies, which may play a key role in the knowledge of the Quaternary evolution of near-shore benthic fauna. A similar example occurs in the 30° S transition zone in the Humboldt Current System [Pyura chilensis, this well-known phylogeographic break was unrecognized by traditional markers [Our results in t System . In the  markers  but dete markers ,92.Since this is the first study in a Magellan near-shore marine benthic species performed with SNPs, further phylogeographic studies using fast-evolving markers and oceanographic biophysical models of the circulation patterns in this complex area are necessary to corroborate and support these findings, and to assess if this isolation by distance pattern could be maintained by local adaptation processes.. 5Harpagifer species in southern South America or at least supported evidence of incipient speciation. Nevertheless, such a conclusion would have been a consequence of intraspecific spatial structure rather than historical genealogical patterns associated with historical divergence. Using different species concepts , our data confirm that Harpafiger in South America does not include two separate evolutionary units as Richardson [H. palliolatus. Based on the low levels of pairwise distances recorded among Harpagifer populations across Pacific Patagonia and the Falkland/Malvinas Islands, our results do not support a scenario of incipient speciation or divergence. Furthermore, the absence of diagnostic alleles between Patagonia and Falkland/Malvinas provides strong evidence that the detected patterns with SNPs in South American populations of Harpagifer are a consequence of contemporary patterns of genetic structure and gene flow. This pinpoints the necessity to verify the phylogenetic status of each evolutionary unit using different approaches before drawing genealogical conclusions based solely on non-targeted SNPs. In the Southern Ocean, traditional DNA molecular markers have been extensively used for species delimitation analyses (see [Since non-targeted SNPs are increasingly used for phylogenetic inferences and species delimitation analyses –55,93, wchardson  hypothesses (see ,41,94,95ses (see ,96,97, bses (see ,57 suggeFinally, as previously demonstrated in shallow marine benthic organisms including fishes , inverte


Preoperative chemoradiotherapy (CRT) has emerged as a potential therapeutic strategy to increase the fraction of patients with pancreatic ductal adenocarcinoma (PDAC) who are candidates for surgical resection. However, treatment response is heterogeneous and ranges from complete response to progression. In this study, we uncovered that the transcription factor STAT3 mediates CRT resistance in PDAC cell lines with high IL-6/STAT3 signaling activity. If further validated, pharmacological inhibition of the IL-6/STAT3 pathway may represent a promising therapeutic strategy to increase responsiveness of PDAC to preoperative CRT.The debate is ongoing regarding the potential role of preoperative chemoradiotherapy (CRT) for patients with pancreatic ductal adenocarcinoma (PDAC), and whether it should be reserved for borderline resectable or unresectable tumors. However, treatment response is heterogeneous, implicating the need to unveil and overcome the underlying mechanisms of resistance. Activation of the transcription factor STAT3 was recently linked to CRT resistance in other gastrointestinal cancers such as rectal and esophageal cancers, but its role in PDAC needs to be clarified. Protein expression and phosphorylation of STAT3 was determined in PDAC cell lines and connected to transcriptional activity measured by dual-luciferase reporter gene assays. Inhibition of STAT3 signaling was achieved by RNAi or the small-molecule inhibitor napabucasin. We observed a positive correlation between STAT3 signaling activity and CRT resistance. Importantly, genetical and pharmacological perturbation of the IL-6/STAT3 pathway resulted in CRT sensitization specifically in those cell lines, in which STAT3 activity was augmented by IL-6. In conclusion, our data underscore the general importance of IL-6/STAT3 signaling for CRT resistance and suggest that pathway inhibition may represents a putative treatment strategy in order to increase the fraction of patients with PDAC who are candidates for surgical approaches. Pancreatic ductal adenocarcinoma (PDAC) is a major devastating type of cancer with an increasing incidence  and is eRadiation therapy (RT) represents a putative treatment strategy to increase the fraction of patients who are candidates for surgery. However, it remains under exploration whether it should be reserved for borderline resectable or unresectable tumors, and whether it should be used in a preoperative or postoperative setting or sequentially ,5,6,7,8.Tyr705) dimerizes and translocates to the nucleus, where it activates its target genes leading to proliferation, survival, and tumor invasion [Signal transducer and activator of transcription 3 (STAT3) has been shown to play a crucial role in the development and progression of PDAC ,10 and iinvasion ,13,19,20Previously, we have demonstrated that the IL-6/STAT3 signaling pathway plays a central role in mediating the resistance of rectal cancer to chemoradiotherapy (CRT) and that inhibition of IL-6/STAT3 signaling leads to the sensitization to CRT in vitro and in vivo ,22.In the present study, we therefore investigated the putative role of STAT3 in controlling CRT sensitivity in PDAC. Indeed, STAT3 is connected to treatment resistance, and the small-molecule inhibitor napabucasin re-sensitizes a priori resistant cells to CRT. Our data underscore the general importance of STAT3 for CRT resistance and point to a novel role for STAT3 as a potential target for radiosensitization in PDAC.® Mycoplasma Detection Kit .Seven human PDAC cell lines were obtained from the American Type Culture Collection : BxPC-3, Capan-1, Capan-2, L3.6, MIA PaCa-2, PANC-1, and PaTu8988t. The ATCC ensures authenticity of these cell lines using short tandem repeat profiling . Cells wTyr705, cells were incubated with 100 ng/mL IL-6 for 10 min prior to lysis. The original Western blot images are shown in Western blot analysis was performed as previously described ,22. In s® Reporter Assay System  for visualization of luciferase activity. Experiments were performed as previously described according to the manufacturers’ instructions [Renilla luciferase reporter plasmid (Promega) for normalization . For analyses, normalized relative light units (RLU) of the STAT3-specific reporter and normalized values of the negative control reporter were divided. To assess STAT3 pathway activation, samples with and without stimulation with IL-6 were compared. Additional information about transfection conditions is presented in The STAT3 CignalTM Pathway Reporter Assay Kit  was used for reporter transfections and the Dual-Luciferaseructions . All cellization . After 2TM technology (Lonza). Briefly, cells growing in log-phase were transfected with small-interfering RNA  targeting STAT3 and a nonsilencing control siRNA . Additional information about transfection conditions and siRNA sequences is presented in Silencing of STAT3 was performed as previously described ,22 usingTo determine the respective surviving fractions (SFs), standard colony formation assays (CFAs) were performed as previously reported ,22. Brie® reagent (Promega) in a plate reader . All experiments were conducted as technical and biological triplicates.Cellular viability assays were performed as previously described ,22. Briet-test in Microsoft Excel (version 2016), and visualized in Grapher software . Correlations were calculated with Pearson’s correlation in Microsoft Excel (version 2016). Statistical analyses of irradiation experiments were performed with two-way analysis of variance (ANOVA) to calculate significant differences between control and treatment groups using Microsoft Excel . p-values < 0.05 were defined as significant.All experiments, except for Western blot analyses, were performed as technical and biological triplicates. Dual luciferase and cell viability assays were analyzed by an unpaired two-tailed Student’s Tyr705 levels in response to IL-6. In all cell lines, IL-6 induced phosphorylation of STAT3, while expression levels of unphosphorylated STAT3 remained unchanged . We used single irradiation doses from 1 to 8 Gy, with or without the chemotherapeutic agent 5-FU. Interestingly, five cell lines showed no difference in response when RT and CRT were compared, suggesting that 5-FU mediated no additional radiosensitization effect A, additiTo functionally assess whether STAT3 confers CRT resistance, we reduced STAT3 expression with RNAi. We selected three cell lines with high STAT3 activity , one cell line with low STAT3activity (PANC-1), and the normal epithelial cell line RPE-1. Successful silencing of STAT3 was validated by Western blot analysis  which h for 1 h A, which There is an ongoing and controversial debate on the potential role of preoperative treatment of PDAC ,7,8. In p = 0.029). Regarding patients with initially unresectable, locally advanced PDAC, Fietkau et al. recently reported in their interim analysis of the randomized CONKO-007 trial, in which patients without tumor progression after induction chemotherapy (gemcitabine or FOLFIRINOX) either received chemotherapy alone or concurrent chemoradiotherapy, that even in this stage of the disease, an R0 resection may be achieved in up to 20% of patients [In this context, it also remains unclear whether preoperative treatment should consist of chemotherapy (preferably FOLFIRINOX), (chemo-)radiotherapy, or a  combination thereof, and whether preoperative treatment should be reserved for borderline resectable tumors, who are at high risk of an R1 resection, or unresectable tumors ,30,31,32patients .While these data point to an important role of preoperative treatment strategies for PDAC, they also highlight two central aspects: First, treatment response is not uniform, and ranges from progression to complete histopathological response. Second, clinicians are faced with the dilemma that the individual patient’s response to it is currently not predictable. Accordingly, there is a strong clinical need to identify the genetic mechanisms and pathways underlying treatment resistance, because this may lead to the identification of effective strategies to overcome it. As a consequence, it may increase the fraction of tumors that respond to preoperative treatment, ultimately resulting in higher rates of R0 resections, which remains one of the most important prognostic factors.Here, we show that IL-6/STAT3 signaling mediates CRT resistance and that pathway inhibition re-sensitizes PDAC cells with high STAT3 transcriptional activity to CRT. In this context, our data support the growing body of evidence that STAT3 may represent a potential therapeutic target to overcome CRT resistance in various tumor entities ,34. RectAs discussed above, there is growing evidence that STAT3 inhibition may represent a potential therapeutic concept for tumors with high STAT3 signaling activity. To translate these findings into the clinic, effective pathway inhibitors are needed. Napabucasin has already been tested in a phase III clinical trial (NCT01830621) for advanced CRC . In thisMechanistically, it remains to be elucidated how STAT3 signaling mediates CRT resistance and which downstream pathways and targets are affected. Regarding napabucasin, evidence suggests that its sensitizing effect in combination with RT may be explained by the ability of both treatments to alter redox homeostasis, which may lead to an increased generation of reactive oxygen species (ROS) ,40,41. IOur results indicate that for a highly selected subgroup of patients with PDAC with high IL-6/STAT3 signaling activity, pharmacological pathway inhibition may represent a promising therapeutic strategy to enhance responsiveness to CRT. If further validated, STAT3 inhibition may be tested in preoperative treatment concepts.


Following massive persecution and eradication, strict legal protection facilitated a successful reestablishment of wolf packs in Germany, which has been ongoing since 2000. Here, we describe this recolonization process by mitochondrial DNA control-region sequencing, microsatellite genotyping and sex identification based on 1341 mostly non-invasively collected samples. We reconstructed the genealogy of German wolf packs between 2005 and 2015 to provide information on trends in genetic diversity, dispersal patterns and pack dynamics during the early expansion process. Our results indicate signs of a founder effect at the start of the recolonization. Genetic diversity in German wolves is moderate compared to other European wolf populations. Although dispersal among packs is male-biased in the sense that females are more philopatric, dispersal distances are similar between males and females once only dispersers are accounted for. Breeding with close relatives is regular and none of the six male wolves originating from the Italian/Alpine population reproduced. However, moderate genetic diversity and inbreeding levels of the recolonizing population are preserved by high sociality, dispersal among packs and several immigration events. Our results demonstrate an ongoing, rapid and natural wolf population expansion in an intensively used cultural landscape in Central Europe. Wilderness areas are rapidly declining across the planet, while available habitats and population numbers of large mammals shrink globally  continuously grow due to effective conservation measures and socioeconomic changes , where Germany is a member state, the wolf is listed in Annex II and IV under the conservation legislation of the EU Habitats Directive (Council Directive 92/43/EEC), with the overall goal of reaching the ‘Favourable Conservation Status, FCS’ . Included in Article 2, the conservation status of the wolf as priority species needs to be monitored by the member states (Article 11). The wolf population in Germany has been monitored since 2001. The main monitoring methods used in Germany are presence sign surveys in combination with camera trapping and genetic analyses , urine and blood stains during the pre-oestrus period collected while snow tracking. Saliva traces were collected from livestock and wild ungulate kills. Tissue samples were collected from wolf carcasses and a tooth from one set of skeletal remains. Blood, hair and saliva samples were collected from some wolf carcasses in addition to tissue samples. Blood samples were collected from injured wolves. Blood, hair or saliva samples were collected from wolves captured for radio collaring.Within the first years of German wolf monitoring, initial genetic analyses were performed at the Institute of Nature Conservation, Polish Academy of Sciences, in Krakow, Poland . DNA from saliva, saliva traces from kills, blood, hair samples and one tooth was extracted using the QIAamp DNA Investigator Kit . In each case of extraction, manufacturers’ protocols were followed.DNA from faecal samples was extracted using the QIAamp DNA Stool Mini Kit . For DNA extraction, mucus was scraped off from the surface of the faeces with forceps. DNA extraction from urine and blood (oestrus) samples was performed following Hausknecht et al.  with sli® No-ROX , 0.4 μM of each primer and 2-μl DNA extract. The PCR protocol started with initial denaturation at 95 °C (3 min), following 40 cycles of 95 °C (5 s) and 60 °C (30 s). The laboratory procedures and protocols for mtDNA sequencing of scat, hair, urine, blood and tissue samples as well as purification of PCR products have been described in Lesniak et al. . Saliva samples and one tooth sample were sequenced using the primers WDloopL and WDloopH254 . A multiple-tube approach was applied including at least four and, for some samples, up to eight or twelve replicates per non-invasive sample. Consensus genotypes with a minimum of ten loci were accepted with ≥2 PCR amplifications of a heterozygote and ≥3 PCR amplifications of a homozygote locus. Some selected samples were included despite a lower amplification success rate. For these individuals, fragmentary genotypes were verified and completed by comparison with the known genotypes of the breeding partner and pups of that breeding pair. One mixed DNA sample  was also included, as it contained the genotypes of the two breeders in that territory.Autosomal microsatellite data were obtained as part of the regular genetic wolf monitoring performed in our laboratory to assess relatedness and origin of the wolves. We used 13 variable unlinked microsatellites along with two sex markers, DBX6 and DBY7 Seddon . MarkersTo identify individual genotypes, the R package DNAtools . As wolf pups are born at the end of April/beginning of May, a wolf monitoring year starts at the first of May and ends at the end of April in the following year. Here, we mention the year in which the monitoring year starts (e.g. 2005 for the monitoring year 2005/2006).Various computer programmes are available for inferring kinship and pedigree reconstruction , allelic richness (Ar), observed heterozygosity (Ho) and unbiased expected heterozygosity (He) were calculated for the different years based on the genotypes of the inferred breeding pairs to evaluate the genetic diversity. Pedigree-based inbreeding coefficients (Fp) for the offspring from breeding pairs were calculated with the inbreeding function in the R package GeneticsPed ver. 1.40.0  based on the Bayesian information criterion , number of effective alleles (Ne), allelic richness (Ar), observed heterozygosity (Ho) and unbiased expected heterozygosity (He) for the year 2015. Furthermore, we used the fast maximum-likelihood clustering method ‘snapclust’ . Testing of the breeder’s datasets for each year from 2005 to 2015 revealed that <6% of the loci across all eleven subsets deviated significantly from HWE (p < 0.05). By comparing inheritance patterns with field data, 7 of 13 loci exhibited consistent Mendelian patterns of inheritance. In 17 of the 431 offspring genotypes (3.9%), one allele at one of the six loci  did not match between parents and offspring. As the rate was low, all 13 loci were used for subsequent analyses. For all 524 identified individuals, the mean number of alleles was 6.62, observed heterozygosity was 0.574 and expected heterozygosity was 0.573. The polymorphic information content for the set of 13 loci was high (PIC = 0.526). The probability of identity (PID) was 2.8 × 10−09 and the probability of identity between siblings (PIDsib) was 1.85 × 10−04, indicating that the presence of individuals sharing the same genotype by chance was very unlikely within the population.We successfully genotyped 1341 samples collected between 2002 and 2015 across Germany, including 872 scats, 126 saliva traces from killed prey, 108 tissue and 108 urine samples, 78 hair samples, 34 blood samples, eight oestrus blood stains, six saliva samples collected from carcasses or from wolves captured alive and one tooth. Overall, we identified 524 individuals in the analysed samples Table . Wolf inPack and pedigree analysis based on genetic and field data allowed us to determine the relatedness among the identified individuals. This was done for 76 different breeding pairs in 51 territories with a total of 145 litters between 2005 and 2015  Tables . Comparep = 0.01, Sen’s Slope = ±0.009)  Figs. . HW01 wap < 0.001, Sen’s Slope = 0.179) from 2.9 in 2005 to 4.8 in 2013 , 12 out of 13 female breeders originated from German packs, while 5 out of 12 male breeders were born in the study area Fig. . The firp < 0.001, mAIc females = 2.131; mAIc males = −2.077). Moreover, we found several cases of serial monogamy where females had between two and four breeding partners  than females . Dispersal distances between the reproducing females and males did not differ significantly . Dispersal distances between the reproducing females and males still did not differ significantly  to form a pair. The underlying idea is that after reaching sexual maturity every wolf will aim to breed within a wolf population consisting of territorial social groups , as their great-great-grandparents were already related. In this context, it should be noted that the family ties of individuals with unknown origin are unresolved in this study. Thus, the degree of inbreeding is likely underestimated throughout the dataset  signs of a founder effect and (ii) a colonization process in Germany similar to that found in other areas. We also detected that (iii) gene flow and dispersal among packs were predominantly male-biased, while average dispersal distances did not differ by sex among dispersers. Furthermore, we found (iv) moderate genetic diversity and inbreeding levels of the recolonizing population compared to other European wolf populations  is high (Reinhardt et al. Supplemental Material Figures S1-S5Supplemental Material Tables S1-S5


We describe our efforts to operationalize the ACMG guidelines in the “real world” to optimize our EHR (PennChart) for the delivery of precision medicine  has simultaneously been adopted into routine practice. A recent Points to Consider Statement by the American College of Medical Genetics and Genomics (ACMG) provides a framework for the optimal integration of genomic data into the EHR.We have taken a two-staged approach to integrating germline and somatic genetic data into the EHR. Currently, most genetic results are reported in unstructured PDF documents. We established common procedures across all Penn Medicine’s clinical genetics services for genetic testing reports, labeling them with a common naming convention and scanning them into a specific Genetic Results document type, which we created specifically for genetic testing results. This document filters both into our Lab (standard results) and PennChart Precision Medicine Tabs. We created the latter tab as a centralized location in the EHR to enable easy access to all genetic data, ensuring that it is not overlooked amid all the other testing that happens over a patient’s lifetime. This approach has standardized the real-time integration of unstructured genetic data into the EHR. Further, it has facilitated our efforts to import legacy data, as we began scanning all genetics documents with the common naming convention several years before implementing the Precision Medicine Tab.3 The second stage of our efforts therefore aimed to integrate structured genetic data into the EHR. The PGI has leveraged Epic’s Genomics Module to record discrete genetic variant information in Human Genome Variation Society (HGVS) nomenclature along with the notation of significance ; transcript, genome build, chromosome, and genomic location are also included. Pharmacogenetic results are entered as diplotypes based on PharmVar star allele definitions . Content experts throughout Penn Medicine collaborated to develop standard operating procedures (SOPs) to ensure institutional consistency for both manual and automated entry of discrete results into the Genomics Module. To date, these SOPs have been developed for autosomal dominant and pharmacogenetic variants with plans to expand to other result types over time, such as cytogenetics and autosomal recessive alleles. Manual entry of discrete genetic data into the Genomics Module is currently completed by genetics providers, who spend less than five minutes per variant.Although the ACMG recommends that genetic results be incorporated into patient records, at minimum, as scanned PDF files or images, it is preferable to store them in discrete, computable format to enable electronic searching, clinical decision support (CDS), and secondary use for research and operations.Interfacing directly with genetic testing laboratories is essential to move from manual to automated entry of discrete results into the EHR. The PGI partnered with Ambry Genetics to implement computerized order entry so providers can place genetic test orders directly into PennChart for import into the Ambry portal. The return of results has followed a phased approach using a Health Level 7 (HL7) interface, first with PDF reports and then with discrete results, which are automatically imported into the Genomics Module, linked to their associated PDF documents, and accompanied by direct provider notification. We are working on expanding our HL7 capacity with other commercial genetic testing laboratories.2 The PGI’s partnership with Ambry Genetics enables the automatic import of results both at the time of initial testing and as variant reclassification occurs. If an update occurs, a notification is sent to the ordering provider and care team for review of the amended report.The ACMG also recommends that updated genetic test results be clearly linked to the original report in the EHR, as the interpretation of results may evolve and result in reclassifications over time.4 Not only should providers be able to retrieve external educational content to learn more about a patient’s genetic findings, but they should also receive automated recommendations at the point of care to facilitate clinical decision-making.6 The ACMG highlights that EHR vendors may not be fully equipped to build CDS systems in isolation due to the complex and dynamic nature of genomic medicine. The multidisciplinary nature of the PGI addresses this challenge by fostering collaboration between clinical, pathology, and information technology (IT) experts to build CDS tools that are seamlessly implemented into routine care.One of the greatest potential benefits of integrating genetic data into the EHR is the ability to link results to CDS.DPYD gene testing.8The PGI has leveraged Epic’s Genomic Indicators, driven by variants that are pathogenic/likely pathogenic or medically actionable, which are tags added to a patient’s record to indicate potential disease risk or drug sensitivity based on his/her genetic testing results. Genomic indicators are displayed on the Snapshot Tab (chart front page) and facilitate clinical decision-making by triggering automated recommendations directly in the EHR targeted to both providers and patients. By using triggered genomic indicators, we prevent variants of uncertain significance from being misinterpreted by nongenetics providers as disease associated. Our initial use cases for CDS provide guideline-concordant recommendations on colonoscopy timing intervals for patients with Lynch syndrome and fluoropyrimidine dose adjustments in patients with dihydropyrimidine dehydrogenase deficiency identified on The ACMG recommends that genetic data in the EHR be made available to patients in understandable and usable form. At the time of pretest counseling, patients are informed that they will receive their genetic results in conjunction with counseling on their clinical interpretation, thereby mitigating any confusion and distress that may arise if patients receive their results in isolation. As such, we have developed a method to retain these data, as permitted by law, until patients are counseled on their results, after which ordering genetics providers manually release them concurrently to nongenetics providers and through PennChart’s secure patient portal. This electronic portal also features the Genetic Profile, a centralized location where patients may view their results, along with annotated educational information written in patient-friendly language for select genetic conditions and pharmacogenetic results.Although our primary objective in developing the Genetic Results document type was to enable centralized document display in the Precision Medicine Tab to improve patient care, it also provides the flexibility to segregate genetic results from other EHR data. Being able to segregate genetic data was found to be important by the privacy officer at our institution to provide the capability to withhold that data when desired for privacy purposes, such as in certain health information exchange use cases. Genetic results from family member records also are categorized as a distinct document type within the EHR so that they are only accessible to genetics providers and cannot be released to external parties as part of the patient’s chart.The PGI has made significant strides in integrating genomic data into the EHR for the optimization of patient care. To date, approximately 21,500 documents have been filtered into the Precision Medicine Tab, including over 3,500 legacy scans and 627 discrete results from Ambry Genetics. We attribute our successes to date to large-scale, collaborative, and coordinated efforts led by committed champions with clinical, research, pathology, IT, and legal expertise throughout Penn Medicine. However, our experiences have not been without challenges. The complexity of genomic medicine requires substantial educational initiatives to ensure that our multidisciplinary team has the knowledge base necessary to effectively impact patient care. In addition, as we expand our discrete reporting interfaces to other laboratories, they, too, must have the infrastructure necessary to connect with our EHR systems. Finally, the PGI has benefited from significant support and investment from Penn Medicine that may not be feasible at other, smaller institutions. As such, we are committed to sharing our SOPs, decision process algorithms, and other documentation with the genomic medicine community to inform broader efforts to optimize the integration of genomic data into the EHR.Future PGI endeavors include (1) developing SOPs beyond autosomal dominant and pharmacogenetic variants for other types of genetic variants, (2) building CDS systems for additional genetic test results, (3) providing guidance on genetic testing indications and familial risk assessment, (4) partnering with additional genetic testing laboratories for computerized order entry and discrete result reporting, (5) collaborating with other health-care facilities to enable transportability of genetic data for patients seeking care at multiple institutions, and (6) maintaining privacy and security protections as genetic data become more accessible electronically, both within and between institutions. We also look forward to working with the genomic medicine community to develop innovative solutions for broader-reaching goals such as unifying reporting standards, storing high-volume genomic data efficiently, supporting variant reclassifications from external knowledge bases, handling genetic results from unique populations, and ensuring equitable access to genomic medicine for all patients. The EHR is a powerful tool for the delivery of precision medicine; we are optimistic that the efforts of the PGI and other similar initiatives will be instrumental in optimizing patient care and accelerating the discovery of novel genomic medicine applications over time.Supplemental Figure 1