Ammodytes heian and revived the species name Ammodytes japonicus to describe sand lances from the northwestern Pacific Ocean. This study used molecular methods to investigate genetic relationships between the two sand lance species in Korea and Japan. In total, 154 specimens were collected from four locations in Korea , and 50 specimens were collected from a single location in Japan (Wakkanai in the Okhotsk Sea). Mitochondrial DNA analysis demonstrated that the individuals from Baengnyeongdo and Tongyeong all belonged to the SOL, whereas those from Gijang, Jumunjin, and Wakkanai included individuals from both the NOL and SOL (over 75% NOL). Population structure analyses were performed on the same individuals using seven microsatellite DNA markers. The population structure analysis based on 201 specimens identified two clusters (named as northern group and southern group), with the admixture proportion (q) of < 0.1 for the northern group in the Backyeongdo and Tongyeong sand lances and < 0.1 for the southern group in the Wakkanai sand lances. The high heterogeneity indicated that the former was probably A. japonicus and the latter probably A. heian. However, the admixture proportion in the Jumunjin and Gijang sand lances was 0.71–0.75 for the southern group, indicating that hybridization and unidirectional introgression from SOL to NOL occurs in southwestern margin of the East Sea. Our findings illustrate the speciation process based on different patterns of gene flow between Korean and Japanese sand lance, which is strongly influenced by both the paleo-climatic change and the contemporary local oceanic current pattern.Based on northern (NOL) and southern (SOL) mitochondrial lineages, recently, it proposed the new species  Interspecific genetic exchange and hybridization are important factors in the evolution of some species , 2. ManyHybridization occurs more often when the ranges of closely related species overlap , 5, and Ammodytes remains.Cryptic species, which are morphologically identical but genetically distinct, may be generated allopatrically or sympatrically. However, genetic identification of cryptic species can be considerably more difficult when they have recently diverged by introgression , 13. TheAmmodytes personatus when referring to sand lances inhabiting the temperate zone of the Yellow Sea, the southern East Sea (Sea of Japan), and the waters south of Japan. Previous morphological and genetic studies on the Korean A. personatus suggested there were two lineages: (1) a northern lineage (NOL) and (2) a southern lineage (SOL) [The taxonomic status of the northwestern Pacific sand lance was first questioned by Turanov and Kartavtsev , who sugge (SOL) –17. The ge (SOL) , 64–67 (ge (SOL) , spawninge (SOL) , and genge (SOL) .Ammodytes heian, and Orr et al. [Ammodytes japonicus to describe the SOL from the same area and southern Japan. Orr et al. [A. heian vs. 132–166 in A. japonicus), the number of dorsal plicae (159–198 in A. heian vs. 144–182 in A. japonicus), body height (40–57% in A. heian vs. 32–53% in A. japonicus), upper jaw length (29–32% in A. heian vs. 30–39% in A. japonicus), and eye diameter (11–15% in A. heian vs. 12–20% in A. japonicus). However, these ranges overlap, making it difficult to distinguish between the two species using morphological features alone.Nonetheless, the two lineages can be distinguished by their mitochondrial DNA (mtDNA) cytochrome oxidase I (COI) sequences, and Orr et al.  describer et al.  revived r et al.  describeMany studies have used highly polymorphic microsatellite DNA markers to identify both individuals and populations , 20, 22.The aim of this study was to make comprehensive genetic comparisons that would reveal relationships among Korean and Japanese sand lances and to understand their microevolutionary history, which might include hybridization or unidirectional introgression.This research did not involve endangered or protected species, and any locations, which performed in this research, are not required for specific permission. A total of 204 adult individuals were collected at four locations around Korean peninsula and one location in Japan during 2010–2015 . The sam5′-TCW ACC AAC CAC AAA GAY ATY GGG AC-3′) primers [® PCR purification kit  and directly sequenced using the ABI BigDye terminator cycle Sequencing Ready Reaction Kit v.3.1 . The mtDNA COI sequences were aligned using ClustalW [Genomic DNA was extracted from muscle tissue or pectoral fin clips using the AccuPrep Genomic DNA extraction Kit . Partial fragments of mitochondrial DNA cytochrome c oxidase subunit I (mtDNA COI) were amplified using VF2 and FishR2  and internal size standard  and denatured for 2 minutes at 95°C. Samples were run on an ABI 3730xL DNA sequencer and fluorescent peak data were sized and scores using Genemapper ver. 4.0 .We designed seven microsatellite primers  according to the methods of Ren et al. . SuccessA. japonicus , A. heir et al. ), A. perr et al. ), and A.r et al. ) from thhttps://www2.unil.ch/popgen/softwares/fstat.htm). Hardy-Weinberg equilibrium within a population was verified using GENEPOP v. 4.3 [For microsatellite analysis, in order to analyze genetic diversity for each locus, we calculated the number of allele (Na), observed heterozygosity (Ho), expected heterozygosity (He) and polymorphic information content (PIC) of the seven microsatellite DNA markers using Cervus v. 3.0.6  and calcP v. 4.3 , 35.Genetic boundaries between populations were inferred using BARRIER software version 2.2 . For genAmmodytes species with those of Orr et al. [Ammodytes japonicus, A. heian, and A. hexapterus, respectively . At Baengnyeongdo in the Yellow Sea and Tongyeong in the Korean Strait, only SOL specimens were collected, whereas specimens from both the SOL and NOL were collected at Gijang and Jumunjin in the East Sea and Wakkanai in the Okhotsk Sea, with NOL being dominant  . In totar et al. , the phyectively .Seven microsatellite markers were used for genetic analysis of the 201 specimens. This analysis determined the number of alleles (Na), observed (Ho) and expected (He) heterozygosity, polymorphic information content (PIC), probability value estimates for deviations from Hardy—Weinberg equilibrium, and inbreeding coefficients (FIS) Tables . AverageValues of pairwise FST were significant for comparisons between the Baengnyeongdo individuals and those in Gijang, Jumunjin, and Wakkanai, similarly comparisons between Wakkanai and all the Korean locations were significant . BARRIERAmmodytes japonicus, A. heian, and A. hexapterus, respectively. The mtDNA results demonstrate that A. japonicus is distributed throughout Japan, China, and Korea, but the distribution of A. heian is limited to Korea and Japan, and that of A. hexapterus is restricted to Japan. The distribution of A. japonicus and A. heian partially overlap in the southwestern margin of the East Sea and in Wakkanai, suggesting the possibility of hybridization between these two species. A more powerful analytical procedure using microsatellite DNA markers will be required to define these relationships. The microsatellite markers demonstrated that all populations had a high degree of genetic variability (He = 0.885–0.963), and the pairwise FST values were significantly different among populations . The STRUCTURE analysis demonstrated that the sand lances were clustered into two groups, but the structure plot indicated that all individuals included mixed ratios from each cluster. The Wakkannai sand lance consisted of more than 90% cluster 2 (northern group), whereas the Baengnyeongdo and Tongyeong sand lances each consisted of more than 90% cluster 1 (southern group), suggesting there may be very little gene flow between the northern group  and the southern group . In addition, DAPC plots showed genetically two groups corresponding to Japanese and Korean sand lance and this is consistent with that of the BARRIER analysis. According to Palero et al. [Palinurus Elephas) showed a clear split into the Atlantic and Mediterranean populations, suggesting the barriers between the Atlantic and Mediterranean transition. Therefore, our results indicated the presence of strong boundary between the Korean and Wakkanai sand lances. Additionally, Ren et al. [Ephinephelus merra populations living in the western Indian Ocean, which suggested the presence of a cryptic lineage. In this study, both the mtDNA and microsatellite analyses indicated significant genetic variation among the sand lances.A minimum spanning network analysis based on mtDNA COI sequences identified three distinct lineages  among the Korean and Japanese sand lances. The genetic distances between these lineages were 7.2–8.5% (A—B), 7.7–8.5% (A—C), and 5.4–6.2% (B—C), respectively. Compared with the study findings reported by Orr et al. , lineageo et al. , the DAPn et al.  suggesten et al.  demonstrFundulus heteroclitus) using ms markers showed two distinct groups, which were consistent with previous study , and the distribution and population structure between two groups were associated with differences of allele frequencies by fixation of clinal alleles [Salmo trutta, putative hybrid populations have higher HW disequilibrium than pure ones, and these disequilibrium could be evidence of highly subdivided genetic structure [Etheostoma bison and E. caeruleum), linkage disequilibrium is expected to be high, and the more frequencies of hybrids have tendency to the more significant deviation from HWE. In this study, although two groups of sand lance have little gene flow, there are evidence of gene flow between two groups in the southwestern margin of the East Sea, indicating that genetically effective migration may occur. Especially, Gijang and Jumunjin sand lances have higher frequencies of mixing through gene exchange by migration, and this could affect their allele frequency and deviations from HWE. Therefore, this departures from HWE could be related to the several factors including the migration. In addition, the microsatellite analyses in this study that indicated the presence of gene flow also suggest that sand lances from the southwestern margin of the East Sea (Jumunjin and Gijang) have a less well-defined genetic structure. This may be explained by a variety of evolutionary mechanisms, including incomplete sorting of ancestral polymorphisms, and sex-biased hybridization [In agreement with our results, previous studies using mtDNA COI  and control region analysis (d = 5.8%) have demonstrated that the Korean sand lance is highly differentiated at different locations , 19. Ind alleles . In case alleles , heteroztructure . In additructure  suggestedization . Consistdization  reporteddization  also sugMerluccius capensis and M. paradoxus from North Benguela have mixed genotypes equivalent to 0.36 and 0.2, respectively. In particular, all M. paradoxus individuals had M. capensis COI sequences, and this resulted from recurrent backcrosses between M. capensis × M. paradoxus hybrid females and M. paradoxus [A. japonicus × A. heian hybrid females and A. japonicus males could have occurred in Gijang and Jumunjin.Our results demonstrated that 71–75% of the Gijang and Jumunjin sand lances (mainly belong to NOL in mtDNA results) were from cluster 1 (southern group). The migration rate inferred from microsatellite markers was highest between Tongyeong  and Gijang (mostly comprise NOL), accordingly, unidirectional introgression occurs predominantly from the SOL  to the NOL (East Sea). Han et al.  discoveraradoxus . TherefoLateolabrax japonicus) and spotted sea bass (Lateolabrax maculatus) have overlapping distributions in the Korean Strait and how some intermediate individuals (Lateolabrax sp.) of mixed genetic composition were formed by unidirectional introgression. Additionally, two North American Atlantic hake species, Merluccius bilinears and M. albidus, demonstrated a high level of introgressive hybridization where their ranges overlapped, suggesting that long-term hybridization and backcrossing had occurred [Hybridization occurs frequently when spawning areas or periods overlap , 57, 58.occurred . Kim et occurred  suggesteSerranus cabrilla individuals in Cabo de Gata (CG) and Cabo de Palos (CP) from the Atlantic group were comparable to 21.7% of fish with an admixed genotype from a Mediterranean population. This may have occurred when migrants were carried south by oceanic currents. Therefore, genetic population structures may be significantly influenced by present current regimes that traverse oceanographic barriers [Hybridization has great potential for rapidly introducing variability into recipient populations if the barriers to recombination can be overcome . For exabarriers . Strong barriers  suggestebarriers , 60.Hybridization can also occur between species when one species is rare and individuals are forced to find mates from a closely related species , 61. KimAnother cause of asymmetric gene flow is sex-biased dispersal or philopatry. Different genetic patterns in maternally inherited markers  and bi-parentally inherited markers  may arise because of sexual differences in spawning behavior or simply by genetic drift and population bottlenecks . If femaAmmodytes heian and revived the species name Ammodytes japonicus to describe the SOL. Orr et al. [Orr et al.  identifir et al.  reportedr et al. . TherefoCareproctus rastrinus living in the northwestern Pacific Ocean and demonstrated that SOJ1 (group 1 in Sea of Japan) and SOJ2 (group 2 in Sea of Japan) were not separated by principal component analysis, suggesting that gene flow may have occurred by secondary contact. The study concluded that the two lineages (SOJ1 and SOJ2) were not cryptic species because, although their mtDNA was well differentiated, they could not be separated by nDNA analysis. Therefore, it is unlikely that the two sand lance lineages in the southwestern margin of the East Sea are cryptic species because they could not be differentiated by microsatellite analysis . The present study serves as a follow-up study which confirms the hypotheses of Han et al. [Speciation generally occurs as a gradual process, by reproductive isolation and genetic differentiation between populations . Hybridin et al. , Ren et n et al.  and Kim n et al.  where thS1 Table(XLSX)Click here for additional data file.S1 FigThe Neighbor-joining tree was constructed under the K2P model, and the numbers on branches indicate bootstrap probabilities from 1,000 bootstrap replications.(TIF)Click here for additional data file.S2 FigEach population is indicated by abbreviation; Baengnyeongdo (BA), Tongyeong (TO), Gijang (GI), Jumunjin (JU) and Japan (JP), respectively.(TIF)Click here for additional data file.S3 FigEach population is indicated by abbreviation; Baengnyeongdo (BA), Tongyeong (TO), Gijang (GI), Jumunjin (JU) and Japan (JP), respectively.(TIF)Click here for additional data file.


Panstrongylus megistus, obtained blood samples from males and females in 31 nests built on trees, drew blood from 89 nestlings at those nests, and genotyped all samples at 14 microsatellite loci, including six new species-specific loci. We comparatively addressed the performance of parentage allocation versus kinship classification of nestlings to infer the genetic mating system of cattle egrets. In line with previous behavioral observations, we found evidence in support of a non-monogamous genetic mating system, including extra-pair paternity (EPP) and conspecific brood parasitism (CBP). Parentage allocation tests detected a higher percentage of nests with alternative reproductive tactics  than the kinship classification method . Overall, these results indicate that rates of alternative reproductive tactics inferred in the absence of parental genetic information could be underestimated and should be interpreted with caution. This study highlights the importance of incorporating samples from candidate parents to adequately determine the genetic mating system of a species. We expand knowledge on the reproductive tactics of colonial waterbirds, contributing novel data on the genetic mating system of the cattle egret, valuable for the design of management strategies for this invasive bird.Colonial waterbirds such as herons, egrets and spoonbills exhibit ecological characteristics that could have promoted the evolution of conspecific brood parasitism and extra-pair copulation. However, an adequate characterization of the genetic mating systems of this avian group has been hindered by the lack of samples of elusive candidate parents which precluded conducting conventional parentage allocation tests. Here, we investigate the genetic mating system of the invasive cattle egret using hematophagous insects contained in fake eggs to collect blood from incubating adults in a wild breeding colony. We tested a protocol with a previously unused Neotropical Triatominae,  Mating systems, i.e., the way in which females and males organize during breeding seasons, are dynamic and can be remarkably variable among species and populations, as well as within populations . Mating Colonial breeder birds, such as egrets, herons and spoonbills (Order Ciconiiformes), exhibit characteristics that could have promoted the evolution of alternative reproductive tactics and are, thus, expected to have rather high rates of extra-pair paternity (EPP) and CBP –9. For eSterna hirundo) for a variety of metabolic studies . Blood samples were collected under a specific permit from the Instituto Chico Mendes de Conservação da Biodiversidade–ICMBio (permit No. 20295–2). All methods used related to capturing and handling the birds, banding and blood collection comply with the ICMBio guidelines. According to SUCEN institutional protocols, nymphs raised in the laboratory and used in the field cannot be reincorporated into laboratory populations. Hence, the insects used for blood sampling in the field, regardless of whether or not they had fed on cattle egrets, were anesthetized with diethyl ether inhalation and immediately incinerated. The field studies did not involve endangered or protected species.This study was carried out in strict accordance with Brazilian laws for research on wild birds. Permits to handle and rear the insects were obtained from the Brazilian Estação Meteorológica do Centro de Análise e Planejamento Ambiental–CEAPLA/IGCE/UNESP, Brazil). Throughout the reproductive cycle, the colony had ca. 800 active nests of cattle egrets built in trees between one and three meters in height. As breeding adults often fly away when humans approach (< 30 m distance) [Panstrongylus megistus Burmeister 1835 (Insecta: Reduviidae) from a disease-free population reared in a controlled environment, previously starved for fifteen days. This species of insect was chosen because it preferably feeds on birds and also because laboratory-bred nymphs are readily available in several places of the Neotropical region. Fiberglass fake eggs [n = 89) that were inside the same nests from which we collected the blood of incubating adults. Blood was obtained from the aorta vein of nestlings using 29-mm gauge needles coupled to 1-ml syringes previously rinsed in 0.3% EDTA. We used EDTA in both sampling procedures in order to impose similar conditions to the blood taken from adults (bug-eggs) and nestlings (vein-puncture). All blood samples were stored in 1.5-ml tubes containing 100% ethanol and kept at -20°C until processed.Sampling was conducted between October and December 2011 in a cattle egret breeding colony established in the city of Rio Claro, state of São Paulo, Brazil . During the sampling period, the mean temperature was 22.3°C and mean humidity was 68.3% (data from the istance) , blood wCHD genes using P2/P8 primers following the method described by Griffiths et al. [loci  with ET550-R as the internal size standard and alleles were called using Fragment Profiler Software Suite v1.2 . PCRs and allele scoring were repeated at least twice for all samples.Genomic DNA was isolated from blood samples using a phenol:chloroform method followed by ethanol precipitation , quantifs et al. . Sex wasven from , plus siherodias . PCR prostuttering or null alleles were verified using Micro-checker v2.2.3 [locus and multilocus, were calculated manually for samples that were genotyped in duplicate, based on Pompanon et al. [Genepop v4.3 [locus, observed heterozygosity, unbiased expected heterozygosity and the Fixation Index, as well as probabilities of exclusion (PE) and of identity between siblings (PISibs) [GenAlEx v6.5 [Evidence of allele dropout, r v2.2.3 . Genotypn et al. . Deviatipop v4.3 , 53. The(PISibs)  were comlEx v6.5  for a rePWR) was computed in Kininfor v.2 [3 simulated pairs of genotypes for each relationship category, using the empirical marker error rates and allele frequencies computed for the sample of 62 unrelated adults as input data, and set a 5% significance level. The same program was run to compute the multilocus Informativeness Index for Relatedness Inference (Ir) and the Informativeness Index for discrimination between two competing relationships (IR), as well as to identify the most suitable of seven different relatedness estimators for the cattle egret genotypic dataset. For these analyses, we set a null hypothesis of parent-offspring versus an alternative hypothesis of full siblings because this provides the highest stringency for inferring relatedness [To estimate the power of the set of microsatellites to discriminate full-siblings from half-siblings, as well as full-siblings and half-siblings from unrelated dyads, the Power for Relationship Inference  [Cervus as previously described, but setting the proportion of sampled mothers to 0.90 . We then used critical LOD scores derived from simulations to carry out ‘paternity’ and ‘maternity’ analyses in Cervus and manually compared the LODs obtained in such analyses to determine which social parents were not related to offspring.Using the ‘bug-collected’ genotypic information for attending adults, we first checked parent-offspring dyads carrying out conventional DNA-based parentage allocation tests implemented in s v3.0.7 . As an os v3.0.7 . Then, ts v3.0.7 . Paramet = 0.27) . To estiCoancestry v1.0.1.7 [ML-Relate [versus the probability of full siblings, which is the relationship expected a priori under the assumption of genetic monogamy. In addition, using Colony v2.0.6.2 [Cervus exploratory analyses described above) and a second set of tests accounting for CBP, assuming ‘polygamy’ for both sexes, with a 0.97 proportion of candidate mothers sampled . The remaining parameters were set equally for both runs: known allele frequencies , long runs, full-likelihood method, no updating of allele frequencies and no sibship size prior. Finally, we clustered genotypes into sib-groups applying the Markov Chain Monte Carlo (MCMC) algorithm described by Herbinger [http://herbinger.biology.dal.ca:5080/Pedigree). The final classification of nestling dyads into relatedness categories was determined only if the results of all methods were in agreement; otherwise, dyads were not classified [We applied the multiple methods approach  to classL-Relate  with 10,v2.0.6.2 , we partassified .Panstrongylus megistus contained in fake fiberglass eggs fed successfully on the blood of cattle egrets under natural conditions (n = 62). Bug-collected blood (30–80 μl per sample) yielded an average of 407 ng/μl of adult genomic DNA.nditions . One hunn = 89) were genotyped at the 14 loci. There was no evidence of large allele dropouts, null alleles and/or stuttering. We found a minimum of two and a maximum of ten alleles per locus in the sample of 62 unrelated adults   than the initial 14-locus dataset, which further validated the exclusion of the three loci. The 11-locus dataset had a multilocus error rate of 0.03, a probability of exclusion over 0.99 and a probability of identity between siblings of 0.00064 , indicating that they were, on average, unrelated. Simulation results showed that only five loci were sufficient to attribute parentage (distinguish PO dyads) with > 0.90 confidence probability  and nestlings . The conventional DNA-based parentage allocation method was initially applied to determine parentage by examining parent-offspring dyads in each nest. At the highest confidence level, Cervus simulations showed that attending males with a LOD score ≤ -2 and attending females with a LOD score of ≤ -1.9 were not directly related to the offspring being tested and could therefore be excluded as true fathers or true mothers, respectively. Using this threshold, the results of the parentage allocation analyses showed that 12 of the 89 nestlings (35.9%) were not related to their attending males and 34 (38.2%) were not related to their attending females , 59.14% were adult-nestling dyads  and 30.56% were pairs of nestlings  was higher than rates achieved in some previous studies conducted with the common tern, the species for which the method was developed , but lo34.0% in , 86.1% iess rate .4% was h34.0% in ). Sampli34.0% in  (see Tab34.0% in ) thereby0–60 min , 65 to 10–60 min . Differeclimates , 64–68. locus [When candidate parents are available, inferences on the genetic mating system of bird species have traditionally been made using DNA-based approaches aimed at either attributing parentage to the individuals observed incubating the eggs and caring for offspring or excluding these individuals as parents . Such anlocus , 56. Whelocus , 5, 11. locus . The parlocus . Conclusloci of similar variability to the ones used here. When working with a wild non-model organism such as the cattle egret, isolating new markers would be more difficult, expensive and time-consuming than sampling elusive candidate parents using the ‘bug-egg’ method. Hence, this method seems to be a very appealing strategy when the goal is to increase confidence in genetic mating system inference.We overcame the parental sampling limitation by using the ‘bug-egg’ method (as described in the previous section), and obtained valuable samples from incubating adults in a wild breeding colony of cattle egrets. We were able, then, to perform conventional parentage allocation analyses to either confirm or exclude attending females or attending males, and reach to reliable estimates of CBP and EPP rates in the nests studied . In paraThe combined use of a minimally invasive sampling method and genetic parentage allocation tests allowed us to more adequately characterize the genetic mating system of the invasive cattle egret. In line with previous behavioral evidence reporting extra-pair copulation and conspecific brood parasitism in this species , 71–74, Extra-pair paternity and conspecific brood parasitism can have variable consequences for the stability and survival of populations, depending on ecological conditions and life history traits , 7. For The novel information on the genetic mating system of cattle egrets revealed in this study also has important implications for the management of populations of this increasingly expanding invasive bird. Managers can now reliably equate the observed reproductive behavior of adults to the underlining genetic mating system of the species to plan more effective control techniques. Research in other breeding populations of the cattle egret in both its native and non-native range is needed to gain a better understanding of the correlates among the flexible genetic mating system, population survival and invasive potential.P. megistus (Triatominae) for collecting blood from incubating cattle egrets. We recommend that researchers planning to use this method on other birds carry out pilot sampling to evaluate the acceptance of fake eggs by incubating adults as well as determine both the number and duration of trials needed to sample both sexes and achieve acceptable sampling success rates. The baseline methodological guidelines provided here can be applied to collect blood samples from elusive adults of other colonial waterbirds.This study presents the first application of the ‘bug-egg’ method to inspect the genetic mating system of a colonial tree-nesting bird. We used a field protocol and validated the use of the We found evidence supporting our hypotheses of the occurrence of alternative reproductive tactics concurrently with genetic monogamy in the cattle egret, which is in line with data from behavioral observations. By performing comparative analyses based on the same familial dataset, a major finding from this study was that inferences on the genetic mating system made in the absence of parental information could underestimate the rates of alternative reproductive tactics and should therefore be interpreted with caution. Thus, we encourage researchers to invest more in obtaining samples from potential parents to enable an adequate assessment of the mating system of the species of interest.In sum, this study contributes to the growing body of evidence supporting the occurrence of non-monogamous mating systems in non-passerine birds. Using a more powerful methodological framework, we showed that wild cattle egrets have a flexible, complex genetic mating system that includes monogamy as well as extra-pair paternity and conspecific brood parasitism. Finally, we expand knowledge on the genetic mating system of colonial waterbirds, enabling a better understanding of the evolutionary ecology of variable mating tactics in this group of organisms, which have been poorly studied.S1 FileAppendix A, Fig A, Tables A-D.(PDF)Click here for additional data file.


The resulting QSOX1 variants showed a striking detrimental effect when added exogenously to fibroblasts: they severely disrupted the extracellular matrix and cell adhesion, even in the presence of naturally secreted, wild‐type QSOX1. The specificity of this phenomenon for particular QSOX1 mutants inspired an investigation of the effects of mutation on catalytic and redox properties. For a series of QSOX1 mutants, the detrimental effect correlated with the redox potential of the first redox‐active site, and an X‐ray crystal structure of one of the mutants revealed the reorganization of the cis‐proline loop caused by the mutations. Due to the conservation of the mutated residues across the PDI family and beyond, insights obtained in this study may be broadly applicable to a variety of physiologically important redox‐active enzymes.The thioredoxin superfamily has expanded and diverged extensively throughout evolution such that distant members no longer show appreciable sequence homology. Nevertheless, redox‐active thioredoxin‐fold proteins functioning in diverse physiological contexts often share canonical amino acids near the active‐site (di‐)cysteine motif. Quiescin sulfhydryl oxidase 1 (QSOX1), a catalyst of disulfide bond formation secreted by fibroblasts, is a multi‐domain thioredoxin superfamily enzyme with certain similarities to the protein disulfide isomerase (PDI) enzymes. Among other potential functions, QSOX1 supports extracellular matrix assembly in fibroblast cultures. We introduced mutations at a cis‐proline amino acid, analogous to a mutation used to trap substrates of a bacterial disulfide catalyst, has a dramatic effect on the physiological function of the mammalian disulfide catalyst QSOX1. As the active‐site region of QSOX1 is shared with the large family of protein disulfide isomerases in humans, the effects of such mutations on redox properties, enzymatic activity, and biological targeting may be relevant across the family.We show that mutation of a conserved  6HF1PDB Code(s):  By analogy to the corresponding mutants of DsbA,E. coli, were supplied to the media of fibroblast cultures at a concentration about six‐fold higher than levels of secreted QSOX1.To test for effects in a system previously shown to require the activity of extracellular QSOX1 in the assembly of ECM,cis‐proline mutants is competition with endogenous QSOX1 for extracellular enzyme localization sites. This explanation, however, is ruled out by the AXXA QSOX1 mutant, which lacks only the Trx active‐site cysteines and contains all other possible surfaces for interaction. Addition of the AXXA mutant to the fibroblast culture medium did not negatively affect ECM or cell adhesion ). Data fitting to the Hill equation was done using Origin to obtain the equilibrium constant Kox. Standard redox potentials were obtained by the Nernst equation with a standard redox potential value (E0) of −240 mV for GSH/GSSG.One micromolar QSOX1(PDI) variants were incubated at RT for 1 h in a volume of 1 mL containing a range of glutathione redox buffers. Redox buffers contained 5–100 μM oxidized glutathione (GSSG) and 10 μM–10 mQSOX1(PDI) variants at a concentration of 10 μM were incubated with SYPRO Orange 5000× (Thermofisher) diluted 1:250 in a 96‐well PCR plate in 20 μL total volume in triplicate. Fluorescence data were collected using an Applied Biosystems Real‐Time PCR System using the fluorescein amidite channel with a temperature gradient of 1 min per 0.5°C from 24°C to 95°C. The peak of the first derivative of fluorescence with respect to temperature was taken as the transition midpoint (apparent denaturation temperature).cis‐proline QSOX1 variants in the absence of antibody, without success. Among the constructs used in crystallization trials were full‐length rat QSOX1, human QSOX1(PDI), and mouse QSOX1(PDI), as well as methylated versions of these constructs. Antibody Fab fragments can aid crystallization due to their high solubility and ability to provide new surfaces for favorable protein–protein interactions, which are required for crystallization.12121. The data set was processed and scaled using DENZO and SCALEPACK.6HF1).Numerous attempts were made in our laboratory to crystallize


Chenopodium quinoa Willd.) from the cold and arid Andes of Argentina has allowed us to track changes and continuities in quinoa diversity over 18 centuries, by coupling genotyping of 157 ancient and modern seeds by 24 SSR markers with cluster and coalescence analyses. Cluster analyses revealed clear population patterns separating modern and ancient quinoas. Coalescence-based analyses revealed that genetic drift within a single population cannot explain genetic differentiation among ancient and modern quinoas. The hypothesis of a genetic bottleneck related to the Spanish Conquest also does not seem to apply at a local scale. Instead, the most likely scenario is the replacement of preexisting quinoa gene pools with new ones of lower genetic diversity. This process occurred at least twice in the last 18 centuries: first, between the 6th and 12th centuries—a time of agricultural intensification well before the Inka and Spanish conquests—and then between the 13th century and today—a period marked by farming marginalization in the late 19th century likely due to a severe multidecadal drought. While these processes of local gene pool replacement do not imply losses of genetic diversity at the metapopulation scale, they support the view that gene pool replacement linked to social and environmental changes can result from opposite agricultural trajectories.History and environment shape crop biodiversity, particularly in areas with vulnerable human communities and ecosystems. Tracing crop biodiversity over time helps understand how rural societies cope with anthropogenic or climatic changes. Exceptionally well preserved ancient DNA of quinoa ( Less  quinoa) .ca 5000 BP [viz. 16th to 19th centuries), agrarian systems in the most arid highlands reverted to a primarily pastoralist economy, wherein small-scale crop farming assumed a limited role, a situation that persists today [In the Central Andes of Peru and northern Bolivia, the rise and fall of past agrarian societies due to political and environmental changes seems a most likely scenario –10. But  5000 BP ,15. Farm 5000 BP , 17. Thets today . Palaeoets today . Two relts today .The question then arises as to how these social and environmental changes affected local crop biodiversity throughout this period. Specifically, have climatic and agrarian changes—and their related transformations in social structures and local economy—led to genetic changes in native crop species? The quinoa crop in the dry Andes of Argentina provides a case study for investigating these issues since local conditions of low temperature and air dryness allowed for the conservation of abundant biological material in what once were residential places, granaries or tombs ,25. In mca 1800 BP), to periods of stable agro-pastoralist societies (ca 1400 BP) and complex corporative societies (ca 800–700 BP). To evaluate the relationship of these ancient quinoas with the present-day germplasm, we studied a reference panel of quinoas collected in 2006–2007 from different environments in the Andean highlands of Argentina [Analyzing genetic markers within a coalescence framework, we track changes and continuities in quinoa diversity in the dry Andes over the last two millennia. Coalescence theory allows to identify the most probable trajectory among the many possible genealogies in a regional gene pool . Then wergentina ,30  genotyped at 24 microsatellite loci (see Section C in He) in the subset of modern quinoas showed highly variable values (range 0.02–0.70), consistent with those found in an independent study on the same samples [s(Fis) and s(LnL)) appear highly variable without any clear geographical pattern. Comparing quinoa samples through time at Antofagasta de la Sierra (hereafter: Antofagasta) shows a trend towards lower allelic diversity  and expected heterozygosity (He) in the modern sample (#12) compared to the ancient ones . Selfing rate (s(LnL)) increased significantly (P<0.05) from the most ancient samples (#17–18) to the intermediate (#13–14) and modern ones (#12).Estimates of the diversity indices and the selfing rates of the 19 populations sampled are summarized in Table E in  samples . SimilarThe discriminant analysis reveals a neat distinction between ancient and modern samples, ancient samples showing little affinity to modern samples, particularly for the geographically closer sample #12 . Among mca 690–796 cal BP) grouped together in spite of differences in seed color . The two seeds from sample #16  were assigned to this same group. Samples #17 and 18  were assigned to a distinct group, which also included the oldest sample #19 , being differently colored . Interestingly, additional structure analysis separates dark-seeded quinoa samples  and white-seeded samples from the same age and locality   from the older samples (#17–19) . AffinitAt Antofagasta, one modern sample and six ancient samples offer a temporal series covering almost two millennia. Analyses of genetic structure of these samples reveal three main genetic groups: modern (#12), intermediate (#13–15), and ancient (#17–19) . The dife.g. ancestral population effective size, time of divergence) were indistinguishable from priors  the demographic scenario in which three genetic clusters belong to three separate cultivated gene pools, with gene flow from a wild compartment producing dark seeds . We estiagricultural intensification initiated 1100 years ago followed by an opposite phase of farming marginalization in the Colonial and Republican periods.Using a coalescent-based approach, this study provides the first evidence of a significant change in the demographic history of quinoa in the Andes over 18 centuries. The analysis of modern and ancient samples from Antofagasta reveals that genetic differentiation among samples from different times cannot be explained by genetic drift. Instead, the most likely scenario in this locality is the replacement of preexisting quinoa gene pools with new exogenous gene pool. This process occurred at least twice in the last 18 centuries: first, between 1364 and 796 cal BP, well before the Inka and Spanish conquests—respectively initiated 568 and 483 years ago in Northwest Argentina —, and thIntensification of agriculture by local societies starting 1100 BP was contemporary to the increasing aridity, which reduced water availability for crops and pastures in the region ,17. LargCh. quinoa ssp. melanospermum and their relative frequency in archaeological remains is indicative of the degree of seed selection by past cultivators [As reported in other Andean regions , modern tivators ,36. The tivators , a featutivators .Another likely cause of the changes in quinoa demographic history relates to evidence of a generalized warfare in the dry Andes in the 750–600 BP period ,39,40, aca 796–690 BP  had a narrower genetic base and higher selfing rates than in the previous periods . As the scenario of genetic drift within a single population is rejected by the coalescent-based analyses, the lower diversity of the cultivated quinoa at Antofagasta ca 796–690 BP is explained more by the displacement of local varieties by introduced ones with a narrower genetic base than by alternative hypotheses of enhanced selection for new cropping systems or loss of genetic resources due to endemic political unrest. In this perspective, agricultural intensification with newly introduced varieties can be considered as a risk-buffering strategy developed by ancient Andean peoples who, like other societies in the world, sought to ensure food security in a context of rising population, political conflicts and deteriorating climate [In this context of coincident changes in climate, crop technology and society, our estimates of effective population size  and genetic differentiation (FST) were calculated in R using the packages adegenet and hierfstat [MLGs) was computed in R using the package poppr [FIS, or using the maximum likelihood approach implemented in RMES (see Section C in adegenet. An approximate Bayesian computation approach using random forests (ABC-RF) [Ancient and modern quinoas were genotyped using 24 microsatellite loci  ,69 was u(ABC-RF) .S1 AppendixSection A. ARCHAEOLOGICAL SITE DESCRIPTIONTable A. Succinct chronology of climatic and social changes in the Southern dry Andes.Section B. SEED SAMPLE DESCRIPTIONTable B. Seed sample description.Section C. DNA EXTRACTION, SIMPLE SEQUENCE REPEAT GENOTYPING, ANCIENT DNA QUALITY CONTROL, AND POPULATION GENETIC ANALYSISTable C. Microsatellite loci, allele expected size, observed size range, allele number, and missing data points in modern (n = 81) and ancient (n = 76 successfully genotyped out of 144) quinoa seed samples.Table D. Percentage of missing data per successfully genotyped quinoa seed sample, calculated across 24 microsatellite loci.Table E. Diversity and selfing rates in the 19 studied quinoa seed samples.Table F. Pairwise FST values between samples.Table G. Prior and posterior probability distribution of model parameters for the approximate Bayesian computation analysis.Figure A. Sequence alignment from QAAT024 locus alleles of 4 modern (EST and CHEN458 samples) and 4 ancient (AP9 and PPE samples) quinoa genotypes.Figure B. Sequence alignment from QAAT087 locus alleles of one modern (CHEN 420) and three ancient  genotypes.Figure C. Inference of number of clusters in DAPC.Figure D. Individual assignment probability to each group from Discriminant Analysis of Principal Components (DAPC).Figure E. Individual assignment probability to each group from STRUCTURE.Figure F. First two principal components for the Principal Component Analysis (PCA)(PDF)Click here for additional data file.S1 FileS1–File-WINKEL-et-al-Allelic-profiles-ReadMe.(PDF)Click here for additional data file.S2 File(Dataset). S2–File-WINKEL-et-al-Allelic-profiles-Dataset-(2018-04-05).(CSV)Click here for additional data file.


This study was conducted to investigate basic information on genetic structure and characteristics of Limousin population in Hungary. Obtained results will be taken into consideration when adopting the new breeding strategy by the Association of Hungarian Limousin and Blonde d’Aquitaine Breeders (AHLBB).Genetic diversity and phylogenetic relationship of 3,443 Limousin cattle from 16 different herds were investigated by performing genotyping using 18 microsatellite markers. Amplified DNA was genotyped using an automated genetic analyzer.e) of the populations was 3.77. Population C had the lowest number of effective alleles (3.01) and the lowest inbreeding coefficient (FIS) value (−0.15). Principal component analysis of estimated genetic distance (FST) values (p<0.000) revealed two herds (C and E) distinct from the majority of other Limousin herds. The pairwise FST values of population C compared to the others (0.066 to 0.120) fell into the range of moderate genetic distance: 0.050 to 0.150, while population E displayed also moderate genetic distance  but only to six populations . FST(C-E) was 0.148, all other pairs -excluding C and E herds- displayed low genetic distance (FST<0.049). Population D, F, I, J, K, L, N, O, and P carried private alleles, which alleles belonged to 1.1% of the individuals. Most probable number of clusters (K) were 2 and 7 determined by Structure and BAPS software.Mean of effective alleles (nThis study showed useful genetic diversity and phylogenetic relationship data that can be utilized for the development of a new breeding strategy by AHLBB. The results presented could also contribute to the proper selection of animals for further whole genome scan studies of Limousins. Beef consumption is related to living standards, diet, livestock production, consumer prices and is dependent on either cultures or religions. In South America beef is the favorite type of meat. Uruguay consumed the most beef per capita in the world in 2017, followed by Argentina. Both countries consumed more than 40 kg of beef per capita [This is why primary goal of Hungarian cattle breeders is to produce a top-quality beef that consumers are willing to purchase, a tender, juicy and flavourful product of good value. Since its foundation in 1989, the Association of Hungarian Limousin and Blonde d’Aquitaine Breeders (AHLBB) has been taking measures to improve quality of beef and to meet demand of consumers with high quality products, by introducing a strict performance testing, qualification and selection program.Since the establishment of Limousine breed (1886), about 70 countries imported significant number of Limousine cattle for breeding  because The highly polymorphic microsatellite markers are widely used as genetic markers for purposes that include population genetics, parentage identification, fingerprinting, genetic mapping and conservation ,6. MicroSixteen different Limousin cattle herds, maintained for commercial use, were included in this study. Blood samples were collected for routine parentage testing, by breeders during their established breeding program, from jugular veins from 3,443 individuals  in tubes containing ethylenediaminetetraacetic acid. Samples were stored at −20°C until genomic DNA extraction, which was performed using the QIAamp DNA Mini Kit . All data concerning registration of herds and codes used in this study were provided by AHLBB. Sampling locations are presented on All 18 microsatellite markers used herein  are recommended by International Society of Animal Genetics (ISAG) for routine parentage control and record exchange between laboratories [IS), genetic distance (FST) indices were calculated by Genalex 6.5 [Statistical analysis: Exact test of Hardy-Weinberg equilibrium (HWE) and exact test of population differentiation were calculated by Genepop 4.2.1 , FSTAT 2alex 6.5 . Data we5, MCM steps: 5×105, repetition: 5, model: admixture, allele frequencies correlated) and for calculation of membership probability of individuals. For estimation of K, Evanno’s method [Bayesian algorithm implemented in Structure was used for inferring the most probable number of clusters (K)  was performed using the allele frequency data of Limousin individuals and the 18 microsatellite markers using Genalex.Nei’s genetic distance was calculated and viewed by Poptree software . BootstrAssignment tests of individuals were performed by Genalex and Geneclass 2.0 softwares  using a Thirty eight of the 288 chi-square tests showed significant deviations from HWE at the 95% confidence interval. Heterozygote excess was calculated in each herd, the highest excess was detected in herd C. Allelic richness ranged between 4.31 and 5.21 . Private alleles were detected in nine herds. Altogether 38 animals  carried one or more private alleles . Herd chIn genetic assignment test (data not shown) 48% of the animals have been allocated correctly to their original groups. In more detail the corresponding values were 90% and 100% in the herds E and C, supporting that these herds are more different from the others. Cumulative exclusion probability value was higher than 0.999 in accordance to the results obtained by Radko et al .Pairwise exact genotypic differentiation tests performed by FSTAT and Genepop showed that—except the pair B, K—all the herds can be treated as separate units, distinct from each other (p<0.05).ST values  fell into the range of moderate genetic distance: 0.050 to 0.150 [ST values in range 0.052 to 0.064) but only to six populations . FST(C-E) was 0.148, all other pairs—excluding C and E herds—displayed low genetic distance (FST<0.049).Consecutive PCA analysis of estimated Fp<0.000, . revealep<0.000, . The paito 0.150 , while pEvanno = 2, Structure programme revealed that the most probable number of clusters among 16 Limousine herds was two  the A, C, N populations remained separated. E herd was more similar to the remaining herds and it shared a node with population F -as we see on the BAPS generated result  which should also be taken into consideration in the breeding plans of the AHLBB.


Core collections (CCs) constitute a key tool for the characterization and management of genetic resources (GR). When the institutions involved in GR preservation decide to define a CC, they frequently prefer to select accessions based not only on strictly objective criteria, but also to add others following expert knowledge considerations . The aim of this study was to evaluate the implications of approaches that combine formal analytical procedures and expert knowledge on the efficiency of CC definition through a case study to establish a pear CC from the Swiss National Pear Inventory. The CC had to represent a maximum of the genetic diversity, not to exceed 150 accessions, and required to include a priority set (SPPS) with 86 genotypes selected based on expert knowledge. In total, nine strategies were evaluated, resulting of combining compositions of the dataset sampled, sampling sizes and methods. The CCs sampled by mixed approaches provided similar scores, irrespective of the approach considered, and obtained similar efficiency in optimizing the genetic diversity retained. Therefore, mixed approaches can be an appropriate choice for applications involving genetic conservation in tree germplasm collections. However, the sheer size of many of these genebanks is often a barrier hampering their characterization and evaluation, limiting thus their further effective use4. As a consequence, the core collection (CC) concept , was proposed long time ago as an approach to overcome this limitation5. Since a core collection is smaller in size compared to the whole collection, it enables characterization and management operations to be handled more efficiently and effectively3.One of the major challenges genebank managers face to is the necessity of increasing the accessibility of their collections to a broad panel of potential users such as plant breeders, geneticists and farmers8. Later on, the widespread introduction of molecular markers for characterization, switched CC selection to be based either exclusively on genetic diversity11, or in combination with morphological and agronomical data13. In that context, many theoretical and practical considerations on how to define CCs have been accumulated in the last three decades, dealing with three interconnected issues highlighted by Odong3: (i) CC can be defined differently depending on their purpose; (ii) there are different statistical methods to define them, and (iii) there is no consensus on how the quality of a CC should be measured. An additional aspect to take into account when defining a CC is the balance between representing total diversity and the usefulness of the core to the potential users14. However, institutions in charge of preserving traditional plant material often prefer favoring the inclusion of some varieties following criteria different from those that arise solely from statistical considerations, and that can be globally covered by the term ‘expert knowledge’. For instance, it is sensible that those institutions prefer including in CC cultivars which have played an important role in breeding history, are popular, prestigious or emblematic among local growers and/or consumers, are used as a standard in research in a given species, or exhibit some phenotypic features of interest. Some of these conditions arise on the unneglectable role that genetic resources play in an ecological and cultural dimension, beyond the strict maximization of diversity conservation16. Therefore, there is a need of evaluating how building a CC based on mixed approaches for entry selection, i.e. combining formal analytical procedures maximizing the diversity with some additional ‘pragmatic’ considerations, affects the efficiency of the core collection. To the best of our knowledge, despite its relevance, this issue has not been addressed to date.Before molecular identification became available, CC selections were established focusing on morphology, eco-geography and/or passport informationPyrus communis L.) accessions from the Swiss National Pear Inventory coordinated by the Federal Office for Agriculture (FOAG). All accessions had been characterized using a common set of 16 SSR markers, but there was a clear interest of institutions and germplasm curators to consider the inclusion of some entries in the CC on the basis of ‘expert knowledge’ considerations. At the same time, this study also allowed evaluating the genetic diversity at the Swiss national-level for this species for the first time, as well as shedding light into the distribution of this material into population subdivisions.In this context, the main aim of this study was to evaluate the implications that using mixed approaches that combine formal analytical procedures and ‘expert knowledge’ have on the efficiency of CC definition. This study is based in a real study case, where this question arose while intending to establish a CC out of 1198 pear (NE = 7.73), 78.6% and 50.0% occurring at frequencies below 5% and 1%, respectively. Further information about the genetic diversity of the Swiss National Pear Inventory is provided in Supplementary Text All the SSR markers considered in this study were polymorphic. Due to complex scoring and unreliable microsatellite profiles using marker CH05c06, we decided to exclude it from the study. A pairwise comparison of multilocus SSR profiles among the 1,198 accessions allowed identifying 186 groups of accessions sharing identical SSR profiles  was identified, suggesting that the diversity could be sub-structured. Thus, a second-level (nested) application of the STRUCTURE software was applied separately on each of the two main groups defined in the first analysis. For the first group (G1), the results indicated a subdivision at KG1 = 2 (ΔKG1 = 443.4)  and KG2 = 3 (ΔKG2-3 = 63.2)  and the proportion of genotypes strongly assigned (qI ≥ 0.80) to each partitioning level19. Results about the genetic structure for Swiss National Pear Inventory are detailed in Supplementary Text The analysis of the 841 unique genotypes using STRUCTURE revealed that the rate of change ΔThe core collection was required to include 86 ‘Priority’ genotypes, selected by the collection stakeholders on the basis of their ‘expert knowledge’, mainly based on their historical relevance or exceptional pomological features. These 86 ‘Priority’ genotypes were hereafter referred as the Swiss Pear Priority Set (SPPS). Genetic characteristics of the SPPS, namely those selected according to ‘expert knowledge’ considerations, were compared with subsets of the same size sampled by the ASLS (A-86) or the M-method (M-86). A comparison was performed at three levels, including distance-based criteria, allelic diversity parameters, and the distribution of the sampled genotypes on the inferred genetic structure of the whole population.E-E, irrespective of the genetic distance used (DB or DCE), A-86 and M-86 maximized distance 2.5–7.0% more than SPPS, and these two subsets were much more efficient in optimizing E-NE. A-86 particularly outperformed the other strategies, as E-NE was 18.9% higher than in the SPPS. However, the SPPS was 8.7–9.4% more efficient in optimizing A-NE distance. These results indicate that SPPS contains a higher level of redundancy. The differential pattern of genetic relatedness between subsets was noticeable on heat maps  remarkable differences were also found between subsets (Table E)  Table . NE, by PS Table .Table 1GDespite the fact that SPPS was selected with no consideration for genetic data, this subset sampled a rather balanced proportion of the four genetic groups inferred in the structure Fig. , althougAll, Table All) in optimizing E-E and E-NE distances and the number of effective alleles per locus (NE), whereas the M-FNKAll method was more efficient in optimizing A-NE distance. The effectiveness was increased in a range from 2.6% for A-NE to 9.2% for E-NE. For DCE, the rest of the allele diversity parameters  and the allele coverage (CV), the efficiency gains of the ASLS method were more modest, with scores 0.9% to 1.7% higher than those obtained for the M-strategy. The genotypes sampled in A-FNKAll and A-OMinAll are represented using a minimum spanning network in Supplementary Fig. Nine CCs, generated by combining different compositions of the dataset sampled (complete dataset or sampling only within the genotypes not included in SPPS), subset sizes (optimized subsets or full-size subsets) and sampling methods (M-method or ASLS method), were evaluated within this study. The nine corresponding sampling strategies used and their acronyms are described in Fig. DCE, SH, HE and NE was essentially identical, and all strategies retained a high number of alleles (CV > 87.5%).The CCs sampled by mixed approaches Table ; Fig. 3 All, the latter clearly outperformed them at optimizing E-NE (19.2% more efficiently), though mixed strategies were 6.8–7.6% more efficient in optimizing A-NE distance. For the remaining criteria, the efficiency of A-FNKAll was similar to the mixed strategies for DCE, SH and HE (<2%), and higher for NE (11.4%). A similar pattern was observed for M-FNKAll, but overall efficiency was closer to that of mixed strategies. The highest differences in efficiency were observed for E-NE, A-NE and NE (3–9% more efficient), whereas for the other criteria, efficiency was essentially identical (<1%). The mixed strategies retained ca. 90% of the alleles present in Swiss National Pear Inventory, while a nearly total recovery (CV = 97–98%) was obtained when using the formal analytical strategies.When the CCs defined using mixed strategies were compared to A-FNKAll) and for one of the mixed strategies (A-OMinAll). A-FNKAll showed a rather unbalanced distribution over the structure . In addition to this spatially and temporally dynamic process, the self-incompatibility system presumably has been a decisive factor in encouraging high levels of diversity in Pyrus spp.The large diversity found in pear, that occurs in species with a complex history like appleet al.3 defined as a CC-I collection. Ideally, each accession in the whole collection should be represented in the core by an entry that is most similar to itself. CCs have been validated following the indications by Odong et al.3 that is, using preferably distance-based indices and criteria not used in the selection phase, supplemented by other classic indices suited to the evaluation of generalist collections such as Shannon Index (SH) and allele coverage (CV).Core collections in this study were sampled with the objective of obtaining generalist CCs. For that reason, all accessions in the entire collection should be maximally represented, agreeing with what Odong SH and CV were included in the sampling strategies. However, core subsets generated using the formal analytical strategies considered, highlighted a trade-off in the effectiveness of optimizing genetic distances, so that the decision must be determined in terms of fitness-for-use. If our CCs had been selected with the aim to represent the extreme values in the collection, optimizing E-NE should have been the objective3 and, therefore, A-FNKAll would be the best-suited strategy. However, as the CCs were designed to represent the accessions in the collection, A-NE is the distance to optimize, and M-FNKAll the most suited analytical strategy.In the CCs sampled by purely analytical methods, the classic indices provided virtually identical results, something expected, as both 27. Moreover, the development of core collections in other species30 has evidenced that core sets maximized for diversity using a set of specific attributes (molecular or phenotypic), at the same time, maximize unknown diversity. The CCs selected by mixed strategies have optimized diversity parameters as efficiently as the least efficient of the purely analytical strategies evaluated as benchmarks.The differences between strategies in the CCs sampled by mixed methods were nearly negligible , revealing a certain “buffer” effect of the SPPS subset. Such effect could be expected, as the SPPS subset accounts for ca. 57% of the final CCs, and the genotypes in it were rarely included in the optimized subsets sampled by analytical methods. However, an additional optimized subset with low sampling intensity (7–8% of the genotypes in the whole collection) was sufficient to efficiently offset the shortcomings of the SPPS subset and represent the genetic diversity found in the whole collection. Small core subsets have previously showed high efficiency optimizing the retained diversityA-NE is the distance to optimize in generalist CCs, mixed strategies clearly outperformed the purely analytical ones. At the same time, a trade-off between A-NE and allele recovery was observed. The Swiss National Pear Inventory shows a particularly high number of rare alleles, with ca. 17% of the alleles being present in just one genotype. Thus, increasing CV from 87–90% of mixed strategies to 97–98% or purely analytical strategies resulted in an increased redundancy (and A-NE distance) due to the higher number of genotypes just providing those unique alleles. Selection of a core collection that fulfills all genetic criteria is impracticable because of the inter-relationships of the evaluated parameters2. Considering all analyzed criteria and the purpose of the CC, the best results for the Swiss National Pear Inventory collection were obtained using the mixed strategies. In collections with lower presence of rare/unique alleles, the balance between A-NE and CV probably would have been smaller. In any case, it seems reasonable to consider that CC sampling strategies such as those tested in this study (combining expert knowledge and optimized subsets using SSR data) are capable of generating CCs that are similarly efficient to those obtained by purely analytical methods.The greatest differences between purely analytical and mixed approaches appeared in the distance criteria. As The balance between representing diversity and the usefulness of the CC to the intended use or user is highly relevant when defining a core collection. Core sets maximized for diversity using molecular attributes can, at the same time, maximize unknown diversity. However, institutions in charge of preserving traditional plant material often prefer favoring the inclusion of some cultivars due to other reasons, i.e., historic value or exhibiting some phenotypic features of interest. We have presented a case study using the Swiss National Pear Inventory, testing the efficiency of CCs sampled using a mixed approach in which part of the genotypes were selected by ‘expert knowledge’ and then supplemented with a highly optimized subset using SSR data. The final CCs selected by this approach, obtained similar (and sometimes higher) efficiency in optimizing the genetic diversity retained within the CC when compared to CCs sampled by purely analytical methods. The results obtained in this study show that mixed approaches could be appropriate choices for applications involving genetic conservation in fruit tree germplasm collections.31 , the number of rare alleles per locus , the number of the effective alleles (NE), and the observed (HO) and expected heterozygosity (HE) were calculated using the SPAGeDi version 1.3 software package32.The multilocus SSR profiles of all accessions were compared. The number of alleles per locus  and to quantify the membership probability of each genotype to the identified groups. The clustering was performed under the admixture model, and correlated allelic frequencies for K values ranging from one to 10, with five independent runs each, with a burn-in phase of 2 × 105 iterations, and a sampling phase of 5 × 105 replicates. The analysis was run using the recessive allele approach34, encoding the genotypes following the recommendations given for polyploid species in the software manual and as used in previous studies37. Structure Harvester ver. 0.6.9338 was applied to estimate the most suitable K value according to the ΔK method defined by Evanno et al.39. Genotypes were assigned to the groups for which they had the highest assignation probability (qI), considering a strong membership coefficient of an accession to a particular group if qI ≥ 0.8040. When the results suggested sub-structuring of the diversity above the selected K value, a second level (nested) STRUCTURE analysis was performed for each group separately42. The results were graphically displayed using DISTRUCT 1.143. Genetic differentiation between groups defined by STRUCTURE was examined through an analysis of molecular variance (AMOVA) using Genodive v2.0b2344.STRUCTURE version 2.3.4The Swiss Pear CC should maximize the representativeness of the genetic diversity contained in the Swiss National Pear Inventory, without exceeding the size of 150 accessions, due to management and budgetary reasons. Additionally, the collection was required to include the 86 genotypes of the SPPS, mainly based on their historical relevance or exceptional pomological features. Therefore, the final collection would be selected using a mixed approach, combining the SPPS and a representative subset of genotypes, selected from the SSR data, while keeping the final core within the required size limits.Dataset composition. Two strategies differing in the accessions included in the dataset were used:i.Complete database: All the genotypes in the Swiss National Pear Inventory were included in the sampling database.ii.Non-priority database: The genotypes corresponding to SPPS were removed from the database prior to the sampling procedure.Sampling sizes. Sampling strategies were performed at two size criteria:i.Minimal subset: The core subset was sampled for 60 individuals (≈7% of the collection), as that size was near the lowest threshold (5%) in the range of CC size considered optimal in fruit tree core collection2. In this strategy, as the sum of the subset and priority accessions did not reach the required size, additional genotypes were selected manually until reaching it. The criterion used was to include those genotypes carrying missing alleles in descending order of Bruvo’s genetic distance45 to the closest accession already included in the core.Supplementary subset: The core subset was sampled for the highest size that, supplemented with the missing SPPS genotypes, resulted in a core with 150 individuals.Optimized sizes. Since the accessions of the SPPS set were chosen according to criteria different from the marker profile, it was expected that the subset selected from SSR data would include only part of the SPPS genotypes. Therefore, first we assessed the subset size needed not to exceed the required CC size, when the subset is supplemented by the missing SPPS accessions .Full size subsets. In this strategy, core subsets with 150 genotypes were sampled:Sampling methods. Two different sampling methods were used:i.Mstrat46, which examines all possible core subsets and select those that maximize the number of alleles for one sample size. The program allows to specify accessions that will always be included in the core subset (kernel), in this case maximization focuses on complementing alleles not included in the kernel accessions. The Shannon-Weaver (SH) diversity index was used as a second criterion to classify core subsets. One hundred replicates and 200 iterations of Mstrat were generated independently when this sampling strategy was used.The maximizing method (M-method) implemented in ii.Core Hunter II47. The software is able to select core subsets using diverse allocation strategies by optimizing many parameters simultaneously, whereby the best solution among all replicas is reported. The allocation strategy used involved optimizing the following five measures simultaneously with equal weight assigned to each one: average and minimum Cavalli-Sforza and Edwards genetic distance (DCE) between core entries, allelic coverage or number of alleles (CV), Shannon-Weaver diversity index (SH), and Nei diversity index (HE). For this method, 80 independent runs were performed.The advanced stochastic local search method (ASLS method) implemented in In total, nine sampling strategies Fig.  were evaThe acronyms of the nine sampling strategies tested in this study are specified in Fig. The evaluation and comparison between CCs obtained from the different approaches were conducted focusing on distance-based criteria, parameters associated to allelic variability and the graphical distribution of the genotypes selected on the genetic structure inferred for the whole collection.versus mixed approaches: (i) the average genetic distance between all the entries of each CC (E-E), (ii) the average distance between each entry and the nearest neighboring entry for each CC (E-NE), and (iii) the average distance between each genotype of the entire collection and the nearest entry in each CC (A-NE). E-E was assessed for the Bruvo (DB) and Cavalli-Sforza and Edwards (DCE) genetic distances48, whereas the two latter were assessed only for DB. For the E-E and E-NE, the larger the value the higher the quality of the CC, the opposite is true for A-NE3. Additionally, the parameters used to optimize core subsets by the ASLS method  were also used to evaluate the quality of the CCs. Lastly, the pairwise Bruvo genetic distances among the genotypes sampled in each strategy were represented using heat maps to graphically display relatedness.Three genetic distance-based criteria were considered when evaluating the quality of the defined CCs through formal analytical procedures 45 using the ‘poppr’ R-package49 and compared for different strategies.The distribution of the genotypes of the CCs on the underlying genetic structure of the whole Swiss National Pear Inventory was evaluated through two approaches. First, making use of the clustering obtained through the Bayesian model-based method, the distribution and representativeness of the genotypes included in each CC on the inferred genetic groups were examined. Second, Minimum Spanning Network (MSN) plots were drawn based on the Bruvo’s distanceTable S1Supplementary Figures and Texts


Plant breeding collects, induces and rearranges genetic diversity followed by selection. Breeding may contribute to diversity in farmers’ fields or significantly reduce it. History has numerous examples of both. The diversity of many crops have gone through domestication, dispersal and modernization bottlenecks. Between these major decreasing processes, diversity has picked up through different evolutionary processes, and plant breeding affected by policies. Major negative effects of plant breeding on diversity have been recorded following the modernization bottleneck, but alternative breeding strategies have come up as well, both in the formal system and in the interphase between formal and farmers’ seed systems. Multiline breeding and participatory plant breeding are introduced as examples to also analyse effects of current developments in technology and policy. This paper intends to shed some light on the questions: how will current developments in technology and policy affect crop genetic diversity? Are we heading for a new bottleneck—either a molecular or a policy bottleneck, or a combination of both? Or could the future become more diverse? We look at the relationship between breeding, policies, and crop genetic diversity in farming systems with a birds-eye view. Notably because of current policy trends we warn for a new diversity bottleneck. The balance between these diversity enhancing and reducing forces determines the outcome in terms of gain or loss of diversity at the end of the breeding process. However, the effect of breeding on diversity in farmers’ fields is more complex. The seed systems that the breeding operation feeds into and various policies greatly affect the outcome in terms of diversity. Can an analysis of past processes that shaped current diversity levels inform us about the future? By reviewing the grand historical developments we analyse the trend leading to the modernization bottleneck, including the roles of plant breeders and policy makers. We also introduce two initiatives by breeders to illustrate processes that go against the trend of reducing diversity.Plant breeding collects, induces and rearranges genetic diversity followed Technological developments in plant breeding are ongoing, and also at the level of policies significant developments occur at the moment. Can trends since the modernization bottleneck be extrapolated, will these current developments give rise to another bottleneck, or will they affect crop genetic diversity in different ways in the coming decades?On a historic scale many crops have gone through a defined process that shaped the genetics and diversity of our current crops. The ‘big picture’ is that during domestication only a limited part of the species’ diversity was selected to serve the needs of early farmers. The cultivated crop thus has a limited diversity compared with the wild species that it originates from Tanksley and McCouch . This haMany crops were introduced in areas outside their centre of domestication. During this process a larger or smaller part of the diversity of the cultivated crop was used as the genetic base in the ‘new’ continent. This created a ‘dispersal bottleneck’ . However, Van der Wouw et al.  and seed certification (AOSCA in 1919 and OECD Seed Schemes in 1958). Since seed qualities can often not be observed from looking at the seed, farmers require a reliable label with variety name and quality guarantees. Trueness to labelling became the basis of seed regulations in the USA. The basis of such variety and seed regulations is farmer protection. This increasingly strict regulation of varietal purity furthermore led to the reduction of within-variety diversity. The importance of within-variety uniformity for breeders increased further with the introduction of Plant Breeder’s Rights in various countries from the 1940s onwards, and their harmonisation through the Convention on the Protection of New Varieties of Plants in 1961  to evolve in their specific environment. They also unlikely are able to pay for the registration and release trials. Also obtaining protection on such varieties is often not a farmers’ goal  are commendable. Unlike the sovereign rights on genetic resources, discussed hereunder, patents expire (after 20 years in most jurisdictions), which means that the technologies and materials become part of the public domain. Biodiversity rights are in principle eternal which could potentially impact breeding for diversity much more.The patent system did not significantly influence plant breeding until the latter part of the twentieth century. Court decisions (in the USA from 1985 onwards), and policy changes (in Europe in 1998) made it possible in different jurisdictions to obtain patent protection for a plant variety (predominantly in the USA) and on plants expressing particular traits (in a larger number of countries). Plants that fall under the scope of such (utility) patents are not automatically free for further breeding. Breeders have opposed to this trend in several countries. This culminated in Europe to the introduction of a ‘limited breeders exemption’ in national and EU patent laws and in June 2017 the European Patent Office decided not to provide patents anymore on naturally existing traits, formulated as ‘products of essentially biological methods’ . The impact on practical breeding has not been devastating during the first 2 decades. A more recent development, putting responsibilities to the user rather than the provider under the “Nagoya ProtocolThese developments create some important dilemmas: complex benefit sharing arrangements will reduce the sustainable use of genetic resources in breeding, which is also an objective of the CBD. Will the change in the burden of proof on benefit sharing towards the user also introduce a burden of proof on countries with respect to their obligations to promote the sustainable use of their genetic resources and their conservation efforts? Do negotiators realise that complex administrative measures in relation the benefit sharing affect smaller breeders more than larger breeding companies that have more administrative and legal capacities, thus reducing the number of breeding programmes further? How do benefit sharing arrangements on digital genomic information affect the current trends towards open data?More important for this paper is the question what such restrictions will mean for breeding in formal and alternative settings aiming at increasing diversity? It is likely that overly complex and expensive arrangements for benefit sharing may stimulate breeders to limit their work within the gene pool that is unrestricted . This will not support breeding for diversity. Higher diversity in output of breeding requires more diversity at the input level. When rights—either private (patents) or national sovereign (biodiversity)—rest on more sources of plant breeding, then using a wide array of genetic resources becomes increasingly difficult. These rules will also make alternative breeding strategies and alternative institutional arrangements such as participatory breeding in informal seed system settings completely impossible. Tracking and tracing genetic resources to operationalise benefit sharing is impossible in a participatory settings aiming at creating populations that are allowed to further evolve under different farming conditions. Such breeding strategies cannot go hand in hand with administrative arrangements required by the new policies. It may be counter intuitive that biodiversity laws will block breeding for diversity, but current regulations seem to do exactly that.Plant breeding and diversity have a complex relationship. The role of breeding to combine as many ‘positive’ traits in one plant or one population by definition reduces diversity within a variety. At the same time, breeders can introduce important levels of diversity in farming systems. Plant breeders and seed producers operate in a complex policy environment and this paper shows that regulations can have a significant impact on diversity produced and used.Crops have gone through different bottlenecks with regard to diversity and breeders have had a significant role, especially in contributing to the modernization bottleneck. At the same time, breeders have ventured into alternative breeding methods and arrangements with the effect that diversity could increase. Breeders also respond to policies which may affect the diversity that they produce. When we look ahead beyond the modernization bottleneck and the trends that increase diversity to some extent, we see some risks and opportunities.A molecular technology bottleneck may appear in plant breeding when gene editing methods support the trend from plant breeding to trait breeding. This trend does however not necessarily affect alternative breeding strategies to be applied or developed in parallel and gene editing may even be used to increase genetic diversity among varieties and within, such as multiline-breeding. Such strategies will require different incentive structures and may require targeted openings or—better—interpretations of current seed laws.However, a ‘policy bottleneck’ is more likely to develop, when (time limited) patents and especially  rights on genetic resources will require complex administrative and legal procedures to negotiate access, arrange for benefit sharing, and resolve conflicts. This will reduce the practical availability of diversity in breeding and will reduce possibilities for smaller operators in formal breeding. It is likely to affect explicit arrangements for breeding for diversity even more, and potentially outlaw breeding methods where tracking and tracing is impossible, such as in most participatory breeding strategies.This paper raises concerns and calls for policy makers to take these into account and find solutions to the challenges that a new bottleneck will create for the sustainability of agriculture.


Familial Hypercholesterolemia (FH) is an underdiagnosed condition in the United States (US) and globally, affecting an estimated 1/250 individuals. It is a genetic risk factor for premature cardiovascular disease and is responsible for an estimated 600,000 to 1.2 million preventable vascular events. Studies show that FH genetic testing can identify a causal gene variant in 60 to 80% of clinically suspected FH cases. However, FH genetic testing is currently underutilized in clinical settings in the US despite clinical recommendations and evidence supporting its use. Reasons for underutilization are not well understood. We conducted a literature review in the PubMed/MEDLINE database and eight peer-reviewed journals. After filtering for and reviewing 2340 articles against our inclusion criteria, we included nine commentaries or expert opinions and eight empirical studies reported between January 2014 and March 2019 in our review. After applying the Consolidated Framework for Implementation Research (CFIR), we identified a total of 26 potential barriers and 15 potential facilitators (estimated barrier to facilitator ratio of 1.73). We further estimated ratios of potential barriers to facilitators for each CFIR domain . Findings derived from our systematic approach to the literature and calculations of estimated baseline ratios of barriers and facilitators can guide future research to understand FH genetic testing implementation in diverse clinical settings. Our systematic approach to the CFIR could also be used as a model to understand or compare barriers and facilitators to other evidence-based genetic testing processes in health care settings in the US and abroad. Familial Hypercholesterolemia (FH) is an underdiagnosed condition in the United States (US) and abroad and a genetic risk factor for premature cardiovascular disease (CVD) ,2,3,4,5.Studies show that FH genetic testing can identify a causal gene variant in 60 to 80% of clinically suspected FH cases and that large-scale DNA sequencing can identify FH cases that were either not clinically detected or potentially missed using an algorithmic approach ,8. A recFH genetic testing, however, is underutilized in clinical practice in the US, with testing being used primarily in research  settings ,20,25. AIn a recent commentary, we (R.M.H.-S. and C.Y.L.) discussed seven key implementation challenges to FH genetic testing in the US . In thisIn March 2019, articles were electronically searched in the PubMed/MEDLINE database. To control for potential PubMed/MEDLINE index discrepancies, identify articles that do not contain keywords , and overcome possible limitations to searching for relevant literature in a single public database, we also searched for articles directly in eight relevant peer-reviewed journals . We seleOne author (R.M.H.-S.) performed the literature search, screened titles and abstracts, and reviewed full articles to determine topic relevance for information extraction. Literature searches were conducted separately using each search string in PubMed/MEDLINE and each of the eight selected journals. Duplicate articles were removed. After screening titles and abstracts, further articles were excluded due to topic irrelevance.The CFIR is a flexible analysis framework that consists of five major domains  and 39 constructs. CFIR domains are listed and defined in The process for selecting implementation theories by scientists as of recent has been described as ‘haphazard’ and primarily driven by convenience or familiarity . The CFIOne author (R.M.H.-S.) extracted information from relevant articles and categorized the information under a CFIR domain. When deemed appropriate, a single identified barrier or facilitator was categorized to more than one domain. Predefined CFIR domain definitions , many facilitators were related to meeting FH diagnosis criteria, DNA sample collection methods, insurance coverage, the availability of genetic counseling services, timeline for returning test results, and the interpretation and use of test results. Barriers under CFIR Domain #1 focused on the monetary costs of FH genetic testing, little access to testing services, concerns about privacy and discrimination, lack of insurance coverage for testing, and issues with recruiting or identifying family members for cascade testing. The estimated ratio of potential barriers to facilitators for CFIR #1 based on the literature is 9/11 or 0.82.Under CFIR Domain #2, Outer Setting, facilitators centered on physical access to testing services and expert consensus to support the use of FH genetic testing in cases of probable or definite FH. Barriers under CFIR Domain #2 were similar to those listed under CFIR Domain #1: recruitment of family members for testing, proximal access to services, concerns about privacy and discrimination, and coverage of costs for FH genetic testing. The estimated ratio of potential barriers to facilitators for CFIR #2 based on the literature is 6/3 or 2.00.Under CFIR Domain #3, Inner Setting, facilitators centered on the use of electronic health records (EHRs) to identify probands and relatives, and collaboration among clinicians. Barriers centered on lack of optimized diagnostic criteria for FH patient identification in EHRs, suboptimal time for assessing family history during clinic visits, less than optimal accuracy of family history reporting, and the poor adoption of paper-based and web-based tools that would help patients organize their family history outside of the clinical visit. The estimated ratio of potential barriers to facilitators for CFIR #3 based on the literature is 4/4 or 1.00.Under CFIR Domain #4, Characteristics of Individuals, facilitators were patient-focused, centering on the use of genetic counseling before and after FH genetic testing, patient readiness to undergo FH genetic testing, patient diagnosis of FH, and patient use of educational materials about FH. Barriers centered on patient concerns and knowledge about FH, low patient readiness to receive genetic test results, and providers perceiving FH genetic as outside of their scope of medical practice. The estimated ratio of potential barriers to facilitators for CFIR #4 based on the literature is 6/4 or 1.50.Under CFIR Domain #5, Process, facilitators centered on coordinated efforts among clinicians involved in the FH genetic testing process and the use of diagnosis codes to facilitate this process. The one barrier centered on the fact that FH risk stratification and subsequent clinical management is mostly guided by FH phenotypes  versus genotype. The estimated ratio of potential barriers to facilitators for CFIR #5 based on the literature is 1/4 or 0.25.To date, no single literature review has sought to identify possible barriers and facilitators to FH genetic testing using the CFIR framework. Moreover, no single analysis has involved the use of ratios to evaluate the presence of the potential barriers and facilitators to FH genetic testing in the US. Future empirical work should confirm the presence or absence of these potential barriers and facilitators across diverse health systems and settings.Our decision to involve ratios of barriers and facilitators in our analysis was inspired by Gleacher et al., who assessed barriers and facilitators to the implementation of an electronic measurement feedback system and used ratios as a quantitative measure to compare the mean number of barriers or facilitators reported by clinicians at two clinics . To contAlso, since health care in the US is largely fragmented (versus integrated) and because patients can be denied insurance coverage of genetic testing , the number of barriers and facilitators experienced by clinicians could significantly vary based on factors that include but are not limited to geographic location; provider knowledge, attitudes, and beliefs about genetic testing; and insurance payor coverage policies. The estimated overall ratio of barriers to facilitators we have attained of 1.73 can be used as a numerical benchmark for future studies that might measure improvement  or as a benchmark or baseline measure that can be used by health systems seeking to implement FH genetic testing. For example, a health system with several facilitators on our list, but only a few of the barriers on our list, will have an overall average barrier/facilitator ratio that is lower than our baseline ratio of 1.73.Researchers are currently using the CFIR as a guide to explore barriers and facilitators to genetic testing for other conditions with Tier 1 genomic applications according to the CDC  ,33. The Keith et al. applied the CFIR and conducted practice interviews and visits to assess implementation barriers and facilitators to clinic participation in a program consisting of five functional components. By cross-referencing, in a two-way matrix, the five program components to each CFIR domain, they were able to understand the influence of program implementation barriers and facilitators among 21 small, medium, and large practices . The FH One key limitation to this review could be our use of the CFIR as a guiding framework for our analysis. The IGNITE-CMG described two key limitations to using the CFIR as a guiding framework to understand the implementation of genomic technologies: (1) the CFIR does not fully capture all domains pertinent to genomic medicine and (2) the CFIR is specifically focused on factors relevant to the health system rather than local community values . They adOther limitations to this review include limiting the search strategy to only a single database and eight peer-reviewed journals; it possible that other potentially relevant publications in unsearched peer-reviewed journals or databases might have been missed . Further, our list of barriers/facilitators is not based on empirical studies alone, as we also included expert commentary to generate a relatively comprehensive list of potential barriers and facilitators to FH genetic testing. Expert commentaries do not often include searchable keywords. Thus, it is possible that commentary articles included in our review from the selected journals were not identifiable in PubMed/MEDLINE using keywords from our search strategy.FH genetic testing has been reported within recent years in diverse locations and health systems within and outside of the US and within adult and pediatric populations  ,19,20,54FH, Lynch syndrome, and breast cancer are diseases considered by the CDC as having sufficient evidence to support the implementation of genetic testing for clinical diagnosis . As mentOur work demonstrates how the CFIR can guide a systematic analysis of the literature to identify potential barriers and facilitators to FH genetic testing in the US. Our systematic approach might also guide the exploration or comparison of barriers and facilitators to other evidence-based genetic testing programs, such as genetic testing for breast cancer-related genes, in diverse health systems and geographic locations outside of the US. We identified a total of 26 potential barriers and 15 potential facilitators to FH genetic testing to calculate an estimated barrier to facilitator ratio of 1.73. Strategies should be taken to reduce the number of barriers and leverage current facilitators to FH genetic testing in the US to increase the likelihood of observing the clinical benefits associated with FH genetic testing. Moreover, future research should consider our findings as a crucial starting point to assess how these potential barriers and facilitators impact key steps within the FH genetic testing process.