Garra cambodgiensis, a stream cyprinid, in eight tributary streams in the upper Nan River drainage basin , Nan Province, Thailand. These populations are under intense fishing pressure from local communities. Based on 11 microsatellite loci, we detected moderate genetic diversity within eight population samples . Allelic richness within samples and stream order of the sampling location were negatively correlated (P < 0.05). We did not detect recent bottleneck events in these populations, but we did detect genetic divergence among populations . The Bayesian clustering algorithms (TESS and STRUCTURE) suggested that four to five genetic clusters roughly coincide with sub-basins: (1) headwater streams/main stem of the Nan River, (2) a middle tributary, (3) a southeastern tributary and (4) a southwestern tributary. We observed positive correlation between geographic distance and linearized FST (P < 0.05), and the genetic differentiation pattern can be moderately explained by the contemporary stream network . The MEMGENE analysis suggested genetic division between northern (genetic clusters 1 and 2) and southern (clusters 3 and 4) sub-basins. We observed a high degree of genetic admixture in each location, highlighting the importance of natural flooding patterns and possible genetic impacts of supplementary stocking. Insights obtained from this research advance our knowledge of the complexity of a tropical stream system, and guide current conservation and restoration efforts for this species in Thailand.Spatial genetic variation of river-dwelling freshwater fishes is typically affected by the historical and contemporary river landscape as well as life-history traits. Tropical river and stream landscapes have endured extended geological change, shaping the existing pattern of genetic diversity, but were not directly affected by glaciation. Thus, spatial genetic variation of tropical fish populations should look very different from the pattern observed in temperate fish populations. These data are becoming important for designing appropriate management and conservation plans, as these aquatic systems are undergoing intense development and exploitation. This study evaluated the effects of landscape features on population genetic diversity of  The historical and contemporary river and stream landscapes have a profound effect on the population demographic and genetic processes of resident fish species . A tropiGenetic variation in natural populations reflects population history and the evolutionary potential of a species , an impoAlthough we recognize a general pattern of landscape effects on population genetic diversity, the boundaries for genetic divergence specific to a river system also vary, depending upon the scale and complexity of local landscapes, fish life history and fish population dynamics. Genetic differentiation often is detected at a drainage basin level  or upstr2 and is divided by the large Sirikit Dam and its reservoir into upper and lower portions. The upper Nan River drainage basin captures all highland headwater streams draining into the main stem of the Nan River and occupies approximately 1/3 of the entire Nan River basin. Due to its heterogeneous landscape containing some pristine highland headwater streams, the upper Nan River basin harbors high fish species diversity—at least 108 species (The Nan River is the largest tributary (740 km) of the Chao Phraya River. Similarly to other tropical rivers, the Nan River is under the influence of seasonal monsoons. The entire Nan River drainage basin covers 34,682.04 km species  of the 6Garra cambodgiensis) is a small-bodied (4–10 cm) cyprinid and a habitat specialist inhabiting rocky bottoms of fast- flowing sections of small-sized and medium-sized streams. This species is widely distributed in large river basins in Southeast Asia, including those of the Chao Phraya, Mekong and Meklong rivers  during the dry season, November–December 2016. Fish distribution was not affected by flooding. Individuals were sampled from their feeding habitats  endorsed a license for animal use for the lead investigator (license number U 1 05299 2559). The Research Ethics Committee of the Faculty of Science, Burapha University, approved the research in June 2016 (Research Ethics Committee meeting report 3/2559).Garra spp. using polymerase chain reactions (PCR). These loci were GC203, GC187 and Sa197 . The PCRs were performed in a thermal cycler  with the following temperature profile: a cycle of 94 °C for two minutes; 40 cycles of denaturation at 94 °C for 30 s, an annealing temperature specific to each primer pair for 30 s , and elongation at 72 °C for 30 s; and a final elongation at 72 °C for 5 min. The PCR products were submitted to a commercial genetic analysis service  for electrophoresis and genotyping on an ABI3730XL DNA analyser. Scores were determined relative to an internal size standard (LIZ 500) using the GeneMapper software v.3.0 .Genomic DNA was extracted using a salt extraction protocol modified from nd Sa197 , Gar3, Gnd Sa197 , PH8A, Jnd Sa197 . A forwaA), effective number of alleles (Ae), heterozygosity , and inbreeding coefficient (Fis) using GenAlEx v.6.5 (Ar) based on the rarefaction approach implemented in F stat v.2.9.3,  of each sample/genetic cluster was calculated based on two methods, the linkage disequilibrium (LD) method (The contemporary effective population size  method  and sibs) method , impleme) method . For theHe) excess compared to the values expected under the mutation-drift equilibrium (Heq).To detect evidence of a recent bottleneck, we evaluated the rapid increase in heterozygosities compared to those expected across loci under a mutation-drift equilibrium assuming a two-phase model of microsatellite evolution (TPM), which is most appropriate for empirical microsatellite data , with 90FST values using an AMOVA framework with an exact test P-value for a given FST based on random permutation procedures  using the software ARLEQUIN v.3.5  and estimated membership coefficients  for each individual. For STRUCTURE analysis, the most likely K value for the dataset was determined by a method proposed by . A K value with the highest rate of change would be the probable K value for the data set. To obtain these probability values, we simulated a range of K values between 1 and 9, with 20 replicated runs for each value of K and a burn-in period of 25,000 and 100,000 Markov chain Monte Carlo (MCMC) iterations (these parameter settings were recommended by the software developer). We used the admixture model with correlated allele frequencies, and default parameter settings. This model is typically a starting point for most analyses. It is a reasonably flexible model for dealing with many of the complexities of real populations  values averaged over 20 simulations between subsequent K values. To obtain the DIC values, the analysis was performed using the CAR admixture model, which assumes spatial autocorrelation of the genomes of individuals in closer geographical proximity compared with those further apart. The spatial interaction parameter (ψ) was set to the default value of 0.6 for analysis. TESS was run with a burn-in of 30,000 sweeps followed by 50,000 sweeps, with 20 independent runs conducted for each value of K, from 2 to 9. For each K, both STRUCTURE and TESS estimated a membership coefficient, accounting for sampling location, for each individual. These coefficients reflected the genetic admixture level within individuals (if any). The display of membership coefficients of individuals for each K value was generated using the Pophelper R web app v.1.0.10 , and a migration rate parameter M, which is the immigration rate m divided by the mutation rate μ. Search criteria in Migrate-n were set to 10 short chains of 10,000 steps, 500 trees recorded and three long chains of 100,000 steps, 5,000 trees recorded and a static heating scheme with the following temperatures 1.0, 1.3, 3.0, and 10,000. Microsatellite mutation was modeled as a continuous Brownian and stepwise process. Migrate-n was run six times with parameter values starting from FST-based estimates, and the distribution of parameter values was compared across runs to ensure overlap of 95% C.I. Effective sample size was 7000 for all runs. Values of long-term, historical estimates of gene flow (M) were converted to proportion of migrants (m). The conversion was calculated using the formula: m = M µ. We tested the effects of sampling site elevation, stream order, distance to the main channel of the Nan River, and percentages of major land-use types within a 4-km radius of sampling locations  on allelic richness using Pearson correlation analysis (IBM SPSS Statistics v.20). We also tested for the isolation by distance (IBD) pattern by performing Mantel’s tests  and pair-wise stream distance among sampling locations, calculated by Google Earth (log transformed). The Mantel tests  were performed in the ecodist package in R , between two neighbor-joining dendrograms, one from genetic distance data and another from stream network data sections.To assess spatial pattern of genetic variation, we examined correlations between landscape features and genetic diversity  as well as with genetic differentiation , we followed an approach used by AIC) scores, adjusted for small sample size (AICc), and Akaike weights (wi). These values were calculated using the AICcmodavg package in R (AICc scores (ΔAICc < 2.0) and highest weights (wi > 0.10) were retained (R2) using the function MRM implemented in the ecodist package in R  to 0.24 ± 0.07 (Sa197). However, for a given locus, we did not observe consistent null allele frequencies across populations. This observation suggested that any deviation from an ideal population may be due to population properties rather than the presence of null alleles. Moreover, the FreeNA analysis suggested only a slight change in pairwise  alleles . We ther∗, P < 0.05, Mann–Whitney U-test), but had comparable values for other measures to the remaining samples. For allelic diversity, the average number of alleles per locus (A) ranged from 8.00 ± 1.92 (Wa) to 12.09 ± 1.88 (Pua), the effective number of alleles per locus (Ae) ranged from 4.31 ±1.70 (Wa) to 6.39 ± 2.41 (Haeng), and allelic richness (Ar) ranged from 7.64 ± 1.92 (Wa) to 11.05 ± 1.87 (Pua). Heterozygosity values were comparable across all samples. Of the 88 sample-locus cases (8 samples ×11 loci), 40 cases showed significant deviation from HWE . All of the deviations were heterozygote deficiencies (Ho <He)  , which wNe estimates for a given population, with the linkage disequilibrium method resulting in an estimate with higher variance compared to the sib-ship methods. Based on the linkage disequilibrium method, Pua had the lowest Ne estimate  and other populations sampled had an estimate with a large 95% CI (upper bound = infinity). The sib-ship method resulted in the lowest Ne estimate in Wa ; most populations had comparable estimates. We did not detect significant heterozygote excess in any of the samples under the two-phase mutation model (TPM). Additionally, the mode-shift test showed a normal L-shaped distribution pattern of the allele frequencies in all samples. The results implied the lack of bottleneck events in the recent history of these populations . Genetic variation from among sub-basins, among sites within sub-basins, and within sites contributed 2%, 2%, and 96% of total variation, respectively. The pairwise FST values ranged from 0.003 (Yao and Sa) to 0.053 (Wa and Sa) . and Sa) . All samThe UPGMA dendrogram based on Nei’s genetic distance also formed robust clusters of Yao and Sa  and of Kon and Meed  . The sam−4 (Wa received from Pua) to 12.5 ×10−4 (Yang received from Pua). Migration from Yang to some northern streams  were similar (m = 10.2 × 10−4–12.3 ×10−4). Adjacent rivers also had slightly higher migration rates than those more distantly located. For example, Yang received immigrants from Pua (m = 12.5 × 10−4) and Wa received immigrants from Sa at higher rates (m = 7.7 × 10−4) than other, more distant sites. The most downstream site, Haeng, received immigrants from all samples. Most samples showed estimated immigration rates comparable to emigration rates. Numbers of migrants per generation (Nem) between sites were low (<0.7). Estimated migration rates suggested that Yang was a likely donor population within the upper Nan River drainage basin, especially to the upstream tributaries, with Nem estimates ranging from 0.313 (to Haeng)-0.636 (to Meed)  .P = 0.015). However, it did not correlate significantly with other landscape attributes, including elevation, distance to the Nan River main stem, number of barriers within tributaries, % forest, % agriculture and % paddy field (P > 0.05)  .FST among samples . Genetic differentiation between populations was moderately explained by contemporary patterns of stream connectivity (R2 = 0.75 for the STREAMTREE model). However, the difference in elevation between sampling locations did not correlate with genetic distance. Based on the AICc and wi criteria, two regression models identified log stream distance and pairwise differences in stream orders as important explanatory variables for spatial genetic variation. However, neither of these variables explained much of the spatial genetic variation (R2 < 0.01) (We found a significant correlation between geographic distance (log (geographic distance)) and linearized  < 0.01) .R2adj = 0.016), but this was sufficient to identify neighborhoods that correspond to a landscape pattern.We detected evidence for at least two distinct spatial genetic neighborhoods separated by the north-south division  were slightly lower than other Garra spp., such as Oriental sucking barb  on Hainan Island, China (G. barreimiae) in the southeastern Arabian Peninsula , had much higher microsatellite variation, with allelic diversity ranging from 8 (Gar8) to 25 (Gar9) alleles and expected heterozygosities ranging from 0.72 (Gar8) to 1.0 (Gar6) (G. barreimiae populations in the southeastern Arabian Peninsula (n = 44 from each location) had 7 to 15 alleles per locus , Mogurnda mogurnda), Overall genetic diversity measures found for d, China , and theeninsula . It is p0 (Gar6) . At anoter locus . It may G. cambodgienis populations did not experience recent bottleneck events. This observation reflects the dynamics between species-specific requirements for suitable upstream habitat and other ecological factors facilitating a longer-term maintenance of stable effective population size and high genetic diversity in tributaries of our system. G. cambodgiensis is a headwater species, with highest abundance in low-order streams. This species inhabits rocky bottoms with fast-moving water, which are somewhat limited to small and mid-size streams . High order streams may be barriers to dispersal. On the other hand, having a reproductive peak during the rainy season with semi-buoyant eggs  and slightly lower effective population size based on the sib-ship method (Nes). This sample represents a population inhabiting the downstream segment of the Wa River, in the Nam Wa sub-basin , Salvelinus fontinalis), Perca fluviatilis), Gadopsis marmoratus), G. cambodgiensis, Among upper Nan River sites, the Wa sample had the lowest allelic richness . We detected four genetic clusters according to the river topology: (1) headwater tributaries (Meed/Kon/Pua) and main stem of the Nan River (including Haeng), (2) a middle tributary (Yang), (3) an eastern tributary (Wa), and (4) a western tributary (Sa). Historical migration rates were relatively low among these sub-basins (Nem < 0.7). The low genetic differentiation between two headwater streams, Kon and Meed, can be explained by their connecting waterways (only 22.5 km apart). The Yang sample from the second part of the Mae Nam Nan sub-basin was more closely related to the main stem cluster than to the remaining clusters. Wa and Sa represented the cluster most distantly related to the headwater streams/main stem cluster. Isolation by distance and a stream hierarchy structure can explain population genetic structure in stream fish species, especially those with dispersal mediated by current , (Catostomus discobolus), Catostomus discobolus) populations in three large river drainage basins of western North America  . For the entire Colorado River basin, there were three evolutionarily significant units (ESUs) of C. discobolus populations divided by segments of the river.The G. cambodgiensis in our study, flooding can facilitate genetic homogeneity for locations along the main stem (more than 100 km apart), but the landscape disconnectivity among sub-basins restricts gene flow between sites in adjacent sub-basins . In Brook trout , FST) and distance (20–25 km) in populations of common roach (Rutilus rutilus) in only one of the two rivers studied, the Stour River, in southeast England.Although the isolation-by-distance pattern of genetic differentiation can be observed in multiple fish species, the geographic distance at which populations diverge varies with the species’ dispersal ability and the degree of restrictions of river flow. For FST = 0.003), located more than 190 km apart in a different sub-basin. The model-based clustering analyses suggested that an upstream site, the Yao River (in the eastern Nam Yao sub-basin), contained genetic material from at least two sources, the main stem cluster and the western tributary cluster (Nam Sa). A similar pattern has been reported by Etheostoma blennioides) from two separate drainages, the Sydenham and Thames rivers, Ontario, Canada, were genetically similar (mean pairwise FST = 0.016). A possible explanation for this weak divergence was an on-going gene flow through Lake St. Clair, an adjacent water body connecting the two rivers. Similarly, Etheostoma caeruleum) samples across four tributaries of the upper Mississippi River . The authors hypothesized that the northward population expansion of this species occurred recently, after the retreat of the last glaciation event . This historical process overwhelmed more recent genetic changes due to life histories  and a contemporary river structure network . For G. cambodgiensis in the Nam Yao-1 and Nam Sa sub-basins, the two sub-basins lie on the same side of a ridge (the east Phi Pun Nam range), and the headwater streams may have been connected in the past. In addition, the two sub-basins are adjacent to the sub-basin of another large tributary of the Chao Phraya River, the Yom River, and partially connected through tributary streams of the Yom River, especially during flooding. For example, the Huai Mae Tha Stream, a tributary stream of the Yom River, flows directly into Sa River. It is possible that G. cambodginensis populations in Sa River share some ancestry with a population in the Yom River. Genetic analysis of additional individuals from the Yom River could be helpful.A surprising finding that conformed to neither the isolation-by-distance pattern nor to the upper Nan River hierarchy structure was high genetic similarity between the Sa and Yao samples , Poecilia reticulata),Mogurnda adspersa), Gasterosteus aculeatus), G orientalis, in the Pearl River, China, contained highly admixed individuals compared to other sampling sites. In P. reticulata populations from Trinidad and Tobago, This pattern of admixture is rather common in tropical monsoonal rivers and streams, especially for smaller-bodied fish species , and there is evidence of slight genetic impacts of supplementary stocking of fry in some locations. The supplementary stocking program was initiated in 2009 and released approximately 200,000 individuals per site per year , Nam Sa, Nam Wa (Wa) and the remaining portion of the upper Nan River/main stem. This spatial-genetic divergence pattern can be used as a guide for identifying conservation and fishery management units in the upper Nan River drainage basin. Any supplementary stocking using broodstock from a different sub-basin should be avoided.One of the key long-term conservation goals is to protect the evolutionary potential of populations . Our resThe high degree of admixture among genetically distinct populations highlights the importance of natural flooding patterns, allowing for admixture, and may reflect the genetic impacts of supplemental stocking. It is possible that this fish species has metapopulation dynamics, with the main stem of the upper Nan River and annual flooding facilitating connectivity among demes. Due to clear sub-division among some sub-basins, future supplemental stocking programs should be planned more carefully . In addiG. cambodgiensis populations in the upper Nan River, Thailand, suggests interactions between habitat isolation  and the flood-mediated dispersal pattern. The high and consistent level of admixture in most populations suggests possible metapopulation dynamics for this species. The genetic analysis also suggests slight genetic impacts of a recent hatchery-assisted stocking program in the area and cautions against extensive reliance on use of broodstock from a different sub-basin.This work is among a few studies providing insights into population genetics of tropical stream fishes in Southeast Asia, a hotspot for freshwater biodiversity. Our work highlights the importance of the historical and contemporary stream network as well as monsoonal flooding patterns on genetic connectivity among the populations of different sub-basins. In addition, the spatial genetic variation of 10.7717/peerj.4487/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.4487/supp-2Table S1Click here for additional data file.10.7717/peerj.4487/supp-3Table S2N), number of alleles per locus (A), effective number of alleles (Ae), allelic richness (Ar), observed heterozygosity (Ho), expected heterozygosity (He), estimated null allele frequencies and fixation index (Fis) values and probability of significant deviation from Hardy-Weinberg equilibrium (P) are given for each population and locus. Values underlined indicate statistical significance, P < 0.00057, after Bonferroni correction = 0.05/88.The indices included the sample size (Click here for additional data file.


Triticum aestivum ssp. leucospermum Korn.) landraces mainly adapted to harsh winter conditions of Eastern Anatolian region of Turkey were characterized at agro-morphological and molecular level. Totally 50 hulled wheat population from Kastamonu, Konya and Kayseri provinces and 15 tir wheats from Kars provinces of Turkey were in-situ collected for characterization in 2013. Some quantitative and qualitative traits of each population were determined.Turkey is one of the important gene centers for many crop species. In this research, some ancient wheats such as tetraploid and diploid hulled wheats together with hexaploid tir wheats (Fit-Fis) and genetic variation (Fst) were determined as 9.45, 0.71, 0.07, 0.67, 0.90, 0.39, 0.87 and 0.39, respectively. A clear genetic deviation from Hardy – Weinberg expectation was observed among population in particular. These results showed considerable genetic variation among landraces rather than within population.Twenty three hulled wheat population collected from Kastamonu province were distinguished into nine emmer and 14 einkorn wheats at morphological level. Additionally, Konya, Kayseri and Kars population were characterized as einkorn, emmer and tir wheat, respectively. Among the evaluated traits, protein ratios of hulled wheats were strikingly higher than registered cultivars. All the populations were also examined by molecular level by using fluorescently labelled 11 polymorphic SSRs primers. The primers exhibited 104 bands, ranging from 6 to 16 with a mean value 9.45 per loci. The clustering analysis separated the germplasm into two clusters which were also divided into two subclusters based on genetic similarity coefficient. Sixty-five population and five checks were analyzed to estimate mean number of alleles (N), expected and observed heterozygoties (He and Ho), polymorphism information content (PIC), Wright fix index (F), genetic deviation from Hardy-Weinberg expectation (These molecular information has revealed genetically diverse einkorn, emmer wheat and tir wheat population could be used as parents for further breeding studies in both Turkey and abroad. Furthermore, the molecular analysis has also generally discriminated the germplasm into ploidy level. T. monoccocum var. monoccocum) and emmer wheat (T. dicoccum L.) grains were firstly found in Cayonu excavation dated back to 6500–7000 B.C. in Turkey [Triticum aestivum ssp. leucospermum Korn.) are also unique bread wheat landraces especially for cultivation in harsh winter conditions of the Lake Van Basin including Kars province for centuries [Hulled wheats are the first domesticated crop species. Two hulled wheat species such as einkorn  cultivars,  and one bread wheat (T. aestivum L.) cultivar, Dogankent were included as controls in both field and laboratory experiments.Totally, 50 hulled wheat populations from Kastamonu (23), Konya (16) and Kayseri (11) and 50 tir wheat population from Kars province of Turkey were in-situ collected. Additionaly, four durum , and fixation index (F = 1-Ho/He) were calculated for each microsatellite locus. Morever, lculated , 22. Fitandraces . Fst is Diversity analysis among wheat accesions was conducted using molecular data. To examine genetic relationships, the unweighted pair-group method with arithmetic mean (UPGMA) was performed to construct cluster based on maximum composite likelihood distance by Mega Software (7.0.14 version) . We alsoAll germplasm were examined at morphologic level considering plant, spike and grain data collected during and after harvest in order to realize true identification. All wheat populations collected from Kars province had naked grain and they were identified as tir wheat while populations collected from Konya and Kayseri provinces had hulled grain “einkorn are emmer wheat”, respectively. However, 23 hulled wheat population collected from Kastamonu province were distinguished into nine emmer wheat and 14 einkorn. Mean plant height, number of grains per spike, plant yield, heading and maturity time, 1000 kernel weight and protein ratio of Kastamonu, Konya, Kayseri and Kars population were shown at Table In addition to quantitative variation, there was enough qualitative variation to distinguish ancient wheats from check cultivars. Einkorn from Kastamonu and Konya and emmer wheat from Kayseri and Kastamonu with grain hull, hairiness and waxiness were completely different from check cultivars in general Table . HoweverFit, Fis and Fst are also known as Wright statistical parameters were calculated to analyse the genetic structure by PopGene32 software. Mean of Fis was 0.87, varying from 0.32 (Xgwm135) to 1 (Xgwm372), and Fit was 0.39 on mean, ranging from 0.22 to 0.79 at corresponding loci and Fst was 0.39 on mean, varying from 0.23  to 0.80 (Xgwm312) (Table Obtained data from 11 labelled SSR primers from different linkage groups (LG) were used for molecular characterization and estimation of basic population genetic parameters summarized at Table T. monococcum and tetraploid T. dicoccum population and T. durum check cultivars while group B fully accounts for hexaploid tir wheats  from 1 to 10. For each K value, the run showing the highest posterior probability of data was considered as STRUCTURE output data. Then, these data were loaded to Structure Harvester and the results suggested that optimal number of K = 6 implying the existence of six main groups among the wheat gene pool. On the other hand, clustering pattern is visualized in a graph to demonstrate the population structure Fig. . AnalyseT. monoccocum) accessions mostly from Kastamonu and Konya. Hexaploid tir wheat (Triticum aestivum ssp. leucospermum) accessions mostly clustered to Q1 and Q6 except for kars11 and kars14 together with registered hexaploid cultivar Dogankent which were situated in different subpopulations such as Q3 and Q4, respectively , with einkorn (The distances between pairs of sub-populations ranged from 0.0303 between Q2 and Q4) to 0.128 (between Q1 and Q3). When considering K = 4, accessions were mainly seperated four subpopulations according to regions that they were collected  for all wheat accessions in this experiment. According to previous studies, He value was 0.650 (0.211–0.899) for Eurasian bread wheat varieties ; 0.70  and mustard landraces (0.12) reported by [Mean orted by , 47, resorted by , 47. Addorted by  resultedJuniperus thurifera L. by [T. dicoccoides) so they suggested that domestication of tetraploid hulled wheat most probably occurred in the Karacadag region of Turkey.Population structure is a component which is most convenient for analyzing genetic structure and a key step for further association studies . Clusterra L. by  is in agra L. by . The larra L. by  who repoThe field experiment under drought conditions showed that einkorn and emmer wheat populations with longer plant height, more 1000 kernel weight and protein ratio together with hairy and waxy leaves and stem characteristics can be used as genitor to develop more drought tolerant new germplasm. Moreover, SSR markers were an effective tools to determine genotypic variation, genetic relationship, different ploidy level and genetic distance among ancient wheat populations. To reveal genetic divergence among different population, both population structure and principal coordinate analyses can be effectively used based on genotypic data. On the other hand, a clear genetic deviation from Hardy – Weinberg expectation among ancient wheat population was good indicator for selecting superior genotypes. We suggest that effectiveness of SSR markers for the separation of wheats in different ploidy level as was in this study be tested in further studies.


Chaetophractus vellerosus, discuss previous hypothesis about the origin of its disjunct distribution and propose an alternative one, based on novel information on genetic variability. Variation of partial sequences of mitochondrial DNA Control Region (CR) from 73 individuals from 23 localities were analyzed to carry out a phylogeographic analysis using neutrality tests, mismatch distribution, median-joining (MJ) network and paleontological records. We found 17 polymorphic sites resulting in 15 haplotypes. Two new geographic records that expand known distribution of the species are presented; one of them links the distributions of recently synonimized species C. nationi and C. vellerosus. Screaming hairy armadillo phylogeographic pattern can be addressed as category V of Avise: common widespread linages plus closely related lineages confined to one or a few nearby locales each. The older linages are distributed in the north-central area of the species distribution range in Argentina . C. vellerosus seems to be a low vagility species that expanded, and probably is expanding, its distribution range while presents signs of genetic structuring in central areas. To explain the disjunct distribution, a hypothesis of extinction of the species in intermediate areas due to quaternary climatic shift to more humid conditions was proposed. We offer an alternative explanation: long distance colonization, based on null genetic variability, paleontological record and evidence of alternance of cold/arid and temperate/humid climatic periods during the last million years in southern South America.Little is known about phylogeography of armadillo species native to southern South America. In this study we describe the phylogeography of the screaming hairy armadillo  The evolution of the Order Xenarthra was bound to South America as they have been considered to be representatives of the initial mammalian stock in this continent . It is tChaetophractus vellerosus, or pichi llorón, presents fossorial habits, an omnivorous diet and is nocturnal during summer but diurnal during winter when individuals leave their burrows around noon, at the warmest few hours of the day [Chaetophractus nationi and C. vellerosus would be the same species, and thus its distribution would extend further to north in Bolivia [The screaming hairy armadillo  the day , 10. It  Bolivia . The spe Bolivia  and a re Bolivia .C. vellerosus in Argentina encompass the provinces of Jujuy, Salta, Formosa, Chaco, Catamarca, Tucumán, Santiago del Estero, San Juan, La Rioja, Mendoza, San Luis, Córdoba, La Pampa and Buenos Aires [The currently distribution range of os Aires . Remarkaos Aires  Fig 1)..C. velleSubsequent studies made by Rabassa et al.  determinC. vellerosus  resulting in 15 haplotypes. The nucleotide composition of sequences was 26.91% C, 29.39% T, 31.06% A, 12.64% G and mean nucleotide diversity (π) among all haplotypes was 0.006460 +/- 0.003750. Samples from Bolivia and from the disjunct populations were monomorphic for haplotypes H1 and H5 respectively.Haplotypes, polymorphic sites, relative frequencies and GenBank accession numbers are given in C. vellerosus CR haplotypes and their source localities. H1 can be considered the oldest  haplotype because it occupies a central position in the network. It is present in 11 of the 23 sampled sites, latitudinally located along the entire distribution range of the species and other 11 haplotypes derive from it. Locations with the highest number of haplotypes were Taco Ralo (TR) = 5 , Usno (US) = 3 , Pellegrini (PE) = 3  and Chamical (CH) = 2 . In the remaining sampling sites, only one haplotype was registered although the number of sampled individuals was high, as in the case of Pipinas (n = 17). No variability was found among Bolivian samples, H1 was the only haplotype present (see Mismatch distributions of different groups are presented in C. vellerosus would have experienced different processes. In the case of contiguous range expansion [contiguous range expansion) or located in distant areas from those occupied by ancestral ones (long distance colonization). In restricted gene flow with isolation by distance pattern [Considering the genealogy of haplotypes and its geographic distribution, xpansion , 20, som pattern , the dis pattern , Neigel  pattern , Nath an pattern  and Slat pattern , 25. The pattern , 27. ThiC. vellerosus and can be visualized in the following relationships between haplotype genealogies and geographical distribution  extracted from These patterns were observed in the phylogeography of Contiguous range expansion .The nucleotide composition of villosus . HoweverData sets 1 and 2, negative values of Tajima’s D indicate population expansion, while a significant positive value was obtained for Data set 3. A positive Tajima’s D signifies low levels of low and high frequency polymorphisms, indicating a decrease in population size (which may be due to a recent bottleneck) and/or balancing selection. On the other hand, Fu’s tests results are negative and significant for all sample sets. Fs takes negative values when an excess of rare haplotypes are found, phenomenon that occurs when populations experienced a recent expansion. Harpending´s raggedness index also supports a population expansion for this species for the three sample sets. These results indicate that C. vellerosus populations would have experienced an expansion. However, we wish to highlight some features of Data set 3 in order to give relevance to positive Tajima´s test results that in principle seem contradictory but could be reflecting an ongoing process toward genetic population stability disguised by recent expansions of H1 and H5 linages. We have to consider that: 1) all haplotypes were found in Data set 3, and relative frequencies do not show as large differences between populations as in Data set 1 and in Data set 2; 2) Data set 3 includes the central haplotype H1 ancestral to all others and other haplotypes located in intermediate positions in the genealogy as H11 and H13, relationships that need time to develop; 3) This genealogy suggest that the area where old haplotypes are distributed can be considered an ancestral distribution range of the species, and 4) the mean number of pairwise differences of Data set 3 are higher than in other groups, reflecting a higher genetic variability  for the three sample sets . In Dataiability . These pC. vellerosus phylogeography cannot be easily classified into a specific phylogeographic category, but shows patterns similar to those that define category V described by Avise [C. villosus colonization front to Argentinian Patagonia, where a single haplotype is distributed along 1500 km to the south [C. villosus haplotype is also present in other northern localities as is the case of haplotype H5 of C. vellerosus, present in both the disjunct coastal population and the core distribution.by Avise : speciesby Avise . A similC.vellerosus to the northwest is continuous [contiguous range expansion some ancestral haplotypes are distributed in the pre-expansive area of distribution, while younger haplotypes arise in population expansion areas and are geographically dispersed [C. vellerosus to the northwest does not fit the definition since H1 is the ancestral haplotype and is distributed in all the core distribution of the species. Further samplings in intermediate areas are needed to know the rate of loss of variability in relation to the advance of colonization wave.In the northwestern expansion front H1 is the only haplotype found, although the twelve sequences included in this study belong to individuals from Bolivian localities spread in an area about 27.000 km² (geographic location in ispersed . TherefoC. vellerosus was previously explained by means of an expansion (i.e. contiguous range expansion) followed by extinction of intermediate populations. Poljak et al. [C. villosus to the east of Pampean Region. These expansions comprised two lineages that split up in different times, probably related to glaciations, without a doubt the most important events that affected climatic and environmental conditions in southern South America in the past. C. villosus (as well as other related armadillo species) are recorded in several paleontological and archeological sites in the east of Pampean Region (i.e. the intermediate area of C. vellerosus). If we accept the hypothesis that C. vellerosus expanded its distribution and reached the coast 0.78 Mya during middle Pleistocene, then became disjunct and after that experienced secondary contacts with the core distribution due to glacial periods of arid climate, it is difficult to explain its current lack of genetic variability. The only haplotype present in the disjunct population (H5) presents the second wider distribution range between all C. vellerosus haplotypes found in this study after H1 and followed by H8 , 33C. vee biotas .T. matacus and Z. pichy into the Pampean Region during the Middle Holocene [C. vellerosus in the intermediate zone between its core distribution and the coastal disjunct population, despite other closely related armadillo species like C. villosus, Z. pichiy and T. matacus are indeed present ([C. vellerosus [http://maps.iucnredlist.org/map.html?id=89604632). In a recent study of fauna association in an archeological site of a coastal locality in southwest Buenos Aires, 40 km to south from Punta Hermengo, Soibelzon & León [Z. pichiy and T. matacus of about 5000 years old, but does not mention C. vellerosus. This finding delimits the age of the last arrive of this species to the area. The lack of genetic variability of disjunct population is in agreement with a recent re-colonization of the area (possibly less than 5000 years).The Quaternary record of Euphractinae and Tolypeutinae shows displacements (expansions/contractions) to eastern Pampean Region that surely occured more than one time during glacial/interglacial cycles, probably in coincidence with glaciations and/or arid phases of the Holocene  so probably both species would share similar environmental requirements. Therefore, it is plausible that C. vellerosus may have reached the coast by the north of Buenos Aires province. An alternative colonization route from south along the Atlantic coast of Buenos Aires province can be proposed based on the work of Soibelzon et al. [C. vellerosus from the coastal locality Punta Hermengo, who lived there 0.78 million years ago. In the second, they argue that expansion of C. vellerosus and T. matacus from central Argentina to the southeast Pampean Region during glacial/interglacial cycles was probably across the "Argentinean arid diagonal"  and of northwestern individuals (living in aridity). Based on the premise that the disjunct population is a relict of an old, wider past distribution, they expected to find different ecological characteristics between individuals of both populations . However, they found no differences between them. A possible explanation would be a modern translocation to the bonaerian coast of a few individuals by humans, given that it was demonstrated that C. vellerosus was part of the medium sized species consumed by the hunter-gatherers since the Holocene in different archaeological sites in Córdoba [Three alternative hypotheses can be proposed regarding the re-colonization routes that based on , that recalities , 45. Then et al.  and Soibn et al.  and refe Morrone ). Given province , it is p Córdoba , Santiag Córdoba –50 and p Córdoba .C. vellerosus inhabited the coast 0.78 Mya, 2) currently there is no genetic variability in an hypervariable fragment of mtDNA in coastal populations, 3) the only haplotype present in the disjunt population is an ancestral haplotype, present in the eastern border of the core population and 4) the alternation of humid warm/dry arid conditions in the area occurred 14 to 16 times during the last million years [C. vellerosus in the Atlantic coast of Buenos Aires province, possibly caused by translocation of individuals by human activity.For the reasons set above, we consider that the hypothesis of isolation due to the climatic changes during the Quaternary that produced the extinction of the species in the intermediate area and resulted in the disjunct distribution is not supported. Alternatively, considering that: 1) on years , we propC. vellerosus population is expanding its distribution range and the front waves of colonization suffer a strong loss of genetic variability, as was reported for other armadillo species. The species reach the Atlantic coast at least twice, one 0.78 million years ago and the other recently. Based on cyclical climatic changes, the evidence of biogeographic response of closely related armadillo species and paleontological and archaeological records in the intermediate area, we propose that the lack of variability of C. vellerosus disjunct population is possibly due to a modern translocation of individuals by human activity, should be interpreted as an evidence that supports the hypothesis that the species recently arrived to the coast by means of long distance colonization, and not by means of a contiguous range expansion.C. nationi from Oruro province (Bolivia) were gently donated by José Carlos Pérez-Zubieta, Universidad Mayor de San Simón . Other 35 samples (24 C. vellerosus and 11 putative C. nationi) were obtained from museum old specimens and faculty collections. Tissues and other data associated with each individual were referenced directly to each voucher specimen and stored along with a field catalog number in the collection of the IDEA . Besides, we added 10 sequences of putative C. nationi from different localities of the department of Oruro, Bolivia, available in GenBank [This study was carried out in strict accordance with the recommendations for care and use of animals of the Ethical Framework of Reference for Biomedical Research, Annex II: Ethical principles for research in animals from laboratory, farms and obtained from nature, Resolution n° 1047/05 of the Ministry of Education Science and Technology, Secretariat of Science, Technology and Productive Innovation, National Council of Scientific and Technical Research (CONICET) of Argentina. Fieldwork authorization L1-00235-11 was extended by the Secretariat of Environment and Sustainable Development, Directorate of Conservation and Protected Areas after review and approval of the capture and tissue collection methods used in this study. One hundred and three specimens were collected on the field, including dry remains and run over animals in different states of conservation. Alive animals were caught by hand. Due to our own previous experience using anesthetics to take blood samples from armadillos on the field [5´-CAATTCCCCGGTCTTGTAAACC-3´), located in the neighboring tRNA-pro gene and DL-H16340 (5´-CCTGAAGTAGGAACCAGATG-3´) [https://www.protocols.io/view/pcr-partial-control-region-chaetophractus-456-bp-kmncu5e in a Biometra T Personal thermocycler.DNA was extracted using the sodium dodecyl sulfate-proteinase K/phenol/RNAse method  and concGATG-3´) . AmplifiDouble-stranded PCR products were purified and concentrated by ethanol precipitation, examined on 1% agarose gels and directly sequenced using the same primers used for amplifications at Macrogen . Sequences were edited with BioEdit 7.0.5.3  and aligwww.fluxus-engineering.com). Loops were resolved following Crandall and Templeton [Arlequin 3.5  was usedempleton .Data set 1 , Data set 2  and Data set 3 . Pairwise differences, Tajima´s and Fu´s tests, mismatch distributions and the goodness-of-fit tests between observed and expected values under an expansion model were calculated for each.Due to the disjunction in the species distribution and the long geographic distance that separates the Bolivian samples from the rest of specimens, we created three different dubsets in order to better understand results. They were: S1 TableGeographic location and repository.(XLSX)Click here for additional data file.S2 TableGeographic information, haplotype composition and GenBank accession number.(XLSX)Click here for additional data file.


The effective population sizes of the examined lines showed patterns opposite to those of population LD. The phylogeny and admixture analyses showed that commercial and purebred chickens were well distinguished, except for Rhode Island Red (RIR) purebred lines of NC (NIAS_RIR_C) and ND (NIAS_RIR_D). These lines are difficult to distinguish clearly because they originated from the same respective breeds. The results of this study may provide important information for the development of genetic markers that can be used in breeding to improve the economic traits of native chickens.The development of genetic markers for animal breeding is an effective strategy to reduce the time and cost required to improve economically important traits. To implement genomic selection in the multibreed chicken population of Korea, an understanding of the linkage disequilibrium (LD) status of the target population is essential. In this study, we performed population genetic analyses to investigate LD decay, the effective population size, and breed diversity using 600K high-density single nucleotide polymorphism genotypes of 189 native chickens in 14 lines . The results indicated that commercial native chickens have less calculated LD (average,  The development of phenotype-related genetic markers and causal variants for the improvement of economically important traits facilitates efficient selection in animal breeding. Accordingly, various phenotype-related markers based on microsatellites and single nucleotide polymorphisms (SNPs) have been used in genome-wide association (GWA) and genome-wide linkage studies for the improvement of livestock animals –3. RecenNe), which describes the demographic status of an observed population ; and the “imported and adapted chicken” , which was imported in the 1960s for industrialization purposes, adapted to the Korean habitat, and maintained as a purebred line  13]. Th. Th13]. In this context, farmers in Korea have considered commercial production of the KNC, which is known for its adaptability to seasonal changes and the breeding of which is associated with lower costs due to environmental control . The KNCNe of KNC populations using high-density SNP data. This genetic information could be useful for the development of genetic markers of economically useful traits of the KNC or for genomic prediction. In addition, it may be useful for the maintenance and utilization of the KNC population and for the establishment of breeding strategies.In this study, we investigated the genetic diversity, extent of LD, and The Institutional Animal Care and Use Committee of Chungnam National University approved this experiment (permit no. CNU-00486). The DNA samples used in the experiment were collected from chicken wing veins based on Assessment of Laboratory Animal Guidelines 1 and 3.n = 7; HF, n = 23; HG, n = 23; HH, n = 23; HS, n = 23; HV, n = 23; HW, n = 23; and HY, n = 7). The following samples from purebred lines were obtained from the NIAS: NC (n = 6), ND (n = 6), NH (n = 6), NS (n = 6), NR (n = 7), and NY (n = 6). Each group of chickens was nurtured under the same standard feeding and environmental conditions used in a government project performed by the NIAS. Genomic DNA (gDNA) was extracted from blood samples collected from the wing veins into EDTA (ethylene diamine tetra acetic acid)-coated tubes. DNA extraction from the blood was conducted following the standard protocol with a PrimePrep DNA Isolation Kit . The quality of extracted gDNA was examined by electrophoresis using 1% agarose gels viewed under ultraviolet light. The concentration of gDNA was quantified using a NanoDrop spectrophotometer . Isolated gDNA samples were stored at –20°C until required for use.In total, we used 187 male chicken samples from 14 purebred lines obtained in Korea from Hanhyup and the NIAS .. Samplep value was less than 0.0001, and the minor allele frequency (MAF) was less than 0.01.High-density SNP array genotyping was performed using an Axiom 600K chicken array , which contains 580,954 SNP markers identified in the chicken genome . Qualityr2 (squared correlation) value among all pairs of syntenic SNP markers. The extent of LD was measured using the following equation:D = freq(A1_B1)*freq(A2_B2)–freq(A1_B2)*freq(A2_B1), freq(A1_B2) is the frequency of the A1_B1 haplotype, freq(A2_B2) is a A2_B2 haplotype frequency and freq(A1_B2), freq(A2_B1) is a frequency of haplotype A1_B2 and A2_B1 in the population, respectively [freq(A1), freq(A2), freq(B1), and freq(B2) were observed frequencies of A1, A2, B1 and B2 alleles, respectively.The statistics of linkage disequilibrium (LD) between genome wide SNP pairs was calculated as an ectively –23. The r2 value for all pairs of SNP marker was estimated in Linkage disequilibrium function of PLINK software (version 1.9) [r2 ~ SNP distance + error) and was plotted in 28 autosomal chromosome areas with an LD window size of 1,000 kb in R software [The ion 1.9) , 24. Thesoftware .To compare the LD structure among breeds, LD correlation was estimated at various marker densities , and correlation patterns were compared among breeds.Ne and LD (r2) without mutation was calculated using the following non-linear equation:r2) is the expected squared correlation of allele frequencies at a pair of loci, c is the genetic distance between SNP markers in Morgans. Genetic distance (c) was inferred based on the ratio between physical distance of each chromosome and length of the corresponding linkage map. The past effective population (Ne) for each population was calculated using the equation [c is the linkage map distance (in Morgans) and r2 is the estimated LD value. Then, Ne was fitted by non-linear least squares regression using the R software [The relationship between equation , 24 as bsoftware .Ne is a value of effective population size for t generation ago, where t = Ne is the effective population size at t = 1/2c generation in the past. This equation indicated that LD values at shorter inter marker distances were a value for the estimated Ne in the past generation ago, while markers at longer distances are an estimated value for the recent and current Ne value.Assuming that the population has been constant in size, the approximation of  t = 12c . That isx and y coordinates, respectively. Population admixture was analyzed using fastSTRUCTURE software [The genetic diversity of the native chicken lines was determined using a multidimensional scaling (MDS) plot, population admixture analysis, and phylogenetic analyses. The MDS analysis was performed in four dimensions using PLINK software (version 1.9) , and thesoftware , and measoftware . Phylogesoftware . To invesoftware .Experimental chickens from two groups were used in this study. One group consisted of purebred chickens from the NIAS and the other group consisted of chickens from eight private purebred lines from Hanhyup (H lines), which are currently being produced commercially ;S1 Tablen = 220) and sex chromosome  SNPs were excluded. Ultimately, 393,418 SNPs on 28 autosomal chromosomes, excluding sex chromosomes and unassigned linkage groups, were used for the genetic diversity and LD analyses. In previous studies, 127,958–473,077 SNPs have been observed in various chicken breeds [In total, we selected 393,418 valid SNPs after quality control of the genotyping results obtained using the Affymetrix 600K Axiom array. The results revealed the occurrence of 69.5% of the total of 580,961 SNPs in the chicken genome. Of these, missing genotype, Hardy-Weinberg equilibrium, and MAF tests led to the removal of 9,314, 132,669, and 35,351 SNPs, respectively (data not shown). Before further calculation, linkage group (n breeds . Furtherr2) values for the whole population were 0.28–0.62 at 20-Kb distances and 0.12–0.34 at 1-Mb distances  over the entire LD distance range. The average LD values for the macrochromosome, intermediate chromosome, and microchromosome were 0.16, 0.14, and 0.12, respectively. The highest r2 values were obtained for the ND (0.40–0.62) and NS (0.34) lines in the 1-Mb distance range  were almost two times higher than those for broiler chickens (0.05–0.12) within the 1-Mb marker interval [To provide comparable LD values for the N-line (NIAS purebred and IAC) and H-line  populations, we calculated LD values for each population using all SNP pairs and plotted them along 1-Mb distances. Overall, chicken lines with less LD were found to have large numbers of effective SNPs, whereas chicken lines with more LD were found to have smaller numbers of effective SNPs. LD  contained five lines with low LD values , and group 2 (G2) contained nine lines with high LD values , and the purebred KNC lines  showed strong correlation with the Hanhyup lines . In addition, when correlations between the purebred KNC line and the Hanhyup RIR, IAC RIR, and IAC Cornish lines were examined, r2 values of 0.274, 0.194, and 0.221, respectively, were obtained. The weakest correlation was between the IAC RIR and IAC Cornish lines (r2 = 0.180) (r2 = 0.359), which remained high even as the extent of LD increased (The complete H and N lines showed a high degree of LD correlation (= 0.180) . Interesncreased . These rNe estimation can aid the identification of changes in the composition of chicken populations caused by genetic drift, the determination of population diversity, and the establishment of selection standards [Ne  and ND had the smallest Ne . The estimated Ne values for the purebred KNC lines NR and NY were 4,360 and 3,923, respectively  had higher Ne values than did the N lines. The NR and NY lines had higher Ne values than did the other purebred chicken lines, possibly because the purebred KNC lines have been maintained in larger populations than have the other IAC lines at the NIAS, as part of efforts to maintain the KNC lines as representative native chicken populations. The results show a 2.71-fold decline in Ne from 5,000 to 1,500 generations ago and a 1.85-fold decline from 1,500 to 700 generations ago. Furthermore, they revealed a 1.73-fold decrease prior to 300 generations ago and a 2.06-fold decline before 125 generations ago. These changes indicate that the recent decline (that occurring in the past 100 generations) has been more gradual than were those occurring in earlier generations. In addition, the chicken populations of G1 and G2 had different LD decay patterns. The decay was 1.1–1.15 times more rapid in G1 than in G2 before 334 generations ago, and 0.3 times more rapid before 125 generations ago. Overall, the decay observed for G1 was 1.1–1.3 times more rapid than that recorded for G2. This result shows that the Ne of G1 is declining faster than that of G2, and that LD is increasing continuously in G1. Thus, we can assume that the genetic diversity of the varieties is decreasing while G1 is maintained as a small population with no influx of foreign resources. The LD and Ne patterns of the HY and HA lines also differed from those of the other Hanhyup lines for the above-described reasons. These two lines have different extents of LD than do other commercial chickens in G1, which can be attributed to line maintenance similar to that practiced by the NIAS.tandards , 35. Appectively ; Fig 5. Ne between G1 and G2 may be attributable to the genetic diversity of populations or the experimental sample size. The Ne values for G2 were estimated using samples that were twice as large as those used for G1; thus, for each generation, the mean value for G2 was higher than the overall mean, and that for G1 was lower than the overall mean. The results could indicate that differences in the extent of LD and genetic diversity between groups were pronounced because the differences between G1 and G2 decreased from 2.4 times to 1.43 times with an increasing number of generations. These assumptions are based on differences in genetic diversity and the extent of populational LD. The G1 populations showed a similar pattern of decline from the ancestral to current populations, although the HF and HV lines showed an approximately two-fold difference in Ne from that estimated for 5,000 generations ago. This result can be attributed to the difference in LD  because the experimental samples for these groups were the same size. The largest Ne value was recorded for the HF line, which had an r2 value 0.27, and the smallest Ne values were recorded for the HV (r2 = 0.41) and HG (r2 = 0.37) lines. For most populations, the Ne value was proportional to LD .The difference in We performed MDS, admixture, and phylogenetic analyses using SNPhylo and TreeMix software to examine the genetic diversity of the 14 Korean chicken lines, and differences in diversity among lines. The purebred KNC lines NR and NY were located at the center of the MDS plot, close to NC and ND, in dimensions 1 and 2 . In contk = 5 admixture result Click here for additional data file.S1 Figr2 values.Inter-marker distances of LD were calculated from 0 to 1 Mbp, and samples of the same size for each chicken line were divided into two groups by converged (TIF)Click here for additional data file.S2 FigNIAS_Hanhyup: all N and H lines; C_Hanhyup: KNC , Hanhyup ; KNC_HRIR: KNC , HRIR ; KNC_RIR: KNC , RIR ; KNC_Cornish: KNC , Cornish ; HRIR_RIR: HRIR , RIR ; RIR_Cornish: RIR , Cornish .(TIF)Click here for additional data file.S1 File(BED)Click here for additional data file.S2 File(BIM)Click here for additional data file.S3 File(FAM)Click here for additional data file.


P=0.011) and spent more time (P=0.009) per case. General GCs reported greater case discussion with other providers (P<0.001), literature review (P=0.026), exploring testing options (P=0.041), electronic medical record review (P=0.040), insurance preauthorization (P=0.05) and fielding patient inquiries (P=0.003). Lesser redundancy in referral indication was observed by general GCs. GCs in general practice carry a higher pre- and post-visit workload compared with GCs in specialty practices. General GCs may require lower patient volumes than specialty GCs to allow time for additional pre- and post-visit activities. Non-clinical activities should be transferred to support staff.With genomics influencing clinical decisions, genetics professionals are exponentially called upon as part of multidisciplinary care. Increasing demand for genetic counselling, a limited workforce, necessitates practices improve efficiency. We hypothesised that distinct differences in clinical workload exist between various disciplines of genetic counselling, complicating practice standardisation and patient volume expectations. We thus sought to objectively define and assess workload among various specialties of genetic counselling. Twelve genetic counsellors (GCs), representing 9.3 clinical FTE, in general or specialty  services at an academic health system developed a data collection tool for assessing time and complexity. Over a 6-week period, the data were recorded for 583 patient visits  and analysed comparing general versus specialty GCs. Variables were compared with hierarchical linear models for ordinal or continuous data and hierarchical logistic models for binary data. General GCs completed more pre- and post-visit activities ( Genetic counsellors are medical providers with a master’s degree in medical genetics or genetic counselling and are the focus of this study.Genetic counselling is a process in which a healthcare provider assesses the likelihood of a disease having a genetic or hereditary basis from a patient’s personal and/or family health history; educates the patient about the genetic or hereditary disease(s) in question; explains the inheritance of the condition; discusses management options for the disease(s) and/or family planning options; when applicable, reviews the genetic testing process, obtains informed consent, facilitates ordering the genetic testing, and interpreting the genetic test results; and finally provides psychosocial support to the patient and his or her family.3 In some sub-specialties of genetic counselling, such as oncology and preconception, the genetic counsellor might work autonomously. In other areas, such as paediatrics, the genetic counsellor might work alongside a medical geneticist. The geneticist has a critical role in conducting a physical examination, providing medical management recommendations and prescribing medications or treatments, as necessary.The early years of genetic counselling were primarily focused on reproductive counselling. However, there has been vast expansion of services into areas such as paediatrics, neurology, oncology, cardiology, metabolic disorders, complex adult onset disorders, newborn screening, infertility/ART/IVF, preconception and genomics, and even pharmacogenomics.www.genetests.org, As of February 2016, there were over 4,000 genes and genetic disorders for which commercial genetic testing was available, resulting in over 55,500 tests (www.genetests.org). In addition to the growing number of genetic tests and increasing complexity of testing, including whole-exome sequencing and next-generation sequencing-based multi-gene panels, there is also greater awareness of genetic counselling services, which is leading to an increase in demand.4 In order to meet the demand for genetic counselling services, expansion of the work force and/or adjustment of current practice models is required to create greater access to genetic services. Adjustment of clinical practice models and establishing appropriate patient volume expectations for genetic counsellors should take into account that each discipline of genetic counselling serves different patient populations that may require varying levels of direct and indirect patient care. To that end, we sought to objectively assess the variation and care complexity or workload in clinical practice among general paediatric/adult, cancer, cardiovascular and prenatal genetic counselling services at a single large academic health system.The field of genetic counselling has shifted from utilisation of observed empiric risks  to confirmatory diagnostic approaches with genetic testing. Commercial genetic tests are being released at a rapid pace, and are often updated in online directories, such as During the two 3-week periods of study (30 May 2014 to 1 July 2014 and 27 October 2014 to 12 November 2014), 583 patient visits , cancer (252), cardiovascular (103) and prenatal (92)) seen by 9.3 clinical full time equivalents (FTE) genetic counsellors were tracked. Per specialty, the numbers of patients seen per week per clinical FTE were as follows: general 7.3 total (5.1 new and 2.2 follow-up), cancer 14.7 total (13.8 new and 0.9 follow-up), cardiovascular 8.6 total (7.2 new and 1.4 follow-up) and prenatal 11.8 total (11.4 new and 0.4 follow-up).P=0.004, P=0.013). Notably, 75.74% (103/136) of the general genetic counsellor visits were paired with a medical geneticist. The general genetic counsellor appointments were less likely to be new visits compared with the other specialties (P=0.010). A greater percentage of patients seen by the general group had a diagnosis/indication that was new to the genetic counsellor, though this did not reach statistical significance. The general group had greater anticipated and perceived complexity than the other disciplines . Compared with all other specialties, the general group was less likely to order testing for a known familial mutation .On average, minimum time spent was greatest for general genetic counsellors compared with all specialties P=0.004, . Visits P=0.040), discussed the case with other healthcare providers (P<0.001), conducted a literature review (P=0.026) and explored testing options (P=0.041). There was a trend that the general group conducted more insurance preauthorizations, but this did not reach statistical significance (P=0.06).Among the previsit activities, the general genetic counsellors had a greater number of activities completed as well as time spent per patient . DetailiThe indications for the genetic counselling referrals are summarised in P=0.002), asked questions for the Ohio Department of Health targeted to children ages ⩽5 years (P<0.001), obtained patient photographs (P=0.004), and spent more time waiting for the MD geneticist (P=0.004). These genetic counsellors less frequently collected reproductive history (P=0.012), collected information about prior evaluations or screening history (P=0.003), educated about genetic conditions (P=0.003), discussed inheritance (P=0.027), discussed management options (P=0.047), and consented for genetic testing (P=0.02).There were no differences observed in the total score or minimum time for the in-person visit . RegardiP=0.05), explored testing options (P=0.05), discussed the case with other providers (P=0.001) and handled patient inquiries (P=0.03).The general genetics had a higher number of post-visit activities completed and minimum time spent than all specialists combined . The gen4 In order to meet this demand, there must be expansion of the number of providers in the field and/or accommodation of the current work force to higher patient volumes. Growth of the genetic counselling field is one of the 2015–2017 Strategic Initiatives being addressed by the National Society of Genetic Counsellors . However, expanding patient volumes can potentially be addressed on an individual or institutional level.With improvements in genetic testing technology and growing awareness among healthcare providers and patients, the demand for genetic counselling services is increasing.This study found no differences for the in-person clinical session requirements for genetic counsellors providing general paediatric/adult, cancer, cardiovascular and prenatal services. However, general genetic counsellors required a significantly greater amount of time for pre- and post-visit activities. The general genetic counsellors also had greater variability among the referral indication and greater physician involvement than the other specialty practices. Therefore, it was not surprising to observe that the general genetic counsellors reported more often discussing the case with other providers, reviewing the literature, researching testing options and reviewing the electronic medical record.Expansion of clinical services requires survey of the current patient care environment for each sub-specialty of the profession with mindful consideration of how to streamline patient care-related activities to maximise the genetic counsellor’s efficiency, in the context of the non-genetics clinical practices in a given institution. The tool used in this survey is capable of identifying patterns and differences in clinical genetic counselling practice. These patterns may reveal activities related to patient care that might be redundant or could be reallocated to support staff. For example, in this study, the general genetic counsellors conducted insurance preauthorization 7.4% and 19.1% of the time pre- and post-visit, respectively. On the basis of this, we identified institutional based financial analysts/counsellors to conduct the insurance preauthorizations on behalf of the genetic counsellors thus reducing the time burden for the genetic counsellor associated with this responsibility. In addition, given the repetitive nature of the cancer and cardiovascular genetic counsellor referrals, standardized templates for documentation were created prior to this study, which contributed to decreased time burdens in these sub-specialties.5–11 In addition to the potential diagnostic yield, whole-exome/-genome sequencing also results in incidental pathogenic mutations , pharmacogenomic information, disease carrier status and a large number of variants of uncertain significance, all of which are manually reviewed by the ordering healthcare provider. A recent study by Williams et al. found that genetic counsellors were spending an average of 420 min (7 h) reviewing all available medical records in additional to time spent completing forms, making telephone calls, presenting to physicians/oversight committees and actually completing a genetic counselling and consenting session.12 Despite the fact that these healthcare providers were conducting standard genetic counselling activities, the scope of their activities varied greatly from genetic counselling sessions where testing for a single gene or syndrome was undertaken. Therefore, it is important to recognise these significant differences when creating practice models and establishing target patient volumes.Among the NSGC’s 2015–2017 Strategic Initiatives is to ‘define and promote best-practice models focused on high-quality, efficient delivery of genetic counselling services’ as well as ‘identify existing and needed tools and technology to support the efficiency of genetic counsellors.’ These are critical initiatives for genetic counsellors to address. Collectively, there are general similarities surrounding in-person-related patient care activities, which these data support. Divergence appears, however, on pre- and post-visit-related activities. This becomes particularly important as the field has entered an era driven by next-generation sequencing, which has brought whole-exome/-genome sequencing into routine clinical practice. At present, clinical whole-exome/-genome sequencing is not a routine test offered in cancer, cardiovascular or prenatal genetics settings. However, in paediatric and neurology genetics clinics, whole-exome/-genome sequencing has a critical role in identifying the genetic aetiology of those with intellectual disability or neurodevelopmental disorders, with a diagnostic yield of 25–40%.13–17 The original time study conducted in 1987 primarily evaluated the work of the medical geneticist in a paediatric setting, and found that clinical genetics services were labour-intensive.13 This was supported by a study in 2008 tracking the workflow of medical geneticists and genetic counsellors at a single institution, which found that each new patient required an average of 7 h of genetics professional time, with an average of 3.5 h for follow-up patient-related activities.15 In 2013, we evaluated the cancer genetic counsellors’ practice, and found that it was more efficient for genetic counsellors to see patients autonomously rather than paired with the medical geneticist.17 What all these studies lacked was a direct comparison among the sub-specialties of genetic counselling. Further, little is published about the workload of the genetic counsellor. As pointed out in the nursing literature, workload measures are not directly correlated with efficiency, complexity of the workload or the role of the work environment on these factors.18–20 Subjective measures of complexity also have a critical role in the perceived workload of the healthcare provider. Therefore, it is important to not only quantify the actual work done but to also capture the healthcare providers’ perceived burden of the work.A small number of studies have evaluated the time-based efforts of genetics professionals.The major strength of our current study is the methodical approach of the data collection. Although the data obtained at this specific health system may not broadly be applicable to other genetic counselling practices, we believe the data collection tool is an adaptable instrument that could be used within any practice to identify opportunities to improve service delivery. A limitation of this study is the genetic counsellors’ years of experience could not be included in the analysis owing to the limited sample size of genetic counsellors. Experience certainly has a role in the efficiency of the provider and would be an important variable to assess in a larger study among genetic counsellors. This study was also limited by not evaluating time or activities related to results disclosure. This is an equally important task conducted by genetic counsellors, which is invariably associated with its own complexities and time requirements. For the purposes of this study, we used the type of test ordered as a surrogate marker for the associated clinical workload. For example, for the average patient, disclosure of a single-site genetic test result would be more straightforward than disclosure of exome sequencing results. An important area for future health services research would be to investigate the workload associated with results interpretation and disclosure for the various types of testing ordered by genetic counsellors.Expanding patient volumes for genetic counsellors will help to create greater patient access to services. This can only occur when we have a greater understanding of what and how patient-related activities are being conducted, so opportunities to improve efficiencies can be identified. This will vary by sub-specialty of genetic counselling. Our data demonstrate that GCs in general practices may require lower patient volumes than specialty GCs to allow time for additional pre-/post-visit activities. We propose that determination of patient volume expectations for genetic counsellors should include consideration of specialty, variation in number of indications for referral and the genetic counsellor’s clinical FTE as well as what non-genetics tasks, e.g., insurance preauthorization, they are performing.Over two 3-week periods (30 May 2014 to 1 July 2014 and 27 October 2014 to 12 November 2014), 12 genetic counsellors representing 9.3 clinical FTE, ranging from 1 to 28 years of experience, at a single academic institution prospectively tracked the data related to patient-care activity. The practice was housed within a genetics/genomics institute and divided into general paediatric/adult, cancer, cardiovascular and prenatal services. At this centre, the general paediatric/adult clinic serves patients with birth defects, developmental delays or other neurological issues, or any other indication not fitting within the other three specialty settings. Three of the genetic counsellors crossed disciplines. The following numbers of genetic counsellors were studied in each discipline: four general , five cancer , three cardiovascular  and two prenatal . Patients were seen as a combination of genetic counsellor only appointments and paired genetic counsellor and physician (geneticist) appointments. All initial consultations in the practice were conducted in person. Indications for follow-up visits include, but are not limited to: discussion of genetic test results, annual follow-up, updating the patient’s personal and/or family health history, review of new or additional genetic testing options.The data collection tool was created by the genetic counsellors based on routine activities that are conducted surrounding patient care. Data were collected in REDCap. The genetic counsellors tracked 69 activities related to appointment preparation (16), in-person interactions (38) and post-appointment tasks 15; . StandarFor all cases, it was indicated whether this was a new diagnosis to the genetic counsellor, whether a trainee was involved with the case and what type of genetic testing was ordered , large panel or exome). Pre- and post-visit, the genetic counsellors subjectively self-rated the case as complex or simple.Individual activity scores were summed to obtain complexity scores for previsit activities , in-person activities (0–77), post-visit activities (0–35) and all activities (0–144). The time intervals were utilised to calculate minimum time spent for the entire genetic counselling session by taking the minimum time for each interval  and adding up those minimum times for each category  of activity. Individual activities were analysed as having been done (score>0) or not done (score=0). Three activities were reported in <5 of 583 patient visits and were not analysed individually: family history by phone (previsit), write a letter of medical necessity (previsit) and complete school/employer forms (in person). Variables were reported using standard descriptive statistics.Variables were compared by discipline with hierarchical linear models for ordinal or continuous data and hierarchical logistic models for the binary data to account for correlation within genetic counsellors, for varying number of patients seen by each genetic counsellor and for the different number of genetic counsellors within each discipline.P values ⩽0.05 were considered significant. No adjustments were made for multiple comparisons. The pretest hypothesis was that the general practice had greater complexity and thus higher time requirements, than the other three specialties.Data were described for all four disciplines and for specialties . Data were compared in two ways: once among the four disciplines and again for general versus specialty genetic counsellors. The comparisons among the four disciplines and of general versus specialty data are reported , 4 and 5


Optimal contribution methods have proved to be very efficient for controlling the rates at which coancestry and inbreeding increase and therefore, for maintaining genetic diversity. These methods have usually relied on pedigree information for estimating genetic relationships between animals. However, with the large amount of genomic information now available such as high-density single nucleotide polymorphism (SNP) chips that contain thousands of SNPs, it becomes possible to calculate more accurate estimates of relationships and to target specific regions in the genome where there is a particular interest in maximising genetic diversity. The objective of this study was to investigate the effectiveness of using genomic coancestry matrices for: (1) minimising the loss of genetic variability at specific genomic regions while restricting the overall loss in the rest of the genome; or (2) maximising the overall genetic diversity while restricting the loss of diversity at specific genomic regions.Our study shows that the use of genomic coancestry was very successful at minimising the loss of diversity and outperformed the use of pedigree-based coancestry (genetic diversity even increased in some scenarios). The results also show that genomic information allows a targeted optimisation to maintain diversity at specific genomic regions, whether they are linked or not. The level of variability maintained increased when the targeted regions were closely linked. However, such targeted management leads to an important loss of diversity in the rest of the genome and, thus, it is necessary to take further actions to constrain this loss. Optimal contribution methods also proved to be effective at restricting the loss of diversity in the rest of the genome, although the resulting rate of coancestry was higher than the constraint imposed.The use of genomic matrices when optimising contributions permits the control of genetic diversity and inbreeding at specific regions of the genome through the minimisation of partial genomic coancestry matrices. The formula used to predict coancestry in the next generation produces biased results and therefore it is necessary to refine the theory of genetic contributions when genomic matrices are used to optimise contributions. A) that represents expected relationships assuming neutrality and does not take into account variation due to Mendelian sampling. Thus, although its use has proved to be efficient to manage genetic diversity, it has some limitations. For instance, individuals from the same (full-sib) family inherit different sets of alleles but they are assumed to be equally related. In addition, since matrix A does not consider differences between genomic regions, optimisation of contributions will, on average, control the rate of coancestry to the chosen value, but some genomic regions may have substantially higher rates than desired.It is generally accepted that control of the rate of coancestry provides a general framework to manage genetic variability. Optimal contribution (OC) methods , 2 permiA is replaced by a realised relationship matrix that is calculated by taking into account variation in the level of relationship between animals of the same family and variation between genomic regions ),This study confirms that the use of genomic coancestry in the optimisation of contributions is substantially more efficient in maintaining genetic diversity than the use of pedigree coancestry. Moreover, the use of genomic coancestry permits the targeting of specific genomic regions to minimise the loss of genetic diversity and the extension of the optimisation procedure to include restrictions for additional regions. This study also highlighted the need to refine the theory of genetic contributions using realised genomic relationship matrices in order to ensure that optimal contribution methods properly manage the genetic diversity available in a population.