In the last 50 years, the diversity of cattle breeds has experienced a severe contraction. However, in spite of the growing diffusion of cosmopolite specialized breeds, several local cattle breeds are still farmed in Italy. Genetic characterization of breeds represents an essential step to guide decisions in the management of farm animal genetic resources. The aim of this work was to provide a high-resolution representation of the genome-wide diversity and population structure of Italian local cattle breeds using a medium-density single nucleotide polymorphism (SNP) array.After quality control filtering, the dataset included 31,013 SNPs for 800 samples from 32 breeds. Our results on the genetic diversity of these breeds agree largely with their recorded history. We observed a low level of genetic diversity, which together with the small size of the effective populations, confirmed that several breeds are threatened with extinction. According to the analysis of runs of homozygosity, evidence of recent inbreeding was strong in some local breeds, such as Garfagnina, Mucca Pisana and Pontremolese. Patterns of genetic differentiation, shared ancestry, admixture events, and the phylogenetic tree, all suggest the presence of gene flow, in particular among breeds that originate from the same geographical area, such as the Sicilian breeds. In spite of the complex admixture events that most Italian cattle breeds have experienced, they have preserved distinctive characteristics and can be clearly discriminated, which is probably due to differences in genetic origin, environment, genetic isolation and inbreeding.This study is the first exhaustive genome-wide analysis of the diversity of Italian cattle breeds. The results are of significant importance because they will help design and implement conservation strategies. Indeed, efforts to maintain genetic diversity in these breeds are needed. Improvement of systems to record and monitor inbreeding in these breeds may contribute to their in situ conservation and, in view of this, the availability of genomic data is a fundamental resource.The online version of this article (10.1186/s12711-018-0406-x) contains supplementary material, which is available to authorized users. Domestication of cattle occurred during the Neolithic agricultural revolution, about 8000 years ago, and was associated with dramatic modifications in the socio-economic conditions of most human populations . Cattle Genomic DNA was isolated using a salting-out protocol from buffy coats of nucleated cells that were obtained from whole-blood withdrawn from the jugular vein using EDTA-containing tubes . The DNAA total of 814 animals (10 to 43 per breed) belonging to 30 Italian cattle breeds  were selected. In addition, two cosmopolitan breeds reared in Italy (Charolais and Limousin) were included because they are used for cross-breeding with local breeds. Thus, they are relevant to study the genetic relationships between Italian breeds. For a short description of each breed included in this study  and expected (He) heterozygosity, the genomic inbreeding, which is based on the difference between the observed and expected numbers of homozygous genotypes (FHOM), and average MAF (≥ 0.05). Historical and recent effective population sizes (Ne) for each breed were estimated with the SNeP package [e and recombination rate. The contemporary effective population size (cNe) was calculated with NEESTIMATOR v. 2 [e_hap) for the haplotypes for each breed was estimated using the R package ADEGENET [PLINK  was also package , which iTOR v. 2 , which iTOR v. 2 . GenotypTOR v. 2 , accordiTOR v. 2 . ExpecteADEGENET .FROH) was calculated for each animal using the method proposed by McQuillan et al. [FROH>4Mb, FROH>8Mb, and FROH>16Mb, based on a minimum ROH length of 4, 8, or 16 Mb, respectively.Runs of homozygosity (ROH) were estimated within each breed using PLINK  by applyn et al. , which dFST were obtained with GENEPOP software [FST and Reynold’s genetic distances) and geographical distances between breeds, a Mantel test was performed [distm function in the R package GEO-SPHERE (http://cran.r-project.org/web/package/geosphere/). Finally, historical relationships and admixture between the considered populations were inferred using the f3 and f4 tests implemented in TREEMIX [f3 and f4 tests implemented in the TREEMIX computer package. In particular, we used the f3-statistics  to determine if A was derived from the admixture of populations B and C, and the f4-statistics  to test the validity of a hierarchical topology in four-population trees.Pairwise genetic relationships were estimated using a matrix of genome-wide identity-by-state (IBS) genetic distances calculated by PLINK  and plotsoftware . To testerformed . We used TREEMIX , which ro and He ranged from 0.297 ± 0.194 (Pontremolese) to 0.358 ± 0.167 (Piedmontese) and from 0.267 ± 0.187 (Mucca Pisana) to 0.353 ± 0.137 (Sarda), respectively. Mucca Pisana showed the lowest mean MAF (0.200 ± 0.162), whereas the Sarda breed showed the highest mean MAF (0.267 ± 0.139). The highest average FHOM were obtained for the Pontremolese (0.195 ± 0.101) and Mucca Pisana (0.183 ± 0.058) breeds, whereas the lowest FHOM was obtained for the Sarda breed (0.006 ± 0.063). Estimated He based on haplotype data (He_hap) was higher than that based on genotype data and ranged from 0.323 ± 0.040 (Mucca Pisana) to 0.395 ± 0.003 (Piedmontese), with several Podolian-derived breeds  having intermediate values (< 0.360). The two He estimates were correlated . Estimated Ne at t generations ago (from 13 to 98) are in Additional file e decreased progressively across generations. Ne was less than 150 for all breeds at 13 generations ago, except for the Sarda breed (Ne = 152). The variation in Ne across generations was smallest for Mucca Pisana, Pontremolese and Garfagnina, for which the estimated recent Ne (13 generations ago) were less than 40. Ancestral populations of the contemporary Sarda, Cinisara and Podolica breeds exhibited considerably larger Ne values, with the largest historical Ne values. We also inferred the size of cNe for each breed (Table e (less than 10) and Sardo-Bruna the largest cNe (1021). Pearson correlations between genetic diversity indices for SNPs are in Additional file o, He and MAF were high , whereas the correlations between these parameters and cNe were quite low (~ 0.150). Recent and historical Ne estimates were not correlated with cNe but moderately correlated with Ho, He and MAF .Genetic diversity parameters are in Table FROH>4 Mb, FROH>8 Mb, and FROH>16 Mb) are in Fig. FROH values decreased with increasing minimum length of the ROH. ROH coverage in the genome differed considerably among breeds, with the highest mean values of FROH across all ROH length categories observed for the Garfagnina, Mucca Pisana and Pontremolese breeds. In particular, the Pontremolese breed had more than 10% of the genome covered by ROH in all length categories, and more than 16% by ROH longer than 4 Mb. In contrast, medium and low FROH were found for the other breeds, which is consistent with their larger Ne and moderate inbreeding (FHOM). The breeds with the lowest levels of inbreeding included Piedmontese and Rossa Siciliana. However, the large standard deviation values indicated high variability in autozygosity levels within each breed.Individual genomic inbreeding was evaluated using ROH data. The distributions of the three ROH inbreeding coefficients  and FROH>4Mb was also negatively correlated with recent and historical Ne . The relationship between the number of ROH and the extent of the genome with ROH longer than 4 Mb per individual are in Additional file We used an MDS plot of the pairwise IBS distances to compare the Italian cattle breeds Fig. . The firWe tested admixture among breeds and groups of breeds by model-based clustering. The ADMIXTURE plots showed the results for K ranging from 2 to 32 Fig. .Fig. 4MoWhen K increased from 8 to 32, breeds were progressively assigned to separate clusters: Rendena and Maremmana at K = 12, Romagnola and Pustertaler at K = 16, Pinzgau and Burlina at K = 20  and lowest between Sarda and Sardo-Bruna (FST = 0.016). To test the correlations between genetic (FST and Reynold’s genetic distances) and geographical distances, we performed a Mantel test. The results showed no concordance between FST and geographical distances among all breeds  . When only the Sicilian breeds were considered, the correlation increased but remained statistically non-significant . We also conducted a Mantel test between Reynold’s genetic distances and geographical distances among breeds but again, no correlation was observed .Details of the level of pairwise genetic differentiation are in Additional file f3 test highlighted clear signs of admixture between Rossa Siciliana and Modicana  are the main causes of small Ne in livestock. In local cattle breeds, selection pressure is usually very weak and AI is almost absent, but conversely, uncontrolled mating of related animals is common, and thus inbreeding and low genetic diversity are the most likely cause of their small Ne [e and recent and historical Ne estimated 13, 20 and 80 generations ago. Estimates of Ne vary a lot with the method used [e from LD provide reliable predictions [e) must be considered with care, even more so when the sample size is less than 15 animals [Although homozygosity may be overestimated for populations that were not included in the design of the SNP array (ascertainment bias) , the Illrvations , even ifr cattle , and arer cattle  using thr cattle . We obsee breeds , 12, 23.e breeds  . The low genetic variability observed for these breeds agrees with the theoretical expectation for populations, which have undergone a severe bottleneck [e should be taken into account and monitored.Across all the breeds analyzed here, generally, the cosmopolitan breeds  showed moderate levels of genetic diversity. Moreover, although some breeds  are endangered or have a small census population size, they did not show signals of low levels of genetic diversity. ROH analysis and Nttleneck . Geneticttleneck . TherefoF estimates (FROH) are considered one of the most powerful approaches to detect inbreeding effects [e [FROH>16Mb obtained for Garfagnina, Mucca Pisana and Pontremolese. Therefore, strong evidence of recent inbreeding (3 generations ago) exists for these Italian breeds, due to the decrease in their Ne. In fact, isolation of breeds with a small population size increases the probability that identical segments are inherited. It is likely that the long ROH are also signatures of the extensive use of a few bulls within herds and of mating among relatives. If long ROH accumulate in the genome of some individuals, they could seriously impact the overall biological fitness [FROH and low Ne values observed for the Italian Brown breed can be ascribed to the intense use of a small number of (closely related) bulls for artificial insemination. More generally, these results highlight that both ancient and recent inbreeding have impacted the genome of Italian cattle breeds and that several local breeds, in particular the four autochthonous cattle breeds of Tuscany , have probably not undergone recent extensive crossbreeding since the long ROH in their genomes have not been broken down [e and thus a low degree of recent consanguinity. Therefore, we conclude that the genetic diversity indices, the effective population size and the genomic inbreeding levels were congruent with the protection status of the local Italian cattle breeds based on their reduced demographic size. The results also reflect the need to implement conservation programs, in particular for the breeds with a limited distribution.Abundant genome-wide SNPs are particularly suitable for detecting genomic regions with reduced heterozygosity and, recently, an alternative method called runs of homozygosity (ROH) was implemented . Current effects  and may  effects , 43. Bec effects –46. Analfects [e . The lenfects [e . Since rfects [e , which a fitness . Indeed, fitness  showed t fitness . Similar fitness . On the ken down . This scFST for the Italian local cattle breeds. Except for Podolian-derived breeds, the MDS grossly separated the breeds according to their genetic origin and/or to their geographical proximity between breeding areas. A previous study on breeds  reared in the ancient Etruria region , reported that they are genetically closer to Near Eastern than to European genetic stocks [e_hap.Determination of the structure and the genetic relationships of a population has proven useful in conservation programs and for developing suitable management practices , 32–35. c stocks . This orc stocks . To bettc stocks , which sc stocks . Our resc stocks , which rFST) among some local breeds from the same geographic area, such as between Sarda and Sardo-Bruna or among the Sicilian breeds. Previously, a similar geographical pattern was described by using a medium-density SNP array in Italian goat [e size that can, in turn, lead to a strong impact of genetic drift. Our results also indicate that the genetic component of some commercial dairy breeds, such as the Italian Holstein, was relatively small in the Italian local breeds. In fact, the lowest FST values of the Italian Holstein was with the Burlina breed (0.066). Moreover, as illustrated by the MDS and ADMIXTURE results, the Italian Holstein breed is genetically distant from the local breeds. Nonetheless, differentiation between some local breeds  and the commercial meat breeds (Limousin and Charolais) was very low (FST ~ 0.04), which suggests, as mentioned above, that the use of these breeds could have affected their genetic background. Among all the local breeds, Mucca Pisana was highly differentiated and presented only low levels of admixture with other breeds; this breed consistently demonstrated higher FST values with the other breeds analyzed. In a recent study on Russian cattle breeds, analogous results were reported for the Yakut cattle, which was the most divergent breed [e size. In fact, Mucca Pisana showed the smallest recent and past Ne estimates. In a study on sheep breeds, Kijas et al. [FST analyses, and can be explained by its demographic history since it was reared for a long time in a geographically separate valley  [e estimates. The reticulations towards the extremity of the networks also suggest increased genetic relatedness between breeds and therefore past hybridization events between these populations, and are consistent with several of the admixture events highlighted by TREEMIX between breeds and groups. Indeed, Neighbor-Net is a robust tool for reconstructing phylogenetic networks. Therefore, the low pairwise Fst values, shared ancestry, admixture events, and reticulations observed on the phylogenetic tree between some breeds, as well as between the Sicilian breeds, all suggest high levels of gene flow between these populations, and as well between some breeds originating from the same area. These results can be attributed to the fact that geographical proximity facilitates the gene flow, and that breeds from the same breeding area are more likely to have common ancestries.The presence of a general North–South distribution of the genetic diversity along the Italian Peninsula was highlighted and confirmed by the low genetic differentiation  . TherefoSerchio) . Indeed Our study represents the first comprehensive analysis of the genomic diversity and population structure of Italian local cattle breeds. All the analyses revealed genetic relationships, gene flow and admixture events for several Italian cattle breeds. However, although most of the breeds have experienced complex admixture events, several breeds have preserved distinctive characteristics and can be clearly discriminated, which is likely due to the effect of different remote and/or recent genetic and demographic factors. The population structure and the low genetic diversity presented here for several breeds represent useful information to guide conservation strategies. Notably, mating plans can have an important role in restraining inbreeding and increasing the census size of these local breeds. Monitoring of inbreeding trends and improvement of the recording systems are strategic for in situ conservation of these breeds, and towards this aim, the availability of genomic data represents a fundamental resource. Moreover, these results highlighted the importance of using genomic information to reveal the genetic structure of each population and provide an objective basis for decisions regarding the conservation of the Italian local cattle breeds. When standardized genotyping arrays are adopted, it is possible to combine various datasets. Therefore, further studies are necessary to provide insights into the genetic composition and origin of Italian cattle breeds, such as the Podolian-derived breeds, using data of other worldwide cattle populations, and for the development of a SNP-based identification test for breed assignment and tracing animal products.Additional file 1. Description of each Italian local cattle breed involved in this study [is study .Additional file 2: Table S1. Name of the breeds, geographic coordinates (longitude and latitude) of the center of origin, sample size before (n-PreQC) and after (n-PostQC) genotyping quality control, and origin of genotyping data.Additional file 3: Figure S1. Trends in historic effective population size (Ne) (from 13 to 98 generations).Additional file 4: Table S2. Pearson correlation coefficients between genetic diversity indices. Observed (Ho) and expected (He) heterozygosity, average minor allele frequency (MAF), inbreeding coefficient (FHOM), contemporary effective population size (cNe), mean ROH-based inbreeding coefficient (FROH>4Mb) and recent and historical Ne estimated 13 (Ne13), 20 (Ne_20 and 80 (Ne_80) generations ago. .Additional file 5: Figure S2. Relationship between the number of ROH and the length of the genome (Mb) covered by ROH per individual.Additional file 6: Figure S3. Distribution of all ROH within breeds according to their size (kb).Additional file 7: Figure S4. Model-based clustering of the estimated membership fractions of individuals from the 32 breeds analyzed in each of the K inferred clusters revealed by the ADMIXTURE software . For a full definition of breeds see Table S1  and geographical distances for all breeds.Additional file 11: Table S4. Results of the f3 test.Additional file 12: Table S5. Results of the f4 test.Additional file 13: Figure S7. Genetic relationship defined with multidimensional scaling analysis between Italian and 62 European cattle breeds. The breeds were grouped according to their geographical origins and distributions. Northern_Europe , England , Spain , France , Germany (Gelbvieh), Switzerland , Turkey . Italian breeds are colored according to their geographical distributions and are the same as those described in Fig.


Fusarium head blight (FHB) is one of the most destructive diseases in wheat worldwide. Breeding for FHB resistance is hampered by its complex genetic architecture, large genotype by environment interaction, and high cost of phenotype screening. Genomic selection (GS) is a powerful tool to enhance improvement of complex traits such as FHB resistance. The objectives of this study were to (1) investigate the genetic architecture of FHB resistance in a North Dakota State University (NDSU) hard red spring wheat breeding population, (2) test if the major QTL Fhb1 and Fhb5 play an important role in this breeding population; and (3) assess the potential of GS to enhance breeding efficiency of FHB resistance. A total of 439 elite spring wheat breeding lines from six breeding cycles were genotyped using genotyping-by-sequencing (GBS) and 102,147 SNP markers were obtained. Evaluation of FHB severity was conducted in 10 unbalanced field trials across multiple years and locations. One QTL for FHB resistance was identified and located on chromosome arm 1AL, explaining 5.3% of total phenotypic variation. The major type II resistance QTL Fhb1 only explained 3.1% of total phenotypic variation and the QTL Fhb5 was not significantly associated with FHB resistance in this breeding population. Our results suggest that integration of many genes with medium/minor effects in this breeding population should provide stable FHB resistance. Genomic prediction accuracies of 0.22–0.44 were obtained when predicting over breeding cycles in this study, indicating the potential of GS to enhance the improvement of FHB resistance. Fusarium head blight (FHB) is one of the most destructive diseases of wheat worldwide. The disease is caused by Fusarium species and can lead to severe grain yield and quality loss. HRSW is a major class of wheat and is used for the finest baked goods owing to its high protein content and superior gluten quality. In the United States, HRSW is annually planted on over 13 million acres, primarily in the Northern Great Plains. FHB epidemics in HRSW producing areas of the US in the early 1990s resulted in devastating economic losses  enable genetic mapping to be performed in a breeding population, and the identified markers can be used for MAS directly . Advanceariation . Taken tA simulation study suggested that GS was more efficient for the improvement of complex traits compared to MAS . In GS, Fhb1 and Fhb5 have been integrated into the breeding pools. The objectives of this study are to (1) investigate genetic architecture of FHB resistance in this breeding population; (2) test if the major QTL Fhb1 and Fhb5 play an important role in this population; and (3) assess potential of GS for FHB resistance enhancement in the NDSU HRSW breeding program.Diverse resistance donors like Sumai 3, Frontana, etc. have been used for the improvement of FHB resistance in wheat breeding programs including the NDSU HRSW breeding program. Well-characterized major QTL like 9 breeding lines from 2011-16 advanced yield trials (AYTs) of the NDSU HRSW breeding program, representing six breeding cycles, and 15 check cultivars were evaluated for FHB resistance from 2011 to 2016  producers and ten 15ADON  producers, collected from fields in North Dakota , until 14 days after anthesis of the latest lines. Eight to twenty spikes from each hill plot were visually scored at 21 days post anthesis using a visual scale   . DiseasePstI and MseI, and then ligated to a common adapter and a barcoded adapter unique to each sample. Equal volumes of the ligated products were pooled and purified with the QIAquick PCR purification kit  for PCR amplification. For the PCR amplification, 50 ng of template DNA was mixed with NEB 2X Taq Master Mix and two primers (5 nmol each) in 200 μL total reaction volume and PCR amplified for 18 cycles with 10 s of denaturation at 98°C, followed by 30 s of annealing at 65°C, and finally 30 s extension at 72°C. The PCR product was cleaned using a QIAquick PCR purification kit. The library was sequenced on an Illumina HiSeq 2500 to generate single-end, 100-bp reads at the Genomic Sequencing and Analysis Facility at the University of Texas Southwestern Medical Center at Dallas, Texas. All sequences were submitted to the National Center for Biotechnology Information Short Read Archive (experiment #SRP144046). SNP discovery and genotype calling was performed using the TASSEL-GBS pipeline  per the manufacturer’s instructions and quantified with a Quant-iT PicoGreen dsDNA assay kit . GBS libraries were constructed based on the protocol of pipeline  with thee genome . SNP mare genome . Missinge genome  implemene genome .IFA-FM227, Fhb1 and one SNP marker  linked to the major QTL Fhb5 were genotyped for the spring wheat breeding lines and check cultivars. Primer sequences for IFA-FM227 were as follows: FAM: GGCGTCGGCGATCCTGCTTA; HEX: GGCGTCGGCGATCCTGCTTAT; Common: CGTCGT CGGCCGCGGGTT. Primer sequences for barc186-80018 were as follows: FAM: GTAGTGATCCAAAGAAATAAAGGAGAT; HEX: GTAGTGATCCAAAGAAATAAAGGAGAG; Common: GTGACAAGTTATAGGTAAGGTCTCCAT. Each SNP was assayed using the KBiosciences Competitive Allele-Specific PCR genotyping system (KASP). Briefly, genotyping reactions were performed in 384-well plates on a Procycle thermocycler (Life) in 4 μL reactions containing two allele-specific primers, one common reverse primer, 1 x KASPaR v4.0 SNP Mastermix (LGC Genomics), and 36 ng of gDNA. After an initial denaturation step of 15 min at 94°C, PCR amplification reactions consisted of 10 cycles of 20 s denaturation at 94°C, followed by 60 s at a touchdown annealing/elongation at 65-57°C (decreasing 0.8°C each cycle), then 29 cycles at 20 s denaturation at 94°C, followed by annealing/elongation 60 s at 57°C. Fluorescence of PCR products was measured on a Roche Light Cycler® 480 and accompanying software (v 1.51) was used to distinguish clusters and call genotypes.Additionally, one SNP marker  for each individual trial. All factors were considered as random. The variance components of error (σ2) and genotype (σ2g) were estimated. The broad-sense heritability was estimated as σ2/(σ2 + σ2g) and was used to eliminate trials with a H2 score < 0.1.where y is the vector of unadjusted phenotypes, μ is the overall mean, In the second analysis stage, we estimated BLUEs of breeding lines across all 10 trials using the R package lme4 . The mody* represents the estimated BLUEs of breeding lines within each individual trial calculated in the first stage, μ is the overall mean, gi is the random effect of the ith genotype and tj is the random effect of the jth trial. The estimated BLUEs were further used in GWAS to identify major QTL and in genomic prediction analysis.where Va) and error variance component (Ve) were calculated with the mixed.solve function in the package rrBLUP (h2) was calculated as Va/(Va + Ve).A relationship matrix was calculated using the 102,147 SNP markers with the R package rrBLUP . Then, ae rrBLUP . Genomicq value smaller than 0.1.Principal component analysis (PCA) was conducted with the 102,147 SNPs to assess population structure using TASSEL v.5 . SNP-basr) between genomic estimated breeding values (GEBVs) and BLUPs of phenotypic values. Random sampling of training and validation sets was repeated 100 times and the mean of r divided by the square root of the estimated heritability was defined as the genomic prediction accuracy.Genomic prediction was evaluated with the statistical model rrBLUP. The rrBLUP model was constructed using R package rrBLUP . PredictTo test prediction accuracy across breeding cycles, we developed prediction models using a training population that includes five breeding cycles to predict breeding lines from the remaining breeding cycles, e.g., to predict 2012 breeding lines, the five breeding cycles tested in the other 5 years  were used as training population.Because genetic relationship between training population and validation population can affect prediction accuracy, we calculated genetic relationships between training population and validation population based on identity-by-state similarity  between all individuals using R package “snpRelate” .To evaluate the effect of marker number on prediction accuracy, we randomly sampled 100, 500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, and 5000 markers to develop prediction models and validate their prediction accuracies. Random sampling for each subset was repeated 500 times and the mean of the Pearson correlations between GEBVs and BLUPs was defined as the genomic prediction accuracy.H2 < 0.1. BLUEs of FHB severity were estimated for the 427 elite spring wheat lines and 15 check cultivars using the unbalanced phenotypic data collected from all 10 field trials at two locations across 6 years. Among the 15 checks, the resistant check variety ND2710 showed the lowest FHB severity (15.9%) while the susceptible check variety ND2398 had the highest disease severity (75.8%). Among the 427 breeding lines, FHB severity ranged from 6.0% to 83.1% and broad variation was found in all six breeding cycles  for the FHB severity was estimated to be 0.43 in this HRSW breeding population. We evaluated genomic prediction for FHB severity with statistical model rrBLUP. Prediction accuracy using fivefold cross validation was 0.35.Based on the marker and phenotypic data, genomic heritability  into expensive yield trials and therefore improve selection efficiency. For example, GS pre-selection of end-use quality traits in wheat has successfully reduced the number of poor performing lines advanced to expensive yield trials (In GS, selection is based on the predicted breeding values from genome wide markers. One potential application of GS is to select promising lines from a large number of newly developed early generation breeding lines (i.e., Fd trials . In thisd trials . In mostd trials .7 lines being tested for the first time, 350 second year F8 lines, 150 F9 advanced lines, and 75 F10 elite lines. Yield tests, quality screening, and selection for other economically important diseases were conducted in each generation. It was reported that genomic assisted selection by combining phenotypic data and predicted values from GS model provided higher selection accuracy than phenotypic selection alone for grain yield and protein content at preliminary yield trial, where phenotypic data is collected from few environments with few or no replications (7 generation, where FHB severity will be estimated using both phenotypic data and predicted values from the initial GS model. GS model will be updated by adding the F7 lines in the training population. Implementing GS pre-selection of FHB resistance on untested early generation lines like F6 lines can be carried out once high prediction accuracy is obtained from the updated prediction model.Prediction accuracy of between-crosses or families is generally low because of distant relationship between training population and validation population . In thisications . We planOne limiting factor to utilize GS in pre-selection of promising lines is high genotyping cost for a large number of selection lines. Similar to other GS studies in wheat breeding populations , we alsoThe 427 breeding lines from the NDSU HRSW breeding program represent the current breeding pool. Few of the new breeding lines showed better FHB resistance than ND2710, one of the first experimental lines released by the NDSU HRSW breeding program from efforts to introduce FHB resistance from Sumai 3 into elite wheat lines adapted to the Northern Great Plains . MultiplAll datasets for this study are included in the manuscript and the SZ and XL conceived and designed the study. YL, ES, JF, JH, AG, MM, SZ, and XL generated and analyzed the data. All authors wrote and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Thymallus thymallus, to study the toxicity of an ecologically relevant concentration of EE2. We also used a full-factorial in vitro breeding design and singly rearing of 1555 embryos and larvae of 40 sib groups to test whether there is additive genetic variance for the tolerance to this pollutant.The presence of a novel pollutant can induce rapid evolution if there is additive genetic variance for the tolerance to the stressor. Continuous selection over some generations can then reduce the toxicity of the pollutant but also deplete the additive genetic variance for the tolerance and thereby slow down adaptation. One common pollutant that has been ecologically relevant for some time is 17alpha-ethynylestradiol (EE2), a synthetic compound of oral contraceptives since their market launch in the 1960s. EE2 is typically found in higher concentrations in rivers than in lakes. Recent experimental work revealed significant genetic variance for the tolerance to EE2 in two lake-spawning salmonid species but no such variance in river-spawning brown trout. We used another river-spawning salmonid, the European grayling We found that exposure to EE2 reduced larval growth after hatching, but contrary to what has been found in the other salmonids, there were no significant effects of EE2 on embryo growth and survival. We found additive genetic variance for embryo viability, i.e. heritability for fitness. However, there was no significant additive variance for the tolerance to EE2.Our findings support the hypothesis that continuous selection has reduced the toxicity of EE2 and depleted genetic variance for tolerance to this synthetic stressor. Chemical pollution is one of the anthropogenic pressures that can threaten salmonid populations –3. SalmoSalmo salar) )N = 7302 singly raised embryos) [C. palaea [C. albellus [Regarding our first main question: We found a statistically non-significant increase in mortality of 1.5 pp. for embryos and 0.4 pp. for larvae. These effect sizes seem comparable to the observed increase in embryo mortality of 0.9 pp. in brown trout that was only significantly different from zero because of an extra-ordinary large sample size (embryos) . In whit. palaea  and aroualbellus .With the observed low mortality, the question of whether there is EE2-induced sex-specific mortality in grayling cannot be solved yet. The study population suffers from a skewed sex ratio  that seWhile EE2- and sham-exposed grayling embryos hatched at similar size, exposure to EE2 reduced larval growth and consumption of yolk sac after hatching by about 4% each during the first 8 days after hatching. We therefore conclude that EE2 is toxic to grayling at early developmental stages. Such a reduction in growth was predicted from recent analyses of physiological reactions to EE2 in Atlantic salmon , 49 but Regarding our second main question: Because grayling males do not provide any parental care, significant sire effects on offspring traits reveal additive genetic variance in full-factorial breeding experiments . The damNo significant additive genetic variance could potentially be due to a type II error . However, such an error is unlikely here because (i) our analysis is based on a large sample size (1555 singly-reared embryos) and 40 sib groups, (ii) our sample revealed overall additive genetic variance (i.e. significant sire effects) on embryo mortality and the timing of hatching, (iii) a parallel study  on other samples of the same 40 families revealed genetic variation in the tolerance to infection by a bacterium, and (iv) singly-reared salmonid embryos are sensitive indicators of environmental stress, and studies based on comparable breeding designs have demonstrated additive genetic variance for the tolerance to other types of stressors, including other types of pollutants , 59, patThe finding of no significant additive genetic variance for tolerance to EE2 pollution in grayling is in sharp contrast to the findings of Brazzola et al.  on lake-As far as we know, there exist no measurements of estrogenic pollution around the spawning ground of our study population. However, this spawning ground is located in the river Aare within a city of more than 40,000 inhabitants, a large sewage treatment plant about 4 km downstream, and several nearby villages  upstream. The sewage treatment process typically removes only about two thirds of the EE2 , and expOur study adds the grayling to the list of salmonids whose embryos and larvae could be experimentally exposed to ecologically relevant concentrations of around 1 ng/L EE2. With the present study, at least one species of each subfamily of the Salmonidae  has now even been tested using the same method of applying a one-dose exposure of 2 pg to embryos developing in 2 mL wells , 22. TogA key question in evolutionary conservation biology is whether populations can adapt to anthropogenic stressors such as chemical pollutants. Such evolutionary responses require additive genetic variance for the susceptibility to the pollutant. Continuous selection over several generations is then expected to deplete such genetic variance but also reduce the toxicity of the pollutant. We tested the susceptibility of a river-spawning grayling population to EE2 and found that a low and ecologically relevant concentration did not induce embryo mortality as it did in some lake-spawning salmonids. However, EE2 was still toxic because it reduced larval growth. We found additive genetic variance for fitness-relevant traits, but no significant genetic variation that would enable the population to adapt to EE2. The low toxicity and the lack of genetic variance for the susceptibility to EE2 support the hypothesis that the marked launch of contraceptive pills and the associated pollution of rivers has induced rapid evolution in river-spawning grayling.


GBS) provide unprecedented power for resolving genetic differences among species and populations. We therefore used GBS to provide a genomewide perspective on the comparative population structure of two palm genera, Washingtonia and Brahea, on the Baja California peninsula, a region of high landscape and ecological complexity. First, we used phylogenetic analysis to address taxonomic uncertainties among five currently recognized species. We resolved three main clades, the first corresponding to W. robusta and W. filifera, the second to B. brandegeei and B. armata, and the third to B. edulis from Guadalupe Island. Focusing on the first two clades, we then delved deeper by investigating the underlying population structure. Striking differences were found, with GBS uncovering four distinct Washingtonia populations and identifying a suite of loci associated with temperature, consistent with ecologically mediated divergence. By contrast, individual mountain ranges could be resolved in Brahea and few loci were associated with environmental variables, implying a more prominent role of neutral divergence. Finally, evidence was found for long‐distance dispersal events in Washingtonia but not Brahea, in line with knowledge of the dispersal mechanisms of these palms including the possibility of human‐mediated dispersal. Overall, our study demonstrates the power of GBS together with a comparative approach to elucidate markedly different patterns of genomewide divergence mediated by multiple effectors.Comparative studies can provide powerful insights into processes that affect population divergence and thereby help to elucidate the mechanisms by which contemporary populations may respond to environmental change. Furthermore, approaches such as genotyping by sequencing ( By contrast, we expected Brahea palms to be more influenced by neutral processes and thus to exhibit an IBD pattern; (4) finally, due to the long historical association between Washingtonia palms and humans, we hypothesized that population structure in Washingtonia could potentially also have been influenced by human‐mediated translocation events.To address the issues described above, we combined GBS with near‐exhaustive sampling of all five native 22.1W. robusta from Sonora on the Mexican mainland. We were therefore able to cover the full distributional ranges of three Brahea species  and one Washingtonia species (W. robusta), while W. filifera could only be sampled from its southern distributional limit in Sierra Juarez, Mexico  Sierra La Laguna (SLL), (2) Sierra Mechudo (SM), (3) Sierra Giganta (SG), (4) Sierra San Pedro (SSP), (5) Sierra San Francisco combined with Sierra Libertad (SFSL), (6) Cataviña (CAT), (7) Sierra Juarez (SJ), and (8) Mexican mainland, state of Sonora (SON). For Brahea, we specified nine populations on the Baja peninsula corresponding to (1) Sierra La Laguna (SLL), (2) Sierra Mechudo (SM), (3) Sierra San Pedro (SSP), (4) Sierra San Francisco (SSF), (5) Sierra Libertad (SL), (6) Sierra Asamblea (SA), (7) Cataviña (CAT), (8) Sierra San Pedro Martir (SSPM), and (9) Sierra Juarez (SJ)  Figure .2.2Total genomic DNA was extracted from silica‐dried leaves using a modified CTAB protocol (Gutierrez‐Rivera in preparation), and 50 μl of DNA from each sample was sent to the Cornell Institute of Genomic Diversity for library preparation and GBS  reference genome  using the software BWA  were excluded from further analyses. Manipulations of the VCF files were carried out using VCFTOOLS. Inbreeding coefficients and observed and expected heterozygosities were calculated for each palm genus separately using PLINK 1.9  present within each genus. sNMF was chosen in preference to more computer‐intensive approaches such as STRUCTURE . Each population was represented by individuals from a given sierra as described previously. For Washingtonia, we used the most divergent sierra (SJ) as an outgroup, while for Brahea, we used B. edulis as an outgroup. We first generated a maximum‐likelihood graph with no migration events based on 1,000 bootstrap replicates. Then, we tested for between one and ten migration events per taxon (m1–m10) and performed likelihood ratio tests to allow stepwise comparison of log‐likelihood values between each pair of migration events. After that we formally tested for admixture using the “three‐population test”   and dispersal barriers are known to contribute toward the geographic structuring of genetic variation in many organisms. We therefore used the Isolation By Distance Web Service 3.23  as described in Benjamini and Hochberg  genotyped at 2,050 polymorphic SNPs. Observed heterozygosity was lower in Washingtonia  than in Brahea , while the opposite was found for expected heterozygosity . Consistent with differences in observed heterozygosity, the genomic inbreeding coefficient Fhat3 was higher in Washingtonia  than in Brahea .We subjected 190 palm samples to GBS, generating a total of 296,358,035 high‐quality barcoded reads, which were assembled into 549,976 tags that aligned uniquely to the a Figure . Additio3.2Washingtonia and Brahea individuals  as shown in Figure W. robusta from the Mexican mainland (SON) and its peninsular conspecifics than between W. filifera and W. robusta from the northern Baja California peninsula. This suggests that any genetic differences between W. filifera and W robusta are smaller than the magnitude of intraspecific variation within W. robusta.To elucidate taxonomic relationships, we constructed a Bayesian phylogenetic tree based on a subset of 54 s Figure . As expes Figure a. HoweveBrahea, the first corresponding to B. edulis from Guadalupe Island and the second comprising B. armata and B. brandegeei  B. brandegeei and B. armata; and (3) B. edulis.By contrast, two distinct monophyletic clades were resolved in i Figure c. Within3.3Washingtonia and Brahea palms on the Baja California peninsula. These analyses were conducted separately for the two clades identified above corresponding to W. robusta and W. filifera (forthwith referred to as Washingtonia) and B. brandegeei and B. armata (forthwith referred to as Brahea). Strong population structure was found in both genera, with majority of pairwise comparisons among sierras yielding moderately large and highly significant Fst values  as well as between the northernmost population of SJ and the other sierras . Additionally, sierras of the northern  and southern  regions of the Baja peninsula were significantly differentiated from one another , whereas negligible structure was found within each of these regions , (2) the southern Baja peninsula , (3) the northern Baja peninsula , and (4) Washingtonia from Sierra Juarez (SJ). For Brahea, there were some differences in the results depending on the analytical approach used. Specifically, sNMF uncovered three distinct genetic clusters , (2) the central Baja peninsula , and (3) the northern Baja peninsula . By contrast, the SPLITSTREE analysis  had genotypes indicative of ancestry in the southern sierras SLL, SM, and SG, whereas two Brahea individuals sampled from CAT had genetic ancestry consistent with SM and SLL.To uncover the main genetic clusters present within d Figure  correspoAT), and  WashingtAT), and  Washingts Figure  also hig3.4Washingtonia was concordant with the previous results, revealing deep divergence between the mainland and peninsula populations and partitioning of the latter into southern  and northern  groups . This indicates that the most likely number of migration events among the sierras was two. Exploring this scenario further, we found evidence for unidirectional long‐distance migration spanning around 450 km, from SG into CAT and from SM into SFSL  tree for s Figure a. After s Figure  and stepL Figure .Brahea palms, TREEMIX analysis supported the partitioning of the peninsular samples into two major groups comprising the northernmost sierras (SJ and SSPM) and the rest of the peninsula  and the southern and mid‐peninsula sierras . Inferred migration events for Brahea pointed toward the possible migration of palms between the southern sierra of SM and the northern sierra of CAT .For the a Figure b. The laT Figure b. Howevea Figure  and none3.5Washingtonia and Brahea , while the amount of precipitation in the driest quarter correlated significantly with genetic distance in Brahea .Mantel tests revealed strong positive correlations between genetic and geographic distance for both 3.6Washingtonia and Brahea. First, latent factor mixed models LFMMs were used to detect loci exhibiting unusual associations with mean temperature of the coldest quarter in Washingtonia and precipitation of the driest quarter in Brahea. This resulted in the identification of 80 SNPs in Washingtonia and 51 SNPs in Brahea after table‐wide correction of the corresponding p‐values for the false discovery rate. Second, we used a multivariate approach implemented in SAMβADA to test for genotype by environment associations. This approach identified 18 significant associations in Washingtonia and none in Brahea.In order to investigate the genomic basis of the associations described above, we used two complimentary approaches to test for signatures of local adaptation in n = 97 for Washingtonia and 51 for Brahea) resolve contrasting phylogenies to the neutral loci . Analysis within SPLITSTREE revealed a striking pattern for Washingtonia in which the neutral loci resolved four groups corresponding to the southern Baja peninsula , the northern Baja peninsula , SJ and SON . Our data therefore lead us to the conclusion that W. filifera is more likely to represent the northernmost population of W. robusta than a separate species. This is in line with a previous study by Bacon et al.  although this was to some extent expected given the geological origin and geographic isolation of Guadalupe Island . This probably reflects the fact that the seeds of Brahea palms are primarily dispersed by water pulses that wash them short distances along canyons  and subsequently re‐established themselves across most of the peninsula  or using multivariate logistic regression (in SAMβADA), which reduces the occurrence of spurious genotype by environment associations (Stucki et al., Brahea were less clear cut. Within the Baja peninsula, the outlier loci failed to resolve the individual sierras. However, phylogenetic trees based on the same number of randomly selected neutral loci also grouped the sierras together, suggesting that genetic differences between the sierras may be too weak to be resolved by a relatively small subset of SNPs. Either this could reflect weaker selective pressures on Brahea palms or alternatively the association between genetic distance and precipitation of the driest quarter in Brahea could be a type I error. Further insights into this and related questions could be gained from detailed physiological studies aiming to establish more clearly how the two palm genera respond to climatic extremes.The equivalent results for 4.4Washingtonia were clear cut, with strong support being found for two distinct south to north migration events spanning around 450 km. By contrast, although one migration event was inferred for Brahea, this was not supported by three‐population tests and should therefore be treated as putative at best.A further unexpected pattern was revealed by cluster analyses of the GBS data. Overall, the majority of individuals were confidently clustered to their respective geographic groups and levels of admixture between the identified populations were low. However, several palms sampled from the northern sierras had genotypes that were clearly consistent with ancestry in the southern sierras, a pattern that is strongly suggestive of recent long‐distance dispersal. To explore this further, we used TREEMIX to infer the most likely number of migration events in both palm species. The results for Brahea is more dispersal limited, Washingtonia cannot be naturally dispersed over more than a few tens of kilometers. Furthermore, if natural agents such as birds or mammals were involved, one would not necessarily expect to find a bias in the direction of migration from north to south, which appears to be the case for the long‐distance migration events inferred in Washingtonia. However, as palms have a long history of relationship with humans and have been extensively used as a source of food, construction materials and more recently as ornaments in cities and gardens, several authors have speculated that humans may have been involved in spreading palms on the Baja peninsula (Cornett, Washingtonia were extensively used as a food source by native people (Cornett Brahea fruit were less appreciated and have even been referred to as “useless” (Minnich et al., Washingtonia but not Brahea prior to European contact (Cornett, Washingtonia. However, the two northern oases containing Washingtonia palms of southern ancestry are also both sites of colonial Spanish missions where agriculture and trade would have been especially well developed (Minnich et al., It is unlikely that these patterns could have resulted from differences in the natural dispersal abilities of the two palm genera because, even though Cornett, . Further Cornett , whereasCornett, . Our res4.5The recent development of cost‐effective methods for obtaining high‐quality genome‐scale data has stimulated growing interest in the genomic basis of ecological divergence. By greatly increasing genotyping coverage, approaches like GBS have made it possible to identify genomic regions and in some cases specific loci responsible for adaptive differences among populations (Savolainen et al., First of all, disentangling IBE from neutral patterns of genetic variation can be challenging (Wang & Bradburd, Second, the accurate detection of locally adapted loci through genotype–environment associations also depends on accurate measures of different aspects of the ecological landscape. Low‐resolution environmental data may reduce the accuracy of results even if the selective environment is known (Hoban et al., Fst, and result in an increase in false positives in Fst outlier tests (Andrews et al., Fst (Frichot & François, Third, in common with virtually all genotyping approaches including restriction enzyme‐based methods, GBS suffers from a number of potential sources of genotyping error (Andrews et al., Washingtonia suggest that at least for this genus our study was successful at detecting genomic regions influenced by selection. Classical approaches like common garden or reciprocal transplant experiments would provide a suitable basis for future confirmatory studies (Anderson et al., 2010; Savolainen et al., A related issue is that stochastic processes during PCR can cause one allele to amplify more readily than the other at a given locus. This can lead to downstream genotyping errors, as heterozygotes can appear as homozygotes, or alleles containing PCR errors can be interpreted as true alleles. However, at least in theory, PCR should not systematically favor one allele over another at a given locus, and therefore, parameters estimated from a large number of loci are unlikely to be substantially biased (Andrews et al., 5Washingtonia and Brahea, reveal associations between genetic distance and climatic variables, identify subsets of loci that appear to be under divergent ecologically mediated selection, and identify long‐distance migration events consistent with human‐mediated dispersal. None of these patterns could be detected with classical molecular markers, indicating the promise of approaches like GBS to dissect apart the contributions of different processes toward genome‐wide patterns of divergence.Comparative studies can shed light on species‐specific properties that may influence dispersal and ecological divergence, while GBS offers an unprecedentedly detailed window on genome‐wide patterns of differentiation. By combining these approaches in iconic North American palms, we were able to uncover markedly different patterns of population structure in None declared.AK, AOR, and JIH designed the research; AK collected samples; AK performed molecular laboratory work; AK, DLJV, and JIH analyzed the molecular data; AK and JIH wrote the manuscript; AOR provided funding; all authors approved of the final version of the manuscript.https://doi.org/10.5061/dryad.5vk6219.All of the genotypes together with environmental data for the sampling locations are available from the Dryad Digital Repository:  Click here for additional data file.


Rhododendron shanii (Ericaceae), a narrowly distributed species found in the Dabie Mountains, China, to study the genetic diversity, population structure, and evolutionary history of the species.We developed microsatellite primers for R. annae, R. chihsinianum, R. decorum, R. denudatum, R. fortunei, R. neriiflorum, R. rex, and R. simiarum).Two terminal sequencing modes of the Illumina HiSeq platform were used to mine simple sequence repeat markers from large‐scale transcriptional groups. In this study, 24 microsatellite loci were screened. The number of alleles ranged from one to 20, and the levels of observed and expected heterozygosity ranged from 0.000 to 1.000 and 0.000 to 0.918, respectively. Most of these primers were successfully amplified in eight congeneric species (R. shanii and congeneric species.These newly developed microsatellite loci will be useful for studying the genetic diversity and population structure of  Rhododendron L.  is a genus of 1024 species of evergreen or deciduous woody plants in the heath family that are mainly distributed in Asia and the highlands of the Appalachian Mountains in North America. Most species have bright flowers that bloom from the end of winter to early summer. One member of the genus, R. shanii W. P. Fang, is endemic to the southern Dabie Mountains   markers have been used to examine genetic diversity in R. shanii, these studies have not clearly revealed the genetic structure and population demographics of this species .Here we developed a set of SSR markers to probe the genetic diversity in five wild populations of R. shanii plants  was used to dynamically remove those low‐quality fragments with 10% chance of error in the base call from the 3′ ends of the sequencing data. The quality of the pre‐processed data was analyzed using FastQC version 0.11.5 , observed heterozygosity (Ho), and expected heterozygosity (He) were calculated using GENETIX version 4.0  were tested using GENEPOP version 3.4  in R. shanii (Table Ae ranging from 5.83 (SBG) to 7.54 (THJ), Ho from 0.650 (SBG) to 0.718 (DZJ), and He from 0.591 (SBG) to 0.685 (THJ). Overall, SBG showed the lowest genetic diversity of the five populations .We identified 25,564 SSRs from 23,264 contigs and designed 17,251 microsatellite primers. The 24 novel microsatellite markers identified in this study are valuable tools that will be useful for investigating population structure, gene flow levels, and mating systems, as well as for conservation genetic studies of


Poecillastra laminaris Sollas 1886, significant mitochondrial  and nuclear DNA (microsatellite) genetic differences were observed between provinces, amongst north-central-south regions and amongst geomorphic features. For Penares sp. no significant structure was detected  across the same areas. For both Neoaulaxinia persicum Kelly, 2007  and Pleroma menoui Lévi & Lévi 1983 (COI) there was no evidence of genetic differentiation within their northern only regional distributions. Of 10 separate species-by-marker tests for isolation-by-distance and isolation-by-depth, only the isolation-by-depth test for N. persicum for COI was significant. The use of archived samples highlights how historical material may be used to support national and international management decisions. The results are discussed in the broader context of existing marine protected areas, and possible future design of spatial management measures for protecting VMEs in the New Zealand region.The United Nations General Assembly has called for greater protection of the world’s deep-sea species and of features such as Vulnerable Marine Ecosystems (VMEs). Sponges are important components of VMEs and information about their spatially explicit genetic diversity can inform management decisions concerning the placement of protected areas. We employed a spatially explicit hierarchical testing framework to examine genetic variation amongst archived samples of four deep-sea sponges in the New Zealand region. For  Analysis of spatially explicit patterns of genetic structure for multiple taxa can be used to evaluate the suitability of the location, size and spacing of marine protected areas (MPAs) and the establishment of MPA networks in the sea2. However, the identification of patterns of connectivity (gene flow), of regional genetic structure, and of genetic diversity hotspots is challenging in the deep sea because the high cost and logistical constraints of sampling make it difficult to acquire specimens at large spatial scales, and across a wide range of depths1. Given ongoing anthropogenic threats to the deep sea that include fishing, dumping, oil and gas exploration, and potential deep-sea mining3, there is an urgent need to better understand patterns of population genetic diversity and differentiation amongst vulnerable marine ecosystems (VMEs) so that appropriate protection mechanisms can be put in place4.Larval dispersal and genetic connectivity are poorly understood in the deep sea, despite their importance in shaping patterns of biodiversity and biogeography, and their ability to provide insights to help guide the protection of rare and endangered species5. VME protection is called for by the United Nations General Assembly (UNGA), and such protection is the responsibility of bodies that manage resource use in areas beyond national jurisdiction (ABNJ) 6. VMEs are typically characterised by taxa that are fragile and susceptible to damage (in particular from fishing gear), have high longevity and slow growth rates, and have a limited ability (or complete inability) to recover from disturbance5. VMEs are often associated with seamounts that are sites of intense fishing activity because some fish species aggregate around seamounts for breeding or feeding7. VME indicator taxa have already been identified for individual regions8. Different measures have been enacted to protect VMEs, including spatial and temporal fishing gear restrictions, move-on rules, and areas closed to fishing, but the efficacy of these protection measures has been questioned10. If VMEs are to be protected adequately by spatial closures then further research is required to improve knowledge of the distribution of VME indicator taxa, to determine if and where genetic diversity hotspots exist, and to quantify the spatial scales at which populations exhibit high levels of genetic diversity and differentiation.The relatively recent concept of VMEs has been advanced to help identify and thereby protect deep-sea species and features. VMEs are “assemblages of marine benthic organisms which are susceptible to anthropogenic disturbance, especially that arising from the impact of fishing gear used in bottom fishing”11. A limited number of studies, employing a range of genetic markers, have failed to reveal consistent patterns of genetic structuring across a range of deep-sea VME indicator taxa in the New Zealand region16. The role of currents as either promoters of, or barriers to, gene flow is likely to be region-specific and requires more consideration at a variety of spatial and temporal scales. For example, in waters south of New Zealand, the Antarctic Circumpolar Current is believed to act as an oceanographic barrier to gene flow of deep-sea octocorals, separating populations to the north  and south (Southern Ocean) at approximately 55°S on the Macquarie Ridge17. In addition, deep water (>1000 m) currents flow from north and south off New Zealand’s east coast and meet along the Chatham Rise18: such flow may act as a barrier to larval dispersal in some regions and for some taxa20 but may also promote mixing and genetic diversity in others16.Whilst genetic differentiation amongst many species in the coastal environment is well documented, population genetic structure of deep-sea taxa is much less well elaborated21, and the design of a network of MPAs in the New Zealand region16. Whilst deep-sea genetic studies in the New Zealand region span a range of VME indicator taxa, the spatial coverage is patchy and therefore difficult to interpret to inform general protection measures. We examined mitochondrial DNA sequence variation to assess population genetic diversity and differentiation of three deep-sea demosponge species, as well as mitochondrial DNA sequence variation and microsatellite DNA variation for a fourth species, all from the New Zealand region. Sponges are VME indicator taxa in the South Pacific Ocean8, and the study species were selected to provide genetic data for a broader project that aims to inform management of VMEs in the South Pacific region. Our study highlights the difficulty that may be faced by researchers of deep-sea taxa in trying to balance the extraction of maximum information from a minimum number of specimens for which genetic markers are not highly informative and/or are difficult to employ. Interpretation provided here will be added to newly published data from VME corals22 and from other taxa (work in progress) to develop a multi-taxon approach to understanding spatially explicit population genetic variation within and beyond the New Zealand Exclusive Economic Zone (EEZ).Understanding the spatially dependent patterns of genetic structure  is important for management planningCOI sequence data were obtained for all four species, 12S data for Penares sp., and N. persicum, and Cytb data for only Poecillastra laminaris  population structure.is Table . Overallis Table , for COIp. Table , for COIui Table . Haplotyoui Fig. , indicatfor Fig. . These rCOI haplotypes were observed for Poecillastra laminaris in the northern province, four haplotypes were only observed in the southern province. For Cytb, nine haplotypes were distributed only in the northern province and seven haplotypes were only found in the southern province. Poecillastra laminaris exhibited clear evidence of spatial genetic differentiation at both markers  tended to be greater in the northern than in the southern province, whereas mean index values tended to be greater in the south region than in the central or north regions , whereas significant differentiation existed within all populations (p < 0.01). The Bayesian phylogenetic trees revealed evidence of geographic structure , and the FST values between the north and central, central and south, and north and south regions were 0.033 (p < 0.05), 0.024 (p < 0.05) and 0.052 (p < 0.05), respectively. Amongst the geomorphic features, the Kermadec Ridge population was significantly differentiated (p < 0.05) from the Campbell Plateau and Chatham Rise populations  to 0.0079 (Hikurangi Margin) (Table Estimates of n) Table . The estn) Table .Table 4T23, despite the fact that the value of such information for spatial planning and conservation prioritisation has long been appreciated24. This situation may be because few guidelines or frameworks exist to help managers with the incorporation of genetic data into their planning, although recent work has started to address this gap23. Whilst predictive habitat suitability modelling for VME indicator taxa in the New Zealand region26 has been developed to inform spatial management processes, to date this has not included the input of genetic data. In the present paper, a multi-species approach has been employed to improve our understanding of spatially explicit deep-sea genetic diversity and genetic structure, to identify the location of genetic discontinuities (putative barriers to gene flow) within the New Zealand region, and to identify patterns of connectivity and dispersal amongst populations of demosponges.It is still unusual for managers to explicitly consider patterns of genetic diversity in the conservation of marine resources15. Using archived material collected over several decades from many regions of the New Zealand EEZ we started with reasonably large samples sizes and with good spatial coverage, but preliminary testing of this material soon revealed that much of it could not be used for genetic analyses, consistent with earlier work on VME vent mussels15. DNA degradation of these historic samples did not only reduce sample sizes, it also hindered the development of DNA markers such as microsatellites for some species, and SNPs for all species. Nonetheless, because sponges are important VME taxa, and because the collection of new material is unlikely to happen in the near future, we proceeded to analyse the workable material from the four sponge species, whilst recognising limitations to the data set. In this study, small sample sizes for all sponge populations inhabiting different sites may fail to reveal the full extent of genetic diversity within such populations. For example, (1) the total sample size was ≥30 individuals for only Poecillastra laminaris and N. persicum, (2) haplotype accumulation analyses showed that all four species did not have enough specimens to recover all expected haplotypic diversity, and (3) in several cases individual population sample sizes were small . Nonetheless, a fuller understanding of the population genetics of many deep-sea taxa must be pursued with less than perfect sample sets because of limited sampling opportunities, and the urgent management need to protect biodiversity, in particular VMEs. Hence, studies such as the present one, and that of Boschen et al.15, attempt to minimise the biases of small sample sizes by using conservative analytical approaches and qualitative interpretations of genetic structure and patterns of connectivity.A major constraint of studies of deep-sea genetic connectivity is often the limited availability of material, both numbers of individuals per population and numbers of populations at a range of depths29. Consistent with these findings, much of the data generated by this study revealed low levels of mitogenome variation. Nonetheless, we observed sufficient genetic variation within and amongst the four species to reveal patterns of genetic structure of sufficient magnitude to be useful to inform management options. Below, we place the patterns or absence of genetic structure for sponges in a broader context, and then we relate this information to conservation management and the establishment of MPAs.Compared with shallow water sponges, studies of the population genetic variation of deep-sea sponges are uncommon, but typically report that the genetic diversity of the mitochondrial genome in demosponges is low and that rates of evolution are slowPenares sp. exhibited an apparent absence of regional genetic differentiation between the provinces, indicative of high levels of gene flow and consistent with results for highly mobile deep-sea species, including fish31 and giant squid32. Sponges are sessile and one possible explanation for panmixia is that Penares sp. larvae may have a long dispersal duration that allows the species to overcome physical barriers between the two provinces (see section below). In addition, the present-day distribution of lithistids probably reflects palaeo-environmental change around the time of the Eocene, whereas astrophorina such as Poecillastra laminaris and other non-lithistids are not so constrained20. Elsewhere, it has been reported that genetic variability in mitochondrial markers is species-dependent in shallow water sponges, with some, but not all, species exhibiting variability over relatively short geographic distances35. Whether this is true of deep-sea sponges remains unknown, given the relatively poor state of knowledge about the genetics of this group.Several patterns of genetic structure amongst the four demosponges were revealed by mtDNA markers within and between the two lower bathyal biogeographic provinces in the New Zealand region. Poecillastra laminaris, which is consistent with low or no gene flow between the northern and southern provinces (indicated by both COI and Cytb). Similar large-scale north-south genetic structure has been reported for corals13, fish36, and a sea star37. Within the two biogeographic provinces, gene flow was evident for all four sponges, even at scales of 1000 s of kilometres. Similar levels of gene flow, with little or no evidence of within-province structure in the same region, have been observed amongst populations of fish38, ophiuroids39 and crustaceans9. Other studies in the region have revealed evidence of genetic structure at smaller spatial scales within provinces, for example for deep-sea amphipods40 and polychaetes9.Between-province genetic structure was observed for 9. Deep-sea demosponges have a wide-range of reproductive strategies41, but no studies have determined empirically the reproductive strategies or modes of larval dispersal for the four species examined in the present study. Studies of other demosponges have reported asexual and sexual reproduction, as well as dispersal by larvae and by floating propagules, and these different strategies corresponded directly with the genetic connectivity patterns amongst different species41. At least eight different larval types are recognised within the Phylum Porifera but the form of those in the suborder Astrophorina, order Tetractinellida, within which our four sponge species are classified, is unknown42. Sponge larvae are generally considered to be lecithotrophic, ciliated and thus mobile to some extent, but with a relatively short (indirect development) or non-existent (direct development) planktonic life42. This limited dispersal potential may result in clustered distributions of sponges43. Detailed information about the reproductive biology of the four species studied here would be highly beneficial for assessing the importance of reproductive and dispersal strategies to population structure and genetic connectivity.Species-specific differences in reproductive strategies may contribute to differences in patterns of gene flow and regional genetic structurePoecillastra laminaris, and the within-province pattern of genetic homogeneity reported for Poecillastra laminaris, Pleroma menoui and N. persicum, may be attributable to oceanic current systems. Currents may promote the long-distance transport of larvae, but may also act as barriers to gene flow or cause retention of larvae45. In the New Zealand region, the major currents with potential to influence population connectivity are associated with the Tasman Front, the Subtropical Front and the Subantarctic Front  depending on if the objective is to conserve biodiversity or to maintain function. In the context of the present work, the primary focus is on the conservation of biodiversity, with a secondary focus on the maintenance of VME function. Protection of deep-sea habitats in New Zealand is afforded to date by 17 seamount area closures (protecting 19 seamounts) and 17 Benthic Protection Areas (BPAs) which were established throughout the New Zealand EEZ in 2001 and 2007, respectively50 , although the final decision on this proposal is still pending. The Challenger Plateau has two BPAs that cover deep water to the south, and a shallower area on the top of the plateau which will provide some protection for genetic sources on the Challenger Plateau. However, there are no BPAs covering any part of the Macquarie Ridge, although two seamount closures to the west and east of the ridge, and a BPA to the south, may provide some protection for the southern source of genetic connectivity. There are protected areas in relatively close proximity to the remainder of the study sites that represent the range of genetic variation observed for Poecillastra laminaris across the region. However, whether these areas protect this diversity will depend on whether they provide suitable habitat for VME indicator taxa such as sponges.Understanding connectivity between and amongst areas is fundamental to designing an effective MPA network, or for modifying the existing distribution of protected areas to create a networkalong the Rise (west-east), and this may not be sufficient to protect the genetic variation observed across the Rise (north-south). Whilst there are no population genetic data for Poecillastra laminaris from within these two BPAs, the haplotypic distributions showed the Chatham Rise population had the highest genetic diversity of all populations sampled in the study. This suggests that further protected areas, or an enlargement of the current BPAs, need to be considered to afford greater protection to the benthic populations associated with the Chatham Rise. This recommendation was also made by Bors et al.9 for other benthic taxa studied on the Chatham Rise.Our findings indicate that additional protected areas on the Chatham Rise should be considered for sponges. The two BPAs on the Chatham Rise cover a very limited depth range 52, are now beginning to provide information that can be used to improve the design of protected areas (such as BPAs and seamount closure areas) in the New Zealand EEZ and beyond. The results from these studies demonstrate the need for a flexible spatial management system that can be periodically adjusted to accommodate increased understanding about the connectivity of a range of benthic taxa in the region as further studies are conducted.Many countries, New Zealand included, have international obligations to protect deep-sea VMEs, but the decision-making process about the placement, size and spacing of MPAs is often hindered by a lack of understanding about genetic diversity and connectivity of VME taxa. Using historically collected samples and a small data set, we demonstrate how archived material may be employed to inform management decisions about the protection of deep-sea VMEs. The results of the present study, and those of previous genetic connectivity studies around New ZealandNeoaulaxinia persicum Kelly, 2007 (family Phymatellidae Schrammen), an as yet, undescribed species of Penares sp. (family Geodiidae Gray), Pleroma menoui Lévi & Lévi 1983 (family Pleromidae Sollas), and Poecillastra laminaris Sollas 1886 (family Vulcanellidae). Specimens were identified using a combination of standard morphological taxonomic techniques53 and molecular information. Samples were sourced from the NIWA Invertebrate Collection . The majority of specimens were from seamount and slope habitats from 122–1507 m water depth. Most were preserved in ethanol, the rest dry-preserved.Four astrophorinid demosponge species (suborder Astrophorina Sollas) were analysed Fig. : NeoaulaN. persicum (Σn = 30), Penares sp. (Σn = 16), Pleroma menoui (Σn = 16) and Poecillastra laminaris (Σn = 55) were assigned to 6, 5, 4 and 8 different geomorphic feature populations, respectively.Samples were assigned to different populations based on the highest level of spatial differentiation, with the result that individuals of 20  northern and southern currents that meet and mix to the east along the Chatham Rise and which give rise to the Subtropical Front may influence larval dispersal, which results in a north–central–south regional-scale pattern of genetic structure and increased genetic diversity on the Chatham Rise; and (iii) current flows, eddies and turbulent mixing associated with topographic features  may restrict larval dispersal amongst such features, thereby contributing to genetic differentiation amongst populations on the features , a further division at ~32°S (the East Auckland Current flows over the northern province18), was applied at a regional level. Tests were also conducted to examine the influence of geographic distance and depth on genetic population structure. Where appropriate, post hoc analyses were conducted to estimate effective population size (Ne), the location of genetic discontinuities independent of the hypothesis testing framework, and to identify patterns of migration.Patchy deep-sea sampling and the different distributions of the four species meant that samples were not available for all species from all locations20 Fig. . To achires Fig. . For N. 12S rRNA (12S), cytochrome c oxidase subunit I (COI) and cytochrome b (Cytb), were used to estimate genetic diversity and population genetic differentiation. Sponge-specific primers for 12S, COI and Cytb were developed from the Poecillastra laminaris mitochondrial genome using Primer 3.2 in Geneious, and amplifications were tested for all four species as described by Zeng55 , and nucleotide diversity (π) using DnaSP v556. Minimum spanning haplotype networks were drawn using Popart  Specimens were colour-coded according to haplotype and their geographic coordinates of collection were plotted by ArcGIS .DNA sequences were checked and edited and multiple sequences were aligned using ClustalW Alignment in Geneious . Representative haplotypes have been deposited in GenBank Table . Bayesia57. The Mantel test was employed to test for isolation-by-depth and isolation-by-distance by comparing the matrix of Nei’s unbiased genetic distances (generated by MEGA v658 to the matrix of depth (m) and the matrix of shortest actual distances (km) between pairs of sites using GenAlEx v6.559.Analysis of molecular variation (AMOVA) was tested between/amongst locations  and amongst populations using ArlequinΦST statistics were calculated to test for genetic differentiation amongst populations. If significant differentiation amongst populations was detected, the location of the genetic discontinuity was identified using the software Barrier v2.260.Pairwise comparisons of population differentiation were performed in Arlequin and significance values estimated after 1,000 permutations. Between-province or within-province θ (effective population size × mutation rate per site), and migration (M) under the sequence model61. Because of small sample sizes for 3 of the 4 species, values were only estimated for Poecillastra laminaris. Average θ values and migration values were estimated for the combined COI and Cytb data set. A full migration matrix model was employed as the migration estimating model, and each Markov chain Monte Carlo (MCMC) run consisted of 10 short chains  and one long chain  with a burn-in period of 10,000 trees.Migrate-n was used to estimate 62.To assess differential sampling efforts, haplotype accumulation curves were generated by calculating estimates of the mean and variance for the number of accumulated haplotypes through 1,000 random permutations, using the R package SPIDERF statistic (FST). Population genetic differentiation was tested using Arlequin  for differentiation between provinces, amongst regions and amongst geomorphic features.Microsatellite diversity was estimated in GenAlEx, including allelic frequencies, number of alleles, and observed and expected heterozygosities. Arlequin was used to estimate Weir and Cockerham’s (1984) unbiased estimator of Wright’s The Mantel test (GenAlEx) was employed to test for isolation-by-depth by comparing the matrix of individual-based genetic distance values (generated by GenAlEx) to the matrix of depth (m) values and isolation-by-distance by comparing the matrix of individual-based genetic distance values to the matrix of shortest actual distances (km) between pairs of sites. These analyses were conducted for all species at the largest spatial scale at which significant population genetic differentiation was not observed.Supplementary Information for the use of spatially explicit genetic variation data from four deep-sea sponges to inform the protection of Vulnerable Marine Ecosystems