Saccharum complex. However, linkage of undesirable alleles with useful genes in wild species has hindered its efficient utilization in sugarcane breeding. A core collection developed previously with smaller number of clones representing WCSRG did not take into account > 120 wild/exotic clones maintained at the USDA-ARS Sugarcane Research Unit in Houma, Louisiana. Moreover, the genome complexity and sub-tropical to temperate growing climate of Louisiana warrant a region-specific core collection that can be used for base-broadening breeding aimed at efficient introgression of desirable alleles. Genetic diversity of 1,485 clones within WCSRG and Louisiana  using 423 SSR alleles showed an average gene diversity (h) at 0.208 among all species groups where Erianthus-like Saccharum species (ELSS), Miscanthus spp., and S. spontaneum each formed a distinct cluster, Saccharum robustum, S. officinarum, hybrid cultivars, and S. edule grouped together in a major cluster, and Saccharum sinense and S. barberi formed distinct grouping. A 309-clone diversity panel (SDP1) was developed that captured the genetic diversity based on the combination of maximum length subtree and manual selection to maximize representation of Louisiana clones and minimize import of clones from Miami. SDP1 shared 324 alleles out of the 423 alleles in the entire population of 1,485 clones and captured the genetic diversity of the entire collection with an average gene diversity (h) at 0.163. The variation within (11–17%) and among (83–89%) the populations in SDP1 were comparable with the entire population of 1,485 clones . The breadth of the genetic variation of SDP1 was exemplified by the intra- and inter-specific diversity of a 190-clone mini-core collection with markers derived from known cold-responsive genes. SDP1 will facilitate genome-wide association studies for identification of trait-specific markers for use in marker-assisted breeding in Louisiana and elsewhere.Sugarcane crop is important for both sugar and biofuels. A world collection of sugarcane and related grasses (WCSRG) maintained at Miami, FL contains > 1,200 non-redundant clones of different species and genera within the  Saccharum interspecific hybrids) is a perennial C4 grass crop belonging to the subtribe Saccharinae under the tribe Andropogoneae of the family Poaceae. Sugarcane is cultivated worldwide in tropical and subtropical regions as a major source of sucrose1, with a global crop value of $61 billion per year2. Sugarcane accounts for 80% of global sucrose production and 45% of sucrose production in the United States3. Sugarcane, in addition to its importance as a food crop, is recognized as the most productive bioenergy crop because of its ability to produce high biomass4.Cultivated sugarcane  (2n = 40–128) and S. robustum (Brandes & Jesw. Ex Gressl) , and four domesticated species, S. officinarum (L.) (2n = 80), S. sinense (Roxburgh) (2n = 81–124), S. barberi (Jeswiet) (2n = 111–120), and S. edule (Hassk) (2n = 60–80)6. Genetic evidence suggests S. robustum as the progenitor of both S. officinarum and S. edule8, and that most modern cultivars, along with S. sinense and S. barberi, are interspecific hybrids between S. officinarum and S. spontaneum. Around 32–39% of the S. sinense and S. barberi genomes come from S. spontaneum, while for modern cultivars, the percentage is around 10–20%10.Historically, six species are considered important in the S. officinarum clones are involved in the genealogy of sugarcane cultivars with only a few being used extensively11. Basic crosses are made with clones of S. spontaneum, S. robustum, and species of other genera within the Saccharum complex to broaden the genetic base12. In Louisiana, the basic breeding program utilizes wild (basic) clones as nonrecurrent parents, where selected recurrent interspecific hybrids are used for backcrossing14.Modern cultivars have limited genetic variation. Fewer than 20 15. Selection in the early stages of breeding using family appraisal followed by mass selection attempts to separate the environmental components of total phenotype17, but there is typically limited improvement in the genetic gains in sugarcane primarily due to the quantitative nature of the traits with low to moderate heritability. Marker-assisted selection (MAS), on the other hand, could enhance the efficiency of early stage selection as well as selection response of difficult-to-phenotype traits18.Commercial sugarcane breeding is labor-intensive and time consuming. In Louisiana, it takes 13 years from crossing to the release of a new variety. Phenotype-based trait selection in conventional breeding can be problematic because of confounding environmental effects. Therefore, accurate phenotypic selection in the early stages of breeding remains a challenge21. However, traditional biparental linkage mapping in heterozygous species, such as sugarcane, with only single-dose markers, may identify low-resolution QTLs due to the lack of information on the number/type of alleles at the segregating locus and limited genetic variation in the biparental population22.Quantitative trait locus (QTL) mapping is widely used to understand the genetics of complex polygenic traits, and it is the first step toward development of trait-based markers for use in MAS. Use of SNP genotyping by sequencing has improved the resolution of QTLs with generation of high-density linkage maps23. Further, a diversity panel allows study at the same time of allelic diversity and haplotypes of genes/alleles associated with traits. GWAS is based on linkage disequilibrium (LD), which is high in sugarcane24 mainly due to relatively few generations between modern cultivars and the limited number of initial clones used in hybridization26. The high LD of sugarcane has been exploited to identify markers associated with various traits using a regional diversity panel or core collection32.Genome-wide association study (GWAS), on the other hand, has been used recently to identify marker-trait associations (MTAs) in several plants. GWAS allows identification of QTLs with resolution at the gene level, as it takes advantage of historical and evolutionary recombination events in a genetically diverse population (diversity panel)Saccharum spp. and related genera from countries around the world have been collected into a “World Collection of Sugarcane and Related Grasses” (WCSRG) maintained at the National Germplasm Repository of the USDA-ARS Subtropical Horticulture Research Station, Miami, FL, USA. Various studies have described the diversity in the WCSRG36. A total of 342 S. spontaneum clones were assessed using stratified random sampling over geographical origins and principal component cluster groups to select a 75-clone core collection33. The authors also evaluated diversity of 32 S. officinarum, 30 S. barberi, 28 S. sinense, and 27 S. robustum based on cluster analysis using principal component analysis of sugar composition34. A 300-clone diversity panel was created based on genetic diversity of 1,002 clones of WCSRG using 231 SSR alleles35. A similar panel was developed through phenotypic characterization of the collection14.35 did not include clones outside of the world collection. For example, clones that have been procured and maintained by the base broadening (basic breeding) program at the USDA-ARS Sugarcane Research Unit in Houma, Louisiana and elite cultivars and parents used in breeding programs in Louisiana were not included in the diversity analysis. Moreover, ~ 250 additional clones have been collected into the WCSRG in the last four years36. Therefore, the core collections previously developed may not fully account for the range of variation among the subtropical and temperate sugarcane clones currently being used in Louisiana. Sugarcane crop grows optimally at ~ 35 °C. Cold stress by temperatures near freezing can compromise crop growth, development, and yield. Temperatures as high as 20 °C can suppress plant growth and below 15 °C can cause tissue injury37. Louisiana represents the far northern limit of sugarcane cultivation in the U.S. with frequent freezing during the crop season. Therefore, a Saccharum diversity panel developed from WCSRG and clones in Louisiana breeding programs would facilitate GWAS studies for identification of trait-specific markers, such as cold tolerance, for use in marker-assisted breeding in Louisiana and other sugarcane industries. Here, we report on the development of an inclusive sugarcane diversity panel (SDP1) and demonstrate the breadth of its diversity with regard to abiotic (cold) stress responsive genes.The panels discussed aboveSaccharum complex, including Saccharum, Miscanthus, Coix, Imperata, and Sorghum, were used36  for simplicity of analysis. For diversity analysis with cold responsive genes, a mini-core of 190 clones (Supplementary Table Saccharum interspecific hybrids), 27 S. spontaneum, 20 S. spontaneum (previously listed as unknown), 15 S. officinarum, seven Eriranthus, five each of S. robustum and S. bengalense, three each of S. arundinaceum and Miscanthus, two each of S. kanashiroi and S. ravennae, and one clone each of S. edule, S. barberi, S. rufipilum, Coix and Imperata.For the diversity panel, 1,485 clones within the 36. Four-hundred-fifty SSR primer pairs, including 277 genomic SSRs39, 127 eSSRs from sugarcane cold-responsive genes40, and 46 eSSRs from brown rust-responsive genes41, were initially tested for polymorphism among 113 Louisiana clones. Eleven SSR primer pairs mapped on nine out of 10 sugarcane monoploid homeologous groups42 and 17 out of 32 pseudochromosomes43 that had high polymorphism index , as described previouslySorghum44 were searched for the presence of simple sequence repeat (SSR) motifs using BatchPrimer3 v1.0 with criteria set to at least five repeats for dinucleotide motifs and three repeats for tri, tetra, penta and hexa nucleotide motifs. Primers flanking SSR motifs ≥ 16 nt from 52 genes were designed using BatchPrimer3 v1.0. In addition, 48 eSSRs from cold responsive genes of sugarcane40 were included.For genetic diversity analysis of the 190-clone mini-core, 9,974 cold responsive genes reported in 21. Briefly, 50 ng of genomic DNA was used as the template in 10 µl PCR containing 2 µl of 5 × buffer, 1 µl of 25 mM MgCl2, 1 µl of 2 mM dNTP mix, 0.1 µl of Taq DNA polymerase  and 0.5 μl of 10 µM forward and reverse primer . A thermal profile of 95 °C for 5 min followed by 35 cycles of 95 °C for 15 s, 58 °C for 15 s, and 72 °C for 30 s, and 72 °C for 10 min was used. The amplification products were resolved in a 13% polyacrylamide gel using a high-efficiency gel electrophoresis system . Amplified fragments  were manually scored as “1”  and “0” (absent) in a binary matrix.The polymerase chain reaction (PCR) and resolution of PCR products were performed following a method described earlier45.Alleles at SSR loci occurring in less than 1% of the clones were discarded prior to downstream analysis to reduce false similarity between clones due to shared absence of alleles while still capturing rare alleles. The polymorphism information content (PIC) was computed for each SSR marker following46 was used to compute gene diversity (h), Shannon’s information index (I), Nei’s genetic distance (D), principal coordinate analysis (PCoA), and an analysis of molecular variance (AMOVA). AMOVA was done for species groups by recorded names: S. spontaneum, S. officinarum, hybrid cultivar, S. robustum, ELSS, S. sinense, S. barberi, Miscanthus spp., and other46. AMOVA was also conducted on species groups devised from the neighbor-joining analysis, and on groups devised from the population structure analysis (described below). Private alleles, population differentiation and gene flow were estimated for the mini-core by FST and Nm values, respectively. For the mini-core, h and I for every locus, genetic diversity within a population (Hs), total heterozygosity (Ht), gene flow (Nm), and Gst were calculated using PopGene 1.3247.GeneAlEx 6.50248 using Dice dissimilarity scores to validate the clustering of the clones. Weighted neighbor-joining algorithms were used to construct a phylogenetic tree with 1,000 bootstrap repetitions to evaluate the robustness and significance of each node. Bootstrap analysis was implemented with 1,000 iterations to compute the minimum number of SSR alleles needed to differentiate the species using Bootsie software49 and allele numbers were plotted against their corresponding CV values in a bi-plot curve.Genetic diversity was also analyzed using DARwin 6.0.1250. Models were run using Bayesian algorithm for K = 2—10, and K = 8 was selected as per the software’s documentation and eight species groups. A standard admixture model was used with an inferred alpha. To accommodate minor alleles, lambda was evaluated at different levels, and a lambda of 0.5 yielded the best models based on the log of the probability of the data. The Markov chain Monte Carlo program converged well before 50,000 iterations, so 50,000 iterations were used for ‘burn-in’, and 25,000 subsequent iterations were used for model parameter estimation. To estimate the number of clusters, an admixture model with correlated allele frequencies was run in 10 models, and two non-symmetric modes were found. One mode occurred seven times and the other was less consistent and occurred three times. An average of the seven runs from the first mode was used for the final result.Assignment of clones to a specified number of clusters (K) and population structure were determined using Structure ver. 2.3.45. Saccharum spontaneum, representing one-third of the population, has a basic chromosome number of 8.The 11 SSR primer pairs resulted in a total of 423 polymorphic alleles .The low frequencies of Miscanthus spp. to 0.24 for S. spontaneum indicating that Miscanthus spp. were the least diverse and S. spontaneum the most diverse. The overall average gene diversity of all species groups was 0.21 (Supplementary Table Miscanthus accessions (14) were 37-fold less than the S. spontaneum (516).The gene diversity (h) ranged from 0.16 for S. officinarum and S. barberi, where S. officinarum had h = 0.21 but an I = 0.33, and S. barberi had an h = 0.21 but an I = 0.33. The I value ranged from 0.25 (Miscanthus spp.) to 0.39 for (S. spontaneum), with an average of 0.33.The entropy measured by Shannon’s information index (I) followed the same trend with those species having higher gene diversity also exhibiting higher entropy (Supplementary Table S. spontaneum (99.3%) and lowest for Miscanthus spp. (54.4%).The percentage of polymorphic alleles within a species was apparently directly proportionate to its population size Fig. . Again, Miscanthus spp. at 0.105, while the shortest genetic distance was between S. officinarum and hybrid cultivars group (0.009), followed by the D between S. officinarum and S. robustum (0.011) . The D value between the rest of the species groups and Miscanthus spp. ranged from 0.07 to 0.105. The next farthest from the others was ELSS (0.024 to 0.086) followed by S. spontaneum (0.032 to 0.060).Based on Nei’s pairwise genetic distance (D), the greatest distance was between the hybrid cultivars group and Miscanthus spp., and S. spontaneum each showed distinct separation from the other species. Saccharum robustum, S. officinarum, hybrid cultivars, and S. edule grouped together in a major cluster with subclusters concentrated independently with S. robustum and hybrid cultivars. Saccharum sinense and S. barberi also showed distinction, but overall had little intra-species diversity. Miscanthus spp. were most distant from other clones followed by ELSS and S. spontaneum.Neighbor joining with a Dice dissimilarity matrix was also used to evaluate genetic diversity Fig. . ELSS, MS. officinarum and S. robustum with no clear distinction between them. Other sub–populations corresponded to hybrid cultivars, S. sinense, and S. barberi. ELSS and Miscanthus spp. comprised one sub-population, whereas S. spontaneum was delineated by two sub-populations.The eight sub-populations delineated by the structure analysis had near direct correspondence to species groups Fig. . The firSaccharum spontaneum grouped by itself with some outliers that grouped with the cluster comprising ELSS and Miscanthus spp. Saccharum officinarum, S. robustum, and hybrid cultivars grouped together but with distinct centroids. Saccharum sinense and S. barberi formed separate clusters that were close to each other bordering the clusters of the S. officinarum and S. robustum clones and between the S. spontaneum and S. officinarum and S. officinarum/ S. robustum/ hybrid cultivars cluster.Principal coordinate analysis (PCoA) showed definite variance between species groups Fig. . The cooS. spontaneum, S. officinarum, hybrid cultivars, S. robustum, ELSS, S. sinense, S. barberi, Miscanthus, and others, which included Coix lacryma-jobi, Imperata sp., Sorghum polumosum, Saccharum edule, and unknown species  were picked from different subclusters maximizing the clones existent in Louisiana to minimize the number of clones to be imported from the WCSRG. SDP1 consisted of 284 clones from Louisiana that represent clusters of clones within WCSRG and Louisiana breeding programs. The remaining 25 clones require importation from the WCSRG. Over 100 of the SDP1 clones were Louisiana hybrid cultivars that consisted of both historic and current clones. Louisiana clones exhibited some clustering, but were fairly well dispersed across the entire study population  and Louisiana sugarcane breeders selection wherein clones  followed by 77.8% for hybrids. Erianthus-like species had lowest allele polymorphism (40.2%).The gene diversity (h) for different species groups were comparable, ranging from 0.13 for Erianthus-like group was the most genetically distant from other species (0.029–0.041) with the highest D (0.041) with S. robustum. Expectedly, D (0.007) was the smallest between hybrid cultivars group and S. officinarum, followed by the equal D (0.016) between S. officinarum and S. robustum and between S. officinarum and S. barberi  showed that Analysis of variance showed significant differences within sub-populations (Supplementary Table Sorghum identified 93 different kinds of SSR motifs. Trinucelotide repeats were the highest (55%), of which the GCC/GGC motif had the highest frequency (22), followed by tetra (24%), hexa (10%), penta (6%), and di (5%)  exhibited three distinct clusters Fig. . ClusterIIB Fig. . On the IIB Fig. . The priIIB Fig. .Figure 6Miscanthus and Erianthus had the highest number of unique alleles when analyzed with cold-responsive genes-derived SSR primer pairs  , a core collection selected from 1,485 clones enriched with clones from Louisiana, based on the diversity analysis and breeders’ input  of the PIC values of the SSR primers used in the entire collection was similar to the range (0.195 to 0.375) reported earlier35. Previous studies have used nearly two times fewer total alleles and average number of alleles per SSR for diversity analysis, such as 205 alleles with 13.67 per locus55, 209 alleles with 5.8 per locus35, and 261 alleles with 7.35 per locus56. Thus, 423 alleles were considered enough to randomly capture a significant proportion of the genetic diversity within the Saccharum genome to effectively discriminate accessions among different species groups. This was evident from the bootstrap results , diversity of habitats, and widest geographic distribution60.Results of the diversity analysis of the WCSRG from the present study and that of Nayak et al.35 where three main clusters: 1) S. spontaneum; 2) S. officinarum, hybrid cultivars, S. robustum, S. edule, S. barberi, and S. sinense; and 3) other species were described. However, species within each cluster were resolved better in our study, where seven clusters in the neighbor-joining analysis  and six clusters with the structure analysis and the PCoA  were identified. Nayak et al.35 left Erianthus and Saccharum nomenclatures as found in the WCSRG database. They used non-Saccharum species as a group, grouping Erianthus spp. with Miscanthus spp. Considering that several Erianthus spp. have been reclassified under Saccharum, it is difficult to comprehend if their analysis found the same distinction that was observed in the present study between ELSS and Miscanthus spp. The distinction between Saccharum spp. and Erianthus-like Saccharum spp., as found in our study, was also observed earlier61.Clustering analysis results of the WCSRG and Louisiana collection in the present study were similar to that observed by Nayak et al.39. The low polymorphism of genic SSRs could be explained by the conserved sequence of the transcribed regions within the Saccharum genus62 or by the low transmission of diverged sequence differences between the Saccharum spp.51. Genetic similarity was high among the sugarcane hybrids, which is likely because only a few parental clones were involved in the development of the foundation clones through nobilization in breeding programs63. Alleles at these loci are possibly fixed as a result of a limited number of founder parents and subsequent selection. The Louisiana sugarcane hybrids formed a distinct group, close to the group containing S. officinarum clones, which is expected because most of the modern cultivars inherited the major part (~ 80–85%) of the genome of S. officinarum during nobilization events64. Besides, Louisiana-bred clones grouped in a subcluster within the hybrids cluster, which may be because they were developed in subtropical conditions under selection pressure from cold temperatures. Similar to the S. spontaneum in the entire collection of 1,485 clones, S. spontaneum clones in subcluster 3 of the mini-core were close to unknown S. spp. clones in cluster 4. This supported our assumption that most, if not all, of the unknown clones in the WCSRG are S. spontaneum. The non-correspondence of genetic similarity of clones within a species group within WCSRG and the mini-core is believed to be due to the varied cold stress response of the clones. This is justified by the high percentage (81.2%) of variation within a species group (population) compared to 18.8% among the species groups  we observed among the clones in WCSRG was similar to that with a set of cold-responsive genes-derived markers among the Louisiana clones in our previous studyTaken together, the genetic diversity analysis based on genotypic data of 1,485 clones helped Louisiana breeders in the selection of 309 clones (20.8%) as the SDP1 that, with an average h at 0.163 (Supplementary Table Supplementary Figure S1.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Table S9.Supplementary Table S10.


Thamnophis sirtalis) and toxic newts (Taricha granulosa). We found that predator resistance and prey toxin levels are functionally matched in co‐occurring populations, suggesting that mosaic variation in the armaments of both species results from the local pressures of reciprocal selection. By the same token, phenotypic and genetic variation in snake resistance deviates from neutral expectations of population genetic differentiation, showing a clear signature of adaptation to local toxin levels in newts. Contrastingly, newt toxin levels are best predicted by genetic differentiation among newt populations, and to a lesser extent, by the local environment and snake resistance. Exaggerated armaments suggest that coevolution occurs in certain hotspots, but prey population structure seems to be of particular influence on local phenotypic variation in both species throughout the geographic mosaic. Our results imply that processes other than reciprocal selection, like historical biogeography and environmental pressures, represent an important source of variation in the geographic mosaic of coevolution. Such a pattern supports the role of “trait remixing” in the geographic mosaic theory, the process by which non‐adaptive forces dictate spatial variation in the interactions among species.Reciprocal adaptation is the hallmark of arms race coevolution. Local coadaptation between natural enemies should generate a geographic mosaic pattern where both species have roughly matched abilities across their shared range. However, mosaic variation in ecologically relevant traits can also arise from processes unrelated to reciprocal selection, such as population structure or local environmental conditions. We tested whether these alternative processes can account for trait variation in the geographic mosaic of arms race coevolution between resistant garter snakes ( Thamnophis sirtalis) evolved resistance to tetrodotoxin (TTX), a deadly neurotoxin found in their prey, the rough‐skinned newt (Taricha granulosa). Predator resistance and prey toxin levels are roughly matched throughout the species’ shared range in western North America, a pattern that is generally regarded as evidence of local coadaptation in the arms race. We found evidence that geographic variation in resistance of garter snakes deviated substantially from population genetic structure, supporting the interpretation that local adaptation of predators has occurred. In contrast, toxin levels in prey were clearly predicted by the genetic structure of newt populations and local environmental conditions, two factors unrelated to coadaptation in the arms race. At first glance, matched weaponry between predator and prey seems to be the sole result of intense arms race coevolution, but our results emphasize that landscape patterns of phenotypic variation are determined by a mixture of natural selection, demographic processes, and environmental effects, which together compose the geographic mosaic of coevolution.Natural enemies often have matched abilities throughout their range, for example, between predator resistance and prey toxins or pathogen virulence and host defenses. This geographic pattern of matched weaponry can arise from coevolution, because counter‐adaptation leads to tightly matched abilities between natural enemies. While coevolution may influence local trait differences, phenotypic variation can also result from other factors, like historical biogeography and local environmental conditions. Common garter snakes  evolved escalated resistance to tetrodotoxin (TTX), the deadly neurotoxin found in their prey, rough‐skinned newts (Taricha granulosa). Predator and prey have correlated levels of resistance and toxin throughout western North America, which has generally been interpreted as evidence of local coadaptation  that disrupt toxin‐binding and confer large increases in resistance  and TTX‐bearing newt species  that confound inferences of coevolution between discrete species pairs. At each sampling site in the mosaic, we measured TTX resistance in garter snakes  and levels of TTX in newt populations to characterize functional (mis)matches between predator and prey. We then tested whether trait differentiation in the weaponry of each species can be further explained by population structure using genome‐wide single nucleotide polymorphisms (SNPs) and by local environmental conditions using bioclim data (www.worldclim.org). If the geographic mosaic of predator resistance and prey toxins results only from coadaptation in response to a mosaic of reciprocal selection, then trait differentiation of each species should be predicted solely by the local armaments of its natural enemy in the arms race. If mosaic variation in resistance and toxin levels is attributable to processes other than reciprocal selection, then trait differentiation may also be predicted by selectively neutral SNP variation or environmental conditions  that spans the geographic mosaic  and newts (n = 138) at nine locations along a latitudinal transect in the states of Washington and Oregon  , y´ = 0 and the 50% dose is estimable x^=−α/β (where α is the intercept and β the slope from the curvilinear regression). Because x^ takes the form of a ratio, the standard error for the estimated 50% dose is calculated using standard methods for the variance of a ratio  was needed, so we transformed the data using x´ = ln(x + 1)  is located on the Z sex chromosome of Th. sirtalis  in each individual. TTX is uniformly distributed throughout the dorsum and levels of TTX in the dorsal skin are tightly correlated with toxicity in other regions  transformation of TTX was needed. The transformed TTX values were used in the subsequent regression and cline‐fitting analyses of TTX along the transect (see below).We estimated levels of TTX in newts using a Competitive Inhibition Enzymatic Immunoassay (CIEIA) and TTX‐specific antibodies  to the whole animal (mg of TTX/newt) using standard methods. For snakes, the 15, 50, and 85% MAMU doses were extracted from the dose‐response curves of each population. As described in Hanifin et al. , and size selected for 500–600 bp fragments using a Pippin Prep . We enriched the adapter‐ligated fragments in the size‐selected libraries using 16 PCR cycles and then purified the product with AmpureXP beads. The snake and newt libraries were each run on two lanes of the Illumina HiSeq 2500 platform  to generate 125 bp paired‐end reads.We generated SNP data from double digest restriction‐site associated DNA sequenced (ddRADseq) to characterize the population structure of predator and prey, which served as a neutral expectation for trait differentiation in the geographic mosaic of coevolution. We prepared ddRADseq libraries separately for garter snakes and newts using the protocol described in Peterson et al. . Genomicprocess_radtags in Stacks 1.46 . For the newt dataset, we used the denovo_map.pl pipeline in Stacks, because a reference genome is not currently available. We used a minimum depth of three (‐m), a distance of three between stacks (‐M), and a distance of three between catalog loci (‐n). For both species, we used populations in Stacks to select loci with a minimum depth of 10× coverage. To avoid linkage among sites within the same locus, we only retained one SNP per locus.Read quality of the raw sequence data was assessed using FastQC 0.11.5 Andrew . We usedST coefficients that were significantly different than those expected under neutrality  < 0.05 was used to identify and remove outlier loci putatively under selection.We used the dartR package in R  for garter snakes. The ref_map pipeline identified an average of 13,501 loci per individual, with a mean of 80× coverage. After additional filtering, we retained 1,027 unlinked neutral SNPs in 132 individuals. For newts, sequencing produced an average of 1,093,529 raw reads per sample (n = 137). After initial quality control in process_radtags, the denovo_map pipeline in Stacks identified an average of 32,469 loci per individual, with a mean of 19x coverage. After further filtering steps in populations, dartR, and Bayescan, we retained 3634 unlinked neutral SNPs in 123 individuals. The final datasets comprised a larger number of SNPs for newts (3634) than snakes (1027), so we reran the subsequent analyses with a random subsample of 1027 SNPs for newts to generate equivalent datasets for each species. Results from the subsample were highly similar to the full dataset, so only results from the full analysis are presented herein.Illumina HiSeq sequencing yielded an average of 2,451,623 raw reads per sample (O) and expected heterozygosity  for each population using the hierfstat package in R  of sampling locations. We assessed the significance of IBD tests with 1000 permutations .We tested for a pattern of isolation‐by‐distance (IBD) along the transect by performing Mantel tests on matrices of linearized pairwise F Rousset . We also Rousset . We cond Rousset  to test ST is a relative measure of among‐population differentiation that is inherently dependent on levels of within‐population diversity (Charlesworth Pairwise Fwww.worldclim.org) for each locality along the transect. These environmental variables should be especially important for ectotherms and are considered principal determinants of the plant communities that define habitat types . We summarized variation in the 19 bioclim variables using a principal component analysis (PCA). The first two axes explained 89.2% of the variation, which we selected as a summary of local environmental conditions along the transect. PC1 generally captured latitudinal variation in annual mean temperature and precipitation, with positive values indicating warm, dry conditions and negative values comprising colder, wet conditions. PC2 captured temporal fluctuations in temperature, with positive values representing greater seasonality and annual temperature range and negative values comprising less annual variation in temperature (Table S5).We also sought to evaluate environmental contributions to trait variation in the geographic mosaic of coevolution. We extracted temperature and precipitation bioclim data .We used environmental PC1 and 2 from the bioclim data to test for a pattern of isolation‐by‐environment (IBE), which is characterized by a correlation between genetic differentiation and environmental distance, for example, because migrants are selected against in foreign environments , and (3) pairwise differences in environmental conditions . We then tested whether population patterns of phenotypic escalation in a focal species  are explained by trait escalation in the natural enemy (TTX of newts), population genetic differentiation (pairwise FST of snakes), and local environmental conditions . All MRM analyses were conducted using the “MRM” function in the R package ecodist  escalation in the armaments of the natural enemy, (2) population structure of the focal species, and (3) local environmental conditions. Multiple regression of distance matrices (MRMs) is an extension of Mantel tests that involve multiple regression of a response distance matrix on explanatory matrices Manly . PartialST) and geographic distance along our latitudinal transect, indicative of strong IBD in both species . Geographic distance is often also spatially correlated with environmental conditions . The strong pattern of IBD along the latitudinal transect made it difficult to disentangle how genetic differentiation and the environment each contribute to variation in coevolutionary traits. This multicollinearity problem is illustrated by the correlations among newt TTX levels, newt pairwise FST, geographic distance, and environmental PC1 .Our goal was to dissect how population structure and local environmental conditions each contribute to trait variation in the geographic mosaic; however, the analysis was complicated by multicollinearity among our latitudinal datasets. The genomic analyses revealed a tight relationship between genetic differentiation (pairwise FST), we used population means of genomic PCo1 and 2 as a measure of genetic differentiation among populations. To specifically address multicollinearity, we used conditional RDAs to isolate the individual effects of genetic differentiation and the environment on each coevolutionary trait . For example, we tested for the effect of genomic PCo1 and 2 on snake TTX resistance, conditioned on the effect of environmental PC1 and 2. Then we repeated the analysis in reverse, testing for the effect of environmental PC1 and 2 on TTX resistance, conditioned on genomic PCo1 and PCo2. Finally, we conducted a marginal test that included all explanatory variables  and used forward model selection to generate a marginal model that only includes variables with a significant effect on snake TTX resistance. RDAs were calculated using the “rda” function in the vegan R package, including the functions “anova” for significance testing, “RsquareAdj” for model fit, and “ordiR2step” for forward selection.To address issues of multicollinearity, we complemented MRM analyses with conditional redundancy analyses (RDAs), which have been proposed as more appropriate for analyses of spatial data (Legendre and Fortin 2). For garter snakes, we fit (1) a cline to phenotypic TTX resistance using the 50% MAMU dose and variance from the ln‐transformed MAMU data and (2) a genetic cline to the frequency of TTX‐resistant alleles in each population. Two different TTX‐resistant DIV alleles occur in the Pacific Northwest: NaV1.4V is generally found at high frequency in the center of the transect, whereas NaV1.4VA occurs predominately in southern populations  from the most likely value of K for each species (K = 2). Both analyses of population structure (PCo1 from the PCoA and K = 2 from STRUCTURE) produced concordant results , so only the analysis of PCo1 is presented in the main text. PCoAs have no explicit population genetic assumptions, whereas STRUCTURE assumes that all loci are unlinked at linkage equilibrium and in HWE. Because the PCoA and STRUCTURE analyses produced similar results despite different underlying assumptions, we are confident that the cline‐fitting analyses provided a representative depiction of population structure in each species. Finally, we reran cline‐fitting analyses with the Elk River population removed from the newt dataset, because the PCoA Fig. . These rc) and width , but could additionally estimate combinations of exponential decay curve (tail) parameters , which represent the distance from the cline center to the tail (δ) and the slope of the tail (τ). The genetic models varied as to whether they estimated allele frequencies at the cline ends (pmin and pmax) or fixed them at 0 and 1. All models were then compared using AIC corrected for small sample sizes (AICc) and maximum likelihood parameters were extracted for the best‐fitting model. We considered cline centers with non‐overlapping two log‐likelihood unit support limits  to occur in significantly different geographic locations . The TTX levels of newts (μg/cm2) also varied among populations  and sexes  along the transect  and newts  were similar, and both species exhibited a strong correlation between pairwise FST and geographic distance indicative of IBD . This pattern is consistent with previous work suggesting limited population structure and a recent northward post‐glacial expansion of both species  or newts .Global FST revealed subtle differences in the geographic population structure of predator and prey (Table S4), a pattern that was also supported by the PCoA and Bayesian clustering (STRUCTURE) analyses  and the conditional RDA (p = 0.021). In fact, after model selection, the marginal RDA included genomic PCo1 as the only explanatory variable, accounting for 81.5% of the variance in newt TTX. The environmental PCs also predicted variation in newt TTX after controlling for snake TTX resistance in the multivariate MRM (p = 0.001) and conditional RDA (p = 0.007), although this may be driven by multicollinearity of latitudinal explanatory variables (see below). Overall, our results suggest that non‐adaptive factors—especially the population structure of newts—are key correlates of population differences in prey toxin levels, which is not the case for predator resistance. Interestingly, the population structure of newts not only predicted differentiation in newt TTX, but also snake resistance in an MRM  and an RDA .Resistance of garter snakes was strictly predicted by prey toxin levels. Newt TTX was the only significant explanatory variable in the MRM , whereas the genomic PCos were marginally predictive of TTX after conditioning on the environmental PCs  contribute to variation in newt TTX; however, the collinearity of environmental PC1 and newt population differentiation makes it challenging to disentangle the individual contributions of each explanatory variable to TTX levels Fig. S2). Notably, the environmental PCs were not included in the marginal RDA after model selection , exhibiting the signature of IBD among newt populations in the Pacific Northwest , and the tight congruence between clinal variation in newt TTX and neutral SNPs reflects the homogenizing effects of gene flow between hot‐ and coldspots Fig. .The notable importance of prey population structure in the geographic mosaic of coevolution points to an influential role of “trait remixing”, a largely untested process in the geographic mosaic theory thought to generate spatial variation at the phenotypic interface of coevolution Thompson . The neuOur results highlight that not all landscape patterns of phenotypic matching in natural enemies are necessarily the result of coevolution and a mosaic of reciprocal selection .The ddRADseq data will be made available on GenBank upon manuscript acceptance. DIV p‐loop sequences are available on GenBank (MT043460‐MT043727). All genomic and phenotypic data and the code for statistical analyses are available on Dryad  illustrating the relationships among neutral FST and latitude and longitude (IBD) and neutral FST and environmental PC1 and 2 (IBE).Supplemental Figure S2. Correlations between each coevolutionary trait and genetic, geographic, and environmental distances.Supplemental Figure S3. Comparison of neutral clines fit to (1) PCo1 values from the PCoA and (2) average ancestry assignment values (K=2) from the STRUCTURE analysis.Supplemental Figure S4. STRUCTURE results for K=2‐4. The most likely number of genetic cluster was K=2 for both species .Supplemental Table S1. Datasets for each sampling location along the latitudinal transect.Supplemental Table S2. For each locality, functional estimates of oral doses of TTX (mg) required to reduce the speed of an average adult Th. sirtalis to 15, 50, and 85% of baseline speed post‐ingestion and the total skin TTX dose (mg) in adult Ta. granulosa.Supplemental Table S3. Population genetic diversity statistics from neutral SNPs in each species.Supplemental Table S4. Pairwise FST statistics for the neutral SNP datasets of each species.Supplemental Table S5. PC loadings for the 19 biolclim variables.Supplemental Table S6. Results from cline‐fitting analyses.Click here for additional data file.


Vitis vinifera) is one of the most important fruit species in the Classical Mediterranean world. It is thought to have been domesticated 6,000–8,000 years ago in the Near East. However, the domestication of its wild relative into wine grapes or table grapes remains largely unknown. In this study, we analyzed 30 table grapes, 30 wine grapes, 30 dual-purpose grape accessions, as well as 30 wild relatives (Vitis vinifera ssp. sylvestris). The phenotypic comparison showed striking differences in berry weight, acidity and the content of aroma. Based on a total of 7,522,958 single-nucleotide polymorphisms, we identified several significant selective sweep regions for table and wine grapes. Besides the well-known sex-determination locus on chromosome 2, the other four highest signals shared by table and wine grapes could not be linked to the known QTLs. The identification of these genomic regions under selection sweep may reveal agronomically important traits that have been selected during grape domestication. This information not only sheds light on the mechanisms of adaptions and diversification, but also guide the genetic improvement in breeding programs.Grapevine ( Approximately 13,000 years ago, human began to domesticate and selectively breed crops . During Vitis vinifera L.) is now economically the most important cultivated fruit crop in the world , significant differences between table and wine grapes were brought about through selective breeding. Table grapes tend to have large, seedless fruit with relatively thin skin, while wine grapes are smaller, usually seeded and have relatively thick skins. At the time of harvest wine grapes tend to be very sweet (approximately 24% sugar by weight), while table grapes have usually around 15% sugar by weight. In addition, wine grapes are often small with concentrated flavors, whereas table grapes are large, bursting with a lot more water.Generally, commercially cultivated grapes can be classified as either table or wine grapes based on their intended method of consumption: table grapes or wine grapes. Though almost all of them belong to the same species (V. vinifera varieties has been studied. Simple sequence repeats were used by vinifera and 59 sylvestris accessions by Genetic diversity among V. vinifera ssp. sylvestris) and 90 domesticated grape accessions (V. vinifera) from a previous resequencing study  was performed with the Genome-wide Complex Trait Analysis software  software was employed and the pairwise r2 values within and between different chromosomes were calculated. For each sub-group, the LD was calculated using the corresponding SNP pairs.The whole-genome SNPs were used to construct the Maximum likelihood (ML) phylogenetic tree with 100 bootstrap using SNPhylo software  and population nucleotide diversity . A 100 kb sliding window with 10 kb step was applied to quantify FST and π by using VCFtools software , were randomly selected. The geographic distributions of these accessions were from total of 23 countries, including China, France, Japan, the United States, Italy, etc.  and 30 of its wild relative  were collected , table grapes had an average berry weight (5.80 g) significantly larger than that of wine grapes (2.27 g). The berry weight of dual-purpose grapes was in between. Additionally, wine grapes tended to have rounder berry shape as the ratio of length/width was closer to 1. The table grape accession TA-268 had the biggest number of 1.698. Among 3 sub-groups, the average seed number seemed similar, but table and dual-purpose grapes did have seedless accessions. In addition, significant differences were also observed in the acid levels among table grapes , dual-purpose grapes  and wine grapes , showing a higher acidity of wine grape. However, among three sub-groups the less significant difference in sugar level was unexpectedly observed. The similar range of sugar level was surprisingly detected between 0 and 8.42 mg/ml for sucrose and 47.36 and 211.83 mg/ml for total sugar. When comparing the brix concentration detected during 2015 and 2017, wine grapes were found to have higher variance. For instance, TA-291, its brix content was 23.41° in 2015 and 20.10° in 2017, but in 2016 this value was only 13.48°. TA-236 was another example, it had 10.85° brix in 2016 but 18.60° in 2017. Moreover, extreme low cases of brix were also found in wine grapes, such TA-242 (7.80° in 2015 and 7.50° in 2017) and TA-183 (9.99° in 2017). For aroma, uptrends could also be observed in the content of hexanal, 2-hexenal, nonanal and (E)-2-hexen-1-ol, indicating their essential role in high quality winemaking.Moreover, phenotypic data of 21 traits on 90 cultivated grape accessions  should be the same grape type as the offspring or be dual-purpose. Additionally, several accessions were siblings sharing the same breeding parents, and particularly noteworthy was that even the same parents could breed offspring of different types (usage). For instance, Cabernet Suntory and Suntory Blanc are both bred from the cross between Koshu Sanjaku and Cabernet Sauvignon, but they were dual-purpose and wine grape, respectively. The same situation was found in Aishenmeigui  and Zaomanao (table grape), whose parents are dual-purpose grapes Muscat Hamburg and Jingzaojing. Together, these observations could further evidence that grape is a highly heterozygous species with complex genetic background and quantitative agronomic traits.V. vinifera reference genome (The sequencing data of 120 grape accessions (4.85 Gb reads) have an average coverage depth of 16.2X for each accession . After me genome , the avee genome . After ae genome . Among 1V. vinifera subsp. sylvestris) from the cultivated grape accessions . In the admixture plot at K = 4 (K = 4).In accordance with expectations, PCA showed substantial genetic diversity among major grapevine categories. 11.7, 4.9, and 3.9% of total genetic variance were explained by the first three principal components, respectively . PC3 sepat K = 4 , it was at K = 4 . Even that K = 4 , K = 4.r2 at a distance of 3 kb while all three sub-groups of domesticated grapevines had similar LD around 350 bp, being concordant to the previous study  was evaluated in wild European and each domesticated sub-group using large-scale genetic markers . For wilus study . Compariistories . Howeveristories . In thisVIT_12s0034g00310 and VIT_18s0075g00100) and three  disease resistance related genes specific for table and wine, respectively, indicating their undergoing of the different breeding process for disease resistance. Additionally, genes VIT_16s0013g01070 and VIT_16s0013g01080 on the flanking sides of the highest signal on chromosome 16 were annotated as ethylene-responsive transcription factor ERF105 and ERF104, respectively. In Arabidopsis, ERF104 and ERF105 were found to play important role in signaling response, plant immunity and plant response to abiotic stresses  that both scored in the top 5% of the Fst and CLR analysis , 5. 2,11stresses . By infestresses , whereasV. vinifera ssp. sylvestris). Given that most table grape accessions were from the East and most wine accessions were from the West, we found a correlation between usage and geography, which is likely to be influenced by religions  with the composite likelihood ratio (CLR) test was applied in table and wine grape accessions, and each had 2119 positively selected signals, covering 450 candidate genes and 491 candidate genes, respectively. Among them, a signal on chromosome 2 at the position of 4.91 Mb was shared by both groups, where the sex-determining locus had been fine-mapped . As one linators .VviAGL11 on chromosome 18 which codes seedlessness (VvMybA1 on chromosome 2 that affects grape skin color, and several other traits like berry firmness (ERF104 and ERF105, respectively, which were involved in the immunity and stress response in Arabidopsis. Thus, we may deduce that the candidate genes at or close at these positions could be very important genes related to the traits selected during the domestication process, such as stress response.Due to the advancement of high-throughput sequencing technology , a huge lessness , VvMybA1firmness  and resifirmness . HoweverDuring domestication, the selection is often aimed at some specific phenotype, where the internal mechanism is to select genes that have a direct relationship with the preferable traits. In this study, we have identified several genomic regions under positive selection that may have been artificially selected during the process of grapevine domestication. The detection of these significant selection regions can lead to the candidate genes that perform the corresponding functions and therefore would have great significance to understand the evolution of grapevine . EconomiPRJNA393611.The WGRS data set generated and analyzed in the current study is available from NCBI under the BioProject accession LK collected the samples and performed the experiments. SD and SW completed the data analysis. XD and MT edited and modified the manuscript. All authors read and approved the manuscript.SW, SD, and XD are employees of the Nowbio Biotechnology Company and they declare that Nowbio Biotechnology Company plays no role in the funding, design, analysis, and publication of this manuscript.The remaining 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.


Fertility is a very important fitness trait in animal production because of its significant economic importance, particularly in species with low reproductive efficiency. For this reason, it should be included as a breeding objective, particularly in horses, where the trait has received less interest than in other species. In this study, we attempted to identify a variable able to detect the lifetime reproductive performance of Italian Heavy Draught Horse (IHDH) mares and to analyze its possible implementation for breeding purposes, with the final aim to increase mares’ fertility efficiency. A phenotypic variable to be used early in life (at least after 3 breeding seasons) has been identified and validated in the study and its genetic component estimated. Results obtained indicated the proposed phenotypic measure of fertility as a good predictor of the lifetime reproductive success in IHDH mares and the variable showed high heritability  for a fitness trait. Therefore, the use of the lifetime fertility rate proposed in this study for breeding purposes seems feasible, although some limitations could occur in the accuracy of individual breeding value estimates of mares and stallions.n = 1950) and estimated (n = 1443) LFR, obtained by LFR-C, and LFR-E; Arcsine transformation of LFR-C and LFR-E were also analyzed in single trait animal models to estimate heritability. Overall, the LFR showed a moderate but significant genetic variation, and the heritability of the trait was high (0.24) considering it is a fitness trait. The arcsine transformation of LFR did not show any improvement of heritability. The present study indicates the possible use of a linear LFR variable for breeding purposes in IHDH breed considering both complete and incomplete reproductive careers.Our aims were to find a phenotypic variable to express mares’ lifetime reproductive performance after 6 breeding seasons (BS) in Italian Heavy Draught Horse breed (IHDH), and to estimate its heritability. At first, 1487 mares in a training dataset were used to implement and validate a set of predictive coefficients (LFR-C) or equations (LFR-E) to estimate a lifetime foaling rate (LFR) after 6 BS, i.e., the number of foals generated divided by the opportunities to do so. Then, 3033 mares in a dataset with at least 3 registered BS, was used to estimate LFR for mares with 3, 4, or 5 registered RS. This dataset contained actual ( Fertility has a well-recognized role in animal production for its implication on the economic efficiency of the whole productive system, independently from the species considered ,2,3. In www.fao.org/dad-is/; update: August 2019). Genetic improvement is based on the evaluation of linear type traits scored in 6-months-old animals at a population level [The study was carried out on Italian Heavy Draught Horse (IHDH), a native breed originated in middle 19th century from crosses of French Breton stallions with local mares . In the middle of 20th century the meat attitude was added to the original heavy draught attitude, still important for some leisure activities and the agricultural work in organic farms. Nowadays the population includes 353 stallions and 2962 mares for a population of 5137 individuals  expressed as number of foals produced after 6 breeding season divided by the foaling opportunities, i.e., 6. The choice of the 6th breeding season was established as the endpoint representing a successful reproductive career for an IHDH mare, i.e., at about 9–10 years of age. Further breeding seasons were not considered. The breed, indeed, has a mean age at first foaling of 3.5 years and an average lifetime career of 9.3 years, with an average reproductive career of 5.8 years ,25. A di® statistical software; SAS® version 9.4, SAS Institute Inc., Cary, NC, USA) allowing the estimates of the no. of foals produced at the 6th breeding season depending on the basis of (i) the previous no. of foals after either 3, 4, or 5 breeding seasons, and (ii) the age at first foaling , obtainy − ŷ| are absolute differences between actual (y) and predicted values (ŷ), respectively, and n is the number of observations;the mean absolute deviation of residuals  calculay − ŷ, where y and ŷ are vectors of actual and predicted values, respectively.the standard deviation of residuals obtained as The training dataset (step 1) contained all reproductive events available from the studbook database for mares born after 1990. To enter the dataset, mares were required to hold an amount of 6 subsequent registered breeding seasons, to belong to environmental units by year of birth with at least 2 observations and to have both parents known. Environmental units considered were groups of farm-studs in the same geographical area  and common rearing system . These divisions were done on the basis of specific characteristics recognized for horse management . Environn = 1950) and predicted  were carried out. The latter one was run to establish which non-genetic effects could be taken into account in the genetic model. Some preliminary genetic models with increasing complexity were also run to see the variations in heritability when different animal-based factors were included , h is the vector for the random effect of order q for EU-BY, a is the vector of random animal effect of order q  and e is the vector of residual effects. Furthermore, X, h, ZaZ and dZ are the corresponding incidence matrices with the appropriate dimension. The assumptions about the structure of (co)variance were as follows:A is the numerator additive relationship matrix, D is the dominance relationship matrix, and I is an identity matrix.A full dataset (step 2) was obtained considering all mares that had at least 3 registered breeding seasons, born after 1990, located in environmental units with at least 2 observations, and with both known parents. The threshold of 3 breeding seasons was chosen to have robust data to predict the LFR at the 6th event (1 or 2 events are too few to obtain robust prediction). The editing process followed the same rules as for the training dataset, and at the end of editing, LFR was calculated for 3033 mares applying either coefficients or equations methods for mares with 3, 4 or 5 registered breeding seasons. The final dataset consisted of a mixture of actual SE h2n = 3033) and for stallions (n = 77) that showed a minimum accuracy of 0.65  calculated with the formula reported in Mrode [Breeding values obtained for all different LFR expression were estimated for all animals in the pedigree. Standardized EBVs considering the mean EBV of recorded mares born in year 2000 and the genetic standard deviation of the trait were obtained. The year 2000 was chosen to set the genetic basis  in an intermediate birth year. Rank correlations analysis for mares with record  as respect to the use of GLM coefficients , i.e., tIn both cases, the arcsine transformation of LFR produced an increase in both the mean and standard deviation as compared to the linear LFR-C or LFR-E . The useHowever, all variables resulted normally distributed as confirmed by significant coefficients of the statistics produced . TherefoThe genetic analysis carried out on the 4 different expression of the LFR is reported in All LFR have shown a detectable genetic variation, although greater for the arcsine transformed LFR as compare to the linear LFR . However, also the herd, dominance and residual variance resulted greater for the arcsine transformed variables, leading to almost similar heritability estimates, i.e., about 0.24. The ratio of the dominance variance on the total phenotypic variance was similar in all the models and was on average 0.074. The inclusion of this non-additive genetic components allowed to slightly decrease the heritability of 0.02, as noticed in preliminary analysis . HoweverThe use of a linear value for LFR or its arcsine transformation did not affect the rank correlation both in groups of mares and in stallions accounting a homogeneous accuracy  and did A low fertility with respect to other livestock species is well known in horses and may have a negative impact on the profitability of breeding ,19. A maThe lifetime fertility rate of the present study has been developed in IHDH, but it can be easily transferred to other horse breeds by looking at their average reproductive career and using this value as a denominator of the ratio. The choice of this LFR as a trait is due to the fact that it allows one to consider the non-foaling events occurring in a mare career to find out the mare reproductive efficiency, as well as to predict the efficiency after just some years of a career (at least 3) thanks to the use of prediction equations. The phenomenon of a better fitting due to the LFR-E, i.e., linear regression, as compared to coefficients (LFR-C) is probably due to the different properties of linear equations as respects to the classification factors. Notwithstanding, the difference observed in this study between the coefficients and equation predictive methods resulted in general very small. Comparable criteria based on residuals analysis have been previously carried out in comparison to predictive methods or models. For example, Albertsdóttir et al.  used theThe comparison of genetic parameters obtained in this study with other literature is not easy because there are few specific studies on horses. The study of Sairanen et al.  has dealIn the present study, small amounts of data have been used in spite of the fact that all the available information at population level were accounted for. However, the estimated standard errors resulted generally low, allowing the deduction that our estimates were not affected by a reduced dataset size. Additionally, the consistency of the genetic parameters and their standard errors both as linear and arcsine transformation seem to indicate the absence of possible artefacts due to the distributional properties of the analyzed variables .In general, the observed genetic trend was positive but almost steady in both mares and stallions, due to the absence of any selection process for fertility in the breed ,25. The In conclusion, the LFR variable calculated at a specific endpoint using actual and estimated number of foals seems a feasible method to express lifetime reproductive success in IHDH mares. The use of estimates of foal production at 6th reproductive event obtained through equations  performed slightly better than the use of coefficients . No improvement in the estimates of genetic parameters and EBVs can be obtained by the arcsine transformation of the LFR. A significant genetic variation was detected for LFR, estimating a high heritability value for a fitness trait. In addition, a small but positive genetic trend was observed, although the breed has not been selected yet for LFR. In spite of a greater than zero heritability value, a careful use of the EBVs is suggested because the prediction leads to an overestimated accuracy for an individual with an incomplete reproductive career. Last, further analysis of the LFR is required for a final general validation of the projection method. Results of the study will allow for breeders to choose individuals with a greater reproductive efficiency for breeding purposes. This aspect is important in all horse breeds due to the low fertility of the species. Once the average length of the reproductive career has been defined, the LFR can also be applied in other breeds as a lifetime fertility trait available for selection.


Although abnormal cortical gyrification has been consistently reported in patients with schizophrenia, whether gyrification abnormalities reflect a genetic risk for the disorder remains unknown. This study investigated differences in cortical gyrification between unaffected relatives (URs) with high genetic loading for schizophrenia and healthy controls (HCs) to identify potential genetic vulnerability markers. A total of 50 URs of schizophrenia patients and 50 matched HCs underwent T1-weighted magnetic resonance imaging to compare whole-brain gyrification using the local gyrification index (lGI). Then, the lGI clusters showing significant differences were compared between the UR subgroups based on the number of first-degree relatives with schizophrenia to identify the effect of genetic loading on cortical gyrification changes. The URs exhibited significantly lower cortical gyrification than the HCs in clusters including medial parieto-occipital and cingulate regions comprising the bilateral precuneus, cuneus, pericalcarine, lingual, isthmus cingulate, and posterior cingulate gyri. Moreover, URs who had two or more first-degree relatives with schizophrenia showed greater gyrification reductions in these clusters than those who had at least one first-degree relative with schizophrenia. Our findings of reduced gyrification in URs, which are consistent with accumulated evidence of hypogyria observed in regions showing patient-control differences in previous studies, highlight that such hypogyria in posteromedial regions may serve as a genetic vulnerability marker and reflect early neurodevelopmental abnormalities resulting from a genetic risk for schizophrenia. Endophenotype strategies have been adopted in an effort to identify genetic underpinnings in the etiology of schizophrenia to allow early detection and prevention of the disease2. An endophenotype is a state-independent and measurable biological trait found in unaffected relatives (URs) of probands at a higher rate than in the general population. Abnormalities in gyrification, the developmental process of forming cortical sulcal and gyral patterns, have been proposed as a candidate structural endophenotype for schizophrenia3, and this hypothesis has been supported by its characteristics of high heritability4 and consistent observations in schizophrenia patients9. In particular, schizophrenia is widely accepted as a disease attributable to genetic factors that initiate abnormal neurodevelopmental processes before the onset of illness11. Given that the formation of cortical folding is largely determined during early neurodevelopment and strongly influenced by genetic factors14, investigating gyrification changes in URs may help identify a genetic vulnerability marker reflecting the early neurodevelopmental etiopathology of schizophrenia.Schizophrenia is highly heritable in that biological relatives of patients have an increased risk of developing the disease, and genetic factors constitute significant risk factors in schizophrenia15. While several inconsistent results have been reported17, reduced gyrification was the main finding in most lGI studies18, and both patients with schizophrenia and patients with first-episode psychosis (FEP) showed common widespread hypogyria in the medial parieto-occipital and cingulate regions19. However, with respect to gyrification abnormalities reported in URs of schizophrenia patients24, the results have been conflicting and inconclusive, and only one existing lGI study reported a negative finding , which might have reduced the probability of finding crucial cortical folding differences caused by genetic risk. Moreover, relatives of schizophrenia patients with different symptomatic and functional statuses have been included as ‘unaffected relatives of patients with schizophrenia’, complicating identification of the pure genetic effects of schizophrenia on gyrification differences that exclude the influence of other psychiatric symptoms. For instance, the Edinburgh High Risk Study reported that more than 40% of URs of schizophrenia patients have psychiatric symptoms26, suggesting the possibility of the inclusion of URs with prodromal symptoms in previous studies, which might have also influenced their general functioning. In particular, negative results may have been observed in an lGI study of first-degree relatives of psychotic disorder probands with schizophrenia, psychotic bipolar, and schizoaffective disorders because these first-degree relatives had relatively low genetic loading for schizophrenia and were recruited based on relationships with probands who had different psychotic illnesses19.The local gyrification index (lGI) method, which considers the intrinsic three-dimensional morphology of the cortical surface, has enabled accurate examination of regionally specific gyral abnormalities in the whole-brain19. As they were characterized by traits such as schizotypy, which is considered a genetic risk factor for schizophrenia, this result suggests that hypogyria in the cingulate may serve as a genetic vulnerability marker for schizophrenia. Furthermore, patients with 22q11 deletion syndrome (22q11DS), which reflects another major genetic liability for schizophrenia, also showed significantly decreased gyrification in a large medial cluster extending from the posterior cingulate to the precuneus regions, supporting the notion that hypogyria in the medial parietal lobe is possibly associated with a genetic risk for schizophrenia15. However, it remains unknown whether reduced gyrification represents an endophenotype specifically for schizophrenia. To our knowledge, there has been no direct investigation of whole-brain gyrification in asymptomatic URs with high genetic loading for schizophrenia, which could increase the probability of detecting pure effects of genetic risk for schizophrenia on cortical gyrification changes.However, URs with axis II cluster A personality disorders exhibited reduced gyrification with a trend approaching statistical significance in regions of hypogyria evidenced in their probands19, we hypothesized that our URs of schizophrenia patients would exhibit hypogyrification patterns in the medial parieto-occipital and cingulate regions compared to HCs as a genetic vulnerability marker. Furthermore, to strengthen the influence of genetic liability on reduced cortical folding, URs were divided into two subgroups according to the number of first-degree relatives with schizophrenia; in this exploratory analysis, we assessed whether URs with higher genetic loading for schizophrenia showed greater hypogyria in the lGI clusters with significant differences between URs and HCs.In this study, we compared cortical gyrification in asymptomatic, high-functioning URs with high genetic loading for schizophrenia and healthy controls (HCs) using the lGI to identify potential endophenotypes for schizophrenia. Based on previous lGI studies reporting reduced gyrification in regions of case-control differences in schizophrenia patients and their relatives28: (1) the “multiplex” group included URs who had at least two first-degree relatives or monozygotic twins with schizophrenia (n = 9) and (2) the “simplex” group included URs who had only one first-degree relative with schizophrenia and at least one other affected second- to third-degree relative (n = 41). No significant group differences were found between the multiplex and simplex UR subgroups except for the sex ratio .The demographic and clinical backgrounds of the groups are presented in Table p < 0.001) had a peak vertex located within the lingual gyrus covering portions of the precuneus, cuneus, pericalcarine, lingual, isthmus cingulate, parahippocampal, and posterior cingulate gyri. The right cluster showing a significantly reduced lGI (p < 0.001) had a peak vertex within the precuneus gyrus comprising portions of the precuneus, cuneus, pericalcarine, lingual, isthmus cingulate, and posterior cingulate gyri. No region had a significantly increased lGI in the URs compared to the HCs  and right precuneus cluster  were demonstrated in the URs compared to the HCs. The results are shown as box plots in Fig. Additionally, significantly reduced mean lGI values in the left lingual cluster . The right precuneus cluster also showed a tendency toward decreased gyrification in the multiplex group , although the difference was not statistically significant. The results are shown as box plots in Fig. To demonstrate the effect of genetic loading on reduced gyrification in the URs as an exploratory analysis, the comparison between the two subgroups of URs in terms of the extracted mean lGI values from the clusters showing significant differences between the URs and HCs was conducted. Compared to the simplex group, the multiplex group demonstrated a significantly reduced mean lGI in the left lingual cluster  connectivity in the precuneus and anterior cingulate cortex (ACC) than the simplex group, suggesting that dysfunction in these regions is related to genetic risk for schizophrenia27. Consistently, our findings demonstrated the effects of genetic loading for schizophrenia on the amount of reduced cortical folding in the posteromedial cortex, which supports the notion that the precuneus/posterior cingulate regions reflect one of the most genetically vulnerable regions for schizophrenia.Notably, our URs with two or more first-degree relatives with schizophrenia  showed greater hypogyrification patterns in the medial parieto-occipital and cingulate regions than those with one first-degree relative and at least one second- to third-degree relative with schizophrenia . It has been hypothesized that the genetic risk for schizophrenia increases proportionally with familial closeness and the number of affected probands19. However, previous studies of URs of schizophrenia patients have reported conflicting results regarding gyrification patterns, mainly due to the heterogeneous characteristics of URs and different methods used to measure gyrification. In particular, the previous work by Nanda et al. (2014), one of the most prominent lGI studies in this line, examined gyrification in first-degree relatives of patients with different psychotic disorders. However, they treated the relatives of patients with schizophrenia, schizoaffective disorder, and psychotic bipolar disorder as an overall group in the same sample to examine whether gyrification qualifies as an endophenotype broadly marking psychosis liability. Furthermore, this group may have included relatives with relatively low genetic loading for schizophrenia , which might also have reduced the probability of finding significant gyrification differences caused by genetic risk for schizophrenia, and thereby resulted in a negative finding. To investigate whether reduced gyrification represents an endophenotype specifically for schizophrenia, URs with high genetic loading for schizophrenia were recruited in our study by including URs who had at least one first-degree relative with schizophrenia and one or more other affected first- to third-degree relatives. In addition, some of the URs in previous studies may have had certain psychiatric symptoms or prodromal syndromes that might also have influenced their general functioning, making it difficult to identify a pure effect of genetic risk for schizophrenia on changes in cortical folding. Compared to these previous studies, our findings were further bolstered by applying strict inclusion criteria for subjects in that URs having prodromal symptoms were screened out by measuring Structured Interview for Prodromal Syndromes (SIPS) scores, and subjects who had any general psychopathology were excluded by assessing Brief Psychiatric Rating Scale (BPRS), Hamilton Rating Scale for Anxiety (HAMA), and Hamilton Rating Scale for Depression (HAMD) scores to ensure they were all asymptomatic; Global Assessment of Functioning (GAF) scores were also evaluated to confirm that all URs were well functioning. Taken together, these analyses allowed us to investigate the pure effect of shared genetic factors of schizophrenia on cortical gyrification changes as a biological trait that was free from the aforementioned confounding factors, which is consistently evidenced in other genetic models for schizophrenia19.Abnormal gyrification has been repeatedly suggested as a candidate endophenotype for schizophrenia11. Longitudinal studies of subjects at genetic high risk have indicated that progressive brain structural alterations predate the onset of psychosis33, suggesting that genetic vulnerability to schizophrenia might affect cortical changes during neurodevelopment. In fact, prenatal genetic insults are known to influence embryonic and early neurodevelopmental processes, such as neuronal proliferation and differentiation, synaptic formation, and myelination, leading to prolonged alterations in cortical structure and connectivity in schizophrenia34. As the formation of gyral and sulcal patterns occurs mainly in early neurodevelopment and is strongly influenced by genetic factors during this period13, investigating gyrification differences in asymptomatic URs with high genetic loading may help reveal the effects of genetic risk on the neurodevelopmental etiopathology of schizophrenia. Because cortical gyrification mainly increases before and shortly after birth and undergoes minor decreases until becoming relatively stable in early childhood14, reduced gyrification in URs relative to HCs may result from deficits in an earlier phase of increasing cortical maturation or an excessive decrease in later neurodevelopmental processes. Furthermore, the alterations in gyrification may reflect underlying deficits in white matter integrity during cortical maturation37, which are known to be disrupted in both schizophrenia patients and their URs39.Schizophrenia is widely accepted as a neurodevelopmental disease in that deficits of the brain are determined by genetic factors early in life but are not fully established until adolescenceThere are several limitations of the present study that should be considered. The cross-sectional nature of the study makes it difficult to investigate whether abnormal cortical folding increases the possibility of developing schizophrenia. Thus, longitudinal MRI analyses in URs of patients with schizophrenia would be required to identify alterations in neurodevelopmental trajectories and confirm the role of gyrification deficits as a vulnerability marker for schizophrenia. Moreover, the sample size for the multiplex UR subgroup was relatively small. Thus, our findings of the effect of the level of genetic loading for schizophrenia on the degree of hypogyria should be considered exploratory and further tested in a larger cohort of both simplex and multiplex UR subgroups.In conclusion, our findings of asymptomatic, high-functioning URs with high genetic loading for schizophrenia demonstrate that genetic vulnerability for schizophrenia is characterized by reduced gyrification, and, in particular, the posteromedial regions may serve as the most vulnerable regions to genetic liability in schizophrenia. Taken together with previous findings in patients with schizophrenia, our findings confirm that such hypogyria may represent a trait characteristic of schizophrenia and reflect early neurodevelopmental disturbances resulting from a genetic risk for schizophrenia. Our findings of the relationship between higher genetic loading for schizophrenia and greater cortical gyrification reduction further support the possibility of hypogyria as a candidate structural endophenotype that may contribute to the understanding of the genetic underpinnings of a neurobiological etiology in schizophrenia. Future investigations of genes involved in the neurodevelopmental mechanisms of cortical gyrification associated with increased risk for developing schizophrenia will identify the genetic influence on the neurodevelopmental etiopathology of schizophrenia.40, a center for prospective, longitudinal investigation of people at high risk for schizophrenia, and from the inpatient and outpatient clinics of the Department of Psychiatry at Seoul National University Hospital. The URs were defined as asymptomatic subjects with high genetic risk who had at least one first-degree relative with schizophrenia and one or more other affected first- to third-degree relatives. This standard has the benefit of including URs with high genetic loading and excluding relatives of patients with sporadic schizophrenia. The probands of URs were assessed for schizophrenia according to the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) by trained psychiatrists. The family history of psychiatric disorders and the degree of genetic loading for schizophrenia were evaluated in URs using the Family Interview for Genetic Studies41. To assess clinical status upon admission into the study, the Structured Interview for Prodromal Syndromes (SIPS)42 was used to screen for prodromal psychotic symptoms. URs with SIPS scores exceeding certain criteria for clinical high risk (CHR) for psychosis (any P1–P5 ≥ 3) were excluded from this study. The URs were also administered the Brief Psychiatric Rating Scale (BPRS) to measure general psychopathologic symptom severity and the Hamilton Rating Scale for Depression (HAMD)43 and the Hamilton Rating Scale for Anxiety (HAMA)44 to assess the severity of depression and anxiety, respectively. In addition, the Global Assessment of Functioning (GAF) scale was also assessed to evaluate the overall functioning of subjects.Fifty URs of schizophrenia patients were recruited from the Seoul Youth ClinicFifty HCs matched for age, sex, handedness, intelligence quotient (IQ), and years of education were recruited via internet advertisements and screened for the presence of psychiatric disorders or symptoms using the Structured Clinical Interview for DSM-IV–Non-Patient Version (SCID-NP). Potential HCs were excluded if they reported past or current axis-I diagnoses and had any first- to third-degree biological relatives with a lifetime history of any major psychiatric disorders.The IQ of all participants was measured with the Korean version of the Wechsler Adult Intelligence Scale (K-WAIS) to estimate intelligence. The exclusion criteria for all participants included a lifetime diagnosis of a psychotic disorder, substance abuse or dependence, neurological disease or head injury, evidence of any other mental illness, or intellectual disability (IQ < 70). The present study was also approved by the Institutional Review Board at Seoul National University Hospital, and written informed consent was obtained from all subjects and from the parents of subjects under 18 years of age after the procedures had been fully explained.3; field of view, 250 mm; flip angle, 9°; and 208 slices. To ensure quality control, the acquired MRI images were visually inspected for any artifacts or malformation of brain structures.The subjects were scanned, and T1-weighted (T1) images were acquired using a Siemens 3T MAGNETOM Trio MR scanner . For the T1 images, a three-dimensional (3D) magnetization-prepared rapid acquisition gradient-echo (MPRAGE) sequence and the following imaging parameters were used: repetition time, 1670 ms; echo time (TE), 1.89 ms; voxel size, 1.0 × 0.98 × 0.98 mmhttp://surfer.nmr.mgh.harvard.edu/) according to the standard and automatic reconstruction algorithm45. This processing stream consists of automated transformation to Talairach space, normalization of intensity, removal of nonbrain tissue, and segmentation of gray/white matter tissue resulting in a white mesh and a pial mesh composed of approximately 150,000 vertices for each hemisphere.The preprocessing of T1 images was performed using FreeSurfer version 5.3.0  on the smoothed outer surface and of their corresponding paired circular ROIs on the pial surface. The lGI value at each vertex of the cortical surface was computed as the ratio between the smoothed outer surface and buried cortex resulting in the measurement of individual maps.p < 0.05, and clusters were corrected for multiple comparisons by Monte Carlo simulation. For clusters showing significant group differences, mean lGI values were extracted for each participant, and independent t-tests were performed to compare gyrification patterns between the groups.Data were analyzed using Statistical Package for Social Sciences (IBM SPSS Version 22). The demographic and clinical characteristics between URs and HCs were examined with independent t-tests or χ2 tests. A whole-brain lGI comparison between groups was performed with FreeSurfer. The lGI values were mapped on the average template subject (fsaverage) for each subject to perform the contrasts of the vertexwise analysis in Query Design Estimate Contrast (QDEC). A general linear model (GLM) for surface-based group analyses was conducted to assess regional group differences in lGI at each vertex for the right and left hemispheres separately while controlling for age and sex. A smoothing Gaussian kernel of 5-mm full-width at half-maximum (FWHM) was applied to the maps. The significant cluster-forming threshold was set to To further investigate the effect of genetic loading on clusters showing significant differences between the URs and HCs, the URs were divided into two subgroups based on the number of their first-degree relatives with schizophrenia. The demographic and clinical variables and the mean lGI values were compared between the two subgroups of URs using nonparametric Mann-Whitney U tests or χ2 tests due to the small sample size.Further information on research design is available in the Reporting Summary


Maintaining a living plant collection is the most common method of ex situ conservation for plant species that cannot be seed banked . Viability of living collections, and their value for future conservation efforts, can be limited without coordinated efforts to track and manage individuals across institutions. Using a pedigree‐focused approach, the zoological community has established an inter‐institutional infrastructure to support long‐term viability of captive animal populations. We assessed the ability of this coordinated metacollection infrastructure to support the conservation of 4 plant species curated in living collections at multiple botanic gardens around the world. Limitations in current practices include the inability to compile, share, and analyze plant collections data at the individual level, as well as difficulty in tracking original provenance of ex situ material. The coordinated metacollection framework used by zoos can be adopted by the botanical community to improve conservation outcomes by minimizing the loss of genetic diversity in collections. We suggest actions to improve ex situ conservation of exceptional plant species, including developing a central database to aggregate data and track unique individuals of priority threatened species among institutions and adapting a pedigree‐based population management tool that incorporates life‐history aspects unique to plants. If approached collaboratively across regional, national, and global scales, these actions could transform ex situ conservation of threatened plant species. Brighamia insignis.Article Impact Statement: Using zoo‐style studbooks and pedigree management will be transformative for the conservation of exceptional plants like  More than 1 million plant and animal species are threatened, prompting calls for transformative changes to reverse this trend and conserve species for the public good IPBES . For manAs many as half of threatened plant species are exceptional; that is, they produce seeds that cannot tolerate traditional storage conditions  or they produce few to no seeds Pence . ConsequeN), mitigate genetic drift and inbreeding, and equalize family size to decrease the likelihood of artificial selection . We also analyzed these data in the zoological population management software (PMx)  is a critical aspect of developing pedigrees. Although this has been recommended within the plant conservation community  PlantSearch database, they currently do not include accession‐level and plant‐level data necessary for pedigree management. The BGCI uses PlantSearch data as the foundation for reporting progress toward the ex situ‐focused Target 8 of the Global Strategy for Plant Conservation. The BGCI and partners are working to expand this widely used tool to develop an optimal studbook‐style resource to support pedigree management of living plant collections. They have explored potential pedigree data models, are working to develop an aggregation tool for pedigree and genetic data as part of the PlantSearch database for prioritized species of concern, and anticipate launching a functional pedigree module for these taxa soon.B. insignis we identified, 3 have no living descendants in the core conservation collection at NTBG. PMx identifies such underrepresented individuals so that their propagation can be increased. Within NTBG's collection, at least 10% of individuals showed equal representation of similar founders and therefore could be removed from the population with minimal impact to the genetic diversity of the managed population. Similarly, in the case of Z. lucayana, a dioecious species, the entire conservation collection at Montgomery Botanical Center  was derived from seed collected from 16 maternal lines. Plants from 1 of 3 subpopulations were less representative of the species’ genetic diversity  showed that most of the captive population was derived from 3 overly fecund individuals  populations. However, many new founders represent half‐siblings; consequently, some lineages are now overrepresented . Similarly, results of a recent study of B. insignis at NTBG show that pollen viability has declined in many plants, which may be evidence of inbreeding depression in this collection . Since the first expedition, there have been at least 20 documented introductions of new genetic material, subsequently distributed to 140 institutions (BGCI PlantSearch database). This history suggests potentially high genetic diversity within the botanical garden population, which could minimize the need for additional wild collection. Alternatively, most living plants could be descendants of only a few highly fecund individuals. Unfortunately, limited provenance information is available for most collections. Many records of wild origin are unreliable, and genetic data will be required to identify unique lineages and generate a more accurate pedigree.The pedigree approach traces all material back to the original founders to ensure that genetic diversity of those founders is maintained and represented equally throughout the metacollection. Tracking founders is difficult, especially if the species has been in collections for hundreds of years, and often requires a molecular genetic approach. The zoo community also employs genetic techniques when needed to unravel uncertain pedigree history and resolve relationships among founders (Ivy & Lacy https://scti.tools) is developing a version of PMx with improved utility for plants called PMexceptional. This new version will also benefit many animal taxa with unusual life histories or unknown paternity or that are managed in groups, including corals, many invertebrates, some fishes, and other vertebrates.Using the zoological community's population management software, PMx  to maintain the pedigree and the core conservation collection. They are also responsible for working with consortium members and safe sites, a dedicated group of other gardens and partners that acquire additional unique and duplicate material, to ensure those individuals are incorporated into the metacollection's pedigree. This trial will allow us to test the new pedigree module in PlantSearch and PMexceptional, to modify them if needed, and to develop training materials to be rolled out to the larger community. This will also facilitate the development of a core set of ex situ collection standards and a set of priority data fields to support collecting in the wild, plant records management, and, ultimately, species conservation applications.The number of threatened exceptional plant taxa that may benefit from this approach could surpass 50,000, presenting yet another challenge Fig. . IdentifWe make the case for considering pedigree‐based management as a way to maximize the conservation value of living plant collections via studbook‐style record keeping and population management software, such as PMx. The genetic management focus of most zoo‐based breeding programs has contributed to the long‐term retention of gene diversity, helped equalize the contributions of founders, and minimized the mean kinship within scientifically managed captive populations. To achieve this, managers use several tools to compile, share, and analyze captive population data Flesness , followiA key to reference codes in Table 1 (Appendix S1) is available online. The authors are solely responsible for the content and functionality of these materials. Queries  should be directed to the corresponding author.Click here for additional data file.