Juncus effusus. We asked whether trait expression reveals signatures of adaptive differentiation by comparing genetic differentiation in quantitative traits and neutral molecular markers (QST—FST comparisons) and relating trait variation to soil conditions of the plant’s origin. Molecular analyses showed that samples clustered into three very distinct genetic lineages with strong population differentiation within and among lineages. Differentiation for quantitative traits was substantial but did not exceed neutral expectations when compared across treatments or for each treatment and lineage separately. However, variation in trait expression could be explained by local soil environmental conditions of sample origin, e.g. for aboveground carbon-to-nitrogen (C:N) ratios, suggesting adaptive differentiation to contribute to trait expression even at regional level.Wetland ecosystems are known to mitigate high nutrient loadings and thus can improve water quality and prevent potential biodiversity loss caused by eutrophication. Plant traits affect wetland processes directly through effects on accumulation or metabolization of substances, and indirectly by affecting microbial transformation processes in the soil. Understanding the causes and consequences of intraspecific variation in plant functional traits and associated ecosystem processes can aid applied ecological approaches such as wetland restoration and construction. Here we investigated molecular variation and phenotypic variation in response to three levels of nitrogen availability for a regional set of populations of the common wetland plant  Wetland ecosystems are known to mitigate high nutrient loadings and thus can improve water quality and prevent potential biodiversity loss caused by eutrophication . The potUnderstanding the causes and consequences of this intraspecific variation is not only a fundamental challenge for ecological research but it can also aid applied approaches in wetland restoration and construction because it is increasingly recognized as important driver of ecosystem functioning , 17. AcrDetecting the signature of adaptive processes in trait variation and differentiation is a challenging task because it requires the partitioning of the observed phenotypic trait variance into genetically and environmentally based variance. In order to minimize the latter, common garden studies are often used to assess and compare trait variation across multiple provenances or genotypes. However, the expressed genetic contributions to trait variance may vary across different environments , limitinQST) compared to neutral genetic differentiation (FST) is interpreted as a signature of directional selection whereas the opposite case, i.e. QST < FST, indicates stabilizing selection. This approach has been employed by an increasing number of studies finding evidence for adaptation to local environments in a wide range of organisms [QST—FST approach can detect signatures of adaptation but not the environmental regimes causing trait divergence and the observed differentiation patterns. Consequently, another more qualitative approach to detect adaptive differentiation is to test for clines in mean phenotypic trait expression along environmental variables [The impact of adaptive processes to genetically based phenotypic trait variation expressed among populations can be assessed by the comparison between such differentiation patterns and neutral expectations based on neutral molecular loci , 23. Larrganisms . The QSTariables , 26.Juncus effusus, looking for signatures of adaptation. Juncus effusus, the ‘Common Rush’ is a perennial, self-compatible herb widely distributed in temperate wetland ecosystems [J. effusus in Germany consists of sympatrically occurring, morphologically rather similar, but genetically highly differentiated, lineages [Juncus effusus populations partially explained by environmental conditions of the source location suggesting a contribution of adaptive divergence [J. effusus and considering possible within-species lineage differentiation we ask:J. effusus and do observed patterns differ among lineages?What is the relative contribution of neutral and selective processes in quantitative trait differentiation of Is there a clinal differentiation of trait expression along soil environmental conditions at the regional scale, indicating adaptive responses?Here we study quantitative trait expression and molecular genetic differentiation in response to differing nitrogen availability among a regional set of populations of the common wetland plant osystems , 28. It osystems , nitrogeosystems , 31 and osystems . A previlineages . A quantvergence . To furtJuncus effusus is not endangered or a protected species in Germany and no specific permissions for leaf and seed sampling were required. Spatial distance among populations ranged from 1.1 km (PWA—PWI) to 222.5 km (ZM—JEM). At each location , seed material was sampled from 4–6 individuals arbitrarily selected across the whole site, resulting in a total of 111 maternal individuals. Leaf material was sampled from the maternal and additional individuals resulting in a larger sample per location  for molecular genetic analysis. Sampled leaf and seed material was dried with silica gel and stored in paper bags at room temperature. For each population and site, a mean seed mass was estimated by measuring length and width of seeds from 3–5 seed families by optical scanning with high resolution and applying image analysis implemented in WinSeedle  and calculating a volume assuming an ellipsoid shape of the seeds. Topsoil data for each location was taken from the LUCAS topsoil dataset [Between 2013 and 2014, seed families, i.e. offspring from a single maternal individual, were collected at 21 locations across central Germany and covering a wide range of habitats.  dataset . In orde2O, 0.8 μl QIAGEN 5 × Q-Solution and 0.2 μM of each forward and reverse primer. PCR conditions were as follows: 95 °C for 15 min; followed by 35 cycles of 95 °C for 30 s; 58 °C for 40 s and 72 °C for 1 min, followed by 72 °C for 15 min. The amplification with M13R or CAG tailed primers was performed in a 5 μl reaction mixture that contained 1.0 μl genomic DNA (~ 20 ng / μl), 2.5 μl QIAGEN Multiplex Mastermix, 0.05 μM of the M13R or CAG tagged primer, 0.25 μM of forward and reverse primer mix and 0.25 μM of fluorescently labelled M13R or CAG primer. A touchdown PCR was run as follows: 95 °C for 15 min; followed by 20 cycles , followed by 20 cycles  and close with 72 °C for 10 min. Fragments were run on an ABI 3130  with size standard LIZ 500 (Applied Biosystems) and scoring was done using GeneMapper 5 (Applied Biosystems).Genomic DNA was extracted from dried leaves using the DNeasy 96 Plant Kit  according to manufacturer’s protocol and quality was checked by gel electrophoresis and NanoDrop Spectrophotometer . Genotyping was done using 14 microsatellite loci  previously described by Michalski and Durka , Michals3-—N and 0.45 NH4+—N mg / kg detected by flow-rate injection analyzer FIAstar™ 5000 Analyzer . All individuals were constantly watered and after one month of transplanting plants received either no or additional fertilizer of weekly doses of dissolved ammonium nitrate (NH4NO3), equivalent to a total of 72 kg N ha-1 yr-1 and 153 kg N ha-1 yr-1, respectively. Thus, the fertilization treatment consistent of three levels: (1) a low nitrogen load with no additional nitrogen (T0), (2) a moderate nitrogen load (T70) and (3) a high nitrogen load (T150). Because of a low initial seed number and insufficient germination- and establishment rate for a number of populations, not enough offspring could be raised to ensure a fully balanced design . In December 2014, seeds were germinated in quickpots  filled with a 2:1 (vol/vol) mixture of commercial soil (Fruhstorfer soil type P) and sand using a climate chamber with a 12 h photoperiod and mean day and night temperature of 30 °C and 20 °C, respectively. In April 2015, seedlings of approximately the same size were transplanted into 3 l pots (17 cm diameter x 18.5 cm deep) filled with a mixture of commercial soil and sand . Sample extracts (N = 10) from 10 g air-dried soil mixture suspended in 40 ml 1 M potassium chloride gave average concentrations of 49.3 mg / kg NOd design . Pots westart) / (H*S)start). Ten weeks after initiating the treatment, all plants were harvested. For each individual, the weight of the above- (AGBM) and belowground biomass (BGBM) was determined after drying at 60 °C for 48 h. A root to shoot ratio was calculated as the quotient between BGBM and AGBM. To assess the biomass quality, we calculated leaf dry matter content (LDMC) as dry divided by fresh mass.At the start of the experiment, initial plant biomass was indirectly assessed as plant height (H), i.e. the mean height of the two longest leaves, times the number of stems (S). We repeated this measurement at the end of experiment to estimate a relative growth rate (RGR = (H*S)end − (H*S)2 solution and analyzed using a pH Meter . Root porosity was estimated using the microbalance method described in Visser and Bögemann [J. effusus roots given in Visser and Bögemann [Two individuals per seed family and treatment were randomly selected for chemical analyses. A representative fraction (~ 10 mg) of above- and belowground biomass was milled and C and N concentrations were measured using a CHNS/O elemental analyzer . From these data, we calculated C:N ratios for above- (AG-C:N) and belowground biomass (BG-C:N) as well as total aboveground N accumulation (AG-N) as N content per g leaf tissue multiplied by the dry weight. The same subset of individuals was also used for pH measurement of the pot soil using the method described by Hoffmann . Air-driBögemann . Eight wBögemann . The thrJ. effusus individuals  computed in GenAlEx  to visuaividuals . Populat among pHe [FST, , VersionTo assess the quantitative trait expression in response to treatment and lineage, we used a linear mixed effect model implemented in package ‘lme4’  for R 4 explainiQST = VAP / (VAP + 2 VWP) [AP) and within (VWP) populations, with the latter given by the seed family component. Variance components were derived from generalized linear mixed models using a Bayesian framework implemented in the package ‘MCMCglmm’ [QST estimation as they allow for flexible experimental designs and return mostly unbiased precision estimates [QST for all populations using models including treatment as fixed, and population and seed family as random effects, and (2) treatment-specific QST values with only population and seed family as random effects. And (3) we calculated lineage-specific across-treatment QST values. For (3), QST values were calculated for only two out of three genetic lineages found (see below), as sample size for the third lineage was very low (see Each quantitative trait genetic differentiation among populations was estimated as + 2 VWP) , by partCMCglmm’  for R [4CMCglmm’ . Bayesiastimates , 53. For low see . CredibiQST) differed from neutral expectations, we followed the approach of Whitlock and Guillaume [QST and a simulation expected under neutrality (QnST). A significant positive or negative deviation from zero directly indicates directional or stabilizing selection, respectively. The distribution of QnST values was calculated by simulating a neutral among-population variance 1000 times as VnP = FST * (2 * VA / (1 − FST)) and then multiplying by a factor r / (npop − 1) with npop the number of populations considered and r being a random number drawn from a chi2—distribution with npop − 1 degrees of freedom, to simulate the sampling distribution around this expectation. For each simulation, the FST value for the respective set of populations was used and a VA value was sampled from the posterior distribution of the models described above. QnST values were then computed using the observed within-population variance. The test statistic was calculated as the difference between 1000 QST values drawn from the posterior distribution of the model and 1000 simulated QnST values and considered to be significant if the 95% credible interval did not include zero.To test whether quantitative genetic differentiation in mean traits (uillaume  by reporHe) and first axis scores of soil parameters. (B) Variation in pairwise trait differentiation (QijST) between populations was explained by pairwise environmental distances jointly considering neutral genetic differentiation (FijST) using a multiple matrix regression approach [In order to assess whether variability and differentiation in trait expression relates to soil environmental characteristics of population origin, we performed several analyses. (A) Soil environmental parameters were reduced by applying a principal component analysis (PCA) on standardized soil parameters using function prcomp from ‘stats’ package for R . Then, tapproach . EnvironHe = 0.187, SD = 0.089; FST = 0.66; SD = 0.120). Bayesian cluster analysis revealed a single most likely solution with samples forming three distinct genetic lineages  subsequently described as lineages Eff1, Eff2 and Eff3, with the latter represented by individuals of only three populations  and LDMC , whereas for most other traits differentiation patterns were more similar , which mainly represented sand and clay content and cation exchange capacity.Overall mean trait expression at the population level varied with soil environmental variation expressed as PCA axis 1 for above- and belowground C:N ratio , number Pairwise genetic trait differentiation across treatments increased significantly with distance in soil environment for aboveground C:N, whereas differentiation in plant height and number of stems mirrored neutral genetic differentiation . A similJuncus effusus consists of multiple, genetically well separated lineages that partly co-occur at the same location but show only limited differences in trait expression. It has been suggested, that the co-existence of these genotypic lineages is a result of an allopatric origin with secondary contact and a divergence in flowering phenology could contribute to the maintenance of sympatric lineages [Supporting recent findings by Michalski and Durka , our reslineages .J. effusus, which can be expected from previous studies investigating the effect of N fertilization [J. effusus showed a weak but significantly higher LDMC under the highest N availability compared to the other treatment conditions. In general, LDMC correlates negatively with relative growth rate [J. effusus did not significantly decrease with N addition . Often,wth rate . N limitwth rate . Higher wth rate  and thuswth rate , 65. Howichalski ).J. effusus. The species is an efficient pioneer species and colonizer due to a fast growth rate [We found high differentiation among populations and lineages at molecular and quantitative trait levels, confirming earlier results , 34, whiwth rate , high sewth rate  and a prwth rate , 68. Botwth rate .QST-estimates for J. effusus under N addition were exceptionally high for some traits such as aboveground C:N (T70: QST = 0.886) and belowground C:N . Similarly, populations of the predominantly selfing Senecio vulgaris showed a strong degree of quantitative trait divergence for growth and life history traits . Neutral differentiation and stabilizing selection as causes for quantitative trait differentiation is often found for rare species with small population sizes and a high level of habitat fragmentation and isolation e.g. Liatris scariosa [Primula sieboldii [Psilopeganum sinense [J. effusus is rather broad, questioning the importance of stabilizing selection for trait expression in this species.Indeed, l traits . A meta-ntiation . In our  et al., . Howeverscariosa , Primulaieboldii  and Psil sinense . It has  sinense . HoweverQST can be biased, possibly limiting the conclusions that can be drawn from QST—FST comparisons. First, maternal effects may affect trait expression in general and can bias QST estimates downwards [QST estimates possibly resulting in QST < FST outcomes without stabilizing selection. Whereas dominance effects for inbred species such as J. effusus might be of less importance [QST—FST approach. However, for our study this bias is not very likely as it would lead to false positive signatures of adaptive divergence only. The use of microsatellite data for QST—FST comparisons has been criticized because the potentially large number of alleles may result in downwardly biased FST estimates [FST estimate (FST = 0.823 (SNP) vs. FST = 0.660 (Microsatellite); Born, unpublished data). This marker bias could query signatures of diversifying selection, which, however, were not found here.Quantitative trait differentiation estimated by ownwards , which hownwards . In our portance , epistatstimates . InsteadGenetically based phenotypic trait variation along environmental or geographical clines has been reported for many plant species –82 and iJ. effusus [cf. However, these correlations were not consistently found for these traits when data of the different N concentrations applied was analyzed separately, suggesting that in different environments the (genetic) basis for trait expression can differ substantially . In the usus cf. , but areJ. effusus which would be indicated by significant interactions between lineage and soil environmental conditions of origin when explaining population mean traits. This would support the idea that the lineages within J. effusus are the result of neutral divergence following e.g. variance [We found only little and inconsistent evidence for selective mechanisms to vary between the lineages within variance .J. effusus resulted in a very pronounced neutral genetic differentiation with few differences between lineages within the species. Adaptive trait differentiation in response to soil environmental conditions might still be present as indicated by significant trait clines but could not be detected by QST—FST comparisons.In summary, increased effects of genetic drift and limited gene flow for the selfing colonizer S1 Appendix(XLSX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S1 FigCircles represent individuals grouped in three distinct clusters indicated by colors .(DOCX)Click here for additional data file.S2 Fig2), organic carbon (OC), potassium- (K), phosphorus- (P), carbonate- (CACO3) and total nitrogen (N) content and cation exchange capacity (CEC)), while colored symbols represent populations and their underlying genotype  in the environmental space.Grey arrows indicate loadings of each soil variable on the two axes Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.


Fusarium head blight (FHB), a threatening disease of cereals worldwide, is also spreading in unusual environments, where bread wheat (BW) and durum wheat (DW) are largely cultivated. The scarcity of efficient resistance sources within adapted germplasm is particularly alarming for DW, mainly utilized for human consumption, which is therefore at high risk of kernel contamination by health-dangerous mycotoxins . To cope with this scenario, we looked outside the wheat primary gene pool and recently transferred an exceptionally effective FHB resistance QTL (Fhb-7EL) from Thinopyrum elongatum 7EL chromosome arm onto a Thinopyrum ponticum 7el1L arm segment, containing additional valuable genes (including Lr19 for leaf rust resistance and Yp for yellow pigment content), distally inserted onto 7DL of BW lines. Two such lines were crossed with two previously developed DW-Th. ponticum recombinants, having 7el1L distal portions on 7AL arms. Genomic in situ hybridization (GISH) analysis showed homologous pairing, which is enabled by 7el1L segments common to the BW and DW recombinant chromosomes, to occur with 42-78% frequency, depending on the shared 7el1L amount. Aided by 7EL/7el1L-linked markers, 7EL+7el1L tetraploid recombinant types were isolated in BC1 progenies to DW of all cross combinations. Homozygous 7EL+7el1L recombinant plants and null segregates selected in BC2F2 progenies were challenged by Fusarium graminearum spike inoculation to verify the Fhb-7EL efficacy in DW. Infection outcomes confirmed previous observations in BW, with >90% reduction of disease severity associated with Fhb-7EL presence vs. its absence. The same differential effect was detected on seed set and weight of inoculated spikes, with genotypes lacking Fhb-7EL having ∼80% reduction compared with unaffected values of Fhb-7EL carriers. In parallel, DON content in flour extracts of resistant recombinants averaged 0.67 ppm, a value >800 times lower than that of susceptible controls. Furthermore, as observed in BW, the same Fhb-7EL also provided the novel DW recombinants with resistance to Fusarium crown rot (∼60% symptom reduction) as from seedling infection with Fusarium culmorum. Through alien segment stacking, we succeeded in equipping DW with a very effective barrier against different Fusarium diseases and other positive attributes for crop security and safety.Prompted by recent changes in climate trends, cropping areas, and management practices,  Triticum durum Desf., 2n = 4x = 28, genome AABB) is the 10th most important crop in the world  and Fusarium crown rot (FCR). Environments where humid and warm conditions occur around the flowering stage are typically prone to FHB, while FCR is prevalent under drier conditions. On a world scale, FHB is predominantly caused by F. graminearum, while F. culmorum and F. pseudograminearum are the main agents of FCR , breeding for FHB resistance has so far been centered mostly on a large-effect QTL, namely Fhb1, located on the 3BS chromosome arm of the bread wheat Chinese cultivar Sumai 3 and its derivatives (T. spelta), is being exploited for FCR resistance breeding  and for yield-contributing traits , as well as allosyndetic  homoeologous chromosome pairing and recombination. This represents a drawback, which limits recovery and affects background stability of target allosyndetic recombinants , present in a 7E(7D) substitution line and in the 7DS·7DL-7el1L of the T4 translocation line, respectively. Pyramiding of the positive traits controlled by 7el1L genes/QTL (see above) with the 7EL-linked Fusarium resistance QTL (named Fhb-7EL) was thus achieved, with Fhb-7EL being shown to map more distally than the 7el1L genes  locus  content , spontaneous pairing between the 7EL arm from the 7E(7D) substitution line and the 7el1L segments of R5 or R112 was expected to be sharply reduced compared with that using the T4 translocation line, as observed in the aforementioned 7el1L+7el2L pyramiding  to verify the ability and extent of the Fhb-7EL QTL in conferring FHB and FCR resistances once stably introgressed into the new genomic context of durum wheat; and (iii) to provide a preliminary assessment of stability and performance of novel recombinant types, in relation to their exploitation in breeding.The objectives of the work described here were (i) to engineer the R5 and R112 7elTh. elongatum Fhb-7EL QTL into durum wheat were two bread wheat recombinant lines, named R74-10 and R69-9  were analyzed by suitable polymorphic markers , were self-pollinated. BC2F2 offsprings were then genotyped, and the resulting homozygous carriers (HOM+) and non-carriers (HOM–) of the specific 7EL+7el1L combination, as well as their self-fertilized progeny, were used in various comparative tests. In these, depending on the type of experiment, the Chinese Spring (CS) 7E(7D) substitution line (2n = 42), original donor to R74-10 and R69-9 of the Fhb-7EL QTL , using total DNAs of T. aestivum and Th. ponticum as genomic probes. Due to the close relatedness between Th. elongatum and Th. ponticum genomes, the latter equally highlights any Thinopyrum spp. introgression into wheat. Total DNAs were extracted from leaves following Thinopyrum probes were allowed to preanneal for 30 min at 58°C. Prior to hybridization with the pre-annealed probes, a blocking mixture, containing 1 mg of autoclaved and unlabeled DNA of Aegilops speltoides , was applied for 1.5 h at 63°C onto denatured chromosome preparations. This additional step led to a preferential block of B-genome chromosomes (not involved in wheat-Thinopyrum rearrangements), which enhanced the overall differentiation among chromosomes/segments of different genomic origin. Hybridization was then carried out for 2 h at 63°C, after which digoxigenin- and biotin-labeled probes were correspondingly detected using anti-digoxigenin conjugated with FITC  and streptavidin conjugated with Cy3 .Standard Feulgen or aceto-carmine staining techniques were applied both to assess the somatic chromosome number in root tip cells of selected genotypes and for quick anther screening from freshly collected young spikes, in view of meiotic metaphase I preparations. To this aim, selected anthers with pollen mother cells (PMCs) at the target phase, kept at −20°C in 3:1 fixative  for up to several weeks, were rinsed in 45% acetic acid, transferred for about 1 h to 2% aceto-carmine in 60% acetic acid at 37°C and squashed in 45% acetic acid, before freezing the slide in liquid nitrogen. For pairing analyses, metaphase I spreads were subjected to GISH  color digital camera and the SPOT™ Advanced Plus imaging software.1 progeny (pentaploid F1s × Simeto) and further genotyping in subsequent generations was facilitated by previously established inter-genomic polymorphism and genetic/physical mapping of several wheat and Thinopyrum spp. group 7 markers in the chromosomal regions of interest  was sufficient to select for heterozygous recombinants in all BC2 progenies , and to discriminate heterozygotes (HET) from HOM+ and HOM− for the 7EL+7el1L segment assembly in BC2F2 progenies of 7A recombinants . The STSLr19130 marker, closely linked to Lr19 , was employed to confirm presence of the Th. ponticum leaf rust resistance gene. A previously developed STS-CAPS assay .The choice of suitable markers enabling discrimination between parental and recombinant types in BCrest see Table 1.PS assay , enablinhttp://wheat.pw.usda.gov/GG3/). For each 10 μl PCR reaction, 1× GoTaq® G2 Master Mix  and 25 ng of DNA were used for all primer pairs, while primer concentration, annealing temperature, and use of additional reagents varied, as reported in For PCR reactions, DNA was extracted from young leaves or half-kernels according to Fhb-7EL locus , isolated in BC2F2 progenies after crossing with Simeto (see above) of R74-10/R112, R74-10/R5 and R69-9/R112 F1s, together with the recurrent parent cv. Simeto, as well as R112 and R5 recipient lines and the CS7E(7D) substitution line (as FHB resistant control), were employed for a single-floret F. graminearum inoculation experiment. The R69-9/R5 corresponding progeny was not available at the time of inoculations, hence was not included in the assay. The infection assay was conducted under controlled conditions (16 h light/8 h dark photoperiod and 22–24°C/20°C corresponding temperature regimes) when plants were at mid-anthesis stage. The inoculum consisted of 1,000 macroconidia of F. graminearum strain 3824 (4 ml−1 concentration), and supplemented with 0.05% Tween-20. The conidia suspension was pipetted through the glumes onto the basal floret of one central spikelet from the tip of the first spike of each plant. Inoculated spikes were covered with a plastic bag for 48 h to maintain high relative humidity. Disease symptoms were assessed at 7, 14, and 21 days post-inoculation (dpi), by calculating the percentage number of visually diseased florets (NDF) out of the total number of florets per spike. Differences in disease severity among genotypes were estimated by means of NDF ± SE (standard error) of 8–10 plants/genotype  and by one-way analysis of variance (ANOVA). Seed number and weight  were assessed for inoculated and non-inoculated spikes of the same infected plants, and the differences among genotypes assessed as described above for disease severity. The same seeds were also used to extract wholemeal flour and determine the DON content (see below).Tetraploid homozygous carriers (HOM+) and non-carriers (HOM−) of the ain 3824 , freshlyain 3824 , suspend2F3 plants from the progeny of one of the tetraploid 7EL-7el1L HOM+, FHB resistant recombinants (R69-9/R112 cross derivatives), as well as of sib HOM− plants and of cv. Simeto as controls, were used in two independent infection experiments with F. culmorum. Twenty plants per genotype were included in each experiment. Seeds were surface sterilized with sodium hypochlorite (0.5% vol/vol) for 20 min and then rinsed thoroughly in sterile water. Seedlings were individually grown in 5 × 5 × 5-cm pots and arranged in plastic trays and maintained at the same light and temperatures regimes as described for the FHB assay throughout the experiments. F. culmorum strain UK99 macroconidia were produced by fungal culture on SNA medium and harvested by washing the culture surface with 2 ml sterile water  upon conversion of raw files into mzXML format through MassMatrix . Standard curves were obtained with six calibration points (2mg–0.00002 mg) of DON analytical standard (Romer Labs). To assess the effect of presence vs. absence of the Fhb-7EL QTL on DON content, each of the three Fhb-7EL carriers  and non-carrier  genotypes was considered as a biological replicate. Bulks were a necessary option, due to limited amount of flour extracted from shriveled seeds of heavily diseased genotypes. For each replicate, seeds from all infected spikes were used to produce a single flour sample, from which three technical replicates were obtained. Values of all biological × technical replicates were analyzed by analysis of co-variance (ANCOVA), which, better than ANOVA, could eliminate the undesirable variable represented by genetic background heterogeneity across genotypes.DON content was determined in wholemeal flour of kernels produced by plants subjected to 2F3–4 recombinant plants (HOM+) from the R74-10/R112/2*Simeto and R69-9/R112/2*Simeto cross combinations, carrying different amounts of 7EL chromatin including the Fhb-7EL QTL, stacked into the same 7el1L segment of R112-7AL arm , at a 25-cm distance between rows and 10-cm distance along the row. In the 2nd experimental year, the trial consisted of spike rows of BC2F4 selections of each HOM+ and HOM− genotype and of cv. Simeto. On separate plants (1st year), data were collected for spike number/plant (SNP), grain number/plant (GNP), TGW, grain yield/plant (GYP), plant height (PH), days to heading (HD), and spike traits, including grain number/spike (GNS), grain yield/spike (GYS), spikelet number/spike (SPN), grain number/spikelet (GNSP), and spike fertility index (SFI). SFI, indicating the ability of the plant to set seeds in relation to the spike biomass, was calculated as the ratio between GNS and weight of spike chaff (g) of mature and oven-dried (48 h at 65°C) spikes. For each parameter, values from 20 to 30 plants per genotype, expressed as means ± SE, were subjected to one-way ANOVA. In the 2nd year trial, besides PH and HD average values/row, the same spike traits mentioned above were analyzed on 25 spikes/genotype (five from each of five rows).Homozygous durum wheat BC arm see Figure 12F4 plant rows was milled into semolina to measure the yellow index (YI) of contrasting genotypes for Psy1 alleles. Using the reflectance colorimeter CR-400 Chroma Meter (Minolta), absolute measurements for L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) coordinates in the Munsell color system were taken using D65 lightning  to spike , with 0 = no disease, and the second digit corresponds to a one-value percentage of the average infection intensity on the leaf area , based on the modified Cobb scale  were considered for F values. When significant values were observed, a pairwise analysis was carried out by the Tukey Honestly Significant Difference test (Tukey test) at 0.95 confidence level.ANOVA and ANCOVA were performed using SYSTAT12 Software . The variable parameter  was considered as a dependent factor against the independent factor “genotype” (G). Additionally, “replica” (R) was included as independent factor in the two-way ANOVA performed for FCR assessment, or as a covariate in the ANCOVA used for DON and YI assays. Three levels of significance (1L/7EL chromosomes of R74-10 and R69-9 6x lines (bearing the Fhb-7EL QTL) and 7AS·7AL-7el1L chromosomes of R112 and R5 4x lines within their shared (homologous) 7el1L regions was assessed in PMCs of their 5x F1 plants processed by GISH. Because of the presence of a normal 7A from the 6x parent, homologous to the 7A of the durum parents at the short (S) arm level, a trivalent configuration was expected to occur if, in addition to 7AS-7AS pairing, that between the 7el1L homologous regions of R74-10/R69-9 and R112/R5 would have also taken place. This was in fact the type of association in which a GISH site at the level of a chiasmate region was observed in the largest majority of PMCs in all F1 types  rather than as a rod bivalent, both associations clearly marked by the GISH hybridization site of the 7el1L segment of R5 or R112  was higher in cross combinations involving the longer 7el1L segment of R112 than in those involving R5, and, concomitantly, in combinations where the shorter 7EL segment of R69-9 was involved compared to those including the R74-10 chromosome , marker-based genotyping was carried out . Afterwards, a PCR assay for the further proximal segment enabled discrimination between parental and recombinant chromosomes, as well as between recombinant types bearing the 7EL+7el1L assembly on 7AL rather than on 7DL , was confirmed by the respective markers  resulting from exchanges within the 7el1L shared chromatin between R74-10/R69-9 and R112/R5 showed the expected trend from the pf values. Except for the R69-9/R5 rf, the other values exceeded the expected 50% of the respective pf, probably due to metaphase I observations providing an underestimate of actual (early prophase I) pairing events  recombinant types were isolated, with chromosome numbers ranging from 2n = 29 to 2n = 32 . A further backcross of selected plants  to the same cv. Simeto brought most of them (over 80%) to the euploid (2n = 28) condition, hence to stabilization of 7A-7el1L-7EL recombinant genotypes in view of further analyses.In the progeny of all cross combinations, recombinant 7D chromosomes  prevailed over recombinant 7As. Nonetheless, sufficient representatives of all 7A novel  levels indicative of normal gametic transmission (> 5%), although P values were higher for progenies involving the R69-9 chromosome than for those involving R74-10.A first check of their stability, also aimed at obtaining homozygous recombinant (HOM+) and non-recombinant (HOM–) individuals, was carried out by screening the BCF. graminearum unequivocally discriminated carriers  from non-carriers  of the Fhb-7EL QTL (Fhb-7EL+) vs. susceptible (Fhb-7EL–) genotypes, the reduction in FHB severity in the former amounted to nearly 93%.Progression of infection through the three time-points  following single-floret inoculation with Fhb-7EL QTL infection progress was much faster, reaching the majority and even 100% of florets and spikelets of the inoculated spike between 14 and 21 dpi , corresponding to 73.5% reduction in seed number/spike in the susceptible plants. In parallel, average TGW calculated for inoculated spikes of FHB resistant 4x genotypes was 33.8 g, in sharp contrast with the 6.7 g average TGW of susceptible genotypes (over 80% reduction). In fact, conspicuous shrivelling  spikes of the same plants Table 5.Fhb-7EL HOM+) and susceptible controls (Fhb-7EL HOM–). The 3 resistant recombinants, taken as biological replicates of the Fhb-7EL+ condition, showed an average value of 0.67 ppm, more than 800 times lower than the 547.4 ppm mean figure of the 3 genotypes representing the Fhb-7EL– condition , as well as on FHB susceptible control plants (HOM− sibs and cv. Simeto), was extended of 1 week with respect to a previous experiment (Fhb-7EL−) and Simeto plants largely overlapped throughout the time-points, collectively showing a highly significant difference vs. Fhb-7EL-bearing plants, especially from 11 dpi onward , characterized by limited SE and brown discoloration of the infected tissue .To monitor visible disease progress accurately, the time-course of the present FCR infection assay, conducted on seedlings of tetraploid periment , with firecorded Figure 5Materials and Methods) included F3−4 HOM+ and HOM− sib plants from R74-10 and R69-9/R112/2*Simeto crosses, as well as the recurrent cv. Simeto. In both experimental years, no major penalty on yield-related traits was found to be associated with presence of alien segments. In the 1st season (2017–18), yield-related traits such as spike number, grain number, and grain yield per plant  or the Psy1-7EL (R74-10/R112 HOM+) allele in place of a Psy1-7AL allele (HOM− lines and Simeto). Presence of Psy1-7el1L from Th. ponticum determined a 37–42% increase compared with all other genotypes, while the Th. elongatumPsy1-7EL allele had no incremental effect vs. the T. durum 7AL resident allele , were selected to transfer Fhb-7EL into the 7AL-7el1L durum wheat recombinant chromosomes of R5 and R112 lines  and in the remaining portions , compared with two recovered in the same progeny in the more distal region, spanning the remaining 23% telomeric end of the arm  bread wheat substitution line in the infection assay , averaging 80%  of action underlying this unique resistance gene.Visual assessments of head blight were fully consistent with the prominently reduced accumulation of the DON toxin Table 6,se e.g., , remainsFhb-7EL QTL is its efficacy also toward another important Fusarium disease, i.e., crown rot (FCR), both in bread wheat  genes spread along the arms, particularly in their proximal halves  were mostly evident in the 2nd year trial  in the context of the initiative “Departments of excellence” (law 232/216), and by Lazio region, FILAS project “MIGLIORA”. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Oenocarpus distichus presents economic, ecological, and dietary potential for pulp market processed in natura. Germplasm conservation and genetic improvement depend on genetic divergence studies. The objective was to quantify genetic divergence in a native population of O. distichus genotypes based on fruit biometrics. The fruit length and width, fruit mass, pulp and seed, pulp and almond thickness, and pulp yield per fruit were evaluated. All fruit biometric characteristics of O. distichus palms show genetic variability. Genetic variations among genotypes are essential for predicting heredity and heterosis, which are essential for improving O. distichus production. Pulp yield and seed mass were negatively correlated. Almond thickness and pulp, seed mass and transverse diameter were positively correlated. Genetic distances between pair of genotypes ranged from 0.07 to 48.10 with three genetically distinct groups. The seed mass, almond thickness and transverse diameter contributed to genetic divergence. Heritability estimates the genetic control that can be obtained from O. distichus germplasm. Correlations between the variable pair reduce the evaluation effort and the resources to measure the genotype allocations in heterogeneous groups presenting high genetic variability. This makes it possible to select individuals for hybridization programs with F1 generation gains. Correlation and relative contribution networks, based on relationships graphical between fruit biometric characteristics, allow the variables selection with less effort and fewer measurements. O. distichus fruit biometric characters are efficient to quantify genetic divergence between genotypes. The sites identification for conservation and germplasm collection in this region is a great challenge, especially for some native palm trees2. Oenocarpus spp. plants have economic, ecological and dietary potential and are exploited through extraction by Amazonian communities3.Palm fruits and palm hearts native to the Amazon are consumed by local communities but are poorly known at the national and international levelsOenocarpus spp. are native to Brazil, but not endemic, including O. distichus Mart., O. bacaba Mart., O. minor Mart. and O. mapora H. Karten and O. pataua Mart.4. Oenocarpus distichus  has high potential in the palm and processed pulp markets, including its use in the production of “bacaba wine”, a nutritious energy drink with commercial potential similar to that of Euterpe oleracea Mart.5.Six species of the genus Oenocarpus distichus phytochemicals have phenolic compounds correlated with antioxidant capacity7 and vitamin E, riboflavin, insoluble fiber, energy, and minerals, especially potassium and calcium8. This plant has oil with organoleptic properties similar to those of olive oil and raw material for the food industry with proteins of high biological value (40% more than soy)5 and adequate to manufacture ice cream and soap9.O. distichus native populations, from the morphological characteristics of their fruits, needs to be better understood for germplasm inclusion. This is necessary for breeding programs to conserve native populations in extractive areas. Oenocarpus distichus genetic improvement and conservation programs depend on fruits biometric characterization and genetic parameter studies of genotypes showing promise for germplasm banks and conservation programs of this plant10.The genetic diversity in E. oleracea11, Theobroma grandiflorum Willd. Ex Spreng.12, Elaeis guineenses Jacq.13 and Phoenix dactylifera L.14 in forest fragments15. Grouping methods, based on prefixed similarity/dissimilarity measures, can identify divergent groups. Group determination, based on a standard, universally, accepted method does not exist, but individuals of the same group should be as homogeneous as possible and different from the others16.Multivariate analyses allow selecting individuals for breeding programs and morphological or molecular characteristic identification to evaluate the divergence of native and exotic plants such as 17. Genetic divergence, important for prospecting in plant breeding programs, allows us to know the available germplasm to evaluate similar or divergent groups to identify hybrid combinations with greater heterozygosity and heterotrophic effect18. Genetic divergence will allow us to know the available germplasm to predict combinations with higher heterozygosity so that there will be more possibilities of recovering fruitful Oenocarpus distichus genotypes in the segregant generations. Distribution among groups will allows the early determination of the best combinations and reduces the unneeded crosses number, prioritizing more contrasting groups for obtention of combination with higher heterosis. Biometric fruit variables differences such as morphological and production are regarded in the dissimilarity quantification. Those genetic variations among Oenocarpus distichus genotypes are essential for predicting heredity and level of heterosis, which are essential for improving Oenocarpus distichus production.Fruit characteristics are important for characterization, individual selection and genotypes choice during recombination cyclesOenocarpus distichus genotypes in a native population in the northeastern region of Pará, Amazon rainforest, Brazil, based on fruit biometric characteristics.The objective of the present study was to quantify the genetic divergence between O. distichus fruit biometric characteristics (F-test < 0.01) show genetic variability (Table e) of 6.67% for fruit length (FL) at 15.09% for pulp mass (PM), indicating high experimental precision. The genetic variation coefficient (CVg) values, mainly of the PM, seed mass (SM) and fruit mass (FM) characters, confirmed the genetic variation between the O. distichus genotypes. The relationship between CVg/CVe was greater than 1 for the characteristics fruit width (WF), FM, PM and SM. Matrix inheritance estimates for average ones (h2mp) ranged from 85.38 for pulp thickness (PT) to 96.81 for FM. The mean values of biometric characteristics of O. distichus fruits and Scott-Knott test at 5% probability are given in supplementary information.All The biometry correlation matrix of the O. distichus fruits indicate estimates for the last two eigenvalues near to zero, resulting in two linear relationships determining the harmful effects of multicollinearity Table . The con19.In addition to the CN, small single values were observed for FM (0.0503) and PM (0.0043) and high condition index FM (46.60) and PM (537.90), indicating serious problems caused by multicollinearity. VIF values can also be used to detect the existence of multicollinearity. The FM and PM variables presented VIFs of 12.53 and 101.37, respectively. The existence of at least one VIF with a value greater than 10 is sufficient for the regression coefficients associated with these values to be highly influenced by multicollinearity, indicating that these variables must be removed from the analyzesThe PM characteristic correlated with FL and PT and FM with AT and SM, all above 70% Fig. . The corO. distichus ranged from 0.07 to 48.10, with a mean of 11.44  and the high h²mp values, confirm the variability and the genetic control of fruit characteristics, allowing selecting and recombining the most promising O. distichus genotypes. CVg/CVe values, greater than 1, indicate gains in selection24 and that the environment has a low impact on the characteristics measured. These values should be considered in breeding and conservation programs for this palm10. Genetic variations in native populations are important to obtain parents to produce superior and divergent individuals for the interest characteristics in the F1 generation26. However, collection intensity may reduce fruit availability for frugivorous animals, compromising gene flow and genetic diversity in the forest fragment27. Therefore, conservation strategies such as not harvesting the fruits in years of low productivity and maintenance of at least 50% of them on the palms are important to maintain consumption by wildlife28.The genetic variability between the Theobroma grandiflorum Willd. Ex Spreng.12 and Plukenetia volubilis L.29. However, they should be excluded from the analyses, because they may lead to false biological interpretations and errors in the genotype selection process, due to the lack of precision in parameter estimation.The high multicollinearity values caused by FM and PM were expected, because they are characteristics collinear with PY and common in studies involving fruit characteristics, as reported for O. distichus breeding programs. This confirms their wide use to relate characteristics of interest and to enable greater effectiveness in the divergent selection and superior O. distichus genotypes, as reported for Capsicum spp. Species30, Passiflora edulis Sims31 and Cocos nucifera L.22.The correlations between the fruit biometric variables, visualized by graphic network analysis, show the potential of this technique to select variables that reduce the evaluation time and resources for measurement in O. distichus individuals and consequently their heterosis in subsequent cycles, as reported for P. volubilis32. Similar groupings with the Tocher and UPGMA methods, the consistent genetic divergences between genotypes 2, 5 and 8 and the low number of groups may indicate a common ancestry for this plant in the region12, similar to that reported for Bactris gasipaes Kunth.33, E. oleracea Mart.11 and T. grandiflorum Willd. Ex Spreng.12 with 3, 5 and 3 groups, respectively. Matrix separation into heterogeneous groups may indicate distinct gene pools for controlled hybridizations and reciprocal recurrent selection and, therefore, be interesting for genetic improvement18. Additionally, the grouping restricts the crosses number, reducing costs and increasing genetic gains in subsequent cycles, due to the reduced in breeding coefficient in the population by excluding similar individuals32.The large number of genotypes with genetic distances above the general average confirms genetic divergence and suggests the crosses’ ability to explore additive genetic values in O. distichus genotypes by geometric distances in scatter plots34, as reported for genetic divergence in C. nucifera22. The projection of dissimilarity measures in 2D projection representation and use of dendrograms are acceptable when the correlation value between measures of original distances and a graphics is higher than 0.9 and values of distortion and stress lower than 20%35. In a graphic dispersion of canonical variables the grouping was compatible with the ones grouped by Tocher’s method. The bigger contribution of the SM, AT and WF characteristics to genetic divergence is important in selection programs, for being more responsive in the superior genotype identification32. The variable FL, even presenting lower relative contribution to genotype divergence, should not be removed from the analyses because it is easy to determine using non-destructive methods36. In addition, most of the genotypes that had lower FL showed higher yields of pulp (supplementary information). This result is in agreement with what is reported in the literature for E. oleracea, where higher fruit lengths result in lower pulp yields37.The concentration of a large part of the total variance in the first two canonical variables (above 80%) shows that it is possible to study the genetic divergence between Oenocarpus distichus genotypes. The contribution of the seed mass, almond thickness and fruit width, for genetic divergence, is higher and for this reason, these parameters should be prioritized in selection processes. Correlation networks facilitate the visualization of the correlations between the variables and the potential to choose those requiring less time for evaluation and fewer resources in the measurements in O. distichus breeding programs.Biometric fruit characteristics are efficient to quantify genetic divergence between O. distichus genotypes, in different groups, and should be used to maintain the genetic variability, conservation and selection of genotypes of this plant for breeding programs in native populations.Grouping methods are effective in allocating O. distichus native genotypes in an Amazonian forest fragment in the municipality of Oeiras do Pará, northeast of Pará, Brazil  , width (WF) (mm) and mass (FM) of fruits, pulp (PM) and seed (SM) (g) mass; and pulp (PT) and almond (AT) (mm) thickness were evaluated , coefficient of genetic variation (CVg%) and variation coefficient (CVe%) were also determined. The means were grouped by Scott-Knott method39.Genetic variability between genotypes was tested with analysis of variance (ANOVA using a Fisher’s test (F-test) at 1% probability. The genetic parameters heritability 43 based on Mojena20 delimited the groups. The relative contribution of the characteristics to genetic divergence was quantified by Mahalanobis generalized distances using the Singh method44.Possible linear relationships in the residual correlation matrices were obtained with multicollinearity testA = h (R) as a function of: H(rij) = ½ {sgn (│rij│−ρ) + 1}, in which ρ (hard limit) = 0, which allows viewing the connections. Network graphs were obtained with the variables with positive correlations connected by a green line and the negative ones by a red line. The line’s thickness represents the absolute correlation value with the thicker the line. The line’s thickness was controlled with a cut-off value of 0.3 and 0.7, meaning that only |rij| ≥ 0.3 and |rij| ≥ 0.7 have their lines highlighted, for easy viewing. The layout for the network was created with Fruchterman-Reingold algorithm45. The analyses were performed with Genes software46 and integration was realized with R47. The integration of the correlation network was evaluated with the “Qgraph” package48.Relationships between the fruit biometric evaluations were represented graphically with correlation networks. The phenotypic correlation were analyzed by weighted matrix with connections between the variables determined by the adjacent matrix


Saussurea polylepis (Asteraceae), a vulnerable species with very limited distribution in a few southwestern continental islands of the Korean peninsula, in order to facilitate future population genetic studies.Nuclear microsatellite markers were developed for S. polylepis. The levels of expected and observed heterozygosity ranged from 0.000 to 0.842 and 0.000 to 0.933, respectively. Sixteen of these simple sequence repeat (SSR) markers were successfully cross‐amplified in five congeneric species, namely S. gracilis, S. grandifolia, and S. tanakae for all 21 loci, and S. maximowiczii and S. pulchella for 18 loci.Based on the Illumina sequence data, a total of 21 microsatellite primer pairs were designed and tested for their suitability. Nineteen of these primers, with two to 11 alleles per locus, were polymorphic in three natural populations of S. polylepis and related species.The SSR markers developed here will be useful for future population genetic studies on  Saussurea DC. (ca. 400 species) is one of the largest genera in the tribe Cardueae (Asteraceae) and is also one of the most speciose groups in the flora of Korea  are endemic, mostly occurring in small‐sized populations in isolated regions. Of these endemic species, S. polylepis Nakai, a perennial herb, is highly restricted to small continental islands (less than 10 locations) in the southwestern corner of the Korean peninsula. Its population size is extremely small, normally less than 30 individuals per population, and it is currently categorized as a vulnerable species ) in the Korean Red List  Sch. Bip., S. gnaphalodes (Royle ex DC.) Sch. Bip., S. costus  Lipsch., and S. ceratocarpa Decne., contain many important chemical compounds that have become important drugs in the international market  in the Korean peninsula are not an exception to this situation . Despite the ecological and economic importance of the genus Saussurea, very few studies on population genetics and molecular marker development have been conducted  Fisch. ex Colla, and S. tanakae Franch. & Sav. ex Maxim., which are commonly found in mountain regions of Korea.S. polylepis sampled from three island populations of Hueksan‐do, Hong‐do, and Ui‐do (Appendix 2) of PCR buffer, 0.5 μL (each 10 mM) of dNTPs, and 0.1 μL (1.25 U/μL) of Taq DNA polymerase . The PCR conditions were: initial denaturation at 95°C for 1 min; followed by 35 cycles at 95°C for 20 s, annealing at 58–60°C for 40 s, and extension at 72°C for 60 s; and a final extension at 72°C for 5 min. The PCR amplicons were then separated with a GeneScan 400 LIZ Size Standard  at the Macrogen Company utilizing an ABI 3730XL DNA Sequencer (Applied Biosystems). The resulting electropherograms were scored using GeneMapper v5.0 (Applied Biosystems). Of the 35 tested primer pairs, 21 produced repeatable amplicons and 19 showed polymorphisms across all eight individuals. These 19 primer pairs were subsequently scored using 48 individuals of S. polylepis sampled from the same three island populations and five individuals from each of the other species, namely S. gracilis, S. grandifolia, S. maximowiczii, S. pulchella, and S. tanakae  were all successfully amplified and polymorphic for the 21 loci, while two species (S. maximowiczii and S. pulchella) were successfully amplified and polymorphic for 18 loci.Cross‐amplification of the 21 primer pairs was conducted in five widely distributed congeneric species of Saussurea. The 21 SSR markers developed in this study will be useful for studying genetic diversity, gene flow, and population structure in S. polylepis. In addition, the resulting population genetic studies will help to develop appropriate conservation strategies and management plans of the endangered S. polylepis on continental islands in Korea. Lastly, the successful cross‐amplification of these markers among commonly found Saussurea species in Korea will provide a novel population genetic tool in one of the most speciose groups in Korea.The results of the present study represent the second set of SSR markers developed for the genus S.A.Y. and S.‐C.K. designed the project. S.A.Y. collected samples in the field, conducted the experiments, analyzed the data, and wrote the draft of the manuscript. S.‐C.K. supervised the study and revised the manuscript. All authors approved the final version of the manuscript.


PRS (one cycle per year) assuming various breeding population sizes and trait genetic architectures. For oligogenic architecture, the maximum relative genetic gain advantage of GARS over PRS was 12–88%, which was observed only during the first few cycles. For the polygenic architecture, GARS provided maximum relative genetic gain advantage of 26–165%, and was always superior to PRS. Average prediction accuracy declines substantially after several cycles of selection, suggesting the prediction models should be updated regularly. Updating prediction models every year increased the genetic gain by up to 33–39% compared to no-update scenarios. For small populations and oligogenic traits, cost per unit gain was lower in PRS than GARS. However, with larger populations and polygenic traits cost per unit gain was up to 67% lower in GARS than PRS. Collectively, the simulations suggest that GARS could increase the genetic gain in small young breeding programs by accelerating the breeding cycles and enabling evaluation of larger populations.Young breeding programs in developing countries, like the Chibas sorghum breeding program in Haiti, face the challenge of increasing genetic gain with limited resources. Implementing genomic selection (GS) could increase genetic gain, but optimization of GS is needed to account for these programs’ unique challenges and advantages. Here, we used simulations to identify conditions under which genomic-assisted recurrent selection (GARS) would be more effective than phenotypic recurrent selection (PRS) in small new breeding programs. We compared genetic gain, cost per unit gain, genetic variance, and prediction accuracy of GARS (two or three cycles per year)  High-yielding and climate-resilient crop varieties are needed to meet increasing demand for food and feed in the developing world (Genomic selection (GS) has been evaluated in many crop improvement programs as an alternative to phenotypic selection . In GS aGS is generally applied to facilitate recurrent selection (RS) schemes  tolerant varieties. A shift to GARS would allow the prediction of breeding value on S0 progenies, potentially shortening the breeding cycle to one generation. Further, GARS could take advantage of the tropical environment to rapidly cycle two to three times per year. The objective of this simulation study was to compare genetic gain, prediction accuracy, genetic variance, and cost under GARS vs. PRS across various genetic architectures, breeding population sizes, and model updating scenarios. We found that by accelerating the breeding cycle and enabling evaluation of larger populations GARS could increase genetic gain and lower cost per unit gain in developing country breeding programs.In most developing countries of the semi-arid tropics, including Haiti and countries in sub-Saharan Africa, sorghum is grown primarily under low-input rain-fed conditions with heterogeneous climate and soil conditions (https://cran.r-project.org/web/packages/AlphaSimR/index.html)  implemented in AlphaSimR. The population parameters used to simulate the founders include effective population size (Ne) of 20 (the approximate number of founders of the Chibas program), number of base pairs = 2x108, and mutation rate = 2x10−8  . All res= 2x10−8 . Genomesh2 = 0.7. For the oligogenic trait, we are referring to a quantitative trait that is controlled by relatively small number of genes, in which most of the trait variation is attributed to the top few genes. In this case, only five loci are responsible for 45% of the trait variation.Traits were simulated with (i) oligogenic or (ii) polygenic architecture by varying the number and effect size of QTL . The olih2 = 0.3. Intra- and inter-locus interactions (dominance and epistasis effects) were not modeled. For each of the two genetic architectures, various population sizes and selection schemes were compared for genetic gain, genetic variance, and prediction accuracies. True breeding value for each individual was calculated by summing effects across all QTL. Genetic gain  was defined and plotted as the true breeding value expressed in standard deviation units of the phenotypic values of cycle 0 (C0) generation.Polygenic architecture was simulated by sampling 500 QTL (50 per chromosome) with effect sizes drawn from a gamma distribution with scale and shape parameter of 1.0 and 0.5, respectively. Variance components were chosen to make heritability For each simulation, a base population (C0 generation) of 400 inbred lines was simulated by randomly mating the founder population, followed by five generations of selfing to create inbred lines. Phenotypic values for each inbred line in the base population was simulated by adding normally-distributed random deviates to each line’s true breeding value. The base population was used as a source of parental lines that were used to create the initial recurrent selection population. The base population is also used as the training population for estimation of markers effects for the genome-wide prediction model.5_pop50, par20_pop200, par100_pop1000 and par200_pop2000, respectively) (File S1A). In all of these scenarios, the top 10% of S1 families were retained each cycle. Since developing countries breeding programs generally have limited field phenotyping capacity, scenarios were also simulated where the number of phenotyped S1 families was capped at 500. In these scenarios , populations were generated by randomly intermating 100 or 200 selected parents to generate 500 S0 individuals, which meant the top 20% or 40% of S1 families were retained, respectively. Assuming that the first selfing generation (S0→S1) was performed during an off-season nursery, a single cycle of PRS was performed each year, for a total of ten cycles of PRS in ten years. For each cycle of PRS, genetic gain, genetic variance, and prediction accuracy were calculated. Prediction accuracy in PRS was calculated as the Pearson correlation between the phenotypic value and true breeding value of the S1 families.PRS was performed by selecting the top 5, 20, 100, or 200 parental lines from the base population based on their phenotypic values. Selected parents were randomly intermated to generate S0 individuals. The S0 individuals were then selfed to generate S1 families. Following the Chibas breeding program’s current PRS method, the S1 families were phenotyped and S1 families with the top phenotypic values were randomly intermated to generate the next cycle. Intermating of 5, 20, 100, or 200 parents generated 50, 200, 1000, or 2000 S0 individuals, respectively   was usedThe GEBVs were used to select the top S0 individuals of C1 population. The selected S0 individuals were randomly intermated to generate the next cycle (C2) recurrent selection population, and so on for 30 cycles. To assess the effect of number of breeding cycle per year, two or three cycles of GARS per year  were compared to PRS. Note, the results for GARS2 and GARS3 represent the same simulation runs, plotted differently to represent the given number of GARS cycles per year. Therefore, given a 10-year time horizon, there were 20 cycles of GARS2 and 30 cycles of GARS3. To align with the 10-year selection cycles of the PRS, only the first 28 and 19 cycles were plotted. During each cycle, the number of parents and populations generated by random intermating of the parents remained constant. Genetic gain, genetic variance, and prediction accuracy were calculated for the different selection schemes, population sizes, and trait genetic architectures. Prediction accuracies in GARS was calculated using a Pearson correlation between the GEBVs and true breeding values of S1 families.Nind = 400) consisted of (i) the 200 top lines from the previous training population based on phenotype values and (ii) the 200 top lines from the previous cycle based on GEBVs. Updating of the training population was performed based on the population size of par100_pop1000, which represents an optimum breeding population size at which genetic gain has plateaued. Changes in genetic gain, genetic variance, and prediction accuracy due to updating were compared to no-update GARS scenarios and PRS.We examined the impact of updating the training populations and prediction models on the genetic gain, genetic variance, and prediction accuracy. Given that cropping-season phenotyping can only be conducted once per year, updating of the training population and prediction model was performed every two and three cycles for GARS2 and GARS3, respectively. The updated training population for the a given cycle . Costs in PRS were calculated for the following activities; planting and field management, pollination, perform replicated yield trial for grain yield, disease resistance/tolerance, stay-green, stem biomass, stem sugar content and other traits. GARS does not require phenotypic evaluation of the S1 population, but estimates breeding values of the S0 individuals. The costs involved in the different activities GARS includes genotyping and phenotyping of the training population, crossing of the parents and genotyping of the S0 individuals. Different genotyping cost, which include US $5, $15, $25, and $35 were used to estimate cost per gain for GARS and compared to that of PRS. Cost per unit genetic gain for the different scenarios were calculated by dividing the cumulative cost per cycle with the corresponding genetic gain.We compared the efficiency of the two selection methods in terms of cost per unit gain. One cycle of PRS in the Chibas sorghum breeding program involves the intercrossing of best parents to generate S0 individuals; planting and selfing of the S0 individuals to generate S1 families; and evaluation of the S1 families in replicated trials. The following costs  were assumed: $38 in the crossing phase, $14 in managing the S0 phase, and $33 replicationt-tests for comparing the genetic gain between GARS and PRS across the different trait genetic architectures and population size. File S3 contains the script used to simulate the breeding program, while File S4 contains the script used to run the cost analysis and the output from the breeding program simulation (genetic gains) that was used to run the cost analysis. Supplemental material available at Figshare: https://doi.org/10.25387/g3.7396781.All relevant data are within the paper and its supplemental files. Figure S1 shows the effects of updating the training population under an polygenic architecture. Figure S2 and S3 describe cumulative cost per unit gain in GARS2 and GARS3, respectively, compared to PRS. Figure S4 shows the costs per unit gain for updating the training population. Figure S5 shows the shift in the trait genetic architecture due to selection. File S1 summarizes the different populations sizes and cost estimates of activities in GARS and PRS. File S2 presents the t-tests in File S2). For the oligogenic architecture, GARS3 yielded significantly higher (P < 0.01) short-term genetic gain (year < 5) than PRS for all population size (P < 0.01) for PRS compared to either GARS2 or GARS3. For the polygenic architecture, GARS3 resulted in higher genetic gain (P < 0.01) over PRS for all population sizes across all selection cycles, except the first year (P < 0.01) than PRS across all population sizes for the polygenic architecture, with maximum relative advantage of 12–99%.We first compared the genetic gain across population sizes, genetic architectures, and selection schemes. The genetic gain per unit time resulting from GARS and PRS differed significantly and depended on population size and genetic architecture  did not result in significant increase in genetic gain (vs. 2D). For polygenic architecture, final genetic gain from GARS3 increased by 36% and 7%, for the same population size increases.  increased by 22% and 3% when population size was increased (from partic gain vs. 2D. creases. . With thP < 0.001). For the oligogenic architecture, genetic variance during early selection years (year < 5) were higher for PRS compared to GARS (P > 0.05) at the smallest population size or (ii) significantly higher (P < 0.01) for GARS than PRS at the largest population size, notably for GARS2. Under the assumptions of limited phenotyping capacity in PRS (maximum population size capped at 500), genetic variance was higher than GARS at the maximum population size . This difference was greater during the early years, but declined in later years. Similarly, the difference in genetic variance between GARS2 and GARS3 was minimal during the early years, but GARS3 lost more variance than GARS2 in later years (P < 0.01). For the polygenic architecture, genetic variance declined to zero only for GARS3 at the smallest population size  given polygenic architecture vs. oligogenic architecture in the later years  and compared these scenarios to no-update GARS and PRS  and polyGiven polygenic architecture, final genetic gain (year 10) was increased by 29% and 13% for updated GARS2 compared to no-update GARS2 and PRS, respectively . For updated GARS3, the increase in final genetic gain was 30% and 40% compared to no-update GARS3 and PRS, respectively . Updating resulted in a greater decline of genetic variance compared to no-update GARS . Predictvs. PRS across population sizes, genetic architectures, and updating scenarios. For small population size, cost per unit gain was lower than GARS regardless of trait genetic architecture and genotyping costs considered. Cost per unit gain was lower by up to 67% in GARS than PRS when larger population size was evaluated, notably at the genotyping cost of $15 or under , and genotyping cost <$15 per sample. With higher genotyping costs, the greater cost efficiency of GARS will be due to larger breeding population (par100_pop1000 and par200_pop2000). The result implied that it would be economically feasible to implement GARS to Chibas breeding program even with relatively expensive genotyping costs (US $15/sample) and moderate breeding population size. When genotyping costs are driven down to $5/sample, GARS is expected to be cost competitive even when traits are oligogenic and highly heritable.Cost-efficiency of evaluating and maintaining the breeding population is also a critical aspect of implementing genomic-assisted recurrent selection  has been used to rapidly generate 100,000-500,000 SNP markers for diverse sorghum germplasm  sorghum varieties that are simultaneously selected for a high grain yield (white hard to semihard grains primarily for human consumption) and forage quality after grain maturity. Our simulations provide guidance on several key decisions regarding resource allocation and capacity building to maximize genetic gain: (1) achieving three cycles of GARS per year (two off-season nurseries per year) will be critical to provide a higher rate of genetic gain; (2) rapid cycling GARS can provide increased genetic gain even at a modest population size ; (3) GARS can be cost-effective for developing countries breeding programs even at modest population size if genotyping cost under US $15; or at higher genotyping costs if larger population can be evaluated; and (4) updating the training population and prediction models using lines that capture new LD will be valuable to maintain high prediction accuracy and genetic gain. It should be noted that the simulation carried out in present study assumes that all selected parents are fertile. In an actual breeding program that employ male sterility system, however, the population segregates for male sterility. Therefore, the crossing pattern of randomly intercrossing the selected parents may differ from the breeding population in which male sterility system segregates in the population.Future studies will need to address other aspects of genetic architecture and resource allocation that could affect implementation of GARS in small young breeding programs in developing countries. These breeding programs, which often evaluate and cross diverse germplasm, may encounter more epistasis than established breeding programs that have fixed favorable genetic interactions  for varietal development. Genomic assisted selection on S1 and subsequent families would reduce the number of inbred families that needs to be phenotyped in replicated plots at multiple locations. It could also lower the cost of inbred line development and allow prediction for a greater number of environments and traits. While the results of the current simulation experiment can be applicable to the varietal development cycle of the recurrent selection in terms of the population size and associated costs, additional analysis may be required to test if complete phenotypic evaluation across multiple location should be done at the S2 and subsequent generations. Rigorous theoretical and empirical evaluation of genomic breeding approaches will be critical for translating technology investments into genetic gains for smallholder farmers.GS can also be applied to prioritize line advancement (


Kryptolebias hermaphroditus, a predomintanly self‐fertilizing fish, to analyze the potential adaptive value of DNA methylation in relation to genetic diversity and parasite loads. We found strong genetic population structuring, as well as differences in parasite loads and methylation levels among sampling sites and selfing lineages. Globally, the interaction between parasites and inbreeding with selfing lineages influenced DNA methylation, but parasites seemed more important in determining methylation levels at the local scale.Parasite‐mediated selection is one of the main drivers of genetic variation in natural populations. The persistence of long‐term self‐fertilization, however, challenges the notion that low genetic variation and inbreeding compromise the host's ability to respond to pathogens. DNA methylation represents a potential mechanism for generating additional adaptive variation under low genetic diversity. We compared genetic diversity (microsatellites and AFLPs), variation in DNA methylation (MS‐AFLPs), and parasite loads in three populations of  Global heterozygosity for individual fish was estimated using the homozygosity by locus index (HL) implemented in the Excel macro Cernicalin v 1.3 . Gills are an important physical and immunological barrier to pathogens in fish  from INSTRUCT to classify individuals as either selfed or outcrossed  using 10,000 randomizations. Differences between selfed and outcrossed groups in the total number of parasites and homozygosity by locus (microsatellites) were analyzed using median Mann–Whitney rank tests in R v. 3.3.We also used the Bayesian clustering algorithm INSTRUCT  was used for the MS‐AFLP analysis following Rodríguez López et al. (We used methylation‐sensitive amplified fragment length polymorphisms (MS‐AFLPs) to assess genome‐wide DNA methylation patterns  and a burn‐in of 5 × 105. We tested for outliers based on the MSL data generated on the comparisons among sampling sites, selfing lineages, and between inbreeding status (inbred or outbred).Epigenetic (MSL) and genetic (NML) differentiation at AFLPs among sampling sites, selfing lineages, and between outcrossed and selfed groups was assessed by AMOVA with 10,000 randomizations. Epigenetic (MSL) and genetic (AFLP and microsatellites) differentiation among sampling sites, selfing lineages, and inbreeding status was visualized by principal coordinates analysis (PCoA). Mantel tests based on distance matrices Mantel,  were use2.4A Kruskal–Wallis test was used to examine the differences on scaled parasite load and bacterial cysts (the most prominent parasite) among selfing lineages. To test the relationship between genome‐wide variation in DNA methylation and parasite loads, the proportion of methylated loci per individual was calculated as the proportion of loci scored as methylated over the total number of loci observed per individual . The proportion (or percentage) of methylated loci has been previously used to analyze differences in epigenetic profiles among groups , and evidence ratios , and were more prevalent in Site 1 , followed by Site 2  and Site 3 . The second most common macroscopic parasites were protozoan cysts, which consisted of small dark oval cysts over the gill arch and filaments. In total, 19.53% of the total number of individuals were infected with these cysts, ranging from 1 to 6 . Protozoan cysts were absent in Site 1, but present in Site 2  and Site 3 . Finally, adult nematodes were found in the gut of only eight individuals (6.25%), ranging from 1 to 3 . Nematodes were only detected in Sites 1  and 2   and bacterial cysts  among selfing lineages.Bacterial cysts were present on the gills and consisted of white to yellow spherical cysts circumscribed by a capsule, which resulted in hypertrophied gill filaments. They were the most common type of pathogen appearing in 83.6% of the individuals screened, with a prevalence ranging from 1 to 19  Figure . Only se3.2No linkage disequilibrium was detected between any pair of microsatellite loci. As expected from the high levels of self‐fertilization of the species, no loci were found to be in Hardy–Weinberg equilibrium, and all 27 microsatellite loci showed an excess of homozygotes. The global homozygosity index (HL) was very high (mean = 0.95), as well the estimated selfing rates Table . At the FST values were found both among sampling sites  and selfing lineages . All pairwise comparisons were highly significant . Selfing lineage 6 was shared between two different mangroves , separated by approximately 100 km. The other five lineages were solely represented in one of the mangroves  Figures . High Fnt Table .q‐values  and 36  as outcrossed   Figure . The claFST = 0.28, p = 0.001) and selfing lineages     Table . These p1) Table .3.3The epigenetic analysis identified 381 MS‐AFLP loci, of which 267 (70.07%) were methylation‐susceptible loci (MSL) and 106 (27.82%) nonmethylated loci (NML). Of the MSL loci, 236 (88.3%) were polymorphic and therefore were used for the variability analysis. Reproducibility comparisons between original and replicated genotypes for 24 individuals revealed 238 loci with an average of 0.5% error rate  and epigenetic  loci. Significant differentiation among selfing lineages was also found on genetic  and epigenetic  loci. Overall, higher genetic and epigenetic variance was found within than between groups  and microsatellites . No MSL epiloci were identified as an FST outlier in any of the comparisons.AMOVA revealed very low but significant differentiation among sampling sites, for both genetic  or MSL epigenetic data  , but not between microsatellites and MSL .No significant differences between selfing lineages were found among lineages for individuals from Site 1 for AFLPs genetic data (selfing lineages: 0) Table . In the 0) Table . Mantel 3.4SE = 0.13, p < 0.001) and was affected by parasite loads and inbreeding status through its interactions with selfing lineage   and the interactions between inbreeding and selfing lineage  as significant predictors. However, this model explained substantially less of the overall variation compared to the first model (weight: 0.17 vs. 0.28) and was 1.39 times less likely than the first one  Figure . The sece Tables –S6.Overall, the results of the single‐taxa models  were very similar to those for scaled parasite loads. The best model to explain the proportion of methylated loci included selfing lineage, and the interactions between selfing lineage and bacterial cysts, and selfing lineage and inbreeding Table .SE = 0.11, p = 0.03) and that DNA methylation levels were also affected by the interaction between scaled parasite loads and inbreeding , with inbred individuals having increased methylation levels with increased parasite loads, while outbred individuals had decreased methylation levels with increased parasite loads  for the proportion of methylated loci, the model with the lowest AIC indicated that selfing lineage, inbreeding, and the interactions between inbreeding and selfing lineage and inbreeding and scaled parasite loads were all significant predictors Table . Howevers Figure .4DNA methylation could play an important adaptive role in organisms with low heterozygosity, including self‐fertilizing species, potentially increasing their plasticity capacity to cope with environmental change  to increase its transmission  Universities Animal Ethics Committees, and under sampling permit number 30532‐1/2011 issued by ICMBio/SISBIO. The authors declare they have no conflict of interest. Click here for additional data file.


Active coral restoration typically involves two interventions: crossing gametes to facilitate sexual larval propagation; and fragmenting, growing, and outplanting adult colonies to enhance asexual propagation. From an evolutionary perspective, the goal of these efforts is to establish self‐sustaining, sexually reproducing coral populations that have sufficient genetic and phenotypic variation to adapt to changing environments. Here, we provide concrete guidelines to help restoration practitioners meet this goal for most Caribbean species of interest. To enable the persistence of coral populations exposed to severe selection pressure from many stressors, a mixed provenance strategy is suggested: genetically unique colonies (genets) should be sourced both locally as well as from more distant, environmentally distinct sites. Sourcing three to four genets per reef along environmental gradients should be sufficient to capture a majority of intraspecies genetic diversity. It is best for practitioners to propagate genets with one or more phenotypic traits that are predicted to be valuable in the future, such as low partial mortality, high wound healing rate, high skeletal growth rate, bleaching resilience, infectious disease resilience, and high sexual reproductive output. Some effort should also be reserved for underperforming genets because colonies that grow poorly in nurseries sometimes thrive once returned to the reef and may harbor genetic variants with as yet unrecognized value. Outplants should be clustered in groups of four to six genets to enable successful fertilization upon maturation. Current evidence indicates that translocating genets among distant reefs is unlikely to be problematic from a population genetic perspective but will likely provide substantial adaptive benefits. Similarly, inbreeding depression is not a concern given that current practices only raise first‐generation offspring. Thus, proceeding with the proposed management strategies even in the absence of a detailed population genetic analysis of the focal species at sites targeted for restoration is the best course of action. These basic guidelines should help maximize the adaptive potential of reef‐building corals facing a rapidly changing environment. Meanwhile, large investments in coral reef restoration and the population enhancement of critical reef‐building species have been made in an effort to revive coral communities and maintain some ecosystem function and services  foundation reef builders; 2) experiencing severe declines in cover . We suggest that efforts be expanded to target Pseudodiploria spp., Siderastrea siderea, Stephanocoenia intersepta, Montastrea cavernosa, Colpophyllia natans, and Orbicella franksi, as these species each meet two prioritization criteria , we propose strategic genetic banking as a complementary activity to active restoration  that can trace their ancestry back to the same sexual reproductive event . Genotypic diversity is the total number of genetically distinct individuals (genets) within a population, whereas genetic diversity is the amount of variation between genotypes on the level of individual genes Fig. . To tracpsbA minicircle non‐coding region . The resolution of Symbiodiniaceae genotypic diversity is constrained by the genetic tools available. The standard internal transcribed spacer 2 . Technological advancements in high‐throughput SNP‐based methods such as genotype‐by‐sequencing (GBS) and reduced representation sequencing methods  have made it possible to assess genetic diversity at a large number of single nucleotide variant (SNV) loci for a reasonable cost  has traditionally been translated into precautionary concerns about genetic risks, especially preventing genetic swamping or outbreeding depression from affecting the integrity of local populations  interventions. Such AGF interventions recognize that local adaptation is a characteristic of local populations that can be used to improve the fitness of distant populations, especially in scenarios of rapid environmental change. Deliberate translocation of organisms, propagules, or genes among populations can be an effective means to facilitate the spread of adaptive alleles, particularly in areas where populations have declined to the point where colony density or genetic diversity may be insufficient to support successful sexual production of offspring  . Therefore, we propose the following strategies to maximize the chance that nursery collections will capture alleles that help coral species adapt to various environments Fig. .Biomarkers have emerged as useful tools in many fields, from plant breeding to human disease prediction. Recently, they have been suggested as attractive candidates for use in coral restoration. In theory, restoration practitioners would use a simple bioassay to uncover information about coral colony performance. This information could then be applied to select colonies for parental stocks in larval propagation, rearing in nurseries, or matching outplants to particular sites in anticipation of future environmental challenges. However, biomarkers are often context‐ and species‐specific . If survival and growth is merely suboptimal in nurseries rather than absent, then propagating poorly performing genets in the nursery at a minimal level may be warranted if they are performing particularly well during outplanting and have other desirable traits . If some corals show poor fitness in the nursery, replace them with other genets from the same patch or habitat type. Excluding poorly performing genets is unlikely to affect representation of the adaptive genetic diversity in the nursery provided representation of all habitat types is maintained. However, it is possible that local adaptation to some reef environments would prove incompatible with the nursery, which is itself a distinct habitat. Although genetic variants in these corals are still valuable as part of the adaptive genetic diversity of the species, we suggest triaging such cases  or omitting the nursery stage by rearing fragments at the native reef  is vanishingly low  can be highly clonal , thus careful site selection for outplanting is paramount (see Outplanting strategies).Survival of outplanted recruits is currently low and presents a bottleneck to the success of sexual recruitment strategies. However, examples of sexual recruits that have survived to sexual maturity and now spawn predictably each year exist .Scleractinian corals have a long history of interspecies hybridization Veron . GeneralGiven the lack of predictive tools for selecting genets a priori (Box 2), genets will likely perform differently during the propagation phase than they do on the native reef. Performance depends on interactions between genets and their local environment that are difficult to predict  partial mortality, (2) rate of wound healing, 3) skeletal growth rate, (4) bleaching and infectious disease resistance or resilience, and (5) sexual reproductive output  can be fragmented more frequently to produce more stock. Corals that grow rapidly in the field generate more structural complexity, which is often a primary goal of reef restoration. Unfortunately, linear extension rate  is not always correlated in the nursery and the field . This is cheaper and faster than assessing egg quality . Sperm motility is likely influenced by genetic factors as well as environmental factors . The cultivation of large adult fragments as “spawning stocks” within nurseries can provide opportunities to monitor the presence/absence of spawning and synchrony in spawn time among genets as well as to collect and rear sexual offspring. Reproductive size will vary considerably among species (Soong and Lang available online).A. palmata prefers relatively shallow, high energy habitats) but more specific guidelines can be difficult to establish due to the lack of baseline data, species‐specific differences in niches  in native sites or habitat types  to outplant sexual offspring. Evidence suggests that increasing recruit size increases outplant survivorship . Additional research on the optimal timing of outplanting sexual recruits is needed.In the natural environment, typical Symbiodiniaceae sources for coral recruits include the water column, sediments, and adult corals .To improve the prospects for leveraging specific genets and traits in future restoration, it is crucial to link coral genets with phenotypic performance and fine‐grain environmental data. We call for an open‐access database to catalog all managed genets in nurseries and outplants. This would also address a specific aim (Action 4a) in the Endangered Species Act recovery plan for nis NMFS . AchieviA way forward is the integration of the recommended actions into a decision‐making framework to decide on how and when interventions should be made, and the development of such a framework is another recommended research priority (Anthony et al. Reef restoration has made significant progress via the development of innovative ways to grow and outplant corals. While the task of restoring reefs in the face of rapid climate change remains daunting and will prove futile if current carbon emissions continue unabated, coral species still harbor a significant amount of genetic variation that can be leveraged to enable rapid adaptation if the restoration community adopts an evolution‐centric strategy. The heterogeneity in response to stress is what provides the hope that reef‐restoration work is worth the time and effort. We posit that this natural heterogeneity can be capitalized upon, using the recommendations herein, in a way that may speed up natural selection and provide a means for certain species and coral ecosystems to persist. Click here for additional data file. Click here for additional data file. Click here for additional data file.