Background: Climate change will have significant consequences for species. Species range shifts induce the emergence of new hybrid zones or the spatial displacement of pre-existing ones. These hybrid zones may become more porous as alleles are passed from one species to another. Currently, hybridization between highly divergent species living in sympatry seems extremely limited. Indeed, this phenomenon involves breaking two barriers. The first is the pre-mating barrier, related to the reproductive phenology of the two species. The second is the post-zygotic barrier, related to the genetic divergence between these species. Here, we were interested in identifying new hybridization patterns and potential implications, especially in the context of environmental modifications.Methods: We sampled Telestes souffia and Parachondrostoma toxostoma wild specimens from different locations across France and genotyped them for SNP markers. We identified discriminant loci using F1-hybrid specimens and parental species and performed principal component analysis and Bayesian model-based clustering to analyze phylogenetic information. Furthermore, we assessed deviation in allele frequency from F1 to F2 and for Hardy–Weinberg equilibrium for F2 and assessed gene function associated with two F2 cohorts.Results: We demonstrate that by breaking the ecological barrier, massive introgressive hybridization is possible between two endemic lineages of Cyprinidae belonging to two distinct genera. For both cohorts studied (=2 cm and >2 cm), a large majority of loci (>88%) presented no deviation in allele frequency and no departure from the Hardy–Weinberg equilibrium. For individuals beyond the 2 cm stage, two phenomena were observed. The first was an allelic imbalance in favor of P. toxostoma, for some genomic regions, with genes involved in developmental regulatory processes, cytoskeletal organization, and chromosome organization. The second was an excess of heterozygous loci coupled with an equilibrium of allelic frequencies for genes involved in immune response and kidney/liver development. Moreover, the 2 cm-sized specimens with high mortality yielded a particular genomic signature.Conclusion: Our study displayed important results for understanding the early stages of hybridization between divergent lineages and predicting the emergence of future hybrid zones in the wild. Moreover, this hybridization generates a wide spectrum of hybrids that are a potential source of important evolutionary novelties. As early as 1948, However, the ability of two species to hybridize and the fate of hybrid individuals is difficult to predict. Indeed, it is unclear how to predict future hybridization behaviors for species that do not currently hybridize. Furthermore, there may be genomic porosity between phylogenetically distant species that are hidden by current environmental conditions. Poecile atricapillus and Poecile carolinensis  or toxostome and Telestes souffia  or blageon. These two species live in sympatry and are phylogenetically distant with a time of divergence estimated at 15 million years, based on the molecular marker whole cytochrome b (Chondrostoma rysela (Günther 1868 in (P. toxostoma specimens reproduce (single spawn) essentially from April until the beginning of May in the range of water temperature from 11 to 13°C in the Buech tributary of Durance, France (T. souffia specimens reproduce (single spawn) during the month of June, in water temperatures from 10 to 14°C. Warmer temperatures and low water levels in May–June will cause the shift of breeding season to the earlier end of March and beginning of May. The most impacted species will be the T. souffia because June would not present the appropriate conditions for reproduction. Moreover, the densities of the two species are reversed along an upstream–downstream thermal gradient . For this study, we investigated 369 specimens represented by field-sampled (T. souffia and P. toxostoma ‘pure’ specimens) and laboratory-bred individuals , and 5,289 single nucleotide polymorphisms (SNPs) based on the P. toxostoma transcriptome.Given the existence of hybridization phenomena, we were interested in identifying new ones, especially in the context of environmental modifications. Moreover, hybridization is a common phenomenon in fishes in general and cyprinids in particular . We, thechrome b . These tchrome b , and suc 1868 in ). The ra, France , whereasTelestes souffia or blageon is a species of about 15 cm with a purplish stripe on the flanks, especially in males. It is protected but considered to be of least concern on the IUCN global red list  and the red list of freshwater fishes of metropolitan France (2019). We used specimens belonging to the lineage T. s. souffia, which is specific to the Rhône and Mediterranean coastal river basins (from the Hérault River to the Var River)  and near threatened on the red list of freshwater fishes of metropolitan France (2019). Its range extends from the southwest (the Adour–Garonne basin) to the southeast  of France. Both species are diploid and have an identical number of homologous chromosomes (2n = 50).The r River) . The ParT. souffia species, we selected four populations from the Rhône basin, especially in the Durance River: ARC_Ts , MAN_Ts , BUE_Ts , and AIN_Ts , which were previously described , SEP_Pt , and AIN_Pt —and two in allopatry—BER_Pt  and ORB_Pt —which were previously described  and one male P. toxostoma . Free-swimming larvae were first housed in small aquaria (40 × 25 × 15 cm); after 4 months, the fish were transferred into larger aquaria for growth (100 × 60 × 40 cm). Fish were fed live Artemia nauplii, complemented with commercial fish food. The P. toxostoma laboratory stock (LAB_Pt) was obtained by crossing one female (population GEN_Pt from the Durance River at Pertuis) and two males (population GEN_Pt from the Durance River at Pertuis). In 2014, nine backcrosses were obtained by crossing one female P. toxostoma (3 years old) from the laboratory stock and three male F1-hybrids (3 years old). Furthermore, 10 F2-hybrids were obtained in 2014 and 194 in 2018 by crossing eight F1-hybrid specimens (3 and 7 years old) by crossing two females and six males (random mating from the F1 population).In 2011, 14 F1-hybrids specimens were obtained by In total, 369 individuals  belonging to the two species and their hybrids  were genotyped for SNP markers by first digesting genomic DNA with PstI restriction endonuclease as previously described  Supplem with theT. souffia for F1-hybrids and F2-hybrids and P. toxostoma for backcross  for the whole hybrid specimens, corresponding to only two haplotypes: ackcross .https://www.biotech.cornell.edu/facilities-brc). Then, two sequencing Illumina NovaSeq 2 × 150 bp (Pair End) runs were generated with 10% Phi-X Spike-in by NOVOGENE (https://en.novogene.com). The quality of the three sequencing runs was checked independently using FastQC, and the demultiplexing step was performed using the ‘process_radtags’ program implemented in the Stacks software (2.53) . The demultiplexing step was performed as follows: reads with an uncalled base were removed, a quality Phred score = 33 was used, and reads were truncated to 80 bp (Min-Max). The corresponding command was:A sequencing Illumina Hiseq 2000 1 × 100 bp (Single End) run was generated with 5% Phi-X Spike-in by Cornell University (e (2.53) . All theP. toxostoma transcriptome  because it corresponded to a model system belonging to the same family, Cyprinidae. We used the genome nomenclature (ENSDARG) rather than transcriptome nomenclature (ENSDART) because we used the chromosome and gene position of D. rerio for mapping the discriminant SNPs between the two species P. toxostoma and T. souffia. Then, we used the ‘ref_map.pl’ program in the Stacks software (2.53). The corresponding command was:We conserved 99% of reads per individual. We used the genome mapping option of Stacks software (2.53) on the criptome . The traP. toxostoma transcriptomes. 2) The output data in SAM file format was sorted and converted to BAM file format using the software SAMtools  ShorSAMtools . 3) ThenSAMtools .P. toxostoma , wild populations of T. souffia , and farmed individuals—F1 , F2 , and backcrossed .Selecting useful SNPs from Restriction-site Associated DNA-sequencing (RAD-sequencing) constitutes an important step in data analysis . Thus, wP. toxostoma, 1 for heterozygotes, and 2 for homozygous T. souffia) based on the whole loci. The distribution of the 67,896 pairwise distances showed a multimodal distribution with three modes. We hypothesized that the first mode, corresponding to the 34 lower distances, included only the DNA and/or extraction replicates. Considering this criterion, we removed 18 specimens: twelve corresponded to DNA replicates (100%), four corresponded to extraction replicates (33%), and two were not replicates and could correspond to contaminated samples . However, for the 18 extraction replicates, twelve (66.66%) did not belong to the first distribution but were closer to their replicate than any other individual: 9/9 F1 (100% retrieved), 3/4 F2 (75%), and 0/5 backcrossed (because the five specimens clustered together).We defined the Euclidean distance between two specimens or replicates as the square root of the sum of squares of genotypic values (0 for homozygous P. toxostoma in wild populations. A particular case involves an SNP present only in a single homozygous state in P. toxostoma populations (coded as 0) and present in a single homozygous state (different) in T. souffia populations (coded as 2). In the following, such SNPs are referred to as diagnostic SNPs.Polarization of the alleles was performed. To do this, the reference allele (coded as 0) was chosen as the nucleotide with the highest proportion of P. toxostoma individuals and the proportion of homozygous genotypes (1/1) in T. souffia individuals. This score varied between 0 and 1 and was exactly equal to 1 in the case of a diagnostic SNP. In the following, an SNP will be considered discriminant if its score is strictly higher than a fixed threshold (0.9). This threshold was chosen from the empirical score distribution (see The definition of a diagnostic SNP was relaxed for discriminant SNPs by defining a score associated with each SNP as the mean average of the proportion of homozygous genotypes (0/0) in tion see .From a theoretical point of view, an F1 genotype for a diagnostic SNP that presents a sum of alleles equal to 1 is heterozygous (0/1). To verify this on our empirical data, a volcanic eruption plot was generated considering discriminant SNPs. In this plot, a point refers to an SNP. The abscissa (x) shows the mean genotype score calculated on the F1-hybrid specimens, and the ordinate (y) shows the standard deviation of the genotype score calculated on the F1-hybrid specimens. The point of coordinates  indicates perfect agreement with the theory. A deviation from the value x = 1 was tested using the classical Student’s t-test (type I error set at 0.05). All SNPs which deviated significantly from 1 were removed. When several SNPs had the same coordinate, the size of the point was proportional to the number of SNPs.i ≤ n, 1 ≤ j ≤ p where Cij was equal to 0, 1, or 2 and expressed the genotype of the jth SNP for an individual i. Here, p refers to the number of SNPs selected using previous procedures. When a value Cij was missing, it was imputed by the mean genotypic value calculated on individuals belonging to the same group as the individual i. The normalization proposed by PCA was performed on the data corresponding to the selected 641 SNPs and 350 individuals. The original data corresponded to (Cij), 1 ≤ We used a Bayesian model-based clustering algorithm implemented in the software STRUCTURE version 2.3.4 . Individuals in the sample were assigned to K populations or a mix of populations if their genotypes indicated that they were admixed . For the ancestry model, we used the option ‘admixture model’ because different alleles for hybrid specimens  could be inherited from different species populations. Furthermore, we selected the option ‘allele frequencies correlated’. The burn-in length was set to 100,000 followed by 1,000,000 iterations using a Markov Chain Monte Carlo as recommended previously . These aConsidering the 204 F2-hybrid specimens, we observed important mortality  occurring during 2 weeks, corresponding to a 2-cm size. Because this event could correspond to a specific post-zygotic barrier underlined by a particular genomic combination, we first analyzed the cohort of 166 F2 individuals who survived (>2 cm), and second, the cohort of 38 F2 individuals who died very young (=2 cm). For both cohorts, we tested two hypotheses related to the gametic and zygotic phases, respectively, during the formation of the F2 genome. Our study corresponded to a segregation distortion analysis .P. toxostoma or T. souffia. The second hypothesis was related to the allelic association to form genotypes in panmixia. The hypothesis H0_2 indicates that the genotypes are in Hardy–Weinberg (HW) equilibrium, while H1_2 indicates HW disequilibrium. We used the exact Hardy–Weinberg test (P. toxostoma or T. souffia). We used the D chi-square statistic from the HWChisqMat function from the HardyWeinberg package : the mean genotype value is equal to 1, for F2-hybrid specimens as for F1s. Otherwise, the genotype mean value is different from the value 1 (H1_1). This test was performed using the Student’s t-test with a type I error set at 0.05. A rejection of H0_1 may be interpreted as a selection of alleles in favor of erg test , using terg test . Disequi package  to ident package .http://www.pantherdb.org/) with a reference constituted by the 617 SNPs corresponding to 542 orthologous ENSDARGs. We further performed Fisher’s exact test and false discovery rate correction. Because significant differences could be found between individuals belonging to the first cohort >2 cm and those belonging to the cohort = 2 cm, two separate analyses were performed.To perform overrepresentation of biological functions, we used the PANTHER classification system (Telestes souffia and Parachondrostoma toxostoma) were reviewed and approved by local regulatory agencies: the AFB (Agence Française pour la Biodiversité) and the DDT  from Alpes-de-Haute-Provence, Hautes-Alpes and Vaucluse (authorization numbers 2007–573 and 2008–636), following national regulations.All sampling and experimental protocols involving the fish in this study , with a higher rate of discriminant SNPs for chromosomes Chr5 and Chr20 , and was removed. Finally, we obtained an intermediate data set constituted of 641 SNPs and 350 individuals. The rest of the SNP corresponded to shared alleles between the two species. The 641 SNPs corresponded to 560 P. toxostoma orthologous ENSDARGs, showing a decreasing exponential distribution. Indeed, 492 of the ENSDARGS were related to one SNP, 58 to two SNPs, 7 to three SNPs, and 3 to four SNPs. The genetic architecture map for pure individuals of each species, F1-hybrids, F2-hybrids, and backcross is presented in To validate the Mendelian inheritance of discriminant SNPs, we analyzed each 908 selected loci in F1-hybrids. As 204 SNPs were missing for the whole F1-hybrids, these were removed. The volcanic eruption plot  shows, fp < 10–6) , BER_Pt, ORB_Pt , GEN_Pt (from the Durance River), and LAB_Pt (corresponding to P. toxostoma laboratory stock); and those belonging to T. souffia ARC_Ts, MAN_Ts, BUE_Ts, and AIN_Ts. The F1-hybrids displayed a central position with low dispersion, indicating a classical 50/50 genomic composition. Similarly, the F2-hybrids displayed a central position but showed a wide range between the two species, indicating genomic heterogeneity due to parental dilution. On the other hand, as expected, the backcross specimens occupied an intermediate position between the F1-hybrid and P. toxostoma specimens.PCA based on 350 individuals and 641 SNPs clearly displayed phylogenetic information on the first axis  . Indeed,P. toxostoma species were assigned to one cluster, and those belonging to T. souffia were assigned to another cluster. The Q-score of the F1-hybrid specimens for the P. toxostoma and T. souffia clusters presented a mean of 0.507 and 0.493, respectively, and a standard error of 0.008, indicating a similar genotype. The F2-hybrid displayed a mean of 0.513 (0.487) and a standard error of 0.06, indicating a mosaic of genotypes. Finally, the backcrossed specimens yielded a mean of 0.866 (0.134) and a standard error of 0.122, indicating a genomic introgression to P. toxostoma.These results were in agreement with the STRUCTURE analysis with k = 2 . The groDanio rerio chromosomes. However, to explore a potential postzygotic barrier (lineage sorting of particular genomic combination), we first analyzed the cohort of 166 F2 individuals who survived (>2 cm) and, second, the cohort of 38 F2 individuals who died very young (=2 cm). Of the 617 discriminant SNPs, only 86 SNPs (13.94%) were significantly (p < 0.05) different from allele frequencies p = q = 0.5, indicating an increase of allele frequencies toward one species  were in HW equilibrium. The loci departing from HW equilibrium mainly presented an excess of heterozygotes  .P. toxostoma (red color in P. toxostoma genotypes (red square in T. souffia (blue color in p = q = 0.5. We detected genomic regions weighted toward P. toxostoma (chromosomes Chr16 and Chr21) and spotted regions , combined with genomic regions weighted toward T. souffia (Chromosome 11) and a spotted chromosome (Chr15). This result clearly indicated a selection toward T. souffia alleles in Chromosomes 11 and 15. However, the HW disequilibrium was less pronounced for this cohort than in the cohort >2 cm . This analysis displayed a very different pattern, as shown in rt >2 cm , indicatP. toxostoma. This pattern was particularly marked on the cohort > 2 cm indicating the presence of filters limiting the genome porosity during fish development. Considering F2 genotypes, we also observed a positive selection in favor of heterozygotes corresponding to an increase in the genomic porosity by maintaining high genomic compatibility.The porosity between the two genomes appears to be high but not complete. Indeed, we observed the signature of endogenous selection in favor of both species, mostly toward P. toxostoma and HW equilibrium (red circle in p = 5.08E-03), regulation of organelle organization , especially in the cytoskeleton , chromosome organization , cell cycle , and negative regulation of transcription, DNA-templated . The 42 genes that presented an excess of heterozygous genotypes , pronephros development , and liver development .Of the 142 genes that presented an atypical pattern, that is, deviation of allele frequencies and/or HW disequilibrium, in the cohort >2 cm, two categories were well represented. The 76 genes that presented allelic frequency toward P. toxostoma and HW equilibrium , mitochondrion organization , vesicle cytoskeletal trafficking , and regulation of protein depolymerization . The second cluster was implicated in positive regulation of potassium ion transport  and positive regulation of sodium ion transport .Of the 70 genes that presented an atypical pattern in the cohort = 2 cm, two categories were well represented. Indeed, 35 corresponded to allelic frequency toward n = 2) observed in the field for more than 25 years . Two explanations can be formulated: The first is that our P. toxostoma transcriptome has non-random areas for which we did not obtain the genes, which results in missing some SNPs. The second is that by using the position of the D. rerio genes, we assumed that the position of the genes on the chromosomes studied had not changed. It is clear that large regions of synteny exist in Cyprinidae . The study of the 617 SNPs showed that a large majority  were evolving under a panmictic model; that is, the alleles of these genes were interchangeable. The set of F2 individuals that we analyzed constitutes a sorting of compatibility between our two species after a succession of pre- and post-zygotic stages, such as gamete recognition, fertilization, embryonic development, and larval metamorphosis. Two phenomena are underlined by our results.The selected SNPs show a very strong discriminating power. Whether using PCA or assignment by STRUCTURE, we clearly separated the different genetic groups: p≠q, directional selection) in the F2 specimens, notably in favor of P. toxostoma, although it was also present in favor of T. souffia. The fact that the extreme majority of these loci were in HW equilibrium implies an unbalanced production of gametes, whose association would be random in aquatic environments. Moreover, we note that certain genomic regions, such as chromosomes Chr5 and Chr20, seemed to display this phenomenon. If these regions are indeed regions of synteny between D. rerio and the two other Cyprinidae species (P. toxostoma and T. souffia), then the structural incompatibility may involve a problem of chromatin interaction in these regions. This phenomenon could be linked with the asymmetric expression of alleles in cyprinid hybrid lineages  while others will be selected against (disruptive selection).The second is related to HW disequilibrium, essentially genes with frequencies P. toxostoma-like, while being in HW equilibrium, were involved in developmental regulatory processes and cytoskeletal organization but were also involved in chromosome organization and DNA regulation, which suggests not only that epigenetic factors are of great importance in the development and maintenance of the hybrids but also that this stability is more related to P. toxostoma alleles. Genes that showed a heterozygous advantage were involved in immune response and kidney/liver development.We were able to identify two broad categories of genes. Genes that tended to be more P. toxostoma. However, these F2-hybrids presented differences in genes involved in mitochondrion organization and positive regulation of potassium and sodium. In conclusion, the ‘genomic landscape’ of F2-hybrids is a mixture of genes whose alleles are fully compatible, at least under laboratory conditions, and genes whose alleles show different evolutionary trajectories  or the regulation of these genes. In the first case, it would be interesting to use the complete set of SNP markers to describe the different selections between gene and intergenic regions. However, the use of the Danio rerio genome remains limited because of the low similarity between Telestes/Parachondrostoma sequences and the Danio rerio genome, and a reference Telestes/Parachondrostoma genome would be necessary. In the second case, it would be necessary to work on RNA sequencing to analyze differential gene expression (DGE) in order to estimate the effects of dominance, co-dominance, and recessivity of the different alleles belonging to T. souffia and P. toxostoma.Our study showed that two endemic species belonging to two different genera had very high genomic porosity with some disadvantaged genomic combinations. We were able to identify certain genes and biological functions associated with these disadvantages. Currently, it is not possible to know whether these patterns result from a structural problem related to the DNA sequences due to allele differences  did not produce hybrid individuals. Currently, we don’t know if this result is due to a genetic incompatibility or a handling problem because nine backcrosses were obtained by crossing female P. toxostoma and male F1-hybrids, with P. toxostoma mitochondrial DNA . These results may be the consequence of imprinting effects.Moreover, it will be interesting to test the parameters involved in the overlapping breeding because the reciprocal crosses . It will be particularly interesting to decipher the gene expression of F1 and F2 hybrid specimens considering their genomic combination, especially for the F2 specimens . Common garden experiments will be associated with field sampling in order to detect new hybrid specimens that underwent the filter of environmental selection. They will have to better understand the part related to endogenous selection from those related to exogenous selection. These topics are essential in the context of global warming and accelerated environmental changes.The results obtained in the laboratory show that the main barrier is not related to genomic incompatibility. We, therefore, deduce that the barriers which currently strongly limit the emergence of a  species , forcing species . P. toxo, France , whereas at 14°C . However souffia . There w souffia , which c souffia . With cl


Phytophthora ramorum, an emergent pathogen that causes the sudden oak and larch death, spreads as reproductively isolated divergent clonal lineages. We use a genomic biosurveillance approach by sequencing genomes of P. ramorum from survey and inspection samples and report the discovery of variants of P. ramorum that are the result of hybridization via sexual recombination between North American and European lineages. We show that these hybrids are viable, can infect a host and produce spores for long-term survival and propagation. Genome sequencing revealed genotypic combinations at 54,515 single nucleotide polymorphism loci not present in parental lineages. More than 6,000 of those genotypes are predicted to have a functional impact in genes associated with host infection, including effectors, carbohydrate-active enzymes and proteases. We also observed post-meiotic mitotic recombination that could generate additional genotypic and phenotypic variation and contribute to homoploid hybrid speciation. Our study highlights the importance of plant pathogen biosurveillance to detect variants, including hybrids, and inform management and control.Invasive exotic pathogens pose a threat to trees and forest ecosystems worldwide, hampering the provision of essential ecosystem services such as carbon sequestration and water purification. Hybridization is a major evolutionary force that can drive the emergence of pathogens.  Genome sequencing of isolates of the pathogen responsible for sudden oak death in the United States and sudden larch death in Europe reveal hybridisation between European and American lineages. Globalization is driving the spread of pathogens into naïve host populations and biotic homogenization provides an advantage to generalists2. Disease outbreaks can have devastating ecological, economic and social impacts; they can reduce biodiversity, displace native species and threaten food security2. Pests and pathogens can affect landscape resilience and reduce the ability of forests to provide essential ecosystem services such as carbon sequestration and water purification3.Emerging infectious diseases caused by filamentous pathogens pose a threat to animals, crops and forest ecosystems worldwide9. The large-scale genomic changes that result from hybridization generate new genotypic and phenotypic combinations upon which natural selection can act to modify traits such as pathogenicity and transmission11. Selective forces can create the conditions that benefit the emergence of lineages with distinct traits that can result in lifestyle or host shifts. For example, human pathogens such as opportunistic yeasts and the malaria parasite have emerged following hybridization12. Plant pathogens have also emerged following hybridization events that facilitated host jumps9, increased virulence14 and host range expansion15.Hybridization can favor the emergence of infectious diseases by generating novel pathogen lineages with increased fitness that can colonize new nichesPhytophthora ramorum (Oomycota) is an exotic plant pathogen that emerged in the 1990s and is regulated and targeted for eradication and quarantines in Europe and North America. It can attack over 125 plant species and is responsible for the sudden oak death in California and Oregon16 and sudden larch death in the UK17 and France18. The pathogen comprises clonal lineages19 that are reproductively isolated and diverged between 0.75 and 1.25 million years ago (MYA)20. Lineages NA1 and NA2 are confined to North America and lineage EU2 is only found in western Europe. Lineage EU1 is broadly distributed in Europe and the West Coast of North America22. Eight additional lineages were recently described in Asia23. Recombination can only take place between individuals of opposite mating types and was demonstrated in the laboratory between lineages EU1 (mating type A1) and NA1 (mating type A2) but the progeny displayed aberrant genotypic and phenotypic variation26.27. Because of the potential for P. ramorum inter-lineage hybridization in western North America where lineages of opposite mating types co-occur22, we analyzed the genomes of samples from Canada, the USA and Europe to characterize the patterns of variation and identified hybrids among two clonal lineages of the pathogen.Preventing the emergence of novel pathogens requires the timely acquisition of data that can inform mitigation actions. Increasingly, pathogen genome sequencing is used within a biosurveillance framework to generate population genomic data across geographic and temporal scales that can be used to identify variants, recombinants and hybrids and inform pathway and risk analysesP. ramorum from a single nursery in British Columbia, Canada, where these lineages had been previously reported. The qPCR lineage genotyping pattern of isolates 16-237-021 and 16-284-032, collected from infected rhododendron plants, do not match the pattern of the known clonal P. ramorum lineages in North America and Europe (Supplementary Table P. ramorum isolates (Supplementary Table Ho = 34.59%) as in the EU1 lineage  and NA2 (mating type A2) clonal lineages of p < 0.0001; Supplementary Table ≥0.999 probability . There are 103 polymorphic sites in the mitochondrial genomes of the four lineages of P. ramorum. The mitotypes of the EU1 and NA2 lineages differ at 42 positions. The NA2 and hybrid P. ramorum mitotypes are identical , and 11 peptidases  of P. ramorum have now spread to natural forests45. This generates the potential for hybridization among those lineages in forests; the spread of a hybrid lineage would be more difficult to contain in natural forests than in nurseries where containment and eradication are possible. Our study highlights the importance of genomic biosurveillance for the detection of plant pathogen variants and hybrids to inform mitigation strategies27.So far, the P. ramorum from nurseries in British Columbia (BC), Canada between 1995 and 2017 (Supplementary Table P. ramorum46 and cultures were obtained from positive samples by plating the lesions on culture media. Lineages of the P. ramorum isolates used for genome sequencing were determined with qPCR assays that targets four SNPs in the cellulose binding elicitor lectin (CBEL) locus using an Allele-Specific Oligonucleotide (ASO) assay47. These isolates were grown on 5% V8 juice agar48 overlaid with a cellophane membrane. After 7 to 10 days, the mycelium was harvested and immediately frozen at −20 °C. The mycelium was shock frozen in liquid nitrogen and pulverized with a Mixer Mill  for 30 seconds at a frequency of 1/30.We obtained isolates of 49. DNA was quantified using the Qubit dsDNA BR assay Kit following manufacturer’s instructions. Illumina libraries were prepared from 100 ng DNA. Libraries were individually barcoded and amplified by 6 PCR cycles before pooling. Sequencing was performed on multiple lanes of the Illumina HiSeq X platform in 150 bp paired end mode. Forty barcoded libraries were pooled in equimolar amounts per sequencing lane. Samples 16-237-021 and 16-284-032 were sequenced on the Ion S5 System. Single-end libraries were prepared with the Ion Plus Fragment Library Kit starting with 100 ng of DNA which was sheared to 400 bp using a Covaris M220 instrument. Libraries were amplified by 8 PCR cycles. Each library was sequenced on an individual Ion Torrent Chip 530 with a read length of 400 bp. Sequencing reads were quality-trimmed using Trimmomatic version 0.3650 in the single-end mode (Ion Torrent reads) or paired-end mode (Illumina reads) using the following settings: LEADING:20 TRAILING:20 SLIDINGWINDOW:5:10 MINLEN:50. Adapter remnants were removed from the Illumina reads with the option: ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10. Ion Torrent adapters were removed in advance using Ion Torrent software.DNA was extracted from approximately 200 mg of tissue following a chloroform extraction protocol including RNAase treatment20, the US51 and Europe52 for a total of 95 P. ramorum genomes. Data is available from NCBI bioprojects PRJNA791184, PRJNA427329, PRJNA559872, PRJNA177509, and PRJNA558041 (https://www.ncbi.nlm.nih.gov/bioproject). More information is provided in Supplementary Data Altogether, we sequenced 48 genomes from B.C. nurseries that we combined with 47 previously published genomes from CanadaP. ramorum NA1 lineage reference genome PR-102_v3.1 using bwa mem version 0.7.1753 with default settings. Mapped reads were sorted using SAMtools version 1.954 and genome-wide average coverage was assessed using Qualimap version 2.2.155 with the bamqc option. Duplicated reads were annotated using the s (https://gatk.broadinstitute.org/hc/en-us). Variant calling was performed in two steps using Genome Analysis Toolkit . First, variants were called for each individual genome using GATK’s HaplotypeCaller with the -ERC GVCF option and the ploidy was set to 2. Next, variant files were merged using GATK’s CombineGVCFs function and then joint variant calling across all individuals was performed with GenotypeGVCFs. Raw variants were reduced to SNPs only and filtered using GATK’s SelectVariants and VariantFiltration functionalities. Only SNPs satisfying the following criteria were retained: QUAL ≥ 500, QD ≥ 10, MQ ≥ 40, FS ≤ 40, SOR ≤ 3, and MQRankSum, BaseQRankSum, ReadPosRankSum ≥ −2 and ≤2. Read depth (DP) was restricted to ≥1000 (≈10% percentile) and ≤10000 (≈99% percentile) to further increase SNP quality and only biallelic SNPs were retained. Next, SNPs were filtered on the genotype level using VCFtools version 0.1.16 (https://vcftools.github.io/man_latest.html). Genotypes with a read depth <10 (–minDP 10) and a genotype quality <30 (–minGQ 30) were set to no-call, and variants with more than 5% missing genotypes were removed. Finally, invariant sites across all samples were removed. For some analyses  the SNP set was further restricted to variants without missing genotypes.Filtered reads from the genomes generated in this study and those obtained from other sources were mapped to the 56 (NCBI accession: NC_009384.157) and variants called as described for the nuclear genome. Variants were restricted to SNPs only and the distribution of “variant confidence normalized by unfiltered depth of variant samples (QD)” was assessed to confirm the homozygous state across individuals. SNPs were filtered using GATK’s SelectVariants and VariantFiltration functionalities with the following criteria: QUAL ≥ 1000, QD ≥ 20, MQ ≥ 59, FS ≤ 20, SOR ≤ 2 and only bi-allelic SNPs with no missing genotypes were accepted. VCFtools was then used to set genotypes with low support  to no-call. Sites with more than 5% missing genotypes and invariant SNPs across all individuals were removed.Trimmed reads were mapped to the mitochondrial contig of the NA1 lineage reference genome PR-102_v3.1P. ramorum was evaluated using principal component analysis (PCA) with SNPrelate (version 1.28)58. Linkage disequilibrium‐pruning was applied on the VCF dataset of 450,656 SNP loci to subsample 3,780 markers with reduced linkage (R2 < 0.20). Ancestry estimation was conducted using Admixture among the P. ramorum lineages with the full SNP set with K = 4, the number of lineages found in North America and Europe. To assess reticulated relationships between P. ramorum individuals a neighbor-net phylogenetic network was reconstructed using SplitsTree version 4.12.859 with pairwise Nei’s genetic distances with the R package StAMPP (version 1.6.3)60.Population structuring and genetic connectivity of the unknown isolates with the four lineages of 61 to perform a genome-wide test of interspecific hybridization between P. ramorum lineages. HyDe considers a four-taxa network consisting of an outgroup and a triplet of ingroup taxa. The distribution of SNP site patterns in the triplet is used to infer a hybrid ingroup lineage that with a probability γ is sister to one lineage and with probability 1 – y is sister to the other lineage. Under the null hypothesis that admixture is absent, the expected value of y is zero. HyDe testing was performed with the ‘run_hyde_mp.py’ script of the HyDe package on all possible triplet combinations of putative parents-hybrid with NA1, NA2, EU1 and the 16-237-021 and 16-284-032 isolates  while EU2 samples were used as outgroup.We used the function hybridization in the R package Adegenet (version 2.1.5) to simulate 10 F1 hybrids between NA2 and EU1 and 10 backcrosses of the hybrid to the EU1 or NA2 parent. We generated a pairwise distance matrix with the dist function (Euclidean distance) and obtained an NJ tree to visualize the position of the observed samples and simulation of hybrids and introgressants. We used the program HyDe (version 0.4.3)62. We tested the 15,265 genes annotated in the P. ramorum NA1 reference genome PR-102_v3.1 to identify genes containing at least 10 phased SNP loci in the two hybrid samples and samples from each P. ramorum lineage. For the conserved hypothetical protein, peroxisomal membrane protein and cell 12 A endoglucanase  was then reconstructed as follows: the single nucleotide haplotypes predicted from a phased genotype were coded with their respective allele; the single nucleotide haplotypes predicted from an unphased genotype were coded as missing data. Finally, sequence alignments were collapsed to unique haplotypes within each P. ramorum lineage. Maximum likelihood (ML) gene trees were inferred using RAxML63 under the GTR model with a GAMMA parameter. Bootstrap support at nodes was determined by 1,000 iterations.Phased haplotypes were obtained by using short genome regions with physical phasing when two or more variants co-occur on the same sequencing read using WhatsHap (version 1.2)ase Fig.  the follase Fig. , the foln different classes  based on k SNP loci and obtaining the probability that an unknown P. ramorum sample belongs to each class. The assignment procedure was repeated 100 times with random samples of k = 1,000 loci. For each replicate, n = 14 putative source populations were considered: NA1 , NA2 (29), EU1 (30) and EU2 (5). We simulated eight hybrid populations  by randomly recombining SNPs from two parent lineages i.e. NA1 × NA2, NA1 × EU1, NA1 × EU2, NA2 × EU1, NA2 × EU2, EU1 × EU2. We also simulated populations backcrossed to one of the parents: (NA2 × EU1) × EU1 and (NA2 x EU1) × NA2. We simulated mutations with 50 random NA2 isolates for which 18.3% of the loci (randomly picked) were mutated by changing one of the alleles. This proportion of mutated loci was estimated by counting in 16_0237_0021 the number of loci for which at least one of the alleles could not have a NA2 origin, given the alleles found in the whole NA2 lineage for this locus (EstimateProportion.py script). The same procedure was done with 50 EU1 isolates with a proportion of mutated loci of 16.8%, to create the second “mutant” population. The naïve bayes classifier algorithm was implemented in a custom Python script using the Python package scikit-learn 0.24.1 and assuming Multinomial distribution of the data64.We designed an assignment procedure to test the most likely parents of the hybrid isolates. This procedure consisted in building a Bayesian naïve classifier model for 65. We used the P. ramorum reference genome (PR-102_v3.1) and the gff file to build the annotation database. Functional class consisted of missense (non-synonymous mutations); nonsense are mutations that cause stop codon loss or gain and frameshifts and silent mutations are synonymous. High impact mutations result in stop codon loss and gain and frameshifts; moderate impact mutations cause a codon to change the amino acid; low modifier mutations are in codons that produce the same amino acid; modifier mutations are in introns, upstream or downstream regions.To assess the potential functional impact of hybridization from genome predictions, we categorized the SNPs with regard to functional class and type using SnpEff (version 4.3) with default settingsP. ramorum lineages and the EU1 × NA2 hybrids on carrot agar (CA) plates for four days at 20 °C; 7-mm plugs were transferred to fresh CA plates (3 replicates) and incubated in the dark at 20 °C. Isolates of the putative parental lineage NA2 (n = 11), EU1 (n = 11), and two hybrid isolates were used in the growth experiments. Host inoculations were performed in triplicate, using different rhododendron leaves. Rhododendron leaves were inoculated by placing a 7-mm plug from a 7-day-old colony of P. ramorum in the center of a detached leaf and measuring lesion development after 3, 4, 5, 6 and 10 days. The original data is available in Supplementary Data We measured growth of isolates of γ-values with standard deviations by subsampling the full dataset of 450,656 SNP loci 500 times for 10,000 random loci. The Z-statistic was used to test for significance of the y-values, and p-values were adjusted for multiple testing using Bonferroni adjustment (α = 0.05/12 = 0.0041). The growth and inoculation experiments  and averaged. One-way ANOVA with multiple comparisons using Tukey contrasts for multiple comparison of means were performed between lineages for growth and lesion size.For the HyDe analysis, we obtained average Further information on research design is available in the Peer Review FileSupplementary Information (new)Description of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Bos taurus (taurine) and humped Bos indicus (zebu) subspecies, which diverged 150–500 thousand years ago. Despite extensive admixture, all African cattle possess taurine mitochondrial haplotypes, even populations with significant zebu biparental and male uniparental nuclear ancestry. This has been interpreted as the result of human-mediated dispersal ultimately stemming from zebu bulls imported from South Asia during the last three millennia. Here, we assess whether ancestry at mitochondrially targeted nuclear genes in African admixed cattle is impacted by mitonuclear functional interactions. Using high-density SNP data, we find evidence for mitonuclear coevolution across hybrid African cattle populations with a significant increase of taurine ancestry at mitochondrially targeted nuclear genes. Our results, therefore, support the hypothesis of incompatibility between the taurine mitochondrial genome and the zebu nuclear genome.The phenotypic diversity of African cattle reflects adaptation to a wide range of agroecological conditions, human-mediated selection preferences, and complex patterns of admixture between the humpless   •Using SNP data, we find evidence for mitonuclear coevolution in hybrid African cattle•Increased taurine ancestry at mitochondrially targeted nuclear genes in several breeds•Observed for genes encoding proteins that interact with mtDNA-encoded proteins•Our results support the hypothesis of mitonuclear incompatibility in African cattle Ecology, Biological sciences, Genetics, Genomics, Evolutionary biology The results of the genetic structure analysis using the fastSTRUCTURE program and an inferred number of clusters of K = 3 are shown in We first established the ancestry of the animals in our sample set using the BovineHD 777K BeadChip data. Filtering and quality control of the BovineHD 777K BeadChip resulted in 562,635 SNPs and 605 individual animals, retained for subsequent analyses . Figure B. indicus genetic material by a relatively small number of Asian zebu cattle, most of which were bulls . re bulls . This sculations .Figure 3Bos species   . Fixed n species .ω = dN/dS) with positive selection indicated by ω > 1  or the zebu  mitochondrial genomes and that two (COX3 and CYB) have undergone positive selection in both mtDNA lineages. These results provide evidence for positive selection that could lead to functional differences between zebu and taurine mitochondrial DNA sequences. However, to conclusively determine if these functional differences exist, biochemical and structural analyses of taurine and zebu mitochondrial proteins and their cognate nuclear partners will be required , and an HMG subset gene on BTA16, are in the region encompassed by selective sweeps detected separately in the EASZ breed and a composite sample of East African zebu cattle , another HMG subset gene, was detected within a positively selected region on BTA4 when African cattle were compared to commercial European and Asian breeds , production, health, and welfare traits and ultimately improve the livelihoods of smallholder farmers . Future Although we provide support for the hypothesis that mitonuclear coevolution exists between the nuclear and mitochondrial genomes of hybrid African cattle populations, this work is necessarily limited by the number of populations sampled and the density of the SNP data used. In addition, the genome-wide approach we used here is not directly amenable to gene-by-gene analyses, which could use whole-genome sequence datasets from large numbers of hybrid animals to directly identify incompatibilities between individual nuclear- and mitochondrial-encoded proteins.david.machugh@ucd.ie).Further information and inquiries about code, reagents and/or data details may be directed to the lead contact, David E. MacHugh , of which 41 were male and 32 were female, from DNA samples that were previously published by our group as part of microsatellite-based surveys of cattle genetic diversity in the early 1990s . This anHigh-density genome-wide SNP array data sets (Illumina® BovineHD 777K BeadChip) corresponding to a total of 456 animals were obtained from published studies  and for BovineHD 777K SNP locations were remapped to the current bovine genome assembly ARS-UCD1.2  using coPLINK v1.9 and the results were plotted using ggplot2 v3.3.3  of individual animal SNP genotype data for the African taurine (MUTU and NDAG), the East and West African admixed  and four Asian indicine  populations was performed using 2 v3.3.3  in the R2 v3.3.3 . The genibutions .B. taurus and B. indicus cattle lineages. For this analysis, the European JRSY and HOLS taurine breeds and the Indo-Pakistan HARI and SAHI Asian indicine breeds were also included to ensure good representation of the B. taurus and B. indicus mtDNA haplogroups—the ‘T’ and ‘I’ groups, respectively , and Asian B. indicus  mtDNA lineages, and the following additional Bos species: B. gaurus – gaur ; B. frontalis – mithun ; B. grunniens – domestic yak ; B. mutus – wild yak ; B. javanicus – banteng ; and B. primigenius – aurochs . The protein-coding sequence for 13 essential OXPHOS genes were aligned using the MAFFT v.7.49 software package  was evaluated using the dN/dS ratio (ω) branch site test for positive selection  and two of the Asian B. indicus breeds (GIR and NELO). The MOSAIC algorithm requires phased haplotypes and a recombination rate map; therefore, SHAPEIT v2 (r900)  was inferred using MOSAIC v1.3.7 . The MOS2 (r900)  was used2 (r900) .B. taurus nuclear genes that encode mitochondrially targeted proteins (Nu-mito genes) in African admixed cattle, we used an approach modified from surveys of mitonuclear incompatibilities in modern admixed human populations  and modern humans (high-mito” genes (HMG) encoding proteins that directly interact with mtDNA-encoded protein subunits in OXPHOS and ribosomal complexes, or that have functions in mtDNA replication (136 genes); and 2) lower confidence “low-mito” genes (LMG), which encode proteins that localize to the mitochondrion (661 genes) but are not classified as part of the high-mito subset. Finally, a third group of “non-mito” genes (NMG) was generated, which includes the bulk of the mammalian proteome that does not localize to the mitochondrion . To determine if there was significant retention of ulations  and a stn humans . Firstlyn humans  was usedn humans  was usedB. taurus ancestry. The first step in this methodology is subtraction of the mean ancestry fraction across the local ancestry estimate for each SNP (the expectation), which produces the deviation in local ancestry at each SNP locus. For each functional gene subset, the number of windows sampled with replacement was the same as the number of HMG subset genes (n = 136). In each case, the mean ancestry deviation was estimated and then averaged across all windows. Bootstrap resampling (1000 replicates) was used to generate a distribution of mean deviations in local ancestry for each of the three functional gene subsets. Overall significance of the distributions was assessed by the proportion of the distribution that overlapped zero. Mean taurine ancestry was determined for each of the gene subsets across all ten populations. Differences in the population means of these were assessed using the non-parametric Wilcoxon signed-rank test in R v3.6.2 (The local ancestry estimates generated using MOSAIC for each SNP across the genome were cataloged and the BEDTools v2.18 software suite  was thenR v3.6.2 .


Genomic selection has a great potential in aquaculture breeding since many important traits are not directly measured on the candidates themselves. However, its implementation has been hindered by staggering genotyping costs because of many individual genotypes. In this study, we explored the potential of DNA pooling for creating a reference population as a tool for genomic selection of a binary trait. Two datasets from the SalmoBreed population challenged with salmonid alphavirus, which causes pancreas disease, were used. Dataset-1, that includes 855 individuals (478 survivors and 377 dead), was used to develop four DNA pool samples . Dataset-2 includes 914 individuals (435 survivors and 479 dead) belonging to 65 full-sibling families and was used to develop in-silico DNA pools. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate genomic breeding values (GEBVs). The correlation between SNP effects estimated based on individual genotypes and pooled data increased from 0.3 to 0.912 when the number of pools increased from 1 to 200. A similar trend was also observed for the correlation between GEBVs, which increased from 0.84 to 0.976, as the number of pools per phenotype increased from 1 to 200. For dataset-1, the accuracy of prediction was 0.71 and 0.70 when the DNA pools were sequenced in 40× and 20×, respectively, compared to an accuracy of 0.73 for the SNP chip genotypes. For dataset-2, the accuracy of prediction increased from 0.574 to 0.691 when the number of in-silico DNA pools increased from 1 to 200. For this dataset, the accuracy of prediction using individual genotypes was 0.712. A limited effect of sequencing depth on the correlation of GEBVs and prediction accuracy was observed. Results showed that a large number of pools are required to achieve as good prediction as individual genotypes; however, alternative effective pooling strategies should be studied to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection. Genomic selection (GS) is becoming a practical and effective breeding tool for many livestock species because of the rapid development of high-throughput genotyping technologies that reduce genotyping costs. The potential application of GS for aquaculture species has been studied , and it For a conventional GS breeding program, two large datasets are required: a training set (reference population) with genotyped and phenotyped individuals and a prediction set (selection candidates) containing only genotyped individuals . The sizEven though the genotyping cost per individual is reducing, implementation of conventional GS is expensive in aquaculture compared to other livestock species because of the fact that the number of selection candidates and their siblings to genotype is large. Therefore, it is of interest to reduce either the number of individuals or the number of markers to genotype without reducing the prediction accuracy significantly. Strategies for reducing the number of markers have been described in many studies . This stPooling of DNA samples and sequencing have provided a cost-effective alternative for a wide range of genomic applications, such as population genetics , genome-in-silico DNA pooling. The effect of the number of pools and sequencing depth on the selection accuracy is studied. The study uses datasets generated for a breeding work to improve resistance against pancreas disease (PD).in-silico pooling.Two datasets from the SalmoBreed breeding population of two year-classes (YC) were used for this study. Dataset-1 was from YC 2013, and dataset-2 was from YC 2015. The trait of interest was resistance to PD. PD is currently among the most economically important diseases in Norwegian Atlantic salmon production. It is caused by a salmonid alphavirus (SAV) of which SAV2 and SAV3 variants are found in Norway. These datasets were generated as part of the SalmoBreed’s annual challenge test for the practical breeding work to improve host resistance against PD. The first dataset was used for real DNA sample pooling and the other was used for Data and tissue samples on 5,223 postsmolts belonging to 273 full-sib families from the SalmoBreed elite population challenged with SAV3 were available. The mortality profiles for this dataset are presented in DNA was extracted from each selected individual, quantified using the Quant-iT PicoGreen dsDNA assay kit, and normalized to a standard concentration for every individual; subsequently, equal quantities/volumes of DNA from each individual were pooled to make a specific pool group . The M1 and M2 pools contained DNA representing 173 and 204 mortalities, respectively, while pools S1 and S2 incorporated DNA from 205 and 273 surviving individuals, respectively. The dead individuals of pools M1 and M2 represented 28 families, while pools S1 and S2 were represented by 35 and 34 families, respectively.Libraries were prepared for sequencing using the Illumina PCR-free genomic DNA sample prep kits which were sequenced to approximately 40× depth. The sequencing was performed with an Illumina NextGen 500 instrument to obtain paired-end sequence reads of 150 bp. Trimmomatic software was used to perform adaptor and quality trimming of the generated sequence reads, and subsequently, high-quality sequence data were aligned to the Atlantic salmon genome reference sequence (assembly ICSASG_v2) using BWA-MEM version: 0.7.13-r1126 . SNP detData from 4,115 postsmolts belonging to 282 full-sib families from the SalmoBreed elite population challenged with SAV3 were available. The mortality profiles for this dataset are presented in Genotypes for the customized NOFSAL2 SNP array with ∼57 K SNPs were available for each individual. Pooled genotypes were generated in-silico as the frequency of alleles of the individuals in the pools and no random error was added to the pool genotypes.bcftools, version: 1.10.2  were obtained using a customized script implemented with : 1.10.2 . The obsThe reference set was used for the calculation of allele frequencies by in-silico pooling of the genotypes of individuals in this set. They were pooled into different numbers of pools  per phenotype group . Summaries of the average number of individuals and families per pool and per phenotype group are presented in The main difference between individual genotypes and pooled DNA regarding estimation of marker effects is that for the latter, a trait value of individuals cannot be assigned to a particular marker genotype exclusively. Therefore, marker effects are to be estimated from marker allele frequencies calculated from pooled DNA.X is the matrix of genotypes dosage for all SNP coded as 0,1, and 2 and for all animals, For the individual genotypes, marker effects were estimated by fitting the marker-based genomic model (SNP-BLUP):Z is the matrix of average allele frequencies for all SNPs and for all pools, For the pools, the marker effects are estimated by fitting a slightly modified marker-based genomic model:SNP-BLUP , which ij, and Estimation of genomic breeding values (GEBVs) for the selection candidates was performed by summing the effects of the markers multiplied by the standardized genotypes. Two GEBV values per individual were predicted using SNP effects estimated using pool data and the individual genotypes.For dataset-1 DNA pooling of samples, the effect of sequencing depth on the accuracy of selection was studied by varying the sampling from originally available 40× to 20×. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and used to calculate GEBVs as described in the estimation of GEBV section.For dataset-2 in-silico DNA pooling, the effect of sequence coverage on the accuracy of selection was studied by varying the sampling times to 40× and 100×. SNP effects from the pool data were calculated based on allele frequencies estimated from the pools and compared with SNP effects estimated from individual genotypes.h2 = 0.3).GEBV is the estimated GEBVs, h2 is the heritability of the trait.The accuracy of selection was calculated as the correlation between predicted GEBVs and phenotypes and weighted by the square root of the heritability  are presented in Differences in SNP frequencies between survivor and dead in-silico DNA pools are presented in The correlations between SNP effects estimated from individual genotypes and based on allele frequencies calculated from the in-silico DNA pools are presented in Two sets of GEBVs for the validation dataset were calculated based on SNP effects estimated from individual genotypes and in-silico pooled DNA. Pearson correlation coefficients between these two sets of GEBVs are presented in For the validation dataset in dataset-2–in-silico DNA pooling, genomic prediction accuracies were calculated as the Pearson correlation coefficients between true phenotypes and predicted GEBVs and weighted by the inverse of the square root of heritability of PD . The resThe accuracy of prediction values for dataset-1–DNA pooling of samples and individual SNP chips are shown in Implementation of a conventional GS in aquaculture species is very expensive because of the very large number of selection candidates and test-sibs to be genotyped. In recent years, cost-efficient GS design approaches either to minimize the number of individuals or the number of markers to genotype, without significantly reducing the accuracy of selection, have been given emphasis . This stThe datasets used in this study were generated as part of the SalmoBreed’s practical breeding work to improve host resistance against PD. PD is currently one of the most economically important diseases in the Norwegian production of Atlantic salmon . The disA sequence of DNA pools from individuals gives estimates of allele frequencies at SNPs with small or no loss in accuracy for a considerably lower cost compared to individual genotyping. Estimation of marker effects relies heavily on the accuracy of allele frequencies calculated from DNA pools. The accuracy of allele frequency estimation from pooled DNA samples depends on some experimental design parameters , such asFor allele frequency calculation and SNP effects estimation, the importance of sequencing coverage decreased as the number of DNA pools increased  and 4. GThe accuracy of GS is expected to increase as the number of genotyped and phenotyped animals in the reference population increases for any trait, in particular, for lowly heritable traits . Our resThe accuracy of breeding values using pedigree information for dataset-2  was 0.48 (the result is not presented). This accuracy was significantly improved by the use of genomic information from the pools, which is in agreement with other reports , especiaResults from using DNA pooling for genomic prediction are lacking. Our results show a trade-off between the number of DNA pools and the loss of prediction accuracy. A reduction in prediction accuracy means a reduction in genetic gain. This has a cost implication that is complex to quantify as it is determined by the trait, the breeding goal, and other specifics of the breeding industry. Assuming $20 genotyping cost per individual and $300 cost of sequencing per sample for a 40× sequencing depth, the annual genotyping cost for 5,000 individuals  is $100,000. However, for the DNA pool scenario, where only 1,000 candidates are genotyped and the reference siblings are pooled, the cost of genotyping varies from $20,600 to $140,000 for a single pool per phenotype to 200 pools per phenotype, respectively. Increasing the number of pools also increases the cost of sequencing and hence an appropriate pooling strategy should strike an optimal balance between cost-effectiveness and accuracy. Furthermore, in the present study, we have presented the prediction of allele frequencies from the pools of DNA using sequencing of the pools. However, calculation of allele frequencies from pooled DNA does not necessarily require sequencing of the pools. It has been reported that allele frequencies from DNA pools can also be calculated by SNP genotyping of the pools using light intensities ; hence, As it is presented in the study, DNA pooling of a reference population can serve as a cost-effective GS approach, but with a potential limitation in that the identity of individuals would be lost and therefore individual characteristics and environmental factors could not be adjusted in genomic modeling, which may result in a loss in accuracy and a biased estimate of a genetic effect. In the current study, pooling within phenotype groups was done randomly; however, DNA pooling of a reference population can serve as a cost-effective GS approach, but with some potential limitations. Results showed that a large number of pools are required to achieve as good genomic prediction accuracies as individual genotypes; however, alternative effective pooling strategies should be exploited to reduce the number of pools without reducing the prediction power. Nevertheless, it is demonstrated that pooling of a reference population can be used as a tool to optimize between cost and accuracy of selection.


Eriobotrya japonica Lindl.). Auxin is known to regulate fruit development, but its role and metabolism during the development of loquat fruit remain obscure. In this study, we used a multi-omics approach, integrating whole-genome resequencing-based quantitative trait locus (QTL) mapping with an F1 population, population genomics analysis using germplasm accessions, transcriptome analysis, and metabolic profiling to identify genomic regions potentially associated with fruit weight in loquat. We identified three major loci associated with fruit weight, supported by both QTL mapping and comparative genomic analysis between small- and large-fruited loquat cultivars. Comparison between two genotypes with contrasting fruit weight performance by transcriptomic and metabolic profiling revealed an important role for auxin in the regulation of fruit development, especially at the fruit enlargement stage. The multi-omics approach identified homologs of ETHYLENE INSENSITIVE 4 (EjEIN4) and TORNADO 1 (EjTRN1) as promising candidates for the control of fruit weight. Three single nucleotide polymorphism (SNP) markers were also closely associated with fruit weight. Results from this study provide insights into the genetic and metabolic controls of fruit weight in loquat from multiple perspectives. The candidate genomic regions, genes, and sequence variants will facilitate our understanding of the molecular basis of fruit weight and lay a foundation for future breeding and manipulation of fruit weight in loquat.Fruit weight is an integral part of fruit quality and directly influences the commodity value and economic return of fruit crops. Despite the importance of fruit weight, its underlying molecular mechanisms remain understudied, especially for perennial fruit tree crops such as cultivated loquat ( Fruits are rich in a variety of nutrients needed by humans to maintain a healthy life, including vitamins, trace elements, and sugars . Fruit wSlIAA17 in tomato led to larger fruits compared with the wild type, and this was caused by increased cell size and thicker pericarp [SlPIN4 in tomato led to parthenocarpic fruits, suggesting a role for PIN proteins in the auxin regulation of fruit set [SlARF7 negatively regulates fruit set and development by modulating auxin responses [SlARF9 plays a negative role in cell division at early fruit developmental stages, and increased SlARF9 expression in transgenic plants led to smaller fruits compared with the wild type [IPT) increased fruit growth by promoting cell division and expansion [Developmental regulation by endogenous auxin in plants is mediated by many processes, including auxin metabolism, i.e. the biosynthesis and conjugation, transport, and signaling of auxin, the balance of which leads to auxin homeostasis , 16. Thepericarp . In the ruit set . ARFs arruit set , 28. SlAesponses . SlARF9 ild type . Genes txpansion .FRUIT WEIGHT2.2 (FW2.2), was identified and cloned in tomato [fw2.2 encodes a negative regulator of cell division, and changes in its regulation caused differences in fruit size [FW2.2, a series of other loci controlling fruit weight were also identified, such as FW1.1, FW2.1, FW3.3, FW4.1, FW4.2, and FW11.2 [SlKLUH, a gene underlying FW3.2 in tomato, was found to control fruit and seed weight by regulating cell proliferation in the pericarp [FW11.3 was reported to control fruit weight by increasing the cell size [Quantitative trait locus (QTL) mapping has been widely used to identify genomic regions and genes associated with fruit weight and size. The first QTL controlling fruit size, n tomato . fw2.2 euit size , 34. In d FW11.2 ,36. SlKLpericarp , 38. FW1ell size . Howeverell size , 41, peaell size , 43, peaell size , 45, graell size , and bluell size , to idenTranscriptome sequencing is an efficient approach to understanding the transcriptional regulation associated with fruit weight/size. A study in pear compared transcriptome profiles between two accessions with contrasting fruit size at three early fruit developmental stages . Their rEriobotrya japonica Lindl.; 2n = 2x = 34) is a subtropical evergreen fruit tree species from the Rosaceae family that is native to China [EjBZR1, was identified, which binds to the EjCYP90 promoter to repress its expression and cell enlargement [Current knowledge about the regulatory mechanisms of fruit weight/size is mainly derived from annual plants, whereas fewer studies are available for perennial plants. Cultivated loquat , population genomic analysis (using germplasm accessions), transcriptome profiling, and metabolic profiling to identify candidate genomic regions and shed light on mechanisms associated with fruit weight in loquat. Results from this study reveal insights into the genetic and metabolic controls of fruit weight in loquat and provide abundant resources for molecular breeding.In this study, we used a combination of QTL mapping  derived from crosses between “Ninghaibai”  and “Dafang” , two loquat cultivars from China (Zhejiang) and Japan, respectively, were directly retrieved from our previous report [1 population , more than 21 Gb of sequencing data (>27.00× coverage) were obtained for each parent, and an average of 8 Gb of sequencing data per sample (9.57× coverage) were obtained for the Fividuals . The refThe identified SNPs were used for genetic map construction with a Bin map method and HighMap software . The SNPQTLs were identified for fruit weight based on the high-density linkage map and the phenotypic data. In total, 17 QTLs were detected on eight LGs, corresponding to eight chromosomes based on the “Seventh Star” genome, including Chr3, Chr4, Chr8, Chr12, Chr14, Chr15, Chr16, and Chr17 , Table 2Fst using a sliding window approach identified genomic regions that were highly genetically differentiated between the small- and large-fruited groups. By focusing on genomic regions above the top 1% Fst line (Fst = 0.31), we found that a large proportion of Chr1 was highly differentiated between the two groups. Moreover, we noticed a region of Chr8 that contained the highest peak above the top 0.1% Fst line (Fst = 0.42). Interestingly and importantly, this highly differentiated region between the two groups on Chr8 overlapped with a major QTL associated with fruit weight on the same chromosome  owing to its high correlation coefficient (0.964) with fruit weight. Data were recorded at 0, 7, 14, 28, 42, 56, 63, 77, 84, and 91 days past anthesis (DPA)  . The fruAn average of 7 Gb of transcriptome sequencing data per sample were obtained for a total of 30 samples  . To utilFst regions, we found 311 DEGs located only in QTLs, 16 DEGs located only in top 1% Fst regions, and eight DEGs located in the overlapping regions. These genes with different expression patterns will facilitate subsequent candidate gene searches.To further identify the different expression patterns between ZP44 and ZP65 during fruit development, the DEGs were clustered into groups using the K-means method , Fig. 4.Considering the possibly important role of auxin metabolism in fruit development, auxin metabolic status was investigated in ZP44 and ZP65 at the same five stages . A totalFst regions; 3) differential expression between ZP44 and ZP65; 4) homology to known genes associated with fruit weight or related functions ; 5) presence of or proximity to markers highly associated with fruit weight ; and 6) presence of polymorphisms in functional domains between small- and large-fruited genotypes. Because of the potentially different genetic backgrounds of the plant materials used for QTL analysis, Fst analysis, and RNA-seq, a candidate gene may not necessarily meet all the criteria. After mining, we identified two candidate genes with relatively more evidence, a homolog of ETHYLENE INSENSITIVE 4 (EjEIN4) and a homolog of TORNADO 1 (EjTRN1) (Auxin Response Factor 5 (EjARF5), S-adenosylmethionine decarboxylase proenzyme 1 (EjSAMDC1), and Growth-Regulating Factor 1 (EjGRF1)  location in QTLs; 2) location within or adjacent to top 1% (EjTRN1) . Three o(EjGRF1) . For vis genomes , S13.1 individuals, as well as commonly known small- and large-fruited loquat cultivars/accessions  segregated in the F1 population, whereas the genotype of Marker 3 was fixed for the F1 population and only showed high association with fruit weight in the natural germplasm accessions. Using the predicted genotypes of Marker 1 and 2 from GATK, the fruit weights for F1 individuals with different genotype combinations were compared. Marker 2 was used for the comparison because there were more data available for the minor class of genotype combinations than for Marker 2′. The results showed that individuals with “G/G and A/A” had lower fruit weight, those with “G/C and A/C” had higher fruit weight, and those with only one “large-fruited” genotype (either “G/C” and “A/A” or “G/G” and “A/C”) were somewhere in the middle , and 3, were identified using a validation panel composed of the extremely small- and large-fruited Fcessions . Marker e middle . These rEjEIN4, EjTRN1, and EjARF5). The whole gene region of EjTRN1 was deeply covered by uniquely mapped Illumina reads for ZP44 and ZP65 (both are homozygous for the whole gene region). Therefore, the sequences were manually retrieved by visualizing the alignment file. For EjEIN4 and EjARF5, we aligned the 3rd generation PacBio HiFi reads of Puye (available in-house from our genome projects) to the Jiefangzhong reference genome and manually retrieved the gene sequences by visualizing the alignments. The accession Puye was used because of its homozygous “small-fruited” genotypes at Markers 1 and 2, as well as the availability of HiFi read data. Strikingly, EjTRN1 turned out to be a highly promising candidate gene. In Arabidopsis, TRN1, an auxin-signaling pathway gene, is involved in auxin transport, cell proliferation, and plant growth [EjTRN1 was differentially expressed between ZP44 and ZP65 , which was highly associated with fruit weight. In Arabidopsis, GRF1 was reported to play a role in the regulation of cell expansion and to influence organ sizes [In addition to the above genes, and ZP65 . In applbidopsis . By inveS6 stage . It is lan sizes , 68. TheFruit weight is an integral part of fruit quality for fruit tree crops such as loquat and directly influences commercial value and consumer acceptance. Despite the importance of fruit weight, its associated markers and underlying genetic mechanisms, which could greatly facilitate the breeding of cultivars with optimal fruit weight performance, remain understudied in loquat. This is largely due to a major obstacle specific to perennial trees: they usually must undergo a juvenile phase of many years before entering the reproductive stage and bearing fruit. Plant hormones, especially auxin, have been reported to play important roles in the regulation of fruit development , 70. How1 population in a genome-wide manner. Because of the large number of co-segregating SNPs, we used a Bin method that has been used previously [High-density genetic maps are an important genomic resource and a prerequisite for QTL mapping and map-based cloning , 71, 72.eviously , 73, 74 eviously , 76.1 individuals, which may only reveal a rough genomic region, or to the limitation of a single year of phenotypic data. However, we did observe three major QTLs that explained 20.0%–49.7% of the phenotypic variation. Compared with QTLs with minor effects [The high-density genetic map and phenotypic data enabled the identification of 17 QTLs potentially associated with fruit weight in loquat. The number of QTLs for loquat fruit weight seems to be higher than that for several other crops, such as blueberry (seven QTLs) , grapevi effects , 47, maj effects . The maj effects , 78. TheFst peak on Chr8 was highly consistent with the QTL peak on the same chromosome. Therefore, the Fst analysis based on loquat germplasm accessions with diverse genetic backgrounds supports the existence of a major locus associated with fruit weight on the lower part of Chr8. It is highly possible that the alleles controlling fruit weight at this locus are prevalent in loquat germplasm. Similarly, the Fst peaks on Chr12 and Chr15 and located within the two major QTLs also support the existence of two other major loci associated with fruit weight. We believe that the three loci on Chr8, Chr12, and Chr15 play an important role in controlling fruit weight in loquat, as evidenced by both QTL-mapping and Fst analysis. Apart from these three major loci, we also observed many genomic regions that were highly differentiated between small- and large-fruited loquat accessions on other chromosomes, consistent with the large number of QTLs identified by the bi-parental QTL mapping approach. Collectively, the overlapping regions between QTLs and top Fst regions are good candidate genomic regions that potentially control fruit weight in loquat.Considering the large number of QTLs and the relatively large corresponding genomic regions, we also used WGRS data from commonly known small- or large-fruited loquat cultivars or accessions from the natural population preserved in the germplasm repository, whose fruit weight has been tested for many years. Corroborating our QTL results, the top Fst analysis, and RNA-seq analysis should be interpreted with caution, as different materials were used in these analyses, although the two materials used for RNA-seq (ZP44 and ZP65) are offspring of two large-fruited accessions (Zaozhong No. 6 and Peluches) included in the Fst analysis. We propose that the overlapping results between the QTL analysis and Fst analysis could be a coincidence but may also have significant relevance and implications. The germplasm accessions used for Fst analysis represent diverse genetic backgrounds, probably covering most of the gene pool for fruit weight/size. By comparison, the two parental cultivars used for the QTL analysis represent relatively narrower genetic backgrounds, and it is likely that some genes/alleles controlling fruit weight/size may already be fixed (not segregating) in the F1 population. From this perspective, the overlapping results could be a coincidence, as it is unknown which genes from the whole gene pool are represented or segregating in the F1 population. An example would be Marker 3 (located within EjTRN1), whose genotypes/alleles are fixed or not segregating in the F1 population but are highly associated with fruit weight in the germplasm validation panel. From another perspective, the genomic regions with high Fst values are highly differentiated between the small- and large-fruited groups. These Fst peaks were captured because the SNP alleles in these regions are quite different between the two groups. Therefore, the overlapping results could cover genes/alleles that were not only segregating in the F1 population but also common/universal in the loquat germplasm. In other words, this could cover genes/alleles that were recombined after human selection and breeding, during which the large-fruited alleles may have accumulated in the large-fruited group, whereas the small-fruited alleles may have accumulated in the small-fruited group. Both Marker 1 and Marker 2 (located in both QTLs and top Fst regions) can serve as examples, as they are highly associated with fruit weight for both the F1 individuals and the germplasm accessions in the validation panel.However, the results from the QTL analysis, Fst analyses, the genes underlying fruit weight/size in the F1 population or in the germplasm accessions may not necessarily be differentially expressed between ZP44 and ZP65. Therefore, when searching for candidate genes, we not only focused on DEGs but also used a series of other criteria, such as whether they were tagged with markers highly associated with fruit weight or homologous to known genes associated with fruit weight. Given that ZP44 and ZP65 were derived from a cross between two large-fruited cultivars, it is highly likely that they are of the “large-fruited” genotype at many loci, including those tagged with Marker 1 and Marker 2. Supported by abundant evidence, EjTRN1 (tagged with Marker 3) may be one of the promising candidate genes that can explain the extremely small fruit weight of ZP44.Because the two materials used for RNA-seq, ZP44 and ZP65, differ from those in the QTL and EjTRN1, EjARF5, and EjSAMDC1  [trn1 and trn2 mutants compared with the wild type and could lead to twisted growth by changing auxin distributions [EjTRN1 seemed to decrease to a lower level significantly earlier in ZP44 than in ZP65 during fruit development, implying that auxin transport and distribution may be affected in ZP44 compared with ZP65. This may contribute to a shorter duration of auxin supply in ZP44, as supported by the expression patterns of EjSAMDC1 as well as the results from metabolomics. In addition to auxin, ethylene may also play an important role in loquat fruit development, as the candidate gene EjEIN4 is an ethylene receptor gene. The ethylene and auxin signaling genes may act together to regulate fruit development. Therefore, the metabolic status of ethylene may be worthy of future research.The role of auxin in fruit development is achieved through the regulation of auxin-responsive genes by gene families such as  (SAURs) –84. Auxiibutions , 85. In In this study, combining bi-parental QTL mapping and population genomics analysis of small- and large-fruited accessions, we identified three major loci on Chr8, Chr12, and Chr15 potentially associated with fruit weight, as well as other genomic regions. We developed three SNP markers associated with fruit weight, which can be further applied to the breeding of loquat cultivars with higher fruit weight. Our results revealed that the auxin signaling pathway may have been under selection over the breeding history of loquat, leading to cultivars with various fruit weights. Results from this study provide insights into fruit weight using a multi-omics approach, with perspectives from genomics, transcriptomics, and metabolomics. Overall, the genomic regions, candidate genes, and other results provide abundant and critical information for understanding the genetic basis of fruit weight in this fruit tree crop, and they lay a solid foundation for future breeding and manipulation of fruit weight/size in loquat.1 population was obtained from crosses between two loquat cultivars, Ninghaibai  and Dafang , which were planted at the Yangdu Scientific Research and Innovation Base of Zhejiang Academy of Agricultural Sciences  in 2012. A total of 130 F1 plants that stably formed flowers and bore fruit since 2017 were selected for investigation of fruit weight at the mature fruit stage in May 2020. The plants were grown under natural conditions with regular management, without flower or fruit thinning or bagging. From each tree, 30 loquat fruits were randomly collected for measurement of fruit weight. The details of the phenotyping were described in our previous report [An Fs report . A total1 population and the two parental cultivars were subjected to whole-genome resequencing using the Illumina NovaSeq 6000 (150-bp paired-end) platform. DNA samples of germplasm accessions were subjected to whole-genome resequencing using the Illumina HiSeq 2000 (125-bp paired-end) platform.DNA samples were extracted using young leaves and the M5 CTAB Plant gDNA Extraction kit . DNA samples of the Fhttps://www.broadinstitute.org/gatk/guide/). The identified SNPs were filtered based on the following criteria: 1) bi-allelic SNPs; 2) quality >30; 3) Depth ≥ 4; 4) missing rate < 0.25; 5) minor allele frequency > 0.05.Clean reads generated from whole-genome resequencing were mapped to the “Seventh Star” reference genome , which wFor linkage map construction, a Bin strategy (patent application number: 201810689069.8) developed by Beijing Biomarker Company  was applied with HighMap software . In brieQTL mapping for fruit weight was carried out using the interval mapping model in MapQTL 6.0 software . To deteFst) values  were calculated using VCFtools [To determine the genetic relationship of 20 commonly known small- or large-fruited loquat cultivars, a phylogenetic tree was constructed using the filtered SNPs and PHYLIP with 100 bootstraps . PrincipVCFtools  with the2(Fold Change)| ≥ 1 and FDR < 0.05. To identify and compare gene expression patterns across different time points, the genes were clustered using the K-means method.Library construction and RNA-seq were performed by Wuhan Metware Biotechnology Co., Ltd. . HISAT2  was usedFruit tissues collected from ZP44 and ZP65 at the above five time points with three biological replicates were used for auxin metabolite profiling. Auxin metabolite extraction and quantification were performed using the AB Sciex QTRAP 6500 ultra-performance liquid chromatography–tandem mass spectrometry (LC–MS/MS) system following standard procedures. The procedures were performed as previously described , except Fst regions were extended from both directions, and sequences (500 bp in length) were extracted from the “Seventh Star” genome. The extracted sequences were mapped to the “Jiefangzhong” genome using Blastn to identify the corresponding genomic regions. To identify loquat homologs to known genes potentially associated with fruit weight, the keywords “cell division/elongation/enlargement/expansion/size”, “organ growth/size”, and “auxin signaling” were searched in the Swiss-Prot database. Related Arabidopsis genes were used to identify loquat homologs in both reference genomes using an All-Against-All Blast and OrthoMCL approach [Fst regions or those that were DEGs were prioritized in the candidate gene search. These candidate genomic regions and genes were plotted on the two reference genomes using TBtools [To make use of the two available reference genomes for the candidate gene search, the coordinates of flanking markers/borders for identified QTL regions and top 1% approach  (inflati TBtools .https://primer3.ut.ee/). Ejβ-actin was used as the reference gene [Expression levels of the candidate genes were validated using quantitative real-time PCR. Primers were designed using Primer3 . In addition, the primers were also used to genotype all known and available Eriobotrya species currently maintained in our loquat germplasm preservation garden . The Sanger results were visualized using DNA Baser v4 software (http://www.dnabaser.com/). Primers are provided in For the candidate SNP marker potentially associated with fruit weight, primers flanking the SNP were designed and used for PCR amplification with PrimeSTAR HS DNA Polymerase . Sanger sequencing of PCR amplicons of the extremely small- and large-fruited loquat germplasm accessions and FThis research was funded by the National Key R&D Program of China (2019YFD1000200), the Key-Area Research and Development Program of Guangdong Province (2018B020202011), Research Start-up Funding from South China Agricultural University, the National Natural Science Foundation of China (31901973), and the Collaborative Innovation Project from the People’s Government of Fujian Province & Chinese Academy of Agricultural Sciences (XTCXGC2021006).1 population and helped with phenotyping. Z.L. and G.H. provided funding support. Shunquan L., X.Y., and X.C. secured loquat germplasm accession materials. Z.P. and C.Z. performed data analysis. Z.P. and C.Z. wrote the original manuscript draft. All authors reviewed and approved the manuscript.X.Y., W.S., and Z.P. designed and supervised the project. C.Z., Shuqing L., and Y.G. performed fruit weight phenotyping. J.C. and H.X. maintained the FAll the raw sequencing data have been deposited at China National GeneBank (CNGB) database under Project accession number CNP0002296, Sample accession number CNS0462964-CNS0463140, and Experiment/Run accession number CNR0460669-CNR0460845.The authors declare that they have no conflict of interest.Horticulture Research online.Web_Material_uhac037Click here for additional data file.


OsCSLD4 in hemicellulose biosynthesis pathway was investigated in details. The OsCSLD4 function-disrupted mutant displayed reduced SSAS tolerance, biomass and grain yield, whereas the OsCSLD4 overexpression lines exhibited increased SSAS tolerance. Collectively, this study not only reveals the potential role of cell wall matrix polysaccharides in mediating SSAS tolerance, but also highlights applicable value of OsCSLD4 and the large-scale screening system in developing SSAS-tolerant rice.Salt–alkali stress threatens the resilience to variable environments and thus the grain yield of rice. However, how rice responds to salt–alkali stress at the molecular level is poorly understood. Here, we report isolation of a novel salt–alkali-tolerant rice (SATR) by screening more than 700 germplasm accessions. Using 93-11, a widely grown cultivar, as a control, we characterized SATR in response to strong salt–alkali stress (SSAS). SATR exhibited SSAS tolerance higher than 93-11, as indicated by a higher survival rate, associated with higher peroxidase activity and total soluble sugar content but lower malonaldehyde accumulation. A transcriptome study showed that cell wall biogenesis-related pathways were most significantly enriched in SATR relative to 93-11 upon SSAS. Furthermore, higher induction of gene expression in the cell wall matrix polysaccharide biosynthesis pathway, coupled with higher accumulations of hemicellulose and pectin as well as measurable physio-biochemical adaptive responses, may explain the strong SSAS tolerance in SATR. We mapped SSAS tolerance to five genomic regions in which 35 genes were candidates potentially governing SSAS tolerance. The 1,4-β-D-xylan synthase gene  In the hemicellulose biosynthesis pathway, the 1,4-β-D-xylan synthase (EC:2.4.2.24) is encoded only by OsCSLD4 (LOC_Os12g36890) gene, which is responsible for catalyzing UDP-D-xylose into 1,4-β-D-xylan. The corresponding product, 1,4-β-D-xylan is a major hemicellulose in primary and secondary cell walls of gramineous plants  × VT/(V0 × W), where A450,A532 and A600 indicate the absorbances at 450, 532 and 600 nm, respectively [The fresh shoots were harvested, weighed, and ground into fine powders quickly with liquid nitrogen, homogenized with 1 mL 10% , respectively. Three independent biological replicates were sampled for this experiment, and each sample contained 5 mixed shoots.The seedlings of SATR and 93-11 on the 0th, 2nd and 4th day after SSAS treatment were sampled, immediately frozen in liquid nitrogen, and stored at −80 °C for the RNA-seq and qRT-PCR analysis. The collected and stored samples were frozen in drikold and shipped to Basebio Biotechnology Co., Ltd.  for RNA isolation, sample quality testing, cDNA library construction, RNA-seq and primary analysis. Three independent biological replicates were sampled for these experiments, and each sample contained 10 mixed seedlings for RNA-seq and 5 mixed seedlings for qRT-PCR, respectively.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (accessed on 20 October 2022) and subsequent accurate alignment of reads was conducted using TopHat2 tool [http://rice.uga.edu/) (accessed on 20 October 2022). The alignment quality of reads was checked using RSeQC package [http://plants.ensembl.org/index.html) (accessed on 20 October 2022). The fragments per kilobase of transcript per million fragments mapped (FPKM) value was calculated in R software and represents the gene expression level [p-value (adjusted-p) < 0.05 using DESeq2 R package (1.20.0) [http://geneontology.org/) (accessed on 20 October 2022) and KEGG database (https://www.kegg.jp/) (accessed on 20 October 2022), respectively. GO and KEGG enrichment analysis was performed using clusterProfiler R package [The quality of sequencing reads was evaluated using FastQC tool  . Gene On package .® All-in-One First-Strand cDNA Synthesis SuperMix for qPCR ’ kit . Eight DEGs were selected for qRT-PCR analysis and the corresponding primers used in this study were listed in TM Green qPCR SuperMix’ kit . Relative expression levels of genes were calculated using the 2−ΔΔCT method [Total RNA was extracted using the ‘Plant Total RNA Isolation Kit Plus’ kit . The cDNA strand was synthesized using the ‘TransScriptT method . Three iThe roots and shoots of SATR and 93-11 on the 0th, 2nd and 4th days after SSAS treatment were collected, ground into fine powders with liquid nitrogen, and dried in an oven, at 100 °C, to a constant weight, respectively. The dried simples were weighed and further used for measuring the content of cellulose, hemicellulose, WSP, ISP and CSP using the corresponding kits  according to the respective operation manual. Three independent biological replicates were sampled for this experiment, and each sample contained the mixed shoots or roots of 13 seedlings.2 segregating population by a cross between SATR  and 93-11 . Then, the 665 most tolerant seedlings and the 835 most susceptible seedlings were screened, identified, and harvested from the F2 segregating population containing 22,250 seedlings against SSAS treatment. Subsequently, the maternal bulk DNA (SATR-bulk) and paternal bulk DNA (93-11-bulk) of the parental plants were prepared by extracting and mixing equal amount of DNA samples from 10 individual seedlings of SATR and 93-11, respectively. In addition, DNA samples were extracted, respectively, from 30 individual seedlings from the 665 most tolerant seedlings (T-bulk) and another 30 individual seedlings from the 835 most susceptible seedlings (S-bulk) of the F2 segregating population; the two groups of DNA samples were mixed, respectively, with an equal amount of the 30 DNA samples in each group, finally to be served as the T-bulk and S-bulk, respectively. Then, the DNA-bulk samples frozen by drikold were shipped to MolBreeding Biotechnology Co., Ltd.  for GBTS using a GenoBaits Rice 40K Chip. The similar detailed protocols and methods for GBTS including DNA library construction, target sequencing and liquid chip, quality control of the sequencing data and initial data analysis are described in the references [For mapping the candidate genes controlling salt–alkali tolerance in SATR, we adopted a genotyping by target sequencing and liquid chip (GBTS) method . We initferences ,68.p < 0.01) and 95% (p < 0.05) statistical confidence intervals were set under the null hypothesis of no causal gene identified within the confidence intervals. Thus, the candidate genes controlling salt–alkali tolerance were judged within the regions above the 99% (p < 0.01) confidence intervals. To further screen candidate genes controlling salt–alkali tolerance in SATR, the gene set within the five mapping regions was intersected with the union of the four gene sets of cell wall biogenesis, salt stress, osmotic stress and drought stress.For SNP-calling, unique SNP sites were filtered in both maternal bulk (SATR-bulk) and paternal bulk (93-11-bulk) by comparing the two bulks. The intersections among unique SNP sites from the two maternal and paternal bulks and SNP sites of the progeny bulks (T-bulk and S-bulk) were taken. Then, the SNP-indexes of the screened SNP sites were calculated. Sliding window analysis was subsequently applied to calculate the SNP-index of T-bulk and S-bulk, and to calculate the ∆SNP-index between T-bulk and S-bulk (T-bulk–S-bulk) based on 1 Mb interval with a 100 kb sliding window. The ∆SNP-index (T-S) plot as well as the corresponding linkage maps were simultaneously drawn and constructed. For the ∆SNP-index (T-S) plot, the 99%  [Eight independent plants of Kitaake and . (2020) . Eight i