Vitis species that are native to the Southwestern United States and useful for breeding grapevine (V. vinifera) rootstocks.Introgressive hybridization can reassort genetic variants into beneficial combinations, permitting adaptation to new ecological niches. To evaluate evolutionary patterns and dynamics that contribute to introgression, we investigate six wild V. arizonica, and by resequencing 130 accessions, we focus on identifying putatively introgressed regions (pIRs) between species. We find six species pairs with signals of introgression between them, comprising up to ~ 8% of the extant genome for some pairs. The pIRs tend to be gene poor, located in regions of high recombination and enriched for genes implicated in disease resistance functions. To assess potential pIR function, we explore SNP associations to bioclimatic variables and to bacterial levels after infection with the causative agent of Pierce’s disease (Xylella fastidiosa). pIRs are enriched for SNPs associated with both climate and bacterial levels, suggesting that introgression is driven by adaptation to biotic and abiotic stressors.By creating a reference genome assembly from one wild species, Altogether, this study yields insights into the genomic extent of introgression, potential pressures that shape adaptive introgression, and the evolutionary history of economically important wild relatives of a critical crop.The online version contains supplementary material available at 10.1186/s13059-021-02467-z. Heliconius butterflies  that encompass ~ 70 species across varied environments. The subgenus Vitis has a disjunct distribution across North America and Eurasia ..X. fastip < 2.65e−2; permutation test) , 16 combinations were enriched for candidate SNPs within pIRs across seven trios  across six wild adiation , becausen nature . To faci riparia , 58, Vitrusca L. , and onemurensis . Howeverf ~ 1 Mb  vs. 25.9f ~ 1 Mb , but reff ~ 1 Mb , 63 and f ~ 1 Mb . Full amf ~ 1 Mb , 66 or tanalyses , 63.V. arizonica reference, we have produced a dataset of ~ 20 million SNPs from high coverage (~ 25×) data representing 130 accessions. We first used the SNPs to investigate genetic clustering and phylogenetic relationships. After the removal of 19 inferred hybrid individuals, we found that individuals from each species formed a monophyletic clade and also that inter-species relationships had strong support  have complex histories of hybridization and introgression with potentially more than one species. The results also imply that 2 to 8% of the genomes of some species owe their origins to introgression  are unlikely to contribute numerous deleterious mutations (and hence likely to be gene poor); (ii) have high recombination rates, where interference among mutations is minimized [iii) come from donor populations with higher effective population sizes (Ne) than recipient species. This last point reflects the fact that high Ne species are generally expected to have a lower deleterious load. In our species, however, we do not find substantial variation in Ne, because nucleotide diversity varies < 2-fold, making it hard to assess any relationship between population size and hybridization outcomes. Our results are, however, consistent with the first two points, because pIRs are gene poor in 5 of 9 trios (relative to randomly chosen genomic regions of the same size) and also because the pIRs in all trios are in regions with higher recombination rates than the genome-wide average . For example, we compared two trios that had V. riparia as the P2 species. The first (GRM) had V. monticola as P3 and the second (GRB) had V. berlandieri as P3. The two trios had 142 and 293 pIRs, with 73 overlapping. This observation suggests the possibility that the introgression events contributing to these 73 pIRs pre-date the speciation of V. monticola and V. berlandieri.Another non-exclusive hypothesis is that introgression is fueled by adaptation. Indeed, a recent simulation study has found that adaptation (and not deleterious load by itself) may be necessary to produce positive introgression statistics, particularly for regions that have high recombination rates and low gene density , like ouV. riparia, because it is the P2 species in all five of these trios. Nonetheless, when we extend our work to include a quantitative phenotype—i.e., an assay of X. fastidiosa quantity after infection—we find that six of nine trios have pIRs enriched for SNPs associated with bacterial level, with three significantly so  data, which were reported previously [https://github.com/andreaminio/FalconUnzip-DClab [The eviously , with thip-DClab . The pipip-DClab , both onip-DClab  to assemFALCON_unzip was applied with default parameters to produce the primary contigs and the associated contigs  that werhttps://github.com/andreaminio/HaploSync) and based on synteny with Vitis vinifera PN40024 chromosomes.An optical map was based on Bionano molecules (Bionano Genomics) that were generated and assembled at the genome center of the University of California, Davis (Ming-Cheng Luo). Ultra-high molecular weight DNA (> 500 kbp) was extracted from young leaves by Amplicon Express . DNA was then labeled with a DLE-1 non-nicking enzyme (CTTAAG) and stained according to the Bionano Prep™ Direct Label and Stain (DLS) Kit  instructions. Labeled DNA was loaded onto the SaphyrChip nanochannel array for imaging on the Saphyr system . Optical maps were assembled with BioNano Solve ver. 3.3 . The optV. arizonica leaves was isolated using a Cetyltrimethyl Ammonium Bromide (CTAB)-based extraction protocol as described in Ref. [To help annotate the genome, we performed RNAseq using both short- and long-read sequencing technologies. Total RNA from  in Ref. . A NanodV. arizonica genome was performed with the pipeline described here: https://github.com/andreaminio/AnnotationPipeline-EVM_based-DClab, an adaptation of a pipeline used previously [V. arizonica were used in PASA ver. 2.3.3 [Vitis custom repeat library reported in Ref. [The structural annotation of the eviously  to use Ir. 2.3.3  to generr. 2.3.3 , GeneMarr. 2.3.3 , and SNAr. 2.3.3 . Gene prr. 2.3.3  with Ortr. 2.3.3  with the in Ref. .V. arizonica, data from previous work [Vitis ESTs  and Vitis mRNA excluding transposable element-related proteins . RNAseq data were processed using Stringtie ver. 1.3.4d [V. arizonica genome using PASA ver. 2.3.3 [ftp://ftp.ncbi.nlm.nih.gov/refseq, retrieved January 17th, 2017) and InterProScan ver. 5.28-67.0 [Transcriptomic data was based on new data from ous work , Vitis E. 1.3.4d  and Trin. 1.3.4d  with botr. 2.3.3  and Magir. 2.3.3 . Proteinr. 2.3.3 . Predictr. 2.3.3  to generr. 2.3.3 . The finr. 2.3.3  hits fror. 2.3.3  results .28-67.0 .Disease-related gene functions were annotated using the HMM models from the Disease Resistance Analysis and Gene Orthology (DRAGO 2) database . All theVitis species in the wild grape germplasm collection at Davis, California. The germplasm reported in this study was collected from 1997 to 2016 as cuttings across the southwestern states and maintained at the Department of Viticulture and Enology, University of California, Davis, CA. Additional File We collected fresh leaf tissue from 130 individuals from six American For the 130 samples, genomic DNA was extracted from leaf samples with the Qiagen DNeasy plant kit. The sequencing libraries were constructed with an insert size of ~ 300 bp using Illumina library preparation kits and were sequenced using the Illumina HiSeq 2500 platform with 2 × 150 bp paired reads to a target coverage of 25× following ref. . The rawhttp://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Filtered reads were then mapped to the reference genome with the BWA-MEM algorithm [https://samtools.github.io/bcftools/) and vcftools v0.1.15 (https://vcftools.github.io/). We kept SNP sites for downstream analysis if they were biallelic, had quality higher than 30, had a depth of coverage higher than five reads, had no more than three times the median coverage depth across accessions, and also had less than 25% of missing data among all samples. Additionally, the following expression was applied under the exclusion argument of the filter function in bcftools: “QD < 2.0 | FS > 60.0 | MQ < 40.0 | MQRankSum < − 12.5 | ReadPosRankSum < − 8.0 | SOR > 3.0”.We filtered and evaluated raw reads using Trimmomatic-0.36  and Fastlgorithm  implemenlgorithm . Joint Slgorithm . We thenthetastat program to measure genetic diversity (π) within species from the ANGSD package v0.915 [K from 1 to 10 and used a minMaf filter of 0.05. We then employed the Cluster Markov Packager Across K (Clumpak) software [K number of clusters. For the overall consensus phylogenetic tree, we used the SNPhylo pipeline (v20180901) to reduce the number of SNPs [We used NgsAdmix to evaluate the genetic structure and the e v0.915 . We testsoftware  to detec of SNPs , using a of SNPs . We used of SNPs  implemen of SNPs  and phan of SNPs  to creatM. rotundifolia with the Vitis subgenera as a prior, with a log normal distribution. For the time estimation, we used the Gamma Site Model distributions with the JTT site substitution model, a strict clock model, and the calibrated Yule model. We plotted multiple tree from BEAST using densitree v2.6.4 [We also created a clock calibrated tree with BEAST v2.6.4  using the v2.6.4 .D statistic and perform a block jackknifing of the statistic to obtain an associated p value, treating non-hybrid samples of each species as populations. The trios that were concordant with the 4-taxon topology of the test and had a p value < 0.001 were then used for more detailed analysis. For the fd and fdM tests of introgression, we used the program Dinvestigate from Dsuite, choosing non-overlaping windows of 1000 bp SNPs throughout the whole genome. We defined a pIR as windows with highest x% of fdM values, where x was determined for each trio by the f4-ratio estimate  [To test for introgression, we used the ABBA-BABA test as part of the Dsuite software . First, /twisst) .μ statistic in the corresponding P2 population and defined those as highly supported sweeps. We first calculated the number of top 5% μ windows from the P2 species overlapping the IRs of each trio, using bedtools with a requirement of at least 50% of the window overlapping with the IR (-f 0.5). For the enrichment analysis, we randomly chose the same number of 10 kb windows as the number of HSSs chosen from the whole genome with 10,000 replicates. We counted the number of the random windows overlapping IRs in the same way as described before. We then compared the distribution of the observed values with the randomly generated distribution and performed a t-test to evaluate statistical significance. For plotting, we calculated average μ values for a 100 kb window using the bedtools mapping function.To detect selective sweeps, we used RAiSD v2.8  for eachVitis parents. The physical location of 1662 markers was provided on the PN40024 V. vinifera reference. Since the V. arizonica assembly was anchored on the PN40024 V. vinifera reference, we were able to transform the markers and recombination values [V. arizonica genome.To estimate the location of pIRs relative to recombination rates across the genome, we employed the genetic mapping data from a previous study . Brieflyn values  and thenV. vinifera cultivar Chardonnay were used in all experiments as reference control plants. Nineteen screens were carried out from 2011 to 2020, and a minimum of four biological replicates of each accession were tested. Disease severity was accessed 10 to 14 weeks post inoculation and ELISA was used to measure the X. fastidiosa levels in the stem. Statistical analysis was performed using JMP Pro14 software  to determine the variability of ELISA for the reference control plants across 19 experiments. In the next step, ELISA values of wild accessions were analyzed with the inclusion of the reference plants to adjust for variation among the 19 screens. PD resistance data for 38 of the 108 accessions used in this study were from a previous study [Evaluations for Pierce’s disease (PD) resistance were carried out using a greenhouse-based screen , 122. AcX. fastidiosa bacterial level [lfmm_ridge and K = 7 to compute the regularized least squares using a ridge penalty. Next, we used the lfmm_test function to estimate the genomic inflation factor based on the median Z scores. We verified that the calibrated p values had a flat distribution with a slight peak near zero  of observed SNPs across the genome. For each randomization, we counted the total number (ti) of randomly assigned candidate SNPs within pIRs. We performed 10,000 randomizations and compared the distribution of ti to the observed value (tobs) to compute a p value. Note that this approach inherently accounts for any differences in the density of SNPs across chromosomal regions.To avoid biases associated to imputation (LFMM needs non-missing data), all GWAS models were applied to the subset of 397,723 SNPs that had no missing data across the dataset of Genome environment association studies identify outlier loci that correlate with the environment while controlling for demographic structure . We usedextract function of the raster package [arizonica= 5,456,474 SNPs; Nlocberlandieri= 3,859,330 SNPs; Nloccandicans= 4,971,805 SNPs; Nlocgirdiana= 3,598,951; Nlocmonticola= 4,664,260 SNPs; Nlocriparia= 5,630,334 SNPs) and the 19 bioclimatic variables. Outlier SNPs were defined using Jeffreys’ rule [We obtained 19 bioclimatic variables from Worldclim  for the  package  in R . Most of the six species analyzed had adequate occurrence data (> 70 entries), except V. monticola with 20 entries. The low values for V. monticola are likely to lead to an overestimation of its geographic distribution. We also obtained 19 bioclimatic variables from WorldClim [We constructed species distribution models (SDMs) to identify whether species having evidence of introgression had overlapping distributions in the present, the Holocene (~ 6000 ya) and the Pleistocene . For each species, we obtained occurrence records by combining coordinates data from the Global Biodiversity Information Facility  that were predicted to have an overlap in the SDMs in the different periods.Additional file 1: Table S1. Statistics of the V. arizonica b40-14 genome assembly. Table S2. Summary table of the BUSCO genes of both haplotypes detected in the V. arizonica genome assembly. Table S3. Accession identifiers and geographical coordinates of the 130 accessions used in this study. Table S4. Chromosome size, raw number of predicted SNPs and number of SNPs after filtering across all 130 samples. Table S5. Genome-wide calculation of nucleotide diversity (휋) within per species. Table S6. Results from genome-wide estimates of introgressions across all combination of trios tested. Table S7. GO terms significantly enriched (p < 0.05) in pIRs. Table S8. Gene density averages per 10 kb and relative enrichment. Table S9. Recombination rates per trio and values relative to the genome-wide estimated rate of recombination. Table S10. Quantitative measurements of Pierce’s Disease evaluations. Table S11. Enrichments of SNPs significantly associated with PD in pIRs. Table S12. Enrichments of SNPs associated to the top three bioclimatic variables per trio in pIRs.Additional file 2: Figure S1. Genetic clustering of all samples (including hybrid samples) detected by the structure analyses from K = 2 to K = 10. Figure S2. Consensus maximum likelihood phylogenetic tree created with IQtree using 15,893 informative sites found by SNPphylo. Figure S3. Clock calibrated phylogenetic tree created with BEAST using 15,893 informative sites found by SNPphylo. Figure S4. Results of the Fbranch statistic using species tree models. Figure S5. Pairs of species that show evidence of introgression present evidence of overlap at three periods: present, Holocene, Pleistocene. Figure S6. Pairs of species that do not show evidence of introgression present no or low overlap at three periods: present, Holocene, Pleistocene. Figure S7. Pairs of species that do not show evidence of introgression but have overlapping distributions at three periods: present, Holocene, Pleistocene. Figure S8. Genomic size distribution of the windows used to calculate the ABBA-BABA statistics per trio. Figure S9. Distribution the fdM statistic values across windows per trio. Figure S10. Distribution the fd statistic values across windows per trio. Figure S11. Manhattan plot of all the middle point value all windows with fdM values for all nine trios. Figure S12. Scatterplot showing correlations between the fdM and fd statistics across all windows in the genome. Figure S13. Mean gene density of across the whole genome and within the pIRs of all the trios. Figure S14. Average weights of phylogenetic relationships in the pIRs. SNPs from pIRs for each trio were split in 10Kb windows to create a phylogenetic tree for each window. Figure S15. Principal component variance LFMM association analyses. Figure S16. QQ plots showing the logarithm of the p-values for a linear regression model, the LFMM model using 7 LF and without controlling for a genome inflation factor and controlling for a genome inflation factor. Figure S17. Manhattan plot of LFMM association results for the concentration of X. fastidiosa. The red lines indicate the threshold at an FDR < 0.05. Figure S18. QQ plots showing the logarithm of the p-values for a linear regression model (gray), and LFMM models using 6-12 LF and controlling for a genome inflation factor. Figure S19. Number of associated candidate SNPs per bioclimatic variables per species.Additional file 3. Review history.Additional file 4: Dataset S1. Genomic windows identified as putative introgressed regions (pIRs) across nine trios.Additional file 5: Dataset S2. Gene functional annotation for the genome reference of Vitis arizonica b40-14 version 1.Additional file 6: Dataset S3. Significant SNPs (FDR adjusted p < 0.05) from GWAS with bacterial levels of the causative agent of Pierce’s Disease.Additional file 7: Dataset S4. Significant SNPs (Bayes’ Factor > 10) from GEA with the top three bioclimatic variables per species.Additional file 8: Dataset S5. Subset of significant SNPs (Bayes’ Factor > 10) from GEA with the top three bioclimatic variables in the P2 species that were in putative introgressed regions (pIRs).


The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size  and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM.Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar.Based on LnVar, three candidate vQTL regions were detected, on The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.The online version contains supplementary material available at 10.1186/s12711-021-00692-5. In Drosophila and Arabidopsis, Hsp90 is described as a stabilizer of developmental and morphological traits . The LD between this SNP and the most significant SNP from the current study (at 41.8 Mb on SSC7) was only 0.11. Although this is not a strong LD, it indicates that this region on SSC7 plays an important role in litter size variability.Within each vQTL region for variability of litter size, several candidate genes were identified. We focused only on genes that are located within ± 50 kb from the most significant SNP in each vQTL region. The most significant SNP (AX-116689108) for both variability phenotypes was located on SSC7 within a non-coding region but notably in the middle of a regulatory element  . In our ild 10.2  but at 3ADGRF1—adhesion G protein-coupled receptor F1  and ADGRF5—adhesion G protein-coupled receptor F5 . The ADGRF1 GO term annotation relates it to transmembrane signaling receptor activity, for which ADGRF5 is an important paralog [ADGRF1 is involved in neurodevelopment of the brain and its functions are related to the effect of docosahexaenoic acid on the brain [ADGRF5 was recently reported to be involved in the prevention of paraquat-related lung injuries and to be important for the function of the respiratory system [ADGRF5 (in combination with a knockout of another gene adhesion G protein-coupled receptor ADGRL4) show perinatal lethality in 50% of the animals [ADGRF1 and ADGRF5 are expressed in human ovary, uterus, prostate, and testis tissues [The two genes that are located within ± 50 kb from the most significant SNP on SSC7 for both traits are  paralog . ADGRF1 he brain , 43. ADGy system  and in ty system , 46. Mor tissues .AP3B2—adaptor related protein complex 3 subunit beta 2, which is expressed in the testis and prostate [Another identified vQTL region for varTNB was also located on SSC7, with the most significant SNP being AX-116320493 and the candidate gene being prostate . Mutatioprostate .GANC—glucosidase alpha, neutral C for the identified vQTL on SSC1 and AHCYL2 adenosylhomocysteinase like 2 on SSC18. In humans, GANC is expressed in several organs related to reproduction in females  and in males  [GANC encodes a member of the glycosyl family, which is a key enzyme in the metabolism of glycogen and is associated with susceptibility to diabetes [AHCYL2 is involved in the development of congenital heart defects in humans, and a GWAS in Chinese cattle also described it as a candidate gene associated with displacement of the abomasum [The two other candidate genes for LnVarTNB were vesicle) . GANC endiabetes , 49. AHCabomasum . It is aabomasum .KATNAL1 katanin catalytic subunit A1 like 1. This gene is associated mostly with an expression in neurons [The last of the most significant SNPs for varTNB was AX-116416839 located on SSC11 within the candidate gene  neurons , but it  neurons . Since tWe identified six novel genomic regions that are associated with the variability of litter size in pigs. Only one vQTL region, on SSC7, partially overlapped between the variability traits obtained with the two methods (LnVar and DHGLM) used here and, in both cases, it contained the same most significant SNP. Both our current findings and those of a previous study of the same population, provide strong evidence for a causative mutation on SCC7 for litter size variability. However, future studies based on sequence data are needed to confirm the genomic regions involved in the control of variability of litter size, since the most significant SNP on SSC7 was detected within a non-coding region. In addition, our results indicate that the LnVar and DHGLM methods that were used to estimate the variability of litter size produced phenotypically and genetically different traits, and that DHGLM yields much more accurate results. Thus, we recommend the use of DHGLM over the simpler method to study and implement selection on the variability of litter size in breeding programs.Additional file 1: Figure S1. Expected and observed − log10 P-values of SNPs associated with litter size variability defined as LnVarTNB and varTNB with inflation factor (lambda) that were estimated using the R package QQperm [Figure S2. Manhattan plot of the genome-wide association study for total number born in the Large White pig population.e QQperm . Figure Additional file 2: Table S1. SNPs significantly associated with the total number born with chromosome number (SSC) and minor allele frequency (MAF).


FST calculations, functional annotations, and the allele frequency difference between cold-tolerant and cold-sensitive groups of animals pointed to multiple candidate genes. Among these, GRIA4, COX17, MAATS1, UPK1B, IFNGR1, DDX23, PPT1, THBS1, CCL5, ATF1, PLA1A, PRKAG1, and NR1I2 were previously related to thermal adaptations in cattle. Other genes, for example KMT2D and SNRPA1, are known to be related to thermogenesis in mice and cold adaptation in common carp, respectively. This work could be useful for cattle breeding strategies in countries with harsh climates, including the Russian Federation.Despite the economic importance of creating cold resilient cattle breeds, our knowledge of the genetic basis of adaptation to cold environments in cattle is still scarce compared to information on other economically important traits. Herein, using whole-genome resequencing of animals showing contrasting phenotypes on temperature maintenance under acute cold stress combined with the existing SNP (single nucleotide polymorphism) functional annotations, we report chromosomal regions and candidate SNPs controlling body temperature in the Siberian cattle populations. The SNP ranking procedure based on regional  Extreme environmental temperatures are a growing challenge for animal agriculture in light of climate changes and globalization ,2,3,4. WGRIA4 as a promising candidate associated with body temperature maintenance under extremely low temperatures in Siberian cattle populations [The genetics of cold adaptation in cattle have been recently studied both at the genome ,13,14,15ulations . The samulations , implyinulations .To find candidate functional variants contributing to cold adaptation in cattle, in this work we utilized a comprehensive set of cattle SNPs of a known functional type  scored based on their ability to explain variation in 34 complex traits . This Fuhttp://broadinstitute.github.io/picard/, accessed date: 12 September 2021). Next, we performed a base quality score recalibration (using cattle known variants: dbSNP148). We followed the best practice guidelines [p-value using Fisher’s exact test to detect strand bias  >  60; (iv) Z-score from the Wilcoxon rank sum test of alternative vs. reference read MQs (MQRankSum)  <  −12.5; and (v) Z-score from the Wilcoxon rank sum test of alternative vs. reference read position bias (ReadPosRankSum)  <  −8. The thresholds for these parameters were adopted from GATK Best Practices [FST calculations between groups of animals.The phenotype measurement procedure was described in our previous study . Brieflyidelines  recommenractices . INDEL vFST [Calculations of FST  between FST  both forFST, FAETH score of individual SNPs and individual SNP allele frequency differences between the two groups of animals. Each SNP with this information was annotated with three fractional  ranks so that the higher the value was, the closer to zero the rank of the SNP would be. The FST rank for each SNP was calculated based on the rank of its higher-valued  window amongst 100,443 regions resulting from the window-based analysis. The ranks for allele frequency differences were calculated based on the list of 16,647,833 autosomal SNPs having at least three successfully called genotypes in each of the two contrasting groups. FAETH score ranks were calculated based on the abovementioned SNPs having FAETH annotation , of which 0.35% were novel according to cattle dbSNP148. The ranks were summed up, and SNPs were sorted based on the resulting sum  were novel, from 829 genes with FST values ≥ 0.7. Three hundred ninety-six loci from 53 genes had FST = 1  and belongs to core cold response genes in this species [THBS1, with two high-rank missense variants. This gene is involved in cattle adaptation to both cold [THBS1 contributes to a molecular pathway regulating the browning of adipocytes [GRIA4, a gene identified as a major candidate in our previous study of a wider Siberian cattle population using genotyping arrays [GRIA4.Among the top results of the SNP ranking we found multiple genes previously related to thermal adaptations in cattle, close  species . Among ti cattle . Additio species . Althoug species  or be lioth cold  and hot oth cold  climatesoth cold . Accordiipocytes , which iipocytes . It is wg arrays . This vaTMBIM6 affect the rectal temperature in Indian goats [FKBP11 is a candidate for involvement in seasonal adaptations in reindeer (Rangifer tarandus) [SLC10A2 is involved in bile acid metabolism under cold stress in mice [GAP43 is upregulated in the hypothalamus of rats exposed to cold. KMT2D contributes to the thermogenic adipose program in mice [SNRPA1 is involved in adaptation to cold in common carp (Cyprinus carpio) [For some genes, we found additional evidence in species phylogenetically close to cattle. For example, according to Swain and colleagues, variations in an goats , while Farandus) . There a in mice . Accordi in mice , GAP43 i in mice . SNRPA1  carpio) .MAATS1 is related to reproduction (inseminations per conception and sperm concentration). GRIA4 is associated with reproductive (first service conception and inseminations per conception) as well as milk traits (milk fat percentage and milk fat yield). A likely explanation for this is twofold: genes involved in cold adaptation contribute to various metabolic processes [Interestingly, many genes from our candidate set are also associated with various economically important traits in cattle. For example, according to CattleQTLdb , MAATS1 rocesses ,55 and tFST analysis contained many genes with a thermoregulatory and/or cold adaptation function. This work would be useful for cattle breeding in countries with a cold climate as it points to genetic variants segregating in cold-adapted populations, to be tested in breeding and genomic selection programs.This study, which utilizes the whole-genome resequencing of animals to show contrasting phenotypes on temperature maintenance under acute cold stress and functional genome annotations, reveals multiple candidate loci controlling the body temperature in the Siberian cattle population. The top genome intervals based on the weighted


Drug-induced liver injury (DILI) is a major cause of drug development failure and drug withdrawal from the market after approval. The identification of human risk factors associated with susceptibility to DILI is of paramount importance. Increasing evidence suggests that genetic variants may lead to inter-individual differences in drug response; however, individual single-nucleotide polymorphisms (SNPs) usually have limited power to predict human phenotypes such as DILI. In this study, we aim to identify appropriate statistical methods to investigate gene–gene and/or gene–environment interactions that impact DILI susceptibility. Three machine learning approaches, including Multivariate Adaptive Regression Splines (MARS), Multifactor Dimensionality Reduction (MDR), and logistic regression, were used. The simulation study suggested that all three methods were robust and could identify the known SNP–SNP interaction when up to 4% of genotypes were randomly permutated. When applied to a real-life DILI chronicity dataset, both MARS and MDR, but not logistic regression, identified combined genetic variants having better associations with DILI chronicity in comparison to the use of individual SNPs. Furthermore, a simple decision tree model using the SNPs identified by MARS and MDR was developed to predict DILI chronicity, with fair performance. Our study suggests that machine learning approaches may help identify gene–gene interactions as potential risk factors for better assessing complicated diseases such as DILI chronicity. As the primary organ responsible for metabolism, the liver is vulnerable to injury caused by drugs and drug metabolites ,2. Drug-Genetic factors have attracted increasing attention, and recent studies have shown that genetic variants in drug-metabolizing enzymes and human leukocyte antigen (HLA) were associated with the occurrence of DILI ,11,12. MPairwise SNP–SNP interactions have been recognized to play a role in assessing disease susceptibility and drug responses ,23. SeveIn this study, we aim to investigate SNP–SNP interaction as a potential risk factor for predicting DILI chronicity using machine learning approaches. We briefly introduce three machine learning methods, including MARS, MDR, and logistic regression. Then, we use simulated data with known pairwise SNP–SNP interactions to evaluate the accuracies and robustness of these methods. Next, we apply the three methods to identify SNP interactions associated with chronic DILI. Finally, we evaluate the predictive performance of the identified SNPs by using a simple decision tree model to assess whether the presence of these SNP–SNP interactions is associated with an increased risk of DILI chronicity.Cases in this study were collected by International Serious Adverse Event Consortium (iSAEC), a large international collaborative study, including partnerships with the United Kingdom, Sweden, Spain, Germany, France, Switzerland, the Netherlands, Australia, and Finland. The study protocols were approved by local ethics committees, and the informed consent was obtained from all subjects involved in the study. Inclusion criteria for DILI cases followed the clinical chemistry criteria defined . SpecifiA dataset with 271 patients (33 chronic versus 238 acute DILI) were used to assess the three machine learning methods for identifying SNP–SNP interaction as a risk factor for chronic DILI. DNA preparation is described here  and stanY is the phenotype, such as chronic DILI ; A is an SNP, B is another SNP, and A × B is an SNP–SNP interaction factor, the parametric model for the logistic regression is as follows:Assuming The statistical methodologies for identifying gene–gene interactions were reviewed thoroughly ,23. HereThe MARS approach is an adaptive algorithm for identifying SNP–SNP interactions, which fits models using flexible regression modeling, including non-linear components and interactions . It was In MARS, the entire dataset was divided into multiple smaller regression subsets through automatic dataset-determined knots. Each of these subsets comes with a basis function , and all significant basis functions are aggregated to obtain the overall regression model. MARS gives a regression-like output model with certain basis functions of the predictors. It builds models of the following equation form:The regression model of MARS is derived directly from the data, through a data-driven automatic set of basis functions with their corresponding coefficients. These basis functions are derived based on automatic data-driven hinge values in the data, also referred to as knots. As a specific linear relationship between the response and predictors in a data subset is being modeled, the knot automatically identifies the point of direction change from that relationship. This point of change becomes a starting point for a new subset of another relationship, and the process continues to the end with new knots and relationships. In this way, the model captures both linear and non-linear relationships, with corresponding interactions.GCV) score. This process will yield the optimized final model. The GCV equation is written as follows:The MARS model is built using a forward-and-backward selection process in the following way: the forward selection first combines all significant basis regression functions, and their interactions, and then backward selection prevents overfitting by pruning the result from the forward selection, removing basis functions one at a time, and selecting the model with the lowest Generalized-Cross-Validation  and 23% (57/250). To test the robustness of the methodologies, we permutated the genotypes of 23 SNPs, except SNP4 and SNP9. In the first test, two of the 250 observations were selected for permutation, while in the second test 10 observations were permutated. Each analysis includes 100 permutations.We also applied these machine learning approaches to a real-life DILI chronicity dataset. All analyses except those specifically mentioned were performed using R (version 3.6.1)  and the Three machine learning methods were evaluated using the simulated dataset with a known SNP–SNP interaction (SNP4 + SNP9). As shown in p = 0.002) and 3.01 , respectively. The other three cited SNPs were not statistically significant.We first checked the association of DILI chronicity with individual SNPs using the Fisher exact test. Five individual SNPs from SNP–SNP pairs selected by the machine learning methods below were tested. As shown in p < 0.001), while the odds ratios of rs6487213 and rs3785157 alone are 3.28  and 1.49 , respectively. Additionally, 30.4% of the combined genotypes of CC + CC by rs6487213 + rs3785157 were reported with DILI chronicity, as compared to 20.8% of the genotype CC of rs6487213 alone. The MDR approach identified a three-SNP interaction, and its odds ratio of 4.19  by rs5417 + rs7658048 + rs12453290 was improved in comparison to the odds ratio of 3.01  by rs5417 alone. However, the SNP–SNP pair (rs5417 + rs3785157) identified by logistic regression did not show improvement over individual SNPs , which is a critical transporter for glucose uptake and metabolism. GLUT1 upregulation plays a key role in T cell activation and in importing glucose to be metabolized through aerobic glycolysis. Similar to GLUT1, GLUT4 was upregulated in activated CD4+ T cells to varying degrees, and the variants in GLUT4 play compensatory and supporting roles in most T cells [Furthermore, the SNP rs12453290 is a variant of the gene MCT4 (SLC16A3), which is a member of the monocarboxylate transporter (MCT) family controlling the lactate uptake. MCT4 plays a role in exporting additional lactate into the extracellular space. Lactate often builds up at sites of inflammation and in hypoxic conditions. Excess lactate importing into T cells will restrict proliferation and cytokine production of cytotoxic T cells. In T cells, T cell receptor activation upregulates MCT1 expression, while its blockage will result in impaired cytotoxic T cell function. MCT4 upregulation causes full macrophage activation, while its deletion will lead to an intracellular decrease in glycolysis and accumulation of lactate. By contrast, the downregulation of MCT4 will promote the cytotoxicity of natural killer cells and boost immune responses .In this study, we investigated three machine learning methods for the study of gene–gene interactions as risk factors for chronic DILI, including MARS, MDR and Random Forest followed by logistic regression. All of the three methods were robust when tested in the simulation dataset with permutation analysis. When we applied these methods to the real-world DILI chronicity dataset, MARS and MDR identified SNP–SNP interactions that showed improved performance regarding the association with DILI chronicity as compared to their individual SNP counterparts. This finding demonstrated that MARS and MDR are superior to the traditional logistic regression approach for identifying SNP–SNP interactions in complex diseases such as chronic DILI.We also developed a simple decision tree model by using the SNPs from the interactions identified by MARS and MDR, and most of these SNPs were mechanistically relevant, either related to the bilirubin uptake or to immunological responses. These SNPs will be further investigated and validated by including non-genetic factors such as drug properties and responses as co-factors. The identification of SNP–SNP interactions for disease susceptibility has become increasingly important, and our evaluated methods for determining potential genetic risk of the synergetic SNP interactions will potentially benefit the diagnostic test of chronic DILI.


B. oleracea varieties, remain largely unknown. In the present study, bolTLP1 was identified in broccoli, and displayed remarkably inducible expression patterns by abiotic stress. The ectopic overexpression of bolTLP1 conferred increased tolerance to high salt and drought conditions in Arabidopsis. Similarly, bolTLP1-overexpressing broccoli transgenic lines significantly improved tolerance to salt and drought stresses. These results demonstrated that bolTLP1 positively regulates drought and salt tolerance. Transcriptome data displayed that bolTLP1 may function by regulating phytohormone -mediated signaling pathways, hydrolase and oxidoreductase activity, sulfur compound synthesis, and the differential expression of histone variants. Further studies confirmed that RESPONSE TO DESICCATION 2 (RD2), RESPONSIVE TO DEHYDRATION 22 (RD22), VASCULAR PLANT ONE-ZINC FINGER 2 (VOZ2), SM-LIKE 1B (LSM1B) and MALATE DEHYDROGENASE (MDH) physically interacted with bolTLP1, which implied that bolTLP1 could directly interact with these proteins to confer abiotic stress tolerance in broccoli. These findings provide new insights into the function and regulation of bolTLP1, and suggest potential applications for bolTLP1 in breeding broccoli and other crops with increased tolerance to salt and drought stresses.Plant thaumatin-like proteins (TLPs) play pleiotropic roles in defending against biotic and abiotic stresses. However, the functions of TLPs in broccoli, which is one of the major vegetables among the  Thaumatococcus daniellii Benth [TLPs in Oryza sativa, 33 in Zea mays, 18 in Vitis vinifera, 25 in Arabidopsis thaliana, 28 in Prunus persica and 42 in Populus trichocarpa [Fusarium oxysporum, Pleurotus ostreatus and Coprinus comatus [Cassia didymobotrya, a 23 kDa TLP was identified, which exerts antifungal activity toward some Candida species [TaLr35PR5, a TLP from wheat, was involved in Lr35-mediated adult wheat defense in response to leaf rust attack [Mald2, a TLP identified in apple, conferred antifungal activity against Fusarium oxysporum and Penicillium expansum in Nicotiana benthamiana [PpTLP from Pyrus pyrifolia or AdTLP from Arachis diogoi can enhance resistance to Rhizoctonia solani [TLPs from rice all exhibit increased resistance to diverse pathogens [TLPs in plants also respond to abiotic stress. Transgenic tobacco plants ectopically expressing AdTLP not only have enhanced resistance to Rhizoctonia solani, but also have improved tolerance to salt and oxidative stresses [ObTLP1 isolated from Ocimum basilicum enhances tolerance to dehydration and salt stresses in Arabidopsis [TLPs showing inducible expression patterns were also identified in carrot and tea, suggesting that these TLPs could be involved in drought response [TLPs have also been demonstrated to play roles in regulating plant growth and development including fruit maturation, lentinan degradation and seed development [TLPs have undergone functional diversification in plant species, and play pleiotropic roles in response to biotic and abiotic stresses as well as in the regulation of growth and development [Thaumatin-like proteins (TLPs) are widely distributed in plants, animals and fungi , and areii Benth . Most TLii Benth ,3,4. In ii Benth ,6. Examichocarpa . However species . TaLr35Pt attack . The recthamiana . In tobaa solani ,13. Tranathogens ,15,16. Ibidopsis . Under dresponse . In addielopment ,22,23,24elopment .Brassica oleracea L. var. italica) is one of the major vegetables among the brassica oleracea varieties, and is widely planted in Asia, Europe and North America [Brassica. Broccoli pre-treating with SA and chitosan showed the highest drought stress recovery in a dose-dependent manner [Bra-botrytis-ERF056 from cauliflower was demonstrated to increase tolerance to salt and drought stresses in overexpressing bra-botrytis-ERF056 Arabidopsis [BrEXLB1 is involved in drought tolerance in Brassica rapa [BnaABF2 from Brassica napus confers enhanced drought and salt tolerance in transgenic Arabidopsis [BolOST1 was dramatically induced by drought and high-salt stress, and the ectopic expression of BolOST1 restored the drought-sensitive phenotype of ost1 [TLP gene named bolTLP1 was identified in broccoli which exhibited a significant positive response to salt stress. However, the role of bolTLP1 is unknown. In the present study, the full-length cDNA of bolTLP1 was cloned, and its expression patterns under salt stress and drought stress were analyzed. Transgenic Arabidopsis and broccoli overexpressing bolTLP1 were generated. The phenotypes of these bolTLP1 overexpressing transgenic plants under salt and drought stresses were observed. The genes and regulatory processes involved in bolTLP1-mediated tolerance to abiotic stress were explored.Broccoli , are classified into a separate group, and they appear to show far genetic distance with other TLP genes  (The expression pattern of t stress C. In addon (0 h) D.bolTLP1, a transformation vector containing the bolTLP1 under the control of the enhanced CaMV 35S promoter was constructed, and used to transform Arabidopsis. A total of 26 independent 35S::bolTLP1 transgenic lines were obtained  survived, whereas almost all vector controls died . The expression levels of 2202 out of the 3284 DEGs were up-regulated in the 35S::bolTLP1 broccoli, and the other approximately 33% of the DEGs displayed lower expression levels in the 35S::bolTLP1 broccoli compared to the vector control plants   . The sig < 0.01) .bolTLP1 broccoli than those in the vector controls , displayed significantly up-regulated expression patterns in the 35S::bolTLP1 broccoli. At least 11 genes involved in the ethylene signaling pathway displayed differential expression patterns between the 35S::bolTLP1 broccoli and the vector controls  were functionally associated with stress response processes. Among the stress response-associated DEGs, approximately 70% of them displayed higher expression levels in the 35S::controls . A largecontrols A–C. The controls .bolTLP1 broccoli compared to the vector control plants. The majority of these genes play crucial roles in sulfur compound synthesis and metabolism  4/5/8/11/14/18/19, auxin transporter 2, IAA3/9/12/19/26/29, inositol-3-phosphate synthase, indole-3-aceticacid-amido synthetase GH3, calmodulin-binding transcription activator 1 (CAMTA 1) and auxin-induced gene X15 and SAUR32 are involved in auxin-mediated regulatory processes (CAMTA 1 [inositol-3-phosphate synthase [Ethylene receptor 2 and ethylene responsive factors (ERFs) including ERF04, ERF05, ERF09, ERF12, RAP2-3, RAP2-10, ERF11, ERF104, TEM1 and TOE3, are involved in ethylene signaling pathway  increases tolerance to salt stress in Arabidopsis [bolTLP1-mediated regulatory network.The molecular mechanisms by which the TLPs respond to abiotic stress remain largely unknown, although investigations have confirmed the roles of several stresses ,17,18,19ization” . These rcontrols B. Most ocontrols . RD2 andc stress ,40. In arocesses A. Simila is a rice cysteine proteinase inhibitor. Ectopic overexpression of OCI can enhance drought stress tolerance in soybean and Arabidopsis [AtCYSa and AtCYSb are two cysteine proteinase inhibitors from Arabidopsis. Overexpression of these inhibitors in transgenic yeast and Arabidopsis increases their resistance to high salt, drought, oxidative and cold stresses [bolTLP1 broccoli , and three such replicated treatments were conducted. Similarly, a total of 48 individual plants from three independent 35S::bolTLP1 broccoli lines were divided into three groups, and unirrigated to evaluate their drought tolerance. In addition, at least three 35S::bolTLP1 Arabidopsis lines in 1/2 MS medium or in soil were treated with 125 and 200 mM NaCl to mimic salt stress. Three 35S::bolTLP1 Arabidopsis lines in 1/2 MS medium were treated with 200 and 300 mM mannitol to mimic drought stress. The 35S::bolTLP1 Arabidopsis planted in soil were also unirrigated to further evaluate their drought tolerance. To validate the reliability of the assessments of salt and drought tolerance in 35S::bolTLP1 Arabidopsis, three replicates were performed for each treatment.Homozygous broccoli seeds of KRJ-012 were kindly provided by Dr. Hanmin Jiang, Tianjin Kernel Vegetable Research Institute, Tianjin, China. The seeds were planted in a greenhouse with a 16 h/8 h light/dark cycle at 25 °C and 22 °C, respectively. The 25-day-old seedlings were subjected to salt and drought stresses. For the salt-stress treatment, at least 20 individual plants were irrigated with 200 mM NaCl. To mimic the drought stress, 30 individual plants were irrigated with 300 mM mannitol. The leaves of each plant were harvested at 0, 4, 8 and 24 h after these treatments, frozen immediately in liquid nitrogen, and then stored at −80 °C. To evaluate the salt tolerance of 35S::bolTLP1 were identified from the broccoli transcriptome data, and the specific primers (bolTLP1-F/bolTLP1-R) were designed to amplify the full coding sequences of bolTLP1 based on these expressed sequence tags (ESTs)  . Total Rbroccoli . The deddeletion .bolTLP1 (qbolTLP1-F/qbolTLP1-R) was designed by Primer premier 5.0 software  was used in quantitative real-time RT-PCR (qRT-PCR) assay. The relative expression levels of bolTLP1 under salt or drought stress were calculated by the comparative 2−ΔΔCT method. Three biological replicates and three technological replicates were performed to ensure the reliability of quantitative analysis.Total RNAs from the leaves of individual plants, which were harvested at different time points after imposing salt or drought stress, were isolated and reverse transcribed to cDNAs. Specific primers of software . The bolbolTLP1 with incorporated Nco I and BstE II restriction sites was amplified (Nco I/BstE II double digestion was sub-cloned into the pCAMBIA3301 binary vector. The recombinant 35S::bolTLP1 plasmids and vectors without exogenous gene insertion (empty vectors) were transformed into A. tumefaciens strain LBA4404. Agrobacterium-mediated Arabidopsis genetic transformation was performed via the floral dip method. The transgenic Arabidopsis lines were screened by spraying 1:10,000 dilute Basta solution and further identified by PCR using specific primers (bolTLP1-F/bolTLP1-R) and combined primers (35S-F/bolTLP1-R). Simultaneously, the plants with empty vector were identified by PCR using specific primer pair (35S-F/NOS-R). The transgenic Arabidopsis plants were further verified by qRT-PCR using unique primers (qbolTLP1-F/qbolTLP1-R) and atActin as an internal control  was then further used for surface sterilization of the seeds for 10 min. The sterilized seeds were planted on Murashige and Skoog (MS) medium with a 16 h/8 h light/dark cycle at 22 °C. The hypocotyls of 7-day-old broccoli seedlings were cut to 0.5 cm and pre-cultured on MS medium containing 1.5 mg/L 6-BA and 0.15 mg/L NAA for 2 days. Then, the truncated hypocotyls were dipped into a suspension of Agrobacterium containing the 35S::bolTLP1 expression vector or an empty vector for 1 min. The infected explants were transferred onto co-cultivation medium with 1 mg/L 6-BA, 0.1 mg/L NAA and 100 μmol/L acetosyringone, and cultured for 2 days in the dark. Subsequently, the explants were washed with sterile water and transferred onto MS medium containing 1.6 mg/L 6-BA, 0.2 mg/L NAA and 200 mg/L cefotaxime for 10 days for callus induction. The differentiated explants were transferred into screening medium supplemented with 3 mg/L Basta for 2 weeks. Finally, buds that continued to differentiate were transferred to the 1/2 MS medium containing 1 mg/L IBA for root initiation. The transgenic broccoli plants were further verified by PCR using prime pair (35S-F/bolTLP1-R) and qRT-PCR using unique primers (qbolTLP1-F/qbolTLP1-R) (The full coding sequence of mplified  and clon control . The 35SlTLP1-R) .bolTLP1 broccoli line were equally mixed and used to perform three batches of independent RNA isolation. Equal amounts of RNA from each independent 35S::bolTLP1 broccoli line were then used to construct sequencing library. In total, two such sequencing libraries from two independent 35S::bolTLP1 broccoli lines and one sequencing library from the vector controls were constructed. The sequencing reaction was conducted by the Illumina HiSeqTM 2500 sequencing platform  with three technological replicates. The clean reads were annotated and mapped to the reference genome of B. oleracea var. oleracea. The expression levels of genes were calculated by the fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM). The significantly expression levels of genes between the 35S::bolTLP1 broccoli and the vector controls were identified based on the thresholds: |log2 (fold-change (35S::bolTLP1 broccoli/vector control))| > 1 and corrected p-value < 0.05. Gene Ontology (GO) analysis of the differentially expressed genes (DEGs) was performed by the agriGO platform. Available online: http://bioinfo.cau.edu.cn/agriGO/ (accessed on 4 May 2018).Leaves from six 30-day-old individual plants of per independent 35S::bolTLP1 broccoli lines  were used to perform three biological replicates. Similar to RNA isolation in transcriptome sequencing, RNAs from leaves of six randomly selected 30-day-old plants per 35S::bolTLP1 broccoli line were isolated and reverse transcribed to cDNAs. The comparative 2−ΔΔCT method was conducted to calculate the relative expression levels of genes using bolActin as an internal control. To further ensure the reliability of qRT-PCR, three technological replicates were carried out.The gene expression profiles detected by comparative transcriptome analysis were verified by qRT-PCR by using the specific primer pairs . Three iTM Gold Yeast Two-Hybrid System  according to the manufacturer’s instructions. In brief, the full-length coding sequences of bolTLP1 with the Nco I and BamH I restriction enzyme sites were constructed into the bait vector pGBKT7 . The positive mating yeast was screened on SD/-Trp-Leu-His-Ade medium with 0.5 mM 3-Amino-1, 2, 4-triazole (3-AT), which could effectively inhibit the autoactivation of bolTLP1. The plasmids from the mating yeast were isolated and used as a template to amplify the candidate genes using the universal primers (T7-F/3′AD-R)  . To furtr pGADT7 . Subsequr pGADT7 .bolTLP1 significantly increased the salt and drought tolerance in both Arabidopsis and broccoli. BolTLP1 may directly interact with stress response-associated proteins RD2, RD22, VOZ2, LSM1B and MDH to regulate a series of genes involved in phytohormone -mediated signaling pathways, hydrolase/oxidoreductase activity, sulfur compound synthesis, and histone variants, which could play important roles in bolTLP1-mediated tolerance to salt and drought stresses in broccoli (BolTLP1 is a potential candidate gene in breeding crops with high tolerance to abiotic stress via genetic engineering.Overexpression of broccoli . BolTLP1


Secale cereale L.) breeding germplasm. In total, 180 inbred breeding lines were genotyped using the state-of-the-art 600 K SNP array and phenotyped for infection type against three distinct field populations of B. graminis f. sp. secalis from Northern Germany (2013 and 2018) and Denmark (2020). We observed a moderate level of powdery mildew resistance in the non-restorer germplasm population, and by performing a genome-wide association study using 261,406 informative SNP markers, we identified a powdery mildew resistance locus, provisionally denoted PmNOS1, on the distal tip of chromosome arm 7RL. Using recent advances in rye genomic resources, we investigated whether nucleotide-binding leucine-rich repeat genes residing in the identified 17 Mbp block associated with PmNOS1 on recent reference genomes resembled known Pm genes.Powdery mildew is one of the most destructive diseases in the world, causing substantial grain yield losses and quality reduction in cereal crops. At present 23 powdery mildew resistance genes have been identified in rye, of which the majority are in wheat-rye translocation lines developed for wheat improvement. Here, we investigated the genetics underlying powdery mildew resistance in the Gülzow-type elite hybrid rye ( In cereals, the causative agent of PM is the ascomycete fungus Blumeria graminis (DC.) speer (Bg), which is capable of inflicting severe grain yield loss (≥ 20%) and quality reduction in cereals8. In periods of conducive conditions, such as frequent precipitation and low to moderate temperatures, Bg can cause severe epidemics by repeated infections and clonal reproduction9. As an obligate biotroph, Bg is highly specialized to its host species, divided into several distinct ‘formae specialis’ (f. sp.)10, and dependent on a living host for survival and reproduction. In the later stages of the growing season, or in periods of unconducive conditions, chasmothecia structures are formed11. The chasmothecia field inoculum facilitates the infection of volunteer plants or the successive autumn sown winter crop, allowing Bg to overwinter as dormant mycelia12. Long-distance dispersal by wind of conidiospores drives the large spatial variability in the Bg population15. In barley (Hordeum vulgare L.), the virulence complexity of B. graminis f. sp. hordei has been observed to increase with the prevailing wind direction from west to east in Europe16. Rather than geographical origin, evidence suggests that local use of host resistance and fungicides are the primary factors influencing the European B. graminis f. sp. hordei population17. These findings underline the seriousness of long-distance dispersal of B. graminis spores, which allows novel and aggressive pathotypes to evolve through the recombination of distinct pathotypes and rapidly spread18. In rye, the causative agent of PM, B. graminis f. sp. secalis, has attracted little scientific interest in recent years20.Powdery mildew (PM) is one of the most devasting diseases globally21. At present, 23 major PM resistance (R) genes have been identified in rye  proteins that recognize pathogen effector molecules, leading to an effector-triggered immunity resistance response45. Canonical NLR genes are composed of three domains46. In grasses, the N-terminus is composed of a coiled-coil (CC) domain believed to be involved in signaling and the induction of cell death47. In the center, a nucleotide-binding adaptor shared by APAF-1, R proteins, and the CED-4 (NB-ARC) domain functions as a regulatory domain determining the protein activation state48. Last, in the C-terminus, a leucin-rich repeat (LRR) domain is involved in effector recognition49. In rye, 770 canonical NLR genes have been identified in the ‘Lo7’ reference genome50.The majority of characterized Triticum aestivum L.) by chromosomal translocation of segments housing R genes42. While PM is effectively controlled by host genetic resistance in cereals, it has historically been rapidly overcome by virulent races of Bg52. Currently, several of the top-yielding hybrid rye cultivars examined in official Danish trials are susceptible to PM disease  develop a marker map for the 600 K high-density SNP array and validate its performance in the assayed germplasm.In this paper, we investigate PM resistance in a less-prevalent elite Gülzow-type hybrid rye breeding germplasm. Our objective was to I) characterize PM resistance in the assayed germplasm, II) identify PM resistance-associated SNP markers to be implemented by marker-assisted selection for breeding novel resistant hybrid rye cultivars, III) investigate whether NLR genes residing in PM resistance-associated blocks on the ‘Lo7’ and ‘Weining’ reference genomes resemble known https://doi.org/10.5281/ZENODO.5086235. Quality filtration of markers for low minor allele frequency, missing markers, and missing individual scores across the panel led to the identification of 261,406 informative markers . Characterization of fundamental performance-related metrics revealed a homogeneous inter- and intrachromosomal distribution of markers (Table To investigate the genetics underlying powdery mildew (PM) resistance, the assayed hybrid rye breeding germplasm was genotyped on the rye 600 K SNP array. With only scaffold positional data available for the array, SNP marker sequences were anchored to the recent ‘Lo7’ rye reference genome and stringently filtered to ensure its accuracy. In total, 591,196 markers were successfully mapped to the reference genome, and the developed marker map was made available at rs Table . Across Blumeria graminis f. sp. secalis (Bgs) populations (Table Bgs population yielded a mean IT of 2.40 ± 1.28 standard deviations (SD), with 47 resistant lines out of which 3 were restorers. The N18  Bgs population yielded a mean IT of 2.71 ± 1.11 SD with 29 resistant lines, out of which 1 was a restorer. D20  Bgs population yielded a mean IT of 2.74 ± 1.10 SD with 20 resistant lines out of which 1 was a restorer. Across the assayed germplasms, 20 out of 88 non-restorer germplasm and 1 out of 92 restorer lines were consistently resistant to all three Bgs populations. Both controls, hybrid cv. KWS Binntto (‘susceptible’) and KWS Serafino (‘resistant’) were susceptible to all three Bgs populations. KWS Binntto had a mean IT of 3.56 ± 0.54 SD and KWS Serafino 3.01 ± 1.01 SD across Bgs populations.To provide a comprehensive phenotypic dataset of PM resistance in the assayed Gülzow-type hybrid rye breeding germplasm, the lines were scored for their infection type (IT) against three distinct ns Table . The linns Table . Across To visualize the resistance spectrum of breeding lines, a circular neighbor-joining dendrogram was constructed, and concentric circles were added to integrate the scored IT Fig. .Figure 2Bgs population by ANOVA showed that the N13 Bgs population differed significantly from the N18 and D20 Bgs populations   with powdery mildew resistance spanning from 882 to 898 Mbp  (Supplementary Table 10 = 37.9) marker within the haplotype block on chromosome arm 7RL at 892.09 Mbp, which explained 16.8% of the phenotypic variance  peak was identified in GWAS-MLM spanning from 743.9 to 754.4 Mbp on chromosome arm 5RL  > 6.72) with PM resistance, 155 were accurately positioned on the ‘Weining’ reference genome  NLR genes, respectively. Positional information, annotation, and NB-ARC and NLR sequences of all ‘complete’ and ‘partial’ NLR genes for both reference genomes have been made available at nes Fig. . Out of Phylogenetic analysis using the NB-ARC domain of NLRs residing in the PM resistance-associated haplotype block on chromosome arm 7RL led to the finding that the majority of the NLRs were represented in both reference genomes Fig. . The ‘WeR genes as references  showed an evolutionary relationship with an Rpp13-like disease resistance gene and four Mla alleles.An additional phylogenetic analysis was performed using the entire reference genome NLR repertoire including NLR genes of characterized ces Fig. . The NLRPm genes, we selected the Rpp13-like NLR gene for further investigation on the basis of its close proximity. The Rpp13-like NLR gene  was the closest NLR gene, residing approximately 2 Mbp from the top-most PM resistance-associated marker identified by BLINK . Protein BLAST of the NLR gene led to 88% sequence similarity with disease resistance Rpp13-like protein 4 in T. urartu, which encodes a 924 aa NLR protein sequence.The homologous 53. In the last decade, a high level of powdery mildew infection in rye was recorded only once in the trials in 2017 at a site in southern Denmark  populations from Denmark and Northern Germany. We observed a moderate level of powdery mildew resistance in the non-restorer germplasm population, and by performing a genome-wide association study (GWAS) using 261,406 informative SNP markers, we identified a strong PM resistance-associated site on the distal region of chromosome arm 7RL.In this study, we investigated PM resistance in the less-prevalent Gülzow-type elite hybrid rye breeding germplasm against three distinct 59. Additionally, the 600 K SNP array was developed using lines from a German hybrid rye breeding germplasm deploying the predominant Pampa-type cytoplasmic male sterility system (CMS)57. In contrast, the germplasm assayed in this study deploys the less-prevalent Gülzow-type CMS system60. Due to the distinct nature of the different hybrid rye breeding germplasms, we investigated the performance of the 600 K SNP array on the Gülzow-type germplasm. With no physical position data available for the 600 K SNP markers, we developed a marker map by anchoring the marker sequences to the ‘Lo7’ reference genome. In brief, we found that the 600 K SNP array yielded a dense panel of informative SNP markers in the Gülzow-type germplasm with markers homogeneously distributed across the rye genome. The level of marker informativeness measured by the polymorphism information content (PIC) was largely comparable with observations made in maize (Zea mays L.). Here, high-density SNP genotyping of 544 diverse CIMMYT inbred lines yielded 362 K informative SNP markers with a mean PIC of 0.2561. In this study, however, we did observe a drop in marker informativeness and density across the pericentromeric region. Similar observations were made in the study by Rabanus-Wallace, et al.50, who reported a considerable reduction in both genetic diversity and gene density within the pericentromeric region of the inbred rye line ‘Lo7’. In barley, this region has furthermore been observed to display a 20-fold lower recombination rate62. Thus, we concluded that the 600 K SNP array performed satisfactorily on the Gülzow-type germplasm in relation to the fundamental characteristics investigated. The high-density array constitutes a milestone in rye genomic resources transitioning SNP-based genomic studies and replaces the previous rye 5 K SNP array by Haseneyer, et al.63.Hybrid rye breeding germplasms are highly secluded with little exchange of material. The existing exchange is subsequently influenced by the deployed fertility control system, which determines the compatibility of foreign material for introgression into heterotic parental populationsR) genes63. The use of high-density SNP typing has allowed whole-genome scans for the identification of often small haplotype blocks significantly associated with resistance64. The creation of the 600 K SNP array and the chromosomal-scale reference genomes ‘Lo7’ and ‘Weining’ in rye has significantly changed the genomic toolbox available for mining novel resistance genes57. Using these recent advances in rye genomic resources, we successfully managed to identify a site on chromosome arm 7RL that was significantly associated with PM resistance. The high level of resolution provided by the dense marker panel revealed that the PM resistance-associated haplotype block spanned 17 Mbp on the distal tip of the chromosome arm 7RL subtelomeric region.GWAS on SNP genotype data has become an effective tool in genome-based plant breeding for the study of oligogenic traits often governed by a few genes with large effects, such as the major monogenic inherited resistance  analysis followed by transformation of a susceptible non-restorer germplasm line to validate the gene89. This would in turn equally show whether the gene is present in the reference genomes and whether PmNOS1 encodes an Rpp13-like NLR protein.In rye, while the rate of decay has only been determined in a few genes related to frost response, which showed a rapid linkage decayBg displaying a large evolutionary plasticity, shortening the process from identification of resistance-associated sites to isolation of the underlying R gene is important for the development of novel resistant cultivars. The relevance of studies in rye is accentuated by the possibility of introgressing novel R genes into the staple cereal wheat by chromosomal translocation lines. To aid further studies in the field, we have provided both a rye600K SNP array marker map anchored using the ‘Lo7’ reference genome and NLR repertoire information of the ‘Lo7’ and ‘Weining’ reference genomes in open-access data repositories.In conclusion, our study demonstrates the immediate value of recent advances in rye genomic resources for the mining of novel resistance genes. These resources now permit accurate identification of delimited resistance-associated haplotype blocks and scanning for trait-associated genes residing within. With pathogens such as Secale cereale L.) lines, 92 restorer and 88 non-restorer germplasm, belonging to the elite Gülzow-type hybrid rye breeding germplasm at Nordic Seed A/S  were investigated. Population structure and information on the genetic characteristics of the germplasm were presented in a recent study by Vendelbo et al.59. The parental populations represent genetically secluded gene pools with restorer  lines carrying a dominant allele for the restoration of male fertility and non-restorer germplasm lines carrying a recessive allele and fertile cytoplasm, which is used to maintain cytoplasmic male sterile  lines. DNA extraction was performed using an adapted SDS-based method according to the USDA90 after Pallotta et al.91 on an equivalent of 75 mg of plant material collected from the coleoptiles and primary leaves of two seven-day-old seedlings per line. The DNA concentration and 260/280 nm ratio of the samples were measured using an Epoch™ microplate spectrophotometer (Biotek®), and evidence of fragmentation was obtained by size visualization on a 1.2% agarose gel.A panel of 180 inbred rye (57.Samples of each line containing 200 ng of high molecular weight gDNA with a ≥ 1.8 260/280 nm ratio were sent for single nucleotide polymorphism (SNP) genotyping at Eurofins Genomics Europe Genotyping . Genotyping was performed using a 600 K SNP array with 600,843 SNP markers on an Affymetrix GeneTitan™ Scanner platformBgs were sampled in Northern Germany and Denmark in the period from 2013 to 2020 to screen the lines against a broad range of Bgs pathotypes prevalent in Northern Europe. Two Bgs populations were collected at the Nordic Seed rye multiplication site in Germany in 2013 (N13) and 2018 (N18) . Ten leaves exhibiting PM disease were carefully collected and rinsed in 0.5 L of water to release Bgs spores. Six pots containing 15–20 susceptible 12-day-old seedlings of the restorer line R277 were then inoculated by spraying a fine mist of the spore solution using an atomizer bottle. Pots were transferred to a separate climate chamber for each population and incubated at 18 °C with 12 h of light using 400 W high-pressure Phillips SON-T Agro lamps. Populations were continuously multiplied in an overlapping two-week cycle. Each week, the tray of 2-week-old seedlings was substituted with a tray of fresh 12-day-old seedlings. Inoculation was achieved by passive dissemination of spores from the ‘older’ tray to the tray with new plants through a steel-grid shelf.As there was no unified information on germplasm resistance to powdery mildew, three field populations of Bgs population was collected in autumn 2020 (D20) at Nordic Seed   using a mixture of hybrid cvs. KWS Binntto, KWS Bono and KWS Florano. Pots with 15 to 20 seedlings of the susceptible mixture were placed outdoors 12 days after sowing (DAS) in August-October 2020. Plants were controlled regularly, and all leaves showing PM disease within the period were collected. Prior to inoculation, leaves were placed in Petri dishes with moist filter paper and set to sporulate at room temperature in light for four to eight hours. Then, a pot containing 15–20 seedlings of the susceptible mixture was inoculated at 12 DAS by horizontally stroking the collected leaves across the seedlings. Inoculated pots were sprayed with a fine mist of water, placed in a container with transparent lids to ensure 100% RH and incubated in the dark at 10–15 °C for 24 h. After incubation in the dark, the pots were transferred to an isolated greenhouse cabin and incubated under 16 h of daylight at 18–24 °C and 8 h of dark at 14–16 °C.An additional Bgs populations was performed using the same procedure as described above. For each population, two trays containing 35 pots of the susceptible mixture were inoculated by brushing 3–4 highly infected pots inoculated 20 days earlier across the tray.Multiplication of trial inoculum for the three Bgs populations. In each greenhouse trial, eight seeds per line were sown in a 28-hole tray using a completely randomized design with two repetitions for each of the two trial replicates. For each tray, a positive (‘susceptible’) control consisting of hybrid cv. KWS Binntto and negative (‘resistant’) control cv. KWS Serafino was included. At 14 DAS, trays were inoculated as described above by brushing 3–4 highly infected pots inoculated 20 days earlier across the tray. After 14 days of incubation, the lines were phenotyped by scoring the infection response on the first and second leaves for each of the eight seedlings per repetition in accordance with a 9-step 0–4 scale by Torp et al.88  interface in R statistical software (v. 4.0.1) by applying various predesigned packages94. The mapping positions of SNPs were hereafter stringently filtered for I) complete SNP sequence alignment and II) a maximum of 1 mismatch to ensure accurate positioning.Positional data of the 600 K SNP markers were obtained by mapping each of the 600,843 SNP marker sequences to the rye reference genome ‘Lo7’ using the NCBI blastn (v. 2.9.0 +) function95. The interchromosomal distribution of the informative marker PIC, marker-to-marker distance and marker density in 10 mb bins on the ‘Lo7’ rye genome were visualized using Circos (v. 0.69.8) in the Galaxy online interface97. A Circos plot was constructed using the pipeline developed by Hiltemann, et al.98.Prior to analysis, markers were filtered for a marker allele frequency ≥ 0.005, missing individual score ≤ 0.2 and missing marker score ≤ 0.1. Fundamental characteristics of SNP marker informativeness, including polymorphism information content (PIC), were calculated using the SnpReady (v. 0.9.6) R package99. To determine whether the population infection-type distribution differed significantly, ANOVA was conducted using R.The distribution of infection types against the three respective PM populations was visualized by density plots using ggplot2 (v. 3.3.3) R packagel is the line id, and ɛ is the residuals. P and R were set as fixed effects, and l was set as a random effect. The random effect and residuals were assumed to be independent normally distributed variables described as follows: l ~ N , and ɛ ~ N . The BLUP solutions for the line effect were used in GWAS. Data were also fitted to a linear mixed model for each of the populations to correct for the effect of replication. In this model ‘P’ was removed.To correct the resistance phenotype for the effects of replication and population, we fitted the data to a linear mixed model using the lme4 (v. 1.1.26) package in R:100. The Manhattan plot was colorized using the RColorBrewer (v.1.1–2) R package color palette99. GWAS using a mixed linear model (MLM) was performed to identify discrete haplotype blocks associated with powdery mildew resistance. Additionally, the Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK) method was performed to identify the top-most powdery mildew resistance-associated marker within each haplotype block101. BLINK uses a multiple loci test for MLM by combining a fixed effects model, Bayesian information content and linkage disequilibrium information to collectively improve the statistical power while simultaneously reducing the computational run time. Markers that are in linkage disequilibrium with the top-most significant marker at a site are excluded in BLINK. A standard Bonferroni-corrected threshold of α = 0.05 was used as the significance threshold.Discovery of PM resistance-associated SNP markers was performed by a genome-wide association study (GWAS) using the genomic association and prediction integration tool (GAPIT) (v.3) package in Rhttps://github.com/steuernb/NLR-Annotator)102.To investigate whether PM-associated sites in the ‘Lo7’ and ‘Weining’ reference genomes housed nucleotide-binding leucine-rich repeat (NLR) genes, annotation was performed using NLR-parser (v.3) and NLR-annotator  method for coding sequences and the ‘Clustal Omega’ method for NLR protein sequences in Geneious Prime (v. 2020.2.3).The gene structure, coding sequence and NLR protein sequence of candidate genes were extracted from RNA-seq and de novo protein data provided in ‘Weining’ and ‘Lo7’ reference genome data repositories104. The tree was constructed after 10,000 bootstrapping iterations with weak nodes (≤ 80% recurrence) collapsed into multifurcations. A circular neighbor-joining tree was generated using the iTOL (v. 5) online tool (http://itol.embl.de/), enabling a colorful visualization of each line infection type spectrum against the three Bgs populations105.Neighbor-joining clustering analysis of breeding lines was performed with Euclidean genetic distance measurement using the ape (v. 5.3) R packageLr1, Lr10, Lr21, Lr22a), stem rust , yellow rust , powdery mildew  and resistance gene analogs  were included. NB-ARC sequences were obtained from the UniProt online database106. Phylogenetic analysis was conducted using a pipeline developed by Toparslan, et al.107 in R. Multiple sequence alignment of NB-ARC domain sequences was performed via msa (v. 1.20.1) using the ‘Clustal Omeage’ method and pairwise genetic distance based on identity calculated with the seqinr (4.2–8) package in R109. A tree was constructed for the ‘Lo7’ and ‘Weining’ NLR repertoires separately and visualized using ggtree (v. 2.2.4) R package110.To investigate the sequence similarity between nucleotide-binding leucine-rich repeat (NLR) genes at PM resistance-associated sites in the ‘Lo7’ and ‘Weining’ reference genomes, neighbor-joining clustering analysis using NB-ARC  domain sequences was performed. Identification of PM resistance-associated sites on ‘Weining’ was performed by mapping PM resistance-associated markers using the same procedure as described previously for ‘Lo7’. As references, NB-ARC domain sequences of available leaf rust (https://inkscape.org/).Graphs and figures were outputted from R in .svg format and manually curated using Inkscape (v. 1.1) , including the collection of plant material,complies with relevant institutional, national, and international guidelines and legislation.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.Supplementary Legends.Supplementary Information 1.Supplementary Information 2.Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.