Crop improvement through cross-population genomic prediction and genome editing requires identification of causal variants at high resolution, within fewer than hundreds of base pairs. Most genetic mapping studies have generally lacked such resolution. In contrast, evolutionary approaches can detect genetic effects at high resolution, but they are limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Here we use genomic annotations to accurately predict nucleotide conservation across angiosperms, as a proxy for fitness effect of mutations.Using only sequence analysis, we annotate nonsynonymous mutations in 25,824 maize gene models, with information from bioinformatics and deep learning. Our predictions are validated by experimental information: within-species conservation, chromatin accessibility, and gene expression. According to gene ontology and pathway enrichment analyses, predicted nucleotide conservation points to genes in central carbon metabolism. Importantly, it improves genomic prediction for fitness-related traits such as grain yield, in elite maize panels, by stringent prioritization of fewer than 1% of single-site variants.Our results suggest that predicting nucleotide conservation across angiosperms may effectively prioritize sites most likely to impact fitness-related traits in crops, without being limited by shifting selection, missing data, and low depth of multiple-sequence alignments. Our approach—Prediction of mutation Impact by Calibrated Nucleotide Conservation (PICNC)—could be useful to select polymorphisms for accurate genomic prediction, and candidate mutations for efficient base editing. The trained PICNC models and predicted nucleotide conservation at protein-coding SNPs in maize are publicly available in CyVerse (10.25739/hybz-2957).The online version contains supplementary material available at 10.1186/s13059-022-02747-2. In quantitative genetics, candidate causal mutations are often detected by statistical associations between genetic polymorphisms and phenotypic differences within species (QTL effects). QTL effects are useful in plant breeding , but they may be confounded by the co-segregation of neutral polymorphisms with causal mutations  . In conti) it is calculated from a multiple-sequence alignment (MSA), which requires cross-species conservation of alignable genomic regions; (ii) it may lack discriminatory power, because even variants with moderate effect on fitness may be highly conserved [iii) it may be biased by functional turnover (shifting selection) and clade-specific conservation. To overcome these limitations, PNC may be predicted throughout the genome, based on annotations which capture the genomic characteristics of fitness effects (genomic annotations). Previous methods like CADD [Despite key advantages, PNC has practical disadvantages which limit its usefulness in quantitative genetics , 6: (i) onserved , 7; and ike CADD , 9 and Like CADD , 11 haveZea mays L.), using computational annotations that are readily available from DNA sequence data and gene-model annotations. Computational annotations have several advantages: low cost, absence of missing values, and ease of portability from one genome to another. They may also provide latent (non-observed) representations of genes and can be used to perform in silico mutagenesis to predict the impact of point mutations on these representations. To achieve high resolution and high accuracy, we use high-resolution genomic annotations to predict PNC in the angiosperm clade, spanning > 140 million years of evolution and recombination events [In this study, we introduce a machine learning method to predict PNC across angiosperms in coding regions in maize  and monocots  [In this study, we aimed at capturing the genomic basis for fitness effects in coding regions in maize, by predicting PNC at nonsynonymous mutations from genomic annotations. PNC was estimated by conservation of DNA bases, in a MSA of 27 diverse plant genomes, from basal angiosperm uminata) . ConsequThe DNA bases (genomic sites) with large fitness effects are subjected to evolutionary constraint, so they tend to be conserved across species (high PNC), and monomorphic within species (no observed polymorphism at the DNA base). Accordingly, monomorphic sites within maize (sites with no observed SNPs in a maize reference panel) tended to be more conserved across angiosperms, compared to polymorphic sites: they were aligned in MSAs over larger evolutionary timescales —used conserved sites as positive examples for large fitness effects, and sites in non-aligned regions as negative examples for neutral effects. PICNC did not rely on within-species variability , so we could train our model on nonsynonymous mutations at any site, even when they were not observed in maize populations . Therefore, we could include data about the genomic sites where polymorphisms are not tolerated by evolution . This helped us avoid survivorship bias at SNP sites and provided many more instances of PNC to learn about the genomic characteristics of fitness effects: 20,136,310 monomorphic sites, instead of 483,448 nonsynonymous SNPs across diverse maize lines  or 103,9k-mer frequency) and protein structure . While mutation type simply characterizes the codon change caused by a nonsynonymous mutation , SIFT score quantifies the impact of the nonsynonymous mutation by calculating the probability of its codon change  [i) monomorphic sites provided more information about PNC; (ii) annotations like SIFT scores and in silico mutagenesis scores from UniRep enabled predictions at single-site resolution; and (iii) leave-one-chromosome-out prediction avoided overfitting to observed PNC  and a high nucleotide conservation (substitution rate < 0.05 in the MSA at the site of the mutation). Observed PNC was used to train a probability random forest with genomic annotations about genomic structure  , 17. In utation) ; in siliPNC see “”. For eaPNC see “”. Our moCompared to a baseline model including SIFT score and mutation type , annotations about genomic structure  contributed to an improved prediction accuracy for PNC, from 72 to 76% Fig. A. ProteiPn/Ps, and the proportion of nonsynonymous SNPs, Pn/(Pn+Ps), within each gene). UniRep variables captured gene variability within maize, for these gene properties: prediction accuracy (Pearson correlation) > 0.35  and selective constraint  or intergenic regions, when using UniRep variables to describe protein features of the nearest gene  and predicted PNC  are both proxies for evolutionary constraint. However, SIFT scores pointed to relatively large sets of SNPs in the maize reference panel; even the smallest SIFT score value pointed to as many as 64,611 out of 483,448 nonsynonymous SNPs in a maize reference panel Fig. A. PredicUnder the hypothesis that predicted PNC identifies impactful genes, the set of genes prioritized by predicted PNC should be enriched for important functional attributes like high gene expression. Prioritization by SIFT score  resulted in a small increase in protein expression, while observed PNC resulted in significant enrichment for highly expressed genes , among 14,646 prioritized genes out of the 24,549 genes containing nonsynonymous SNPs . Based on these functional enrichments, predicted PNC prioritized genes involved in primary metabolism Fig. : Fig. S7For a more detailed description of prioritized genes, we identified the pathways that were significantly enriched among the genes prioritized by predicted PNC, observed PNC, or SIFT score. According to the CornCyc database , gene prP < 0.05 based on random permutations of SNP weights), while prioritization by the lowest SIFT score (9576 SNPs) did not result in a significant increase in prediction accuracy , plant height (PH), or grain yield (GY). We tested the hypothesis that predicted PNC was larger at causal variants for fitness-related traits in hybrid panels. Under this hypothesis, we expected that (i) weighting SNPs with predicted PNC increased the accuracy of genomic prediction; and (ii) prioritizing SNPs with larger predicted PNC resulted in further gains in accuracy. Expectation (i) was not met for any of the agronomic traits , but not in a diverse panel representative of genetic variability in maize (Ames-H). Importantly, SNPs that are rare in maize are not necessarily rare in NAM-H, because of the half-sib family structure, in which 24 different donor parents are crossed to the same recurrent parent . TherefoGenomic prediction for other agronomic traits (PH and DTS) was not improved by PNC. This lack of improvement may be due to a weak relationship between these traits and evolutionary constraint, as proxied by PNC across angiosperms. Consistently, in maize, hybrid vigor and inbreeding depression are substantially larger for traits related to seed weight and grain yield, compared to traits related to plant morphology and flowering time –35. InteOur results about the characteristics of prioritized SNPs and genes suggest that predicted PNC is more useful than observed PNC to identify causal variants for fitness-related traits, since it can select fewer variants and produce stronger functional enrichments. Our approach (PICNC) addressed two important caveats of observed PNC, which limit its usefulness for quantitative genetics and breeding applications: missing information outside MSAs, and sensitivity of conservation to fitness effects . Our preBased on GO and pathway enrichment analyses, predicted PNC pointed to genes in primary metabolism: biosynthesis and energetic catabolism Fig. , Table 1Our approach was validated by cross-population genomic prediction . Compared to within-population genomic prediction, cross-population genomic prediction accuracies are typically much lower—and sometimes close to zero –53, becaOur approach exemplifies important benefits of this coming generation of protein structural machine learning annotations for predicting PNC without resorting to experimental data. These annotations may be produced by protein representation learning, using techniques like UniRep  or more Our results demonstrate the usefulness of our methodology. They also open possibilities for improved detection of fitness effects, by including broader sets of variants , novel genomic annotations , and different evolutionary timescales . Moreover, further improvements of SNP prioritization could be achieved by combining our approach with complementary techniques. Recent studies in human genetics have inferred relationships between genomic annotations and functional impact of mutations. These include methods based on evolutionary data, like CADD , 9 and LTo detect candidate causal variants at high resolution, we used nucleotide conservation and machine learning to predict the impact of mutations at single DNA sites. Our methodology benefited from genomic annotations which described protein structure by deep neural networks and estimated the structural impact of mutations by in silico mutagenesis. In maize, nucleotide conservation predicted by our approach performs better than observed nucleotide conservation. It results in significant functional enrichments and improves genomic prediction for grain yield across elite populations. Therefore, our approach (PICNC) could enable breeding applications which require the identification of candidate causal variants at high resolution, like cross-population genomic prediction and genome editing.Sorghum bicolor), and Yugu1 , or, by default, the longest transcript. Mutations in the coding region of representative transcripts were characterized at two types of DNA bases: monomorphic sites and SNP sites. Mutations at monomorphic sites were 20,136,310 random nonsynonymous substitutions in the maize genome at the selected genes, while those at SNP sites were the 483,448 observed nonsynonymous substitutions in a reference panel representative of inbred lines in maize [The B73 maize reference genome and its gene-model annotations under version 3 were downloaded from Ensembl Plants , 77. Nucin maize , 78.j, phylogenetic nucleotide conservation (PNC) wj was binary: wj = 1 if the neutral score (tree size) was > 5 and the ratio of conservation score to neutral score was > 0.95 , wj = 0 otherwise.Publicly available data from a multiple-sequence alignment (MSA) across angiosperms was previously published in maize , 79: neuk-mer frequency and transposon insertion) and protein structure .Each mutation in coding regions was characterized by genomic structure , SIFT score, and SIFT class  were predicted using SIFT 4G , 82, 83.wj = 0 or 1) was estimated by probability random forests [ranger [wj = 0) and positive examples (wj = 1) of evolutionary constraint, wj was set to missing in intermediate cases where substitution rate > 0.05 or tree size < 5 . The probability P(wj = 1) was estimated by 1000 trees per forest, 50,000 sites per tree (sampled with replacement), and at least 100 sites at each terminal node. Mutation effect, SIFT score, and SIFT class were always included as baseline predictors, while a third of remaining genomic annotations  were randomly sampled as predictors for each tree, based on recommendations for regression random forests [The relationship between genomic annotations and observed PNC . When estimating the importance of genomic annotations and assessing the effect of random forest parameters on classification accuracy , random forests were validated at monomorphic sites in chromosome 8 and trained (at monomorphic sites) in remaining chromosomes  and alternate proportions of genomic annotations sampled per tree  and validation in the remaining chromosomes (excluding k). For each left-out chromosome k, the optimal hyperparameters were ntrees=1000  and pvariables=In leave-one-chromosome-out prediction, alternate numbers of trees per forest (m forest ).Validation in the sorghum genome consisted of fitting the PICNC model in the whole maize genome, as described above see “”, and prk-mer frequency, Unirep variables, and in silico mutagenesis scores were computed in version 3.1 of the sorghum reference genome, as described above , estimated in a filtered set of SNPs  in the Hapmap 3.2.1 reference panel [Cis eQTL effects were the statistical associations  between SNPs and 3′ RNA-seq expression of genes, in a diverse panel of 299 inbred lines [Cis eQTL effects in germinating shoot or germinating root were estimated for the SNPs with minor allele frequency ≥ 0.05 in this panel, in a linear regression model including the PEER factors from [Predicted PNC in maize was validated by measures of functional importance of SNPs: within-species conservation, ce panel , imputedce panel . Cis eQTed lines , 93. Cisors from  as covarors from . Chromators from , 95.mgcv [cis eQTL effects (by cubic regression splines), and chromatin accessibility (as factors), while accounting for chromosome (as factor) and whether the site was included in the MSA .In maize, PNC was validated by experimental SNP annotations in a generalized additive model fitted in the R package mgcv . PNC wasIn sorghum, predicted PNC was validated by MAF, calculated in the diverse panel of Lozano et al. , 89.log(x + 1) where x is RNA abundance in fragments per kilobase of transcript per million mapped reads (FPKM) or protein abundance in distributed normalized spectral abundance factor (dNSAF). Experimentally validated gene ontology (GO) annotations [Pn) and segregating synonymous SNPs (Ps) were based on a maize reference panel (MAF ≥ 0.01 in Hapmap 3.2.1). The ratio Pn/Ps and proportion Pn/(Pn+Ps) were calculated for each gene with enough observed segregating synonymous SNPs (Ps ≥ 5).Predicted PNC in maize was validated by gene properties: gene expression, gene ontology, pathway annotation, and number of segregating SNPs. Gene expression was quantified by RNA abundance across 23 tissues, and protein abundance across 32 tissues . In all otations  were retotations . In enriotations , and GO otations  (AdditioPn/Ps and proportion Pn/(Pn+Ps) calculated in the diverse panel of Lozano et al. [In sorghum, predicted PNC was validated by the number of segregating SNPs: ratio o et al. , 89.t-test and Fisher’s exact test, respectively.In validations by experimental gene annotations, genes containing sites with n=1106) and a collection of biparental crosses having B73 as their common parent  [m=103,905 nonsynonymous SNPs in the coding regions of the 25,824 genes selected in this study.Two panels of hybrid maize lines were analyzed to assess the usefulness of predicted PNC for genomic prediction: a diversity panel  . These pj in genomic prediction models, fitted in hybrid maize panels:y is the n-vector of mean phenotypic values; Q is a n × 4 matrix depicting population structure by a column of ones (for the intercept) and the three principal components from the Hapmap 3.2.1 panel, with respective effects α; e is the vector of errors; G is the n × n genome-wide relationship matrix such that the n-vector u consists of genome-wide breeding values:xil is the genotype of hybrid i at genome-wide SNP l, pl is the estimated frequency of SNP l in hybrid panels.Predicted PNC  if b = 1, …, 20.The significance of Figure S1. Distribution of neutral tree size  by category of DNA bases. Figure S2. Classification accuracy for different hyperparameters of the probability random forest in PICNC. Figure S3. Prediction accuracy of regression random forests for experimental gene annotations, by UniRep variables. Figure S4. Concordance between importance of protein features (UniRep variables) for phylogenetic nucleotide conservation (PNC) across species and measures of gene variability within species. Figure S5. Classification accuracy for PNC for different categories of DNA sites: CDS (nonsynonymous mutations), Intron+UTR, Intergenic. Figure S6. Difference in SNP variability at genes prioritized by SIFT score or phylogenetic nucleotide conservation (PNC), in maize or sorghum. Figure S7. Enrichment of genes prioritized by SIFT conservation (1 − SIFT score) or phylogenetic nucleotide conservation (PNC), for gene ontology (GO) classes. Figure S8. Prioritization of nonsynonymous SNPs in genomic prediction for agronomic traits in hybrid panels.Additional file 1: Table S1. Functional information about gene models in the maize reference genome: experimental gene annotations and percentile for SIFT conservation and phylogenetic nucleotide conservation (PNC). Table S2. Number of genes prioritized by SIFT conservation or phylogenetic nucleotide conservation (PNC), in each CornCyc pathway. Table S3. Prioritization of nonsynonymous SNPs by SIFT conservation or phylogenetic nucleotide conservation (PNC): number of selected SNPs in hybrid panels and expected number of putative deleterious mutations per inbred line based on minor allele frequency (MAF) in reference panel.Additional file 2: Additional file 3. Review history.


Striga hermonthica is an obligate, root‐parasitic plant of cereal hosts in sub‐Saharan Africa, causing devastating yield losses. Understanding the molecular nature and allelic variation of VFs in S. hermonthica is essential for breeding resistance and delaying the evolution of parasite virulence.Parasites have evolved proteins, virulence factors (VFs), that facilitate plant colonisation, however VFs mediating parasitic plant–host interactions are poorly understood. S. hermonthica genome and identified secreted proteins using in silico prediction. Pooled sequencing of parasites growing on a susceptible and a strongly resistant rice host allowed us to scan for loci where selection imposed by the resistant host had elevated the frequency of alleles contributing to successful colonisation.We assembled the D than the genomic background, consistent with balancing selection.Thirty‐eight putatively secreted VFs had very different allele frequencies with functions including host cell wall modification, protease or protease inhibitor and kinase activities. These candidate loci had significantly higher Tajima's S. hermonthica to overcome different layers of host resistance. Understanding the maintenance of variation at virulence loci by balancing selection will be critical to managing the evolution of virulence as part of a sustainable control strategy.Our results reveal diverse strategies used by  236: 316–318Westwood, .See also the Commentary on this article by  As a consequence, they have evolved sophisticated surveillance systems to detect and protect themselves against parasite invasion  domain‐containing effector (Suppressor of Host Resistance 4z (SHR4z)) into cowpea host cells, whereupon it triggers rapid turnover of the E3 ubiquitin ligase, VuPOB1, a positive regulator of the host's defence response  and candidate VFs. The assembled genome was then used as a reference for an experimental, population genomics analysis, to compare DNA sequence variants in bulked (pooled) samples of S. hermonthica grown on a susceptible (NERICA‐7) or resistant (NERICA‐17) rice host   Fig. . A similS. hermonthica (Del.) Benth. seeds was collected from individuals parasitising maize in farmers' fields in the Kibos region of Kenya . To obtain S. hermonthica for genome sequencing and the bulked sample analysis (BSA), rice seedlings of the varieties, NERICA‐7 and NERICA‐17, were grown in rhizotrons and infected with germinated S. hermonthica seeds  were harvested from NERICA‐7 and NERICA‐17, divided into 20 mg aliquots and immediately frozen in liquid nitrogen. The 300 individuals from NERICA‐7 and NERICA‐17 were divided into three pools of 100 individuals . DNA was extracted from the six pools (Supporting Information Methods An accession (population sample) of c. 2.7 billion reads were generated from the short insert libraries and 0.76 billion reads from mate‐pair libraries. This corresponded to c. 230× and c. 54× coverage of the S. hermonthica genome, respectively. The cleaned and filtered reads were used to assess the S. hermonthica genome size, repetitiveness and heterozygosity, compared with 12 other plant species (Table ga (https://github.com/jts/sga). This analysis showed S. hermonthica was highly heterozygous and therefore the software Platanus, which is specifically designed for highly heterozygous genomes, was chosen to assemble the S. hermonthica genome   Table . For theS. hermonthica transcriptome dataset of cDNAs collected from S. hermonthica individuals at eight developmental stages, generated by the Parasitic Plant Genome Project (PPGP) , Mimulus guttatus (v.2.0), Solanum lycopersicum (ITAG2.4), Oryza sativa (IRGSP1.0) and Sorghum bicolor (79), were used to determine consensus gene models in the genome. Third, an ab initio method was used for de novo prediction of genes in the S. hermonthica genome using the software, Braker, with default settings  and O. sativa (v.7). A Blastp analysis was also conducted against the pathogen–host interaction database  (http://www.phi‐base.org/index.jsp). Blastp analyses were run locally using the NCBI Blast package (version: ncbi‐2.3.0+) and a hit was taken to be significant if e‐value < 10−5, bit score and percentage identity > 30. Protein motifs and domains were determined by searching databases including Pfam, PANTHER, GENE3D, CDD, PRINTS, PROSITE, ProDom and SMART with InterProScan GO terms for individual proteins retrieved from the corresponding InterPro descriptions.Putative protein functions were assigned to rthoFinder v.2  were inferred using the software OS. hermonthica proteins were predicted using SignalP v.3.0 and 4.1  were significant when the corrected P‐value was < 0.1, according to a chi‐squared test with a false discovery rate (FDR) correction for multiple testing , the intronic region and coding region, and the numbers of nonsynonymous SNPs were determined. To quantify the allele frequency differences between control and virulent pools for these candidate virulence genes, the proportion of SNPs with high fixation index (FST) values in the significant window was calculated  from the roots of NERICA‐7 rice plants . FPKM values for each gene at each time point were used to calculate a fold change in expression relative to the haustorial sample and significance assessed with a one‐way ANOVA using the aov function in R  , the closely related nonparasitic plant M. guttatus (28 140) and to A. thaliana (27 416)  Fig.  when com11) Fig. , dominat11) Fig. , a patte6) Table .usco analysis of gene set completeness   Fig. . Of the ome Fig. . Both Sttus Fig. . Similarnil Fig. . Of the nts Fig.  and thesnts Fig. . This isS. hermonthica and 12 other plant species identified 22 624 orthogroups in total, of which 12 278 contained S. hermonthica genes. Of these, 327 were significantly expanded and 104 were contracted in the S. hermonthica genome, identified by Café analyses  between ses Fig. . Low braana Fig. . This miors Fig. . Both prS. hermonthica (11.4% of the proteome)   Fig. , many ofns Table , providiome Fig. . Genes ens) Fig.  suggestiied Fig.  and wereied Fig. , many ofS. hermonthica secretome that were enriched to a higher degree than observed in the secretome of the closely related nonparasitic plant, M. guttatus  domains that were enriched in the S. hermonthica secretome   Fig. . The degS. hermonthica individuals were harvested from either a resistant (NERICA‐17) or susceptible (NERICA‐7) rice cultivar, and pools of these individuals were subjected to genome re‐sequencing. After aligning the reads to our reference genome, we detected 1.8 million SNPs in genic regions. These genic regions were split into 150 741 1 kbp windows and, of these, 194 (0.13%) contained SNPs with large and consistent allele frequency differences between the bulked pools of S. hermonthica selected on the resistant vs the susceptible hosts  and subtilisin‐like protease (SHERM_08443)) or were expressed at very low levels (e.g. the peroxidase (SHERM_00887), glycosyl hydrolase (SHERM_(20042), both aspartyl proteases (SHERM_16482 and SHERM_26730) and an unknown protein (SHERM_03853)) Dataset . HoweverMost of the 38 genes had significantly differentiating SNPs in their promoter regions (from the start site to 2 kbp upstream). Some of these SNPs may lead to a change in the regulation of gene expression Fig. . Some geS. hermonthica. To test this prediction, we compared Tajima's D between candidate loci and the rest of the genome, expecting to see more positive values . Some loci had particularly high Tajima's D values, for example the two receptor‐like protein kinases  with a negative ΔD (DVir − DCon) of −0.9. This suggests strong selection resulting in one common haplotype in the virulent pools in contrast with two or more haplotypes at intermediate frequencies in the control pools. There were also large positive ΔD values: 0.71, 0.16 and 0.20 for one of the putative receptor‐like protein kinases SHERM_01541, one of the aspartyl proteases, SHERM_16482, and the peroxidase SHERM_00887, respectively. This suggests that a rare haplotype in the control pools is present at intermediate frequency in the virulent pools. Overall, these changes indicate that selection on the resistant host caused changes in frequency of multi‐SNP haplotypes at these loci, haplotypes that may have been created by areas of low recombination or by recent invasion of new variants under positive selection  exhibited functions relating to cell wall modification. Cell wall modification is a critical step in plant invasions by many different parasites, including D between the control and virulent pools, consistent with selection favouring one haplotype on the resistant NERICA‐17, out of several haplotypes present in the population. This gene contained SNPs in the promoter region and nonsynonymous SNPs in the coding region. In A. thaliana and O. sativa, TBL proteins belong to large gene families with functions related to cell wall modifications. At‐TBL44 has been implicated in pectin esterification  for contribution to virulence, with large allele frequency differences between our control and virulent pools, including many gene families. It is likely that many additional candidate VFs would be revealed by repeating this comparison on other resistant hosts. An important question for the future will be to determine how individual VFs  are implicated in overcoming resistance for specific hosts or across a range of hosts. Ideally this requires a high‐throughput, efficient, transformation system for D in S. hermonthica to be negative, perhaps reflecting population expansion following the spread of agriculture, but our candidate loci had significantly higher Tajima's D on average, consistent with balancing selection on these loci maintaining multiple alleles. Further understanding the maintenance of variation at virulence loci will be critical to managing the evolution of virulence as part of a sustainable control strategy .Table S7 Enriched GO terms associated with Buscos that were missing from the genomes of only the two Striga species, only the two Cuscuta species or both the two Striga species and the two Cuscuta species.Table S8 Predicted subcellular location of Striga hermonthica proteins according to their closest orthologue in Arabidopsis thaliana.New Phytologist Central Office.Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries  should be directed to the Click here for additional data file.


Meanage = 12.81 years, SD = 0.48) completed questionnaires regarding parent-adolescent conflict, depressive symptoms, and NSSI. Genomic DNA was extracted from saliva and buccal cells from each participant. Bootstrapping techniques displayed statistically significant moderated mediation. The results showed that the positive association between parent-adolescent conflict and adolescent NSSI was in fact mediated by depressive symptoms. Moreover, this indirect link was moderated by the COMT gene rs4680 polymorphism. Specifically, the risk effect of parent-adolescent conflict on adolescent NSSI via depressive symptoms was stronger for adolescents with Val/Val genotype than for those with Met/Met or Val/Met genotype. These findings underscore the importance of examining the interaction between genes and the environment to understand how and when parent-adolescent conflict impacts adolescent NSSI.Existing research suggests that parent-adolescent conflict is associated with increased risk for adolescent non-suicidal self-injury (NSSI). However, adolescent NSSI reactions to parent-adolescent conflicts exhibit large individual differences. This study sought to explore whether depressive symptoms mediates the relationship between parent-adolescent conflict and adolescent NSSI, and whether this mediating process is moderated by the COMT gene rs4680 polymorphism. A total of 673 adolescents  in the age range of 12 to 15 years ( Non-suicidal self-injury (NSSI) refers to intentional destruction of body tissue  without the idea of suicide . The preRecently, the relationship between parent-adolescent conflict and NSSI has drawn increasing attention. According to the pragmatic hypothesis of the integrated theoretical model of NSSI, NSSI is an effective and simple means of immediately regulating distasteful emotional experiences and social situations . NSSI isThe affect-regulation model of self-injury proposes that self-injury is a maladaptive way of reacting to acute negative emotional arousal . At the Hypothesis 1 (H1).Depressive symptoms will mediate the link between parent-adolescent conflict and adolescent NSSI.Although parent-adolescent conflict is considered as a significant risk factor for the development of NSSI, not all adolescents are equally influenced by parent-adolescent conflict. Based on the differential susceptibility model , adolescHypothesis 2 (H2).The rs4680 polymorphism will moderate the positive indirect link between parent-adolescent conflict and adolescent NSSI via depressive symptoms. This indirect association will be significant among adolescents with Val/Val genotype but much weaker among adolescents with Met/Met or Val/Met genotype.A moderated mediation model was designed to explore the role of depressive symptoms and COMT rs4680 in the relationship of parent-adolescent conflict and adolescent NSSI. Meanage = 12.81 years, SD = 0.48 years). There were 364 adolescent male participants and 309 adolescent female participants.In this study, cluster sampling method was used to choose subjects. 673 students in the age range of 12 to 15 years old were recruited as subjects from two middle schools in Guangdong province  in the past six months. These NSSI behaviors are relatively common among adolescents  and haveχ2 goodness of fit test showed that the genotype distribution of the COMT gene rs4680 polymorphism was consistent with Hardy-Weinberg equilibrium .Genomic DNA was extracted from the saliva and buccal cells of each participant. Magnetic Bead Method Nucleic Acid Extraction Kit providing by a commercial company in China was used for DNA extraction. Single nucleotide polymorphism (SNP) genotyping was performed by the Wuhan Tianyi Huiyuan Biotechnology Co., Ltd., Wuhan, China. The detection rate of genotyping was 98.54%. Genotyping the COMT gene rs4680 polymorphism yielded three groups as follows: 35 were with Met/Met genotype, 276 were with Val/Met genotype, and 362 were with Val/Val genotype. The α coefficient for this scale was 0.84.Considering the significant impact of gender ,36, age Before the research, we obtained approval from the Ethics in Human Research Committee of the School of Education, Guangzhou University . Second, with the consent of the parents and students, the experimenter distributed questionnaires to the students in their classes. Third, students were required to collect their saliva to saliva collector. The whole process of filling out the questionnaire and saliva collection took 45 min.SPSS 20.0 was used for descriptive statistical analysis. We then used the Process Macro Model 4 for mediation analysis and Model 59 for moderated mediation model analysis . Next, tSD = 0.57), 1.93 (SD = 0.82), 1.70 (SD = 0.43) and 1.07 (SD = 0.23), respectively. The item mean score of family financial difficulty, parent-adolescent conflict, depressive symptoms and NSSI were 1.33 , and depressive symptoms positively predicted NSSI . The residual effect of parent-adolescent conflict on NSSI was significant . The bias-corrected percentile bootstrap method showed that the mediating effect of depressive symptoms between parent-adolescent conflict and adolescent NSSI was significant .Mediation tests were conducted using the PROCESS macro for SPSS . This mediation model  examed tβ = 0.15, SE = 0.07, t = 1.97, p < 0.05, 95% CI ). As shown in β = 0.17, SE = 0.06, t = 3.05, p < 0.01, 95% CI ), and the interaction effect between depressive symptoms and the COMT gene rs4680 polymorphism on NSSI was significant . As shown in The moderated mediation model tests were conducted using the PROCESS macro for SPSS . The results are presented in SE = 0.03, 95% CI ) than for those with Met/Met or Val/Met genotype . Therefore, the mediation pathway “parent-adolescent conflict → depressive symptoms → NSSI” was moderated by the rs4680 polymorphism, which formed a moderated mediation model.We further tested whether the indirect effect between parent-adolescent conflict and adolescent NSSI via depressive symptoms was conditioned by the COMT gene rs4680 polymorphism. The bias-corrected percentile bootstrap results indicated that the indirect effect was stronger for adolescents with Val/Val genotype  might reduce the frequency of parent-adolescent conflict. Second, this study confirmed that parent-adolescent conflict could enhance the level of NSSI by increasing adolescent depressive symptoms, which provides worthy implication for future intervention. Acceptance without judgment, dimension of dialectical behaviour therapy, has been identified a useful skill for NSSI . FinallyLimitations notwithstanding, in a sample of Chinese adolescents, our study demonstrated that the depressive symptoms significantly mediated the relationship between parent-adolescent conflict and adolescent NSSI. Furthermore, COMT rs4680 moderated the first and second half of the mediating effect of “parent-adolescent conflict—depressive symptoms—adolescent NSSI”. Specifically, the risk effect of parent-adolescent conflict on adolescent NSSI via depressive symptoms was stronger for adolescents with Val/Val genotype than for those with Met alleles.


Juglans regia 700 K SNP array data (∼295 K SNPs) on mother trees (MArray). We identified 578 unique genomic regions linked with at least one trait in a specific treatment, 874 predicted genes that fell within 20 kb of a significant or suggestive SNP in at least two of the three GWAS datasets , and 67 genes that fell within 20 kb of a significant SNP in all three GWAS datasets. Functional annotation identified several candidate pathways and genes that play crucial roles in photosynthesis, amino acid and carbohydrate metabolism, and signal transduction. Further network analysis identified 15 hub genes under WW, WS and WR conditions including GAPB, PSAN, CRR1, NTRC, DGD1, CYP38, and PETC which are involved in the photosynthetic responses. These findings shed light on possible strategies for improving walnut productivity under drought stress.Uncovering the genetic basis of photosynthetic trait variation under drought stress is essential for breeding climate-resilient walnut cultivars. To this end, we examined photosynthetic capacity in a diverse panel of 150 walnut families (1500 seedlings) from various agro-climatic zones in their habitats and grown in a common garden experiment. Photosynthetic traits were measured under well-watered (WW), water-stressed (WS) and recovery (WR) conditions. We performed genome-wide association studies (GWAS) using three genomic datasets: genotyping by sequencing data (∼43 K SNPs) on both mother trees (MGBS) and progeny (PGBS) and the Axiom™  Juglans regia L.) was domesticated in ancient Persia and is today cultivated for its edible nuts throughout temperate and semi-arid regions from Asia to Europe and the Americas , and placed in a petri dish filled with distilled deionized water for 24 h. Surface water on the leaves was removed through tissue paper and the leaves were weighed [turgor weight (TW)] and dried at 70°C. After 24 h, dry weight (DW) of samples was recorded and RWC was calculated as follow: (FW-DW)/(TW-DW) × 100 .2 mol−1; 25 ± 2°C and ~ 50–60% relative humidity) and photosynthetic active radiation of 1200–1500 μmol photons m−2 s−1. The infra-red gas analyser system (IRGA) was manually adjusted and the levels of CO2 and H2O references were fixed before measurements. For each time-point, measurements were taken on only undamaged leaves during two consecutive days from 9 a.m. till 3 p.m. Net photosynthetic rate (Pn in μmol CO2 m−2 s−1), transpiration rate (Tr in mmol H2O m−2 s−1), stomatal conductance (gs in mol H2O m−2 s−1), and intercellular CO2 concentration (Ci in μmol CO2 mol−1 air) were recorded from two plants per treatment. Pn, Tr and gs were used to calculate instantaneous and intrinsic WUE . Pn/iC ratio (CE) was taken as an estimate of carboxylation efficiency of Rubisco . Afterwards, a 800 ms saturating flash at 6000 μmol (photons) m−2 s−1 was applied to determine the maximum chlorophyll fluorescence (FM), variable fluorescence (FV = FM- F0) and maximum quantum yield of PSII (FV/FM) using the equation in In the first experiment, parameters obtained for the 150 families from the analysis of chlorophyll fluorescence were recorded under mild (3 weeks after drought) and severe (5 weeks after drought) stress. For measuring chlorophyll −2 s−1. Fluorescence intensities were recorded at four time points: 50 μs (O), 2 ms (J), 60 ms (I), and maximum fluorescence at around 1 s (P). Measurements related to the OJIP test were calculated based on the approaches described by Strasser et al.  [In the second experiment, chlorophyll fluorescence parameters of 60 selected extreme families (very drought tolerant to very sensitive) were measured under severe stress (24 days after drought) and recovery (two weeks after re-irrigation). Polyphasic Chl a fluorescence transients (OJIP-test) were measured using a portable fluorometer  in the middle part of the sapling in young fully-expanded walnut leaflets with 3 replicates for each treatment (control or drought) after 20 min dark adaptation. To fully ensure that all PSII centers are open, plants were allowed to dark-adapt overnight, and the lights were extinguished in the greenhouse until measurements were concluded pre-dawn (between 1 and 5 a.m.). The fluorescence measurements were taken by a saturating light of ~3000 μmol m0, 2004) ,29. The The response of genotypes to drought stress for all measured traits was expressed as a relative change in water-stress compared with well-water conditions using the drought stress index (DSI) described in Wójcik-Jagła et al. (2013)  and calcStatistical analyses were performed using Minitab software  and R environment  and the related R packages. Descriptive statistics and normality tests were run on both of the data and their residuals, respectively. The results of descriptive statistics were plotted using ggplot2 R package . Analysihttps://dnatech.genomecenter.ucdavis.edu/illumina-library-sequencing/).In this study, 95 out of 150 mother trees were chosen for retrospective tissue sampling and mother-tree genotyping. In other words, genotype data are only available for 95 of the 150 mother trees. Mature fresh leaves were sampled from each family and mother tree , immediately frozen in liquid nitrogen, and lyophilized prior to DNA extractions. Genomic DNAs of 95 mother trees and 150 families  were isolated from young leaflets from 40 mg of dry leaves using the E-Z 96 Plant DNA Kit  according to the manufacturer’s instructions. The DNA concentrations were determined using Qubit dsDNA High Sensitivity (HS) Assay Kits , after adjusting to 50 ng/μl 10 μl aliquots  were used for the library preparation. GBS was performed on 245 walnut samples using a two-enzyme GBS protocol previously described  with a fSNP calling was performed using the TASSEL 5 GBS v2 SNP-calling pipeline  and the The 95 mother trees were genotyped at 609658 SNPs evenly distributed throughout the walnut genome using the high-density walnut array from Affymetrix  as descri) the Fixed and Random Model Circulating Probability Unification (FarmCPU [ii) the Multiple Loci Linear Mixed Model  were 3.38E-6, 2.44E-5, and 2.29E-5 for the MArray, MGBS, and PGBS datasets, respectively. A lower suggestive P-value of 9.99 × 10−5 (-log10P = 5) was also used to detect the suggestive SNP–trait associations for gene set enrichment and network analysis.GWAS was performed for 30 photosynthetic traits under well-water (WW), water-stress (WS) and recovery (WR) conditions. Specifically, the phenotypic data included: (i) average performance of families for each trait under WW, WS and WR conditions; (ii) DSI and DRI; (iii) PC1–PC5 of phenotypic data; and (iv) PCs of DSI and DRI values. Three SNP panels including the array data on 94 mother trees (n = 295 685 SNPs), GBS data on 87 mother trees (n = 40 828 SNPs) and GBS data on 136 families (n = 43 607 SNPs) were used separately for GWAS. A subset of traits was measured on 60 selected families, in which case GWAS was performed using just 60 individuals. Single trait GWAS was conducted by applying two different models implemented in the R package Genome Association and Prediction Integrated Tools (GAPIT v3.0 ): (i) th(FarmCPU ), and (iel (MLMM ). Populael (MLMM . Populathttps://www.ncbi.nlm.nih.gov) BLASTX function. A SNP was considered to be located within a gene if it falls within a gene sequence. Then, based on the LD decay in our walnut panel [−5) implemented in Gene Ontology  and Kyology (GO ) databaslogy (GO  based onlogy (GO . Furtherlogy (GO .Web_Material_uhac124Click here for additional data file.


Olive, the emblematic Mediterranean fruit crop, owns a great varietal diversity, which is maintained in ex situ field collections, such as the World Olive Germplasm Bank of Córdoba (WOGBC), Spain. Accurate identification of WOGBC, one of the world’s largest collections, is essential for efficient management and use of olive germplasm. The present study is the first report of the use of a core set of 96 EST-SNP markers for the fingerprinting of 1273 accessions from 29 countries, including both field and new acquired accessions. The EST-SNP fingerprinting made possible the accurate identification of 668 different genotypes, including 148 detected among the new acquired accessions. Despite the overall high genetic diversity found at WOGBC, the EST-SNPs also revealed the presence of remarkable redundant germplasm mostly represented by synonymy cases within and between countries. This finding, together with the presence of homonymy cases, may reflect a continuous interchange of olive cultivars, as well as a common and general approach for their naming. The structure analysis revealed a certain geographic clustering of the analysed germplasm. The EST-SNP panel under study provides a powerful and accurate genotyping tool, allowing for the foundation of a common strategy for efficient safeguarding and management of olive genetic resources. Olea europaea subsp. europaea) tree crop species many efforts have been devoted to the collection and conservation of genetic resources. This has led to the establishment of over 100 ex situ field collections in Mediterranean countries and beyond , for each value of K, as well as calculating an ad hoc statistic ∆K [A model-based clustering method was applied to infer genetic structure and to define the number of clusters using the STRUCTURE v.2.2.4 software . Thirty istic ∆K  using STistic ∆K . Resultsistic ∆K  to obtaiThis study reports the development and use of a set of 96 EST-SNP markers for the fingerprinting of the accessions maintained at the WOGBC collection. The panel of EST-SNP under study allowed the accurate identification of a high number of cultivars, the largest to date. They have also proven to be useful for the assessment of redundant germplasm and homonymy cases, thus demonstrating their utility for efficient safeguarding and management of the olive germplasm. In this sense, our findings reinforce the need of a priori identification of the new accessions to avoid the accumulation of identical material through prospecting surveys and exchange of plant material in olive germplasm collections. Besides, the thorough characterisation of the WOGBC collection by means of EST-SNP markers has enabled the implementation of a protocol to efficiently curate and safeguard olive genetic resources. The utility of this set of markers for cultivar identification, as well as the relatively wide range of variability detected, suggest their use across laboratories and germplasm collections. Thus, a global use of the SNP panel developed in the present study would not only contribute to accurate identification and removal of identical accessions within each olive germplasm collection, but also, and above all, to the discovery of the presence of identical genotypes among germplasm collections, a task still difficult in olive. In turn, co-ordinated efforts across all olive germplasm banks would also contribute to identifying globally unique genotypes whose safeguard and backup should be prioritised at both national and international olive germplasm collections. Recent efforts based on different techniques  have allowed the discovery of a considerable number of SNP markers very useful for identification, diversity, and marker-assisted selection studies in olive. Overall, the ability to integrate and combine all this information on SNP genotyping of olive cultivars with an international consortium initiative will allow the development of a public SNP database, which will have important application for efficient and cost-effective management of olive genetic resources and better safeguard of them.


Through selective genotyping of pooled phenotypic extremes, we identified a number of loci and candidate genes putatively controlling timing of stem elongation in red clover.Trifolium pratense L.). This trait is of ecological and agronomic significance, as it affects fitness, competitivity, climate adaptation, forage and seed yield, and forage quality. We genotyped replicate pools of phenotypically extreme individuals (early and late-elongating) within cultivar Lea using genotyping-by-sequencing in pools (pool-GBS). After calling and filtering SNPs and GBS locus haplotype polymorphisms, we estimated allele frequencies and searched for markers with significantly different allele frequencies in the two phenotypic groups using BayeScan, an FST-based test utilizing replicate pools, and a test based on error variance of replicate pools. Of the three methods, BayeScan was the least stringent, and the error variance-based test the most stringent. Fifteen significant markers were identified in common by all three tests. The candidate genes flanking the markers include genes with potential roles in the vernalization, autonomous, and photoperiod regulation of floral transition, hormonal regulation of stem elongation, and cell growth. These results provide a first insight into the potential genes and mechanisms controlling transition to stem elongation in a perennial legume, which lays a foundation for further functional studies of the genetic determinants regulating this important trait.We have identified candidate genes controlling the timing of stem elongation prior to flowering in red clover (The online version contains supplementary material available at 10.1007/s00438-022-01942-x. Trifolium pratense L.), the above-ground biomass increases fast. The timing is therefore decisive for the seasonal pattern of productivity, and in addition, it affects the competitive interactions with weeds and companion species. The timing also affects forage quality, as an increasing proportion of stem tissue reduces the digestibility. In spite of its importance in relation both to plant breeding and to fitness in natural ecosystems, there is very limited information available on the genetic control of the timing of transition to reproductive development in the large genus Trifolium. We therefore set out to identify genetic markers, chromosomal regions and candidates for genes and processes controlling timing of reproductive development in red clover, a perennial legume used extensively in production of silage and hay in temperate regions.The timing of sexual reproductive development is an important agronomic attribute of all agricultural crops, and a trait with implications for fitness in natural populations. Proper timing in relation to the environmental conditions, in particular temperature and photoperiod, ensures a large and timely harvest of seed crops, but is also important in grassland species, in which the above-ground biomass is grazed or harvested for use as animal feed. For example, during the first stage of reproductive development of red clover  and Medicago truncatula , FLOWERING LOCUS C (FLC) and FRIGIDA (FRI)) appeared to be missing, while orthologs of genes with similarity to CO and FRI were present. Putterill et al. . Sequencing pools of DNA from many individuals using reduced representation genotyping, e.g., genotyping-by-sequencing  were sown in a greenhouse in September 2015 and grown at approximately 16 °C with a 20 h photoperiod ). The number of days from sowing until the first elongating internode was 2 cm long  was recorded for each plant. The 52 earliest and 52 latest individuals  were selected for genotyping by pool-GBS. Pools were created as follows: DNA was extracted from leaf tissue of each individual with the DNeasy 96 Plant kit (Qiagen). The 52 individuals in each of the two phenotypic groups  were randomly divided into three subgroups of 17–18 individuals and equal amounts of DNA from each individual in each subgroup were combined in a pool, creating a total of six DNA pools. Each DNA pool was distributed into 15–16 wells per 96-well plate to create replicate GBS libraries. One plate each was used for PstI and ApeKI single-digest GBS library preparation according to Elshire et al.  or 86 bp (PstI). Reads were aligned with BWA-MEM  < 0.95 and the reference allele frequency  to 0 if p(freqref ≠ 0) < 0.95, and (iv) filtered out loci with low coverage  that remained after removing read counts with filter iii; Next, we integrated all SNP frequency data into one matrix with six samples and all polymorphic loci, and applied filters v–vi: (v) we discarded SNP positions with more than two remaining alleles across the six samples per GBS library type; (vi) we retained only markers with an RAF between 0.05 and 0.95 (informative loci) in at least one sample, and frequency data in all six samples.We used a custom python script to apply filters on the SNAPE-pooled reference allele frequency (RAF) data. Filters i–iv were applied in the following order and per sample: (i) SNP positions were deleted if the reference allele was not A, C, G, or T; (ii) SNP frequencies were set to missing data per sample when the two observed alleles were both different from the reference allele, or when the sum of the reference and the alternative allele read counts was lower than 30, (iii) using the Bayesian estimates of the probability of allele presence provided by SNAPE-pooled, we set the alternative allele frequency  to 0 if p(freqhttps://gitlab.com/truttink/smap/) and a detailed description of the working procedure and guidelines are available online in the User Manual (https://ngs-smap.readthedocs.io/en/dev/home.html). In short, module SMAP delineate defines GBS loci by locating the outer positions of ‘stacked’ read mappings ). SMAP delineate thus selects regions of the genome consistently covered by read mapping across the sample set, while simultaneously delineating start and end points for read-backed haplotyping of the SNPs identified with SNAPE-pooled (see above). The SMAP package further exploits polymorphisms in read mapping positions as additional information to define haplotype strings. A set of indexed BAM files with mapped reads, a custom BED file with GBS locus positions, and a VCF file with SNP positions then serve as input for the module SMAP haplotype-sites. SMAP haplotype-sites evaluate the read-reference alignment at each polymorphic position within a locus and creates a short haplotype string per read that combines the call of neighboring polymorphisms (SNPs and SMAPs) across the genome region covered by the GBS locus. It then counts the read depth per unique haplotype per locus, integrates all haplotype counts across all loci and samples, quantifies the relative abundance of haplotypes per locus and finally outputs the haplotype frequency table. Here, a GBS locus that contains multiple haplotype alleles is referred to as a haplotype polymorphism (HTP). SMAP delineate was run with parameters: mapping_orientation stranded, min_mapping_quality 30, min_cluster_length 50, max_cluster_length 130, min_stack_depth 5, max_stack_depth 1500, min_cluster_depth 30, max_cluster_depth 1500, max_stack_number 20, min_stack_depth_fraction 5, completeness 0, max_smap_number 20. A VCF file with SNP positions was used for haplotyping after filtering SNPs with data in at least one pool, only 2 alleles across all DNA-pool samples with data, and an RAF SNP frequency between 0.05 and 0.95 in at least one of the pools (see above). SMAP haplotype-sites was run with parameters: mapping_orientation stranded, partial include, mapping_quality 30, min_read_count 30, no-indels, min_haplotype_frequency 5 (meaning MAF > 0.05 in at least one of the DNA pools). After SMAP haplotype-sites, only loci with at least 30 reads  in each of the six pools were retained, because downstream analyses required a complete genotype call matrix.Next, haplotypes, of which there may be more than two variants per GBS locus, were called and their relative frequencies estimated using the SMAP package  was performed in The Unscrambler X v.10.3 , using minor allele frequencies (MAF) of all identified SNPs with a known chromosomal location.PstI and ApeKI), both SNPs and HTPs with significantly different allele frequencies in early and in late elongating pools were identified using three different methods; (i) an FST-based test utilizing replicate pools . This resulted in a total of six FST values for each SNP and HTP variant. A Chi-square test was used to identify significant FST’s at different P-levels, using the test statistic X2 = 2NFST, where 2N = the sum of genotyped gametes in the two populations  were calculated for different P-levels asl is number of SNPs or haplotypes tested and d is the number of SNPs or haplotypes identified as significant, and a P-level corresponding to an FDR of 0.05 was chosen. The formula for calculating FDR is modified compared to the standard With method 1, for each SNP or HTP, allele frequencies in each of the six pools were compared with the average allele frequency in the three pools of the contrasting phenotype. Pairwise F Hedrick . Only SNP-levels, using the test statistic Z2 = V1 = Nind = 52 individuals in each phenotypic class, and V2 = Nrep = 3 replicate pools in each phenotypic class. Subscripts L and E refer to the late and early phenotypic classes, respectively. Here, V1 considers the variance due to the sampling of 52 individuals, and V2 considers the subsampling of 17 or 18 individuals out of 52, and all other experimental variance between  replicates. Corresponding estimates of the false discovery rate (FDR) were calculated for different P-levels as l is number of SNPs or haplotypes tested and d is the number of SNPs or haplotypes identified as significant, and a P-level corresponding to an FDR of 0.05 was chosen.With method 2, a Chi-square test was used to identify SNPs and HTP variants with significantly different average allele frequency in early versus late elongating pools at different ST values into population-specific and locus-specific components. Loci with different allele frequencies in the compared populations are identified as those, where the locus-specific component is necessary to explain the observed variation.For the analysis with BayeScan, allele frequencies were converted into absolute allele numbers, using the number of haploid genomes that had been sequenced in each pool as the total number of alleles . The allele numbers from the three replicate pools of each phenotypic group were summed, resulting in one value per marker (SNP or HTP) for both the early and the late group, which were analyzed using standard input parameters. BayeScan uses logistic regression to decompose locus-population FA. thaliana and M. truncatula were identified using coding sequences of the genes (CDS) as query in blastn searches (blast.ncbi.nlm.nih.gov).SNPs and HTPs identified by all three methods were retained for further description by searching for potential candidate genes in the 50 kb upstream and downstream genome region flanking each significant locus. Sequences and gene annotations were retrieved from the red clover genome sequence  varied from 23 to 94 Fig. . Of the PstI and ApeKI libraries, respectively. After SNP calling and further filtering, we obtained 12,074 and 91,136 SNPs with read depth > 30 in each of the six pools and MAF > 0.05 in at least one of the six pools from the PstI and ApeKI libraries, respectively (Supplementary File 1). Of these, 66,458 SNPs (64%) had a known chromosomal location, the rest were located on unplaced scaffolds. Haplotype calling and filtering  resulted in 4653 HTPs with 2–13 haplotype variants per HTP locus (3.6 on average) based on the PstI libraries, and 41,073 HTPs with 2–20 haplotype variants per HTP locus (3.6 on average) based on the ApeKI libraries. Of these, 30,215 (66%) had a known chromosomal location. For the combined PstI and ApeKI libraries, this equals an average density of one HTP with known chromosomal location per 5.4 kb of the chromosome-anchored part of the reference genome sequence.The six pools were genotyped by pool-GBS and a total of 229 M and 327 M high-quality reads were obtained for the A PCA of MAF data for the 66,458 chromosome-anchored SNPs showed that there was a large amount of random variation in allele frequencies among the replicate pools. However, the first principal component, explaining 22% of the variation, separated the three early pools from the three late pools  detected a higher number than the method based on error variance (method 2) (Supplementary File 2). Across marker types, we detected a total of 20 loci that were significant according to all three methods. Of these, 15 had a known chromosomal location; 10 were detected based on the PstI data set and 5 on the ApeKI data set . Commercial seed was used in the present study; the exact number of generations is not known, but is likely to be around 4 and therefore the chromosomal segments inherited from the 33 parents are likely to be rather long. However, with this many parents, there are theoretically up to 66 different haplotypes present per chromosomal segment. De Vega et al.  between these loci and the allelic variation that controls the phenotypic trait. If LD decays at short distances, the required marker density is high. The level of LD in a synthetic variety depends on the number of parents and the relatedness between them  as well as the number of generations after the original crossing between these parents . FRIGIDA (FRI) is an activator of FLC expression, while several genes in the autonomous and vernalization pathways are repressors of FLC. FRI overrides the effect of components of the autonomous pathway and thereby creates a requirement for vernalization, and vernalization overrides this effect of FRI. Orthologs of FRI and FLC do not appear to be present in the legumes studied so far , another gene that regulates FLC in A. thaliana. ALP1 is a PIF/Harbinger class transposase that has acquired a novel function in epigenetic gene regulation in the plant kingdom  with some of its accessory components. PRC2 takes part in epigenetic regulation not only of FLC but of a range of genes, e.g., the central flowering signal gene FT . Several other REM genes are involved in inflorescence meristem identity or development of flowers  harbor in total 5 genes belonging to the REM family of B3 DNA-binding domain proteins. This family comprises 45 members in In addition to the flowering-related transcription factors mentioned above, we found several genes belonging to families that are more generally known to be involved in transcriptional or post-transcriptional gene regulation. At least five of the identified loci  contained genes encoding proteins with putative roles in RNA processing, ribosome biogenesis and transcriptional or post-transcriptional gene silencing  gene, two DExH-box ATP-dependent RNA helicases and ribosome production factor (RPF)). RDM and DCL genes are involved in the RNA-directed DNA methylation (RdDM) pathway of transcriptional gene silencing (TGS) , which takes part in the photoperiodic, vernalization and autonomous signaling pathways of floral transition and a range of other processes in A. thaliana  stimulate stem elongation in legumes like pea and A. thaliana is mainly accomplished through their interaction with DELLA proteins   group of the C2H2 zinc finger protein family was located in 7_14.52. There are at least 19 IDD-like genes in M. truncatula  was located in 1_1.16. OFPs are transcriptional repressors that regulate multiple aspects of plant growth and development, which are likely achieved by interaction with different types of transcription factors and/or by directly regulating the expression of target genes such as Gibberellin 20 oxidase (GA20ox) . Sucrose, transported from photosynthetic or storage tissues through the phloem, can be hydrolyzed by either sucrose synthases or invertases, and the latter has been shown to be responsible for making hexoses available for cellulose synthesis, as invertase mutants, but not sucrose synthase mutants, strongly reduce growth in both Lotus japonicus and A. thaliana , of which we found one in locus 7_14.52, are transcription factors mediating auxin-induced gene expression , which in petunia has been shown to be responsible for short-distance transport of strigolactone  between membranes to stimulate signaling pathways leading to development and stress responses , an ACTIN DEPOLYMERIZATION FACTOR (ADF) and an FORMIN-like gene . These proteins play key roles in remodeling and function of actin Supplementary file2 (DOCX 18 KB)Supplementary file3 (XLSX 36 KB)Supplementary file4 (XLSX 24 KB)Below is the link to the electronic supplementary material.


Bipolaris sorokiniana, that causes spot blotch (SB) disease of wheat, is a major production constraint in the Eastern Gangetic Plains of South Asia and other warm, humid regions of the world. A recombinant inbred line population was developed and phenotyped at three SB-prone locations in India. The single nucleotide polymorphism (SNP) for SB resistance was identified using a bulked segregant RNA-Seq-based approach, referred to as “BSR-Seq.” Transcriptome sequencing of the resistant parent (YS#24), the susceptible parent (YS#58), and their resistant and susceptible bulks yielded a total of 429.67 million raw reads. The bulk frequency ratio (BFR) of SNPs between the resistant and susceptible bulks was estimated, and selection of SNPs linked to resistance was done using sixfold enrichments in the corresponding bulks (BFR >6). With additional filtering criteria, the number of transcripts was further reduced to 506 with 1055 putative polymorphic SNPs distributed on 21 chromosomes of wheat. Based on SNP enrichment on chromosomal loci, five transcripts were found to be associated with SB resistance. Among the five SB resistance-associated transcripts, four were distributed on the 5B chromosome with putative 52 SNPs, whereas one transcript with eight SNPs was present on chromosome 3B. The SNPs linked to the trait were exposed to a tetra-primer ARMS-PCR assay, and an SNP-based allele-specific marker was identified for SB resistance. The in silico study of these five transcripts showed homology with pathogenesis-related genes; the metabolic pathway also exhibits similar results, suggesting their role in the plant defense mechanism.The pathogenic fungus,  Bipolaris sorokiniana (Sacc.), causes SB disease in wheat, seedling blight, common root rot, seedling rot, and seed rot (Sb1-Sb4) have been identified as having major effects on SB resistance , which has the large sizes of the subgenomes and more than 85% repeated sequences . It is dcriptome . RNA-Seqcriptome . Genotypcriptome .GPC-B1 in wheat to 0.4 cM from 30 cM ((gl3) gene of maize was allocated to a ∼2 Mb area by BSR-Seq, and a single gene, MYB transcription factor, was found  gene om 30 cM . The gloas found . RamirezTo meet future food demands, climatic resilience and disease-resistant wheat combined with good agronomic value can potentially improve its productivity . To date12 generation. Yangmai6 is a Chinese source of SB resistance, and Sonalika is a susceptible cultivar of Mexican origin that has been under cultivation in India for more than five decades/during the green revolution. The parents of the RILs used in this study are similar with respect to the agronomical and phenological traits but harbor different SB resistance QTLs -derived recombinant inbred lines (RILs) were generated from the cross YS#24 × YS#58. The parental lines YS#24 and YS#58 are stable RILs selected from the Yangmai6 × Sonalika cross and advanced to the Fnce QTLs . To devence QTLs .5 and F6) were evaluated at three hot spots in India, namely, Agricultural Research Farm, BHU, Varanasi ; Borlaug Institute for South Asia, Samastipur, Pusa , Bihar; and Uttar Banga Krishi Vishwavidyalaya, Coochbehar , West Bengal, during two consecutive crop seasons 2013–14(F5) and 2014–15(F6). The F7 generation was evaluated only at BHU in crop season 2015–16. At each location, all RILs were planted along with their parents (YS#24 and YS#58) in two replications following a randomized complete block design (RCBD). Each line was sown in two rows of 2 m, keeping row-to-row and plant-to-plant distances of 20 and 5 cm, respectively. To ensure disease build-up and spread, two rows of the susceptible genotype Sonalika was planted after every 20th row and in alleys along with the plots. The susceptible spreader rows served as an inoculum source for epidemic development in addition to the direct inoculation on the RILs , and 45 days after sowing (at second irrigation), respectively. A total of five irrigations were given to maintain sufficient moisture in the field.The 211 RILs  was used to create an artificial epiphytotic. The pathogen was multiplied on sorghum grains, suspended in water (104 spores per ml), and sprayed in the evening at the time of flag leaf emergence as described earlier −ti = time (days) between two disease scores, n = number of dates when disease was recorded.The highly aggressive  earlier . Disease earlier . The perTo increase the statistical power and reduce false positives, multiple bulks were selected independently from each of the three locations in both years . AUDPC wSeeds of each RIL of the resistant and susceptible bulks (30R + 30S RILs) and their parents (YS#24 and YS#58) were grown separately in a greenhouse; seedling leaves were harvested 14 days after sowing for RNA isolation. Total RNA was extracted from the leaf tissues of each RIL using a standard TRIzol method  and treated with DNase I to remove residual DNA contaminants. RNA samples were purified using the RNeasy Mini Kit  and the quantitative and qualitative estimation was performed using the NanoDrop 1000 (Thermo Fisher Scientific) and the Agilent Bioanalyzer 2100, respectively. An equimolar concentration of RNA from 10 resistant individuals of RILs was pooled together to make resistant bulk 1, and the same was done to prepare the other resistant and susceptible bulks. Before cDNA library preparation, we enriched the RNA samples for transcripts using the absolute mRNA Purification Kit . The cDNA libraries were constructed, and Illumina paired-end adapters and barcode sequences were ligated onto the cDNA fragments . The poohttp://hannonlab.cshl.edu/fastx_toolkit). Identification of SNPs was done; using a reference-based approach, the UniGene build63 was used as reference transcriptome (NCBI:ftp://ftp.ncbi.nhi.gov/repository/UniGene/Triticum_aestivum/Ta.seq.uniq.gz). Since UniGene represents only the genic portion, the complexity of the wheat genome was further reduced  were finally used to create the tetra-primer ARMS-PCR (The tetra-primer amplification refractory mutation system-PCR (ARMS-PCR) is a simple and economical technique that produces an allele-specific reaction  The prinARMS-PCR .4)2SO4], 0.33 pmol of each outer primer (forward and reverse), 0.5 pmol of inner forward primer, 0.83 pmol of inner reverse primer, 2.5 mM MgCl2, 0.125 mM of each dNTPs, 1U of Taq Polymerase . PCR reactions were performed in an Agilent SureCycler 8800 using the following program: initial denaturation at 95°C for 5 min, 38 cycles consisting of denaturation at 95°C for 1 min, annealing at 62°C for 90 s, extension at 72°C for 2 min, followed by a final extension at 72°C for 10 min. The PCR product was resolved on 3.5% agarose gel and stained with ethidium bromide to visualize.The genomic DNA of RILs used for BSR-Seq was extracted from the leaves of young seedlings using a cetyltrimethylammonium bromide (CTAB) protocol . The DNA(KEGG) database.To perform the functional analysis of the transcripts, Blast2GO v 2.5 was used . It is ah2) values of AUDPC were lowest at BISA14 (0.65) and highest at BHU14 (0.87). Therefore, resistant and susceptible bulks were constituted by taking the samples independently using extreme phenotypes (resistant and susceptible) from both tails as illustrated in In all environments, the AUDPC of the resistant parent YS#24 was consistently and considerably higher than that of the susceptible parent YS#58 . The meaThe bulk samples utilized for BSR-Seq produced 429.40 million raw reads, containing 32,634.40 million base pairs. The number of raw reads in the sequenced samples ranged from 30.40 million (7.08%) in the resistant bulk-3 to 171.70 million (39.99%) in the susceptible parent . The hig.Each sample yielded an average of 80% high-quality  RNA-Seq reads that were matched to the wheat reference transcriptome. Susceptible bulk-1 had the highest percentage alignment (80.38% with 535390 SNPs), followed by susceptible bulk-3 (80.09% with 613480 SNPs), and resistant bulk-1 had the lowest percentage alignment (79.01% with 669279 SNPs) . The res.A total of 1,379,122 SNPs were identified on 91,855 transcripts of wheat samples used in the present investigation . The putThe distribution of trait-linked SNP markers in the A, B, and D genomes for each homoeologous group and percentage of the total number of SNPs is given in The maximum number of polymorphic SNP with BFR >6 was found on chromosome 5B (198 SNP), followed by 3B (136 SNP) . Thus, aTa_S61830716_1262_3) from chromosome 5B amplified 142 bp fragments in both the resistant parent and resistant bulk-1, whereas a 142 bp fragment was not amplified in the susceptible parent, but in a few samples of susceptible bulk-1, a faint band appeared in lane 10–12  shared homology with acetyl-CoA acetyltransferase, whereas two transcripts of 5B (gnl/UG/Ta#S17985740 and gnl/UG/Ta#S61830716) shared homology with proteinase/protease and the remaining two with phospholipase C1 and exohydrolase proteins . A totalvia transcriptome sequencing is an attractive strategy to reduce genome complexity in wheat  reduces the cost remarkably when repetitive sequences are enriched in the genome and enabled a rapid and detailed understanding of a near-complete set of transcripts and SNPs linked to the trait . In thisSb2 (Sb3 (Sb4 (Sb1) for SB was reported on the 7D chromosome by R-gene, the putative SNPs with enriched BFR can then be converted into high-throughput SNP assays and genotyped across the individuals that were used to assemble the bulk , transcription is de-repressed by pathogen-induced signals. The studied transcripts here are shown to have homology with pathogenesis-related genes. The transcript gnl/UG/Ta#S61799095 of 3B identified in this study showed homology with acetyl-CoA acetyltransferase, which is conferred for its pathogenesis-related activity in rice . It is aignaling . The majnt lines . Thus, tXTaSb_S61830716_1262_3 could be productive in screening wheat germplasm for SB resistance. In future projects, the SNPs discovered for SB resistance across the wheat genome will be visualized by converting them into Kompetitive Allele Specific PCR (KASP) markers for establishing a high-throughput genotyping platform, useful for MAS of the target genes. The newly developed SNPs marker could also be converted to a qPCR-based assay for large-scale application in crop improvement and study of the molecular biology of SB resistance.In this study, BSA combined with RNA-Seq (BSR-Seq) appears to be useful for SNP discovery in bread wheat for SB resistance, later used to develop a new marker assay. The marker


GLP1R, NFYA, NANOG, COX7A2, BMPR1B, FOXP1, SLC29A1, CNTNAP4, and KIT, which exert important roles in skeletal morphogenesis, chondrogenesis, obesity, and embryonic and fetal development. These findings are helpful to better understand the genetic mechanism for body size and reproduction phenotypes, while the phenotype-associated SNPs could be used as the molecular markers for the pig breeding programs.With the increasing popularity of genomic sequencing, breeders pay more attention to identifying the crucial molecular markers and quantitative trait loci for improving the body size and reproduction traits that could affect the production efficiency of pig-breeding enterprises. Nevertheless, for the Shaziling pig, a well-known indigenous breed in China, the relationship between phenotypes and their corresponding genetic architecture remains largely unknown. Herein, in the Shaziling population, a total of 190 samples were genotyped using the Geneseek Porcine 50K SNP Chip, obtaining 41857 SNPs for further analysis. For phenotypes, two body measurement traits and four reproduction traits in the first parity from the 190 Shaziling sows were measured and recorded, respectively. Subsequently, a genome-wide association study (GWAS) between the SNPs and the six phenotypes was performed. The correlation between body size and reproduction phenotypes was not statistically significant. A total of 31 SNPs were found to be associated with body length (BL), chest circumference (CC), number of healthy births (NHB), and number of stillborns (NSB). Gene annotation for those candidate SNPs identified 18 functional genes, such as  Domesticated pigs account for a high proportion of meat production in the world . In the From the perspective of breeders, phenotypic records for body measurement traits are vital for evaluating the production performance and making a future breeding schedule. The body measurement traits present low-to-moderate heritability ,6,7,8. Thttps://www.animalgenome.org/cgi-bin/QTLdb/SS/index, accessed on 9 January 2023), there are 35,846 QTLs related with 693 different traits in pigs that have been mapped by 773 publications. Among the 1281 QTLs associated with litter traits, there were only 466 QTLs related to total numbers born (TNB), 374 QTLs related to number of healthy births (NHB), 138 QTLs related to numbers of stillborn (NSB), and 149 QTLs related to the number of mummified pigs (MUMM). Regarding the body measurement traits, 188 QTLs related to chest circumference (CC), and 219 QTLs related to body length (BL). The Shaziling pig is primarily raised in the Hunan Province of China and is characterized by low growth rate, good meat quality, and strong resistance to diseases [The last decade has witnessed the application of genome-wide association studies (GWAS) in identifying the genetic markers covering the whole genome for diverse body measurement and reproduction phenotypes. According to the latest data from PigQTLdb  and reproductive traits  were measured and collected, respectively. Subsequently, based on the Geneseek Porcine 50K SNP Chip data, the SNPs were detected in those Shaziling pigs. The GWAS was conducted to identify potentially critical SNPs and QTL regions. This work paves the way for the future genetic improvement of pigs with efficient growth and reproduction performances.Our studied population (Shaziling pigs) were raised on the conservation farm . Animals were subjected to the same nutritional and management conditions. Firstly, the BL and CC of 190 sows (from 240 to 265 days) were measured according to the method applied in previous research . BrieflyThe ear tissues of 190 Shaziling pigs were collected for genomic DNA extraction according the protocol of Accurate SteadyPure Universal Genomic DNA Extraction Kit . The concentration and quality of genomic DNA were testing by Nanodrop 2000  and 1.5% agarose gel electrophoresis, respectively. The final qualified DNA samples were genotyped using Geneseek Porcine 50K SNP Chip .p-value > 10−6 for the Hardy-Weinberg equilibrium (HWE) test. Furthermore, we excluded the SNPs located on the sex chromosome and unmatched regions.For phenotypes, the R (version 4.2.1) was used to perform descriptive statistical analyses to check the data quality including the mean, standard deviation, coefficient of variance, minimum and maximum values for each trait. The plot of normal distribution and correlation coefficients among phenotypic values were all visualized and calculated by the “GGally” package in R. For the genotypes, PLINK software (version 1.90 beta) was used to conduct quality control to achieve high-quality common SNPs . BrieflyThe heritability estimates of phenotypes were calculated using a restricted genomic-relatedness-based restricted maximum-likelihood (GREML) analysis, as implemented in GCTA software (version 1.94) . BrieflyPrincipal component analysis (PCA) was performed to identify the population structure of Shaziling pigs to determine whether principal components (PCs) should be included in the GWAS model. Simply, the eigenvalues and eigenvectors were calculated by GCTA software based on high quality filtered SNPs. The lollipop-plot of the top ten principal components were visualized using the “ggplot2” package in R. A heat map was plotted against the kinship matrix to exhibit the level of relatedness among all individuals within the Shaziling population.y represents the vector of phenotypes  for each individual; Wα included the population structure effect and fixed effects, briefly, age (in days) was used as fixed effects for BL and CC, and the individual of boar used for mating was used as fixed effects for TNB, NHB, NSB, and MUMM; Xβ is the marker effect to be tested; μ~N  represents the polygenic effect; e~N  refers to the residual effect, and K is the kinship matrix generated from the SNPs. Similar to previous GWAS research [The association analysis between each SNP marker and phenotypic data was performed using the mixed linear model in the GEMMA software . The modresearch ,20, the http://asia.ensembl.org/biomart/martview/, accessed on 18 October 2022). Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed in the enrichment module of KOBAS 3.0 [p-value < 0.05.To identify the candidate genes of each significant SNP, we searched for annotated genes according to their physical positions (within 500 Kb upstream or downstream) on the pig reference genome (Sscrofa 11.1) . BL and CC were weakly correlated . However, there was no correlation between body measurement traits (BL and CC) and reproduction traits (TNB and NHB). Descriptive statistics for the traits of BL, CC, TNB, NHB, MUMM, and NSB analyzed in the current study are shown in As shown in −6  and 2.38 × 10−5 , respectively.In the Shaziling population, a total of 190 samples were genotyped using the Geneseek Porcine 50K SNP Chip. A total of 41,857 SNPs were obtained for further marker analysis after a series of quality filter procedures. The distribution of the SNP information on eighteen autosome chromosomes was presented in The principal component analysis was performed for the tested Shaziling individuals to avoid the false-positive results caused by population stratification, which was considered as an important influencing factor to the reliability of the GWAS result. The results showed that the first four principal components could be included in the GWAS model as covariates . BesidesGLP1R gene corresponding to the significant SNP (ALGA0040227) was significantly associated with BL. Moreover, at the suggestive level, another 4 SNPs located on chromosome 7 were also associated with BL, including ALGA0039856, ASGA0032589, ALGA0040238, and INRA0024788, which were respectively mapped to genes SNRPC, NFYA, GLP1R, and GLP1R. Regarding the CC trait, 4 SNPs reached the significant level. Among these 4 SNPs, H3GA0003059 and ALGA0004562 on chromosome 1 were mapped to genes NANOG and COX7A2, respectively. ASGA0050356 and ALGA0065112 located on chromosomes 11 and 12 were respectively mapped to genes FAM216B and CACNG1. Furthermore, 9 SNPs  at the suggestive-level located on chromosome 13, 8, 12, 1, 16, 4, 1, 12, and 12, respectively, were also detected to be associated with trait CC, and the genes closest to the 9 SNPs were FOXP1, BMPR1B, DNAH9, ENSSSCG00000060802, CCNG1, COX6C, RIMS1, CCDC57, and CCDC57, respectively. The GWAS results are displayed in SMG1 gene on chromosome 3 was found to be nearest to suggestive SNPs (CASI0010189) related to TNB. In addition, 3 SNPs  in significant-level located on chromosome 8, 1, and 7, respectively, were detected to be related with trait NSB, and the genes nearest to the 3 SNPs were ARAP2, GTF2H5, and SLC29A1, respectively. Moreover, 9 SNPs  in suggestive-level located on chromosome 4, 6, 8, 3, 18, 7, 17, 4, and 7, respectively, were also detected to be related with the NSB trait, and the genes nearest to the 8 SNPs were FAM49B, CNTNAP4, KIT, POR, SPAM1, TFAP2B, BPIFA3, ENSSSCG00000060440, and NEDD9. However, no suggestive or significant SNPs were found to relate to MUMM and NHB  and reproductive phenotypes (NHB and NSB) of the Shaziling population, the corresponding candidate gene was used for enrichment analysis. The significant GO terms mainly included “GO:0008528: G protein-coupled peptide receptor activity”, “GO:0002063: chondrocyte development”, “GO:0006687: glycosphingolipid metabolic process”, “GO:0043588: skin development”, and “GO:0005246: calcium channel regulator activity” A. On theTo our knowledge, this is the first study evaluating the relationship between body measurement and reproductive phenotypes for female Shaziling pigs, and their associated genetic architecture by GWAS analysis. Previous studies have indicated that body measurement traits could be the indicator of reproduction potential in sows ,23,24 anGLP1R, which was the nearest gene for three different SNPs on chromosome 7. According to a previous study, GLP1R was expressed in primary osteoclasts, bone marrow cells, and osteoblasts [GLP1R displayed negative effects on bone strength and quality, the apparent symptoms including impaired mechanical properties, a significant decrease in the bone’s outer diameter and cortical thickness [GLP1R is related to the G protein-coupled peptide receptor activity. Interestingly, the G-protein coupled receptor family has been reported to correspond with regulating limb patterning and skeletal morphogenesis, as well as morphogenesis during embryonic development [GLP1R might be the critical regulator of skeletal bone mass, so it was supposed to be a strong candidate gene for BL. Except for GLP1R, we found that the candidate gene NFYA was also associated with BL. It has been demonstrated that NFYA was an essential modulator in governing embryonic cartilage growth [NFYA isoforms exerts different functions on myoblasts, with NFYAs increasing cell proliferation and NFYAl promoting differentiation [NFYA might play important roles in modulating the formation of myoblasts and cartilage that ultimately affect the development of BL. Regarding the CC, the candidate genes were NANOG, COX7A2, CACNG1, FOXP1, BMPR1B, DNAH9, CCNG1, COX6C, RIMS1, and CCDC57. Among them, researchers found that NANOG-expressing human bone marrow mesenchymal stem cells (MSCs) had much higher capabilities for expansion and osteogenesis [COX7A2 and RIMS1 were identified as candidate genes for osteoarthritis [COX7A2 was highly expressed in cartilage [CACNG1 is thought to participate in modulating postmenopausal osteoporosis via Ca2+ regulation during muscle contraction [CACNG1 is involved in the process of calcium channel regulator activity. In recent years, the forkhead box protein (FOX) family has been reported to play important roles in different kinds of physiological bone processes [FOXP1 was enhanced under the regulation of circFOXP1 [FOXP1 level was accompanied by the augmented expression of PPARγ, which serves as a master regulator in adipocyte differentiation [FOXP1 in adipose cells affects adaptive thermogenesis and boosts diet-induced obesity [FOXP1 is involved in the process of osteoclast development. It has been established that fat is the primary factor impairing body size. FOXP1 might play essential roles in lipid metabolism and osteogenic differentiation, so the FOXP1 gene could be considered as a strong candidate gene for the CC. In sheep, BMPR1B was detected as a candidate gene for variation in mature size [BMPR1B participates in skeletal patterning, and is mainly found in differentiated chondrocytes and osteoblasts, and is also expressed in mesenchymal pre-cartilage condensations [COX6C was identified as a critical gene involved in regulating osteoblast mineralization and mitochondrial bioenergetics [Body measurement traits such as BL and CC are usually associated with growth traits and meat production. For example, it has been reported that BL and CC were positively associated with body weight . BL is aeoblasts . In addihickness . Our GO elopment ,37. Thise growth . Moreoventiation . We contogenesis . A GWAS rthritis ,42, and artilage . In anottraction . Go analrocesses . For exaircFOXP1 . Additiontiation . Over-ex obesity . GO resuure size . In addinsations ,51. COX6ergetics .SMG1 was the closest gene for SNPs CASI0010189 on chromosome 3. It has been reported that SMG1 is a necessary kinase for mouse embryogenesis. The absence of SMG1 would be fatal to embryonic development, mainly due to the profound developmental defects  [ARAP2, GTF2H5, SLC29A1, FAM49B, CNTNAP4, KIT, POR, SPAM1, TFAP2B, BPIFA3, and NEDD9, that were potentially associated with NSB. For ARAP2, a GWAS analysis in Chinese Holstein cows found that it was associated with loin strength (LS), a reproduction-associated body-shape trait [GTF2H5 gene mutations induced severe clinical manifestations including multiple-organ failure [SLC29A1, also called ENT1, belongs to the solute carrier (SLC) family. It was initially found from a human placental DNA library [SLC29A1 might be a potential causal biomolecule for NSB. CNTNAP4 is a member of the neurexin superfamily and is essential for synaptic function and neural development [CNTNAP4 is necessary for the proliferation of embryonic neural progenitor cells (NPCs) in mice [CNTNAP4 gene was associated with fertility traits [KIT mutation murine model, a reduction in placental vascularization was present, which finally resulted in the placenta developing irregularly and embryos presenting with severe growth retardation and dying in the uterus [KIT gene participates in the glycosphingolipid metabolic process. Intriguingly, an earlier study indicated that glycosphingolipid composition is important for the growth and differentiation of trophoblast cells during pregnancy [KIT might be the important regulator for healthy embryonic development. It should be regarded as a strong candidate gene for NSB.TNB and NSB are key indicators for measuring sow reproduction performance. In TNB, gene annotation results showed that d heart) . In dailpe trait . Cows wipe trait . Whole e failure . SLC29A1 library . Accordi library ,58. Althelopment . In addi in mice . Notablyy traits . In the e uterus ,63. Moreregnancy . These rPOR gene was responsible for their death [SPAM1, a hyaluronidase, played roles in binding to sperm during capacitation, elevating cumulus dispersal efficiency and the capacity of sperm to across the cumulus of oocytes [SPAM1 is involved in glycosaminoglycan degradation pathways. Of note, in the developmental process of embryos and fetuses, glycosaminoglycans play essential roles in cell morphogenesis, growth, differentiation, and cell migration [SPAM1 may affect the expression pattern of the glycosaminoglycan degradation pathway, which ultimately affects embryonic development. TFAP2B is a member of the transcription factor TFAP2 family, also called AP-2. Earlier researchers found that TFAP2A-deficient mice suffered from anomalies, such as anencephaly, congenital heart disease, and body-wall defects [TFAP2B-deletion led to neonatal mice death due to the massively increased apoptotic cell death for renal epithelial cells [TFAP2B−/− mice exhibited developmental defects in the ductus arteriosus and limbs [TFAP2B could be regarded as a strong candidate gene for NSB, because of its central role in teratogenicity. NEDD9, a target gene of TGF-beta, was identified in Yorkshire sows as a candidate core gene for litter size [NEDD9 regulates the invasion and proliferation of ectopic endometriotic stromal cells [Recently, a study of prenatal and postnatal deaths for three sibling fetuses revealed that the combination of CNV and SNV in the mutated ir death . SPAM1,  oocytes ,67. KEGGigration ,69. SPAM defects ,71,72. Aal cells ,74. A sund limbs . Thus, Tter size . Besidesal cells .The duration of pigs’ farrowing and estrous cycles are considered as critical factors of reproduction traits. A short duration of farrowing is vital for piglet survival as a delay can elevate the number of stillborn . AnotherChip-based SNP markers have been widely used in genomic studies because of their abundance in the genome and their low cost . In thisGLP1R, NFYA, NANOG, COX7A2, CACNG1, FOXP1, BMPR1B, COX6C. SMG1, ARAP2, GTF2H5, SLC29A1, CNTNAP4, KIT, POR, SPAM1, TFAP2B, and NEDD9, were identified as important candidate genes that may regulate the underlying genetic architecture of porcine body size and reproductive traits. Although the current study is limited by its small sample size, it helps us to understand the genetic basis of porcine body measurement and reproduction traits and could be potentially applied in pig breeding programs.By the genome-wide association study in the Shaziling pig breed, we identified 31 SNPs in total that were potentially associated with body size and reproductive traits of interest. Eighteen functional genes, including