Ten generations of divergent selection for intramuscular fat (IMF) content was performed in rabbits. Through the generations, the IMF content has improved. Different Bayesian animal models were used to assess the importance of maternal genetic effects on the genetic control of IMF and fatty acid composition of the two rabbit lines divergently selected for IMF content. Additionally, maternal genome-wide association studies were carried out in these lines to identify genomic regions affecting the IMF and intramuscular fatty acid composition of the offspring. The maternal genetic variance explained an important part of the phenotypic variance of the IMF and intramuscular fatty acid composition. Potential candidate genes were identified on the associated genomic regions related to the traits under study.TANC2, ACE, MAP3K3, TEX2, PRKCA, SH3GL2, CNTLN, RPGRIP1L and FTO) related to lipid metabolism, binding, and obesity. These regions explained about 1.2 to 13.9% of the total genomic variance of the traits studied. Our results showed an important maternal genetic effect on IMF and its fatty acid composition in rabbits and identified promising candidate genes associated with these traits.Maternal genetic effects (MGE) could affect meat quality traits such as intramuscular fat (IMF) and its fatty acid composition. However, it has been scarcely studied, especially in rabbits. The objectives of the present study were, first, to assess the importance of MGE on intramuscular fat and fatty acid composition by applying a Bayesian maternal animal model in two rabbit lines divergently selected for IMF. The second objective was to identify genomic regions and candidate genes of MGE that are associated with the traits of these offspring, using Bayesian methods in a Genome Wide Association Study (GWAS). Quantitative analyses were performed using data from 1982 rabbits, and 349 animals from the 9th generation and 76 dams of the 8th generation with 88,512 SNPs were used for the GWAS. The studied traits were IMF, saturated fatty acids , monounsaturated fatty acids , polyunsaturated fatty acids , MUFA/SFA and PUFA/SFA. The proportion of phenotypic variance explained by the maternal genetic effect ranged from 8 to 22% for IMF, depending on the model. For fatty acid composition, the proportion of phenotypic variance explained by maternal genetic effects varied from 10% (C18:0) to 46% (MUFA) in a model including both direct and additive maternal genetic effects, together with the common litter effect as a random variable. In particular, there were significant direct maternal genetic correlations for C16:0, C18:1n9, C18:2n6, SFA, MUFA, and PUFA with values ranging from −0.53 to −0.89. Relevant associated genomic regions were located on the rabbit chromosomes (OCU) OCU1, OCU5 and OCU19 containing some relevant candidates ( Many traits of interest in livestock production are affected by the mother of the individual in whom the trait is observed . MaternaAnimal management and the experimental procedures were approved by the Ethical Committee of the Universitat Politècnica de València, according to Council Directives 98/58/EC and 2010/63/EU, (reference number 2017/VSC/PEA/00212).The data used in this study were obtained from two rabbit lines involved in a divergent selection experiment aimed at modifying the intramuscular fat (IMF) content in the Longissimus thoracis et lumborum (LTL) muscle. This experiment was carried out over 10 generations (during the period from 2011 to 2019) in a rabbit selection nucleus located at the farm of the Institute of Animal Science and Technology of the Universitat Politècnica de València, Valencia, Spain. The base population consisted of 13 sires and 83 dams. Up to the 7th generation, each line consisted of 8 sires and 40 dams per generation. From the 8th generation on, the number increased to 10 sires and 60 dams per line. The selection was based on the offspring phenotypic value of IMF. For each dam, two full sibs  of first-parity offspring were sacrificed at nine weeks of age, and their IMF values were measured. The dams were then ranked according to these measurements. The top twenty percent of dams, as determined by this ranking, were selected to provide all the females for the next generation. Each sire was mated with five dams, and, in order to minimize the inbreeding, only one male offspring of the sire from the highest-ranking mating was selected for breeding the next generation. Further information on the divergent selection experiment can be found in previous publications ,16. The Rabbits were slaughtered at 9 weeks of age by exsanguination after electrical stunning. Carcasses were stored at 4 °C for 24 h, and then LTL muscle was excised from the carcass. LTL muscle minced and lyophilized. The IMF content of 1st to 8th generation samples were determined by near-infrared reflectance spectroscopy (NIRS) using the calibration equation developed by . Twenty Five bivariate Bayesian animal models  of increModel 1. Animal model with direct additive genetic effects:Model 2. Animal model with direct additive genetic effects and maternal genetic effects:Model 3. Animal model with direct additive genetic effects and common litter effects:Model 4. Animal model with direct and maternal genetic effects as well as the common litter effects:Model 5. Animal model with direct and maternal genetic effects, and the maternal environmental effects:,In all models, data were assumed to be conditionally distributed as a multinormal distribution. For instance, in model 5:The direct additive genetic effects and the maternal genetic effects had a multinormal distribution:A is the genetic relationship matrix; Ime and n). Likewise, for model 3 the common litter effects  matrices, and corresponded to the variance of the maternal environmental, litter and residual effects, respectively. Flat priors were assumed for the fixed effects , The following model named MGWA  was fittribed in , using ts 0.9987 . The MGWs 0.9987 . The poss 0.9987 .Oryctolagus cuniculus” genome (OryCun 2.0.97). The functional annotation to uncover the biological functions of the annotated genes was performed using the DAVID v6.8 online annotation database [Candidate genes in the genomic regions associated with the traits of interest were identified using the Ensembl release 99 databases . The refdatabase ,29. ThosA previous study  using th at 0.42  and Iber at 0.42 , and a h at 0.67 . In catt at 0.67  and also at 0.67 . The posoc pigs,  using a  model 2 . Neverth model 2 .C2), a posterior mean value of 0.18 was found with model 3, which decreased to 0.14 when model 4 including maternal genetic effects was used. This result suggests that the estimate of the litter effect found with model 3 partly includes the maternal genetic effects. The estimates for the litter effect and maternal environmental effect were similar in our study due to the reduced number of litters per dam. Similar results of C2 were reported in some breeds of pigs, with estimates of 0.14 for Large White, 0.16 for Pietrain, and 0.18 for Duroc [Regarding the litter effect . Furthermore, the exclusion of the common environmental effects results in an overestimation of maternal and direct heritability (model 2). As stated by , in geneh2d . Another study with data of seven generations of these same experimental lines, using a model similar to model 3 of our study, reported a similar result of A (0.56) .C2 did not exceed 0.09 for C18:1n9, MUFA and PUFA, and ranged from 0.11 to 0.18 for the other fatty acids, except for SFA, the only one with C2 (0.21) higher than for the maternal genetic effect. In Iberian pigs, the common environmental litter effect had an important impact on the fatty acid composition, ranging from 0.06 to 0.53 [C2 values ranged from 8% (for C18:2n6) to 15% (for C20:4n6) [Regarding individual fatty acids,  found sid) and maternal genetic (m) correlations between IMF and fatty acid composition and the correlation between direct and maternal genetic effects (dm) for fatty acid composition. The posterior mean of direct genetic correlations was strong and positive between IMF and C14:0, C16:1, and SFA, ranging from 0.92 to 0.97, whereas it was strong and negative for C18:0 with an estimate of −0.80. For C18:1n9, C18:3n3 and MUFA, the d was moderate and positive ranging between 0.36 and 0.52. The IMF was negatively correlated with C20:4n6 (−0.63) and with PUFA (−0.51). A previous study reported similar results for C14:0, C16:0, C18:0, C16:1, and C18:3n3, but different estimates for other fatty acid compositions, showing a lower genetic correlation for SFA (0.30), and higher correlations for C18:1, C12:2n6, C20:4n6, MUFA and PUFA compared to the present findings [m) between IMF and its fatty acids was of the same sign as d (except for C16:0 and SFA). C14:0, C16:1, C18:1n9 and MUFA had a relevant m with IMF, ranging from 0.52 to 0.74, whereas C20:4n6 was strongly and negatively correlated with IMF (−0.81). For the other fatty acids, m was weak with a wide HPD95%, providing little information on the true value of the parameter. The genetic correlation between the direct and maternal additive genetic effect (dm) within the trait was almost negative, except for the C20:4n6, which showed a wide HPD95%, including the zero. The direct-maternal genetic correlations were important for C16:0, C18:1n9, C18:2n6, SFA, MUFA and PUFA, giving estimates between −0.53 and −0.89. A considerable negative correlation suggests that both direct and maternal genetic effects are influenced by common genetic factors but have opposing influences on the expression of offspring traits. These results are in line with the conclusions of other studies that were reviewed by [The relationships between IMF and the fatty acid composition are of interest because both affect organoleptic and nutritional properties of meat . Table 4findings . They usfindings  and cattfindings ,43. The iewed by , who repiewed by , a definThere are few studies focusing on conducting GWAS of maternal genetic effects and their relationship to animal phenotype in livestock. To our knowledge, two GWAS studies in dairy cattle investigated the maternal genetic effects on stillbirth and dystocia  and bodyADAMTSL1, CNTLN, BNC2 and SH3GL2  genes are located on porcine chromosome 1 and appear to be related to synaptic vesicle endocytosis and are associated with lipid binding [This associated region harbors the potential candidate genes d SH3GL2 . SH3GL2  binding . A previ binding .RPGRIP1L and FTO) were found in this region; these genes have biological functions directly related to fat metabolism and deposition. The FTO (Fat mass and obesity-associated protein) gene plays an important role in adipocyte proliferation, regulation adipocyte differentiation, regulation of white and brown adipocyte differentiation, key regulators of feed intake/appetite and energy homeostasis. The association between the FTO gene and obesity in humans has been demonstrated in many populations, and this gene has been identified as an important candidate gene for fat mass, fat-deposition-related traits and obesity [FTO gene plays an essential role in postnatal growth by influencing milk fat composition and regulating lipid storage [The genomic regions identified on OCU5, represented by a single genomic window, included genes with major biological functions directly relevant to the traits studied. Consistently, a maternal genomic region of 10.1 to 10.6 Mb on OCU5 was found to be associated with certain fatty acid composition traits. This region explained 3.5%, 5.1% and 4.3% of the genomic variance for C16:0, SFA and PUFA/SFA ratio, respectively. This result would be expected, as the main component of SFA is the palmitic fatty acid (C16:0). Two important putative candidate genes  was found to be linked to fat-related traits such as marbling, back fat thickness, back fat between 3rd and 4th last ribs, and IMF content [FTO gene located on OCU5 was significantly associated with growth and meat quality traits in rabbits. In cattle, the FTO gene has been associated with growth, carcass, marbling score and traits related to meat quality [FTO gene as a primary candidate among the genomic regions that are associated with obesity [Several studies confirmed that the  content ,59,60. O content  who repo quality ,62,63 as quality . In addi obesity .RPGRIP1L (retinitis pigmentosa GTPase regulator-interacting protein-1-like), which is located upstream of the FTO gene. RPGRIP1L is involved in adipogenesis and insulin-regulated adipocyte metabolism. It encodes a protein with a conserved C2 domain commonly found in calcium-dependent membrane proteins. This protein binds to phospholipids, inositol polyphosphates, and intracellular proteins. Studies have shown that RPGRIP1L is associated with hyperphagic obesity, reduced leptin sensitivity and is expressed in adipose tissue. It is also associated with anthropometric and metabolic parameters, including measures of adiposity and affecting milk fat composition [FTO and RPGRIP1L genes have a co-expression pattern and that the effects of obesity associated with the FTO region could be modulated by changes in RPGRIP1L expression, leading to downstream effects on leptin signalling [TANC2 gene with fat content in the dorsal muscle of common carp fish (Cyprinus carpio). Another GWAS revealed that TANC2 was significantly associated with C14:0 fatty acid in Japanese Black cattle [ACE (angiotensin-I-converting enzyme) gene plays a direct role in fatty acid transport, lipid localization and binding, the process of cell proliferation, differentiation, apoptosis and angiogenesis [ACE gene with overweight, obesity and fat distribution in humans has been previously reported [MAP3K3 gene is involved in fatty acid metabolism and regulates the expression of leptin, which is secreted by adipocytes to regulate food intake and energy expenditure and plays a crucial role in maintaining body weight homeostasis [TEX2 gene was associated with C18:1n-9 fatty acid in Japanese Black cattle [PITPNC1 , also known as the M-rdgB beta gene, is incorporated in lipid metabolism through encoding for a protein of the Nir/rdgB family that is implicated in wide-ranging cellular functions such as regulation of lipid transport, metabolism, and signalling, among others [PITPNC1 gene and the C18:3n-3 content of IMF [PRKCA  gene was found to be associated with IMF accumulation in bovine, as it regulates the insulin receptor signaling pathway [SCD, PLIN2, ERLIN1, and MTMR2). Manhattan plots of the IMF, C16:0, SFA, C16:1n-7, PUFA, and PUFA/SFA are shown in The neighboring gene within this genomic region is position ,66. Sevegnalling . On OCU1gnalling . Of the gnalling  found a k cattle . Furtherk cattle . ACE . Two other annotated genes (SH3GL2 and CNTLN) were detected in a genomic region on OCU1 associated with C16:0, SFA, C16:1n-7, PUFA, and PUFA/SFA fatty acids. A genomic region on OCU5 was associated with C16:0, SFA, PUFA, and PUFA/SFA fatty acids and included two important candidate genes directly related to lipid metabolism, binding, and obesity (FTO and RPGRIP1L). Our results confirm the polygenic nature of IMF and fatty acid composition traits, which are controlled by many genes distributed on many chromosomes. Further studies and fine mapping analyses are needed to validate the associations detected by GWAS. In addition, it is important to determine the relationship between the milk fatty acid composition of the dams and the fatty acid composition of the meat of their offspring, comparing the two High-IMF and Low-IMF rabbit lines.To our knowledge, this is the first study to assess the importance of maternal genetic effects and identify maternal genomic regions for intramuscular fat and intramuscular fatty acid composition traits in rabbits. Our results revealed that maternal genetic effects are not negligible and should be included in the genetic evaluation models for these traits. Ignoring maternal genetic effects led to an overestimation of direct heritability. Furthermore, excluding common environmental effects led to an overestimation of maternal and direct heritability. Therefore, models 4 and 5 seem to be the most appropriate models to estimate the genetic parameters for IMF and its fatty acid composition. The proportion of phenotypic variance explained by maternal genetic effects for IMF ranged from 8 to 22%, depending on the model. Using model 4, important maternal genetic effects for fatty acids ranged from 10% to 46%. The genetic correlation between the direct and maternal genetic effects within the trait was almost negative. It was important for C16:0, C18:1n9, C18:2n6, SFA, MUFA and PUFA, with estimates between −0.53 and −0.89. This suggests that direct and maternal genetic effects have opposing influences on the expression of offspring traits, although they share a common genetic basis. Maternal genetic effects had important influences on IMF and fatty acid composition and are recommended to be included in genetic evaluation models for these traits. A maternal genomic region associated with IMF was detected on OCU19, which contains some relevant candidate genes (


Intense selection of modern pig breeds has resulted in genetic improvement of production traits while the performance of local pig breeds has remained lower. As local pig breeds have been bred in extensive systems, they have adapted to specific environmental conditions, resulting in a rich genotypic and phenotypic diversity. This study is based on European local pig breeds that have been genetically characterized using DNA-pool sequencing data and phenotypically characterized using breed level phenotypes related to stature, fatness, growth, and reproductive performance traits. These data were analyzed using a dedicated approach to detect signatures of selection linked to phenotypic traits in order to uncover potential candidate genes that may underlie adaptation to specific environments.ANXA4 and ANTXR1 genes, for fatness in a region containing the DNMT3A and POMC genes and for reproductive performance in a region containing the HSD17B7 gene.Analysis of the genetic data of European pig breeds revealed four main axes of genetic variation represented by the Iberian and three modern breeds . In addition, breeds clustered according to their geographical origin, for example French Gascon and Basque breeds, Italian Apulo Calabrese and Casertana breeds, Spanish Iberian, and Portuguese Alentejano breeds. Principal component analysis of the phenotypic data distinguished the larger and leaner breeds with better growth potential and reproductive performance from the smaller and fatter breeds with low growth and reproductive efficiency. Linking the signatures of selection with phenotype identified 16 significant genomic regions associated with stature, 24 with fatness, 2 with growth, and 192 with reproduction. Among them, several regions contained candidate genes with possible biological effects on stature, fatness, growth, and reproductive performance traits. For example, strong associations were found for stature in two regions containing, respectively, the In this study on European local pig breeds, we used a dedicated approach for detecting signatures of selection that were supported by phenotypic data at the breed level to identify potential candidate genes that may have adapted to different living environments and production systems.The online version contains supplementary material available at 10.1186/s12711-023-00858-3. In the last decades, pig breeding has focused mainly on improving growth rate, carcass leanness, and reproductive performance  of a limToday, many local breeds are used on a relatively limited scale and the information on their phenotypic traits and genetic diversity is available only for a few of them, such as the Iberian and the Meishan breeds . NonetheRecent genetic characterization of 20 European local pig breeds showed that some local breeds are clustered according to their geographical distribution , while others suffer from introgression or admixture with modern pig breeds  –10. ConsTo better understand the genetic basis that underlies variation in phenotypic traits of local pig breeds, several genome-wide association studies have focused on detecting associations of loci with different phenotypic attributes, such as morphological, production, or meat quality traits –14. HoweThe aim of this study was to localize genomic regions associated with signatures of selection of local pig breeds for production traits by combining genetic and phenotypic data at the breed (population) level. Most of the data used for this study have been previously published , 8, 9. IThe current study was based on genetic data collected from 19 populations of European local pig breeds and seven populations of modern breeds. The dataset included SNP genotyping data obtained with a medium-density array  for all Quality control of the SNP genotypes from the array was performed for the entire dataset using standard filters: only autosomal SNPs with less than 10% missing data were retained. Following this step, 10 individuals with more than 3% missing genotypes were clear outliers and were thus discarded.Discovery of de novo SNPs was carried out on DNA-pool sequencing data using the CRISP software  with defThe genetic structure of the pig populations and the covariance between the populations were assessed from the individual SNP genotyping data using principal component analysis (PCA), admixture analysis , and popTo verify the quality of the DNA-pool sequence data, we compared these data with the SNP array genotyping results. Allele frequencies of the array SNPs in the DNA-pool sequence data were estimated using allele counts extracted with the Samtools mpileup  and PoPoA database of phenotypic traits of European local pig populations  was usedThe means for each variable and each breed were calculated and scaled. Since the phenotypic database was composed of results from different studies, some variables were missing for some breeds  with the approach of [The association of breed scores to allele frequencies was tested for all SNPs for all breed scores . The result of the test was an asymptotic p-value for each SNP for each phenotypic trait. Since Model (1) is only valid for intermediate ancestral allele frequencies, only SNPs for which the ancestral allele frequency estimate, roach of , using tSscrofa11.1). Regions that contained at least four significant SNPs less than 200 kb apart were further annotated. The detected genes within these regions were reviewed in the literature for potential biological effects on phenotypic differentiation.Based on association statistics for individual SNPs, we identified regions of association and extracted the candidate genes from the annotation of the reference genome , which were visible in the first three principal components Fig. . PC1 andThe results of the admixture analysis show that the number of homogeneous clusters in the dataset is difficult to determine. The cross-validation procedure was conducted for up to 40 clusters and resulted in a general decrease in cross-validation error with an increasing number of clusters  . Genomic regions identified for fatness phenotypic group ranged in length from 3.1 to 590.5 kb, with a maximum of 30 SNPs, and included between 1 and up to 12 annotated genes. The region with the strongest signal included the DNMT3A, POMC, and EFR3B genes. Regions identified for the reproduction phenotypic group ranged in length from 12.8 to 1282 kb, with a maximum of 170 SNPs. The region with the strongest signals was located on Sus scrofa (SSC) chromosome SSC6 and contained the YES1, ENOSF1, TYMS, and CLUL1 genes. A comprehensive view of the location of detected selection signals for production traits are shown on Additional file Genomic regions identified for the stature phenotypic group ranged in length from 3.1 to 362.2 kb, with a maximum of 35 SNPs, and included from zero to four annotated genes. The region with the strongest signal included the For each phenotypic group, we estimated the enrichment/depletion of significant SNPs in different functional categories, i.e. intergenic, upstream of genes, genic (coding regions or introns), and downstream of genes Fig. . We obseDeciphering the genetic basis of variation in complex traits can help understand their biology and evolution, and improve breeding, selection programs, and conservation plans for animal genetic resources. One approach to address this question is to link genetic to phenotypic variation by identifying genomic regions that have evolved in response to selection. Establishing such links requires a collection of data on genetic groups  that share a common origin and that have information on adaptive traits that were phenotyped in a standardized fashion. Livestock species generally meet these criteria and therefore provide good models for mapping genomic regions associated with selection. Here, we studied local pig populations that were genetically characterized using individual genotyping and DNA-pool sequencing and for which a common phenotypic database was recently established.Using individual genotyping data of a large set of European breeds, we found that their genetic variation is structured around four main genetic groups: Duroc, Large White, Landrace, and Iberian. The origin of the Duroc breed is unclear, as it originated from the American continent and was then brought back to Europe. Its history implies that it was separated from the European breeds for a long time, which is evident in our genetic analysis by its rather large genetic distance from most European breeds, except for the Mora Romagnola breed from Italy, which has a history of crossbreeding with the Duroc breed . In addiWhile genotyping with a commercial SNP array is sufficient to characterize the genetic relationships and population structure of local pig breeds, the density of the array used here limits the resolution and power to detect potential associations with breed phenotypes, especially in local breeds, for which a large proportion of SNPs included on commercial arrays are not segregating. To alleviate this, a cost-effective alternative to SNP genotyping is DNA-pool sequencing of populations. In the dataset used here, we confirmed that allele frequency and genetic structure estimates obtained from DNA-pool sequencing are consistent with those obtained from a SNP array, while providing more comprehensive information on the genetic diversity of local pig breeds.longissimus dorsi muscle color,), which are strongly influenced by pre-slaughter handling stress [Parallel to the comprehensive information on genetic diversity of local pig breeds, we developed an original approach based on PCA to characterize their phenotypic diversity for traits related to stature, growth, fatness, and reproduction, which have been described in a comprehensive analysis . Local pg stress . In addiLocal pig breeds exhibit a wide variety of external phenotypic characteristics, including body size and body weight. This could be due to genetic differences or could reflect management of the breed. For example, males and females of the Schwäbisch-Hällisches breed are medium to large in size and are typically raised under well-developed management systems. In contrast, some of the less studied breeds (e.g. Moravka) are smaller and lighter  and manae.g. the Schwäbisch-Hällisches pig, which has a growth potential comparable to the genetically-improved modern pig breeds [Knowledge on the growth performance of local pig breeds is limited. Production systems may not be sufficiently adapted to the needs of local breeds, which could affect their growth. For example, Brossard et al.  argued tg breeds , 41.It has been reported that local pig breeds have lower protein and higher lipid deposition capacities than modern pig breeds . The higRegarding reproduction traits, sows of local breeds typically exhibit a relatively high age at first parturition, few litters per sow per year, long lactation periods, small litter sizes, and high piglet mortality . Our PCAWhile some of the observed breed differences in phenotypes can be explained by differences in production systems among local breeds, our genetic association results suggest that some of these differences may be genetic. In order to be able to test for such genetic associations, the methods used here require all breeds to have phenotypic information on the studied traits. To accomplish this, we imputed missing phenotypes of some breeds using PCA (see Methods). The resulting imputed phenotypes are likely to be biased towards the average of all breeds. This is for example most likely the case for the Sarda breed, which is considered to be a very small breed (in terms of body size) but had intermediate imputed size. This certainly limits power to detect genetic associations  but will likely not create false positives.To detect genomic regions associated with phenotypic differentiation of European pig populations, we built upon a method developed for genotype-by-environment associations , consideTurning now to the association results, the genome scans for selection associated with broad phenotypic trait groups revealed 234 regions associated with stature, fatness, growth, and reproduction traits. However, the number of regions identified differed substantially between trait groups, with 192 regions identified for the reproduction traits group but only two for the growth traits group. While the relative magnitude of these numbers could change slightly with the definition of a “significant region”, it is clear that the reproduction trait group exhibits more significant regions than the others. This could be the result of different, non-exclusive reasons. First, it seems that the permutation procedure to obtain p-values was less efficient for the reproduction trait scores than for the others, based on the slightly biased distribution of the p-values on the pp-plot of Fig. MAP9 gene but also close to the KIT gene, which is associated notably with coat color, and the other on SCC15, containing the TMEM237 and MPP4 genes. This illustrates the complementarity of the two approaches, and empirically highlights the gain in power that can be obtained by testing specific hypotheses , as was shown in simulations studies [Relatively little overlap was found between the significant regions discovered with the trait-association approach here and the trait agnostic approach applied to the same DNA-pool sequencing data . Only tw studies , 21.ANXA4 gene. The ANXA4 gene encodes a calcium-dependent phospholipid-binding protein that is involved in various membrane processes. This candidate gene and region was previously proposed as a quantitative trait locus (QTL) for stature in cattle [ANTXR1, which is involved in cell morphogenesis, cellular development process, and cytoskeleton organization. Part of this region was also previously proposed as a QTL for stature in cattle [DNMT3A (responsible for CpG methylation), POMC (prohormone), and EFR3B . The DNMT3A gene was previously shown to affect stature and body weight in cattle and humans [POMC gene, which encodes the precursor of several peptide hormones that contribute to regulation of feed intake and energy balance via the leptin/melanocortin pathway [POMC gene have previously been associated with longissimus dorsi muscle area and backfat thickness in cattle [HSD17B7 gene. This gene encodes an enzyme involved in the biosynthesis of sex steroids and cholesterol [A strong association was found for stature and fatness in a region on SSC3 that contains the n cattle , 48. Anon cattle . For thed humans –51 and ad humans . Another pathway . Polymorn cattle –56 and wn cattle , 58. An lesterol , 60; andARHGAP28, which was previously associated with the number of vertebrae in pigs, thus affecting carcass length [EIF2AK1 (located on SSC3) was identified, with a role in the inhibition of protein synthesis in response to stress. This gene was previously associated with body mass index in pigs [Several other regions have also been identified as signatures of selection for stature and fatness but, although their connection to a biological function in production traits is not so direct, they do show some association with the phenotypes. These regions could be useful for further/future studies on signatures of selection. For example, SSC6 contained a region that might be associated with adaptation of stature and harbors the gene s length . In the  in pigs .KIT gene. This gene encodes the tyrosine kinase receptor and is associated with coat color in pigs. Another signature of selection associated with coat color was found on SSC6 in the growth and fatness groups, which contains the MC1R gene that plays a major role in controlling the transition from eumelanin (black or brown) to pheomelanin (yellow to red) [MC1R or KIT genes, probably due to the different informativeness of the SNP-chip, as well as differences in the statistical approaches.Some of the regions that have been discovered here must be interpreted with care. For instance, the studies included in the meta-analysis differ in terms of production conditions. Therefore, correlations between phenotypic groups could create non-causal signals with genes. An example of such a non-causal signal found in the growth and fatness analyses is a region on SSC8 that contains the  to red) . Since c to red)  (e.g. Wh to red)  did not In this study, we exploited several pig datasets to conduct a large meta-analysis of their genetic and phenotypic diversity. We presented how the two types of diversity can be linked with a statistical approach that builds on methods proposed to detect local adaptation. This led to the identification of genomic regions associated with breed divergence in production performance. The results exemplify how DNA-pool sequencing data associated with phenotype data at the breed level can offer a cost-effective alternative to individual-based approaches such as genome-wide association studies to identify and understand genetic factors associated with the evolution of quantitative traits.Additional file 1: Table S1. Summary of sequencing statistics . The taAdditional file 2: Figure S1. Plot of admixture cross-validation error from K = 2 to 40. Figure S2. A matrix of missing phenotypic variables in a database of European local pig breeds. The yellow color represents missing variables. SFA = saturated fatty acid content, PUFA = polyunsaturated fatty acid content, Piglets W weight = piglets weaning weight, MUFA = monounsaturated fatty acid content, Litter W birth = litter weaning weight, LD IMF = longissimus dorsi intramuscular fat content, M = male, F = female, Death WN = death rate to weaning, BFT = backfat thickness, GM = gluteus medius muscle, ADG1 = Average daily gain during the lactation period, ADG2 = Average daily gain in the growing period from weaning to 30 kg of weight, ADG3 = Average daily gain during fattening period from 30 to 60 kg. Figure S3. Projection of the (A) stature, (B) growth, (C) fatness and (D) reproduction imputed data obtained with iterative principal component algorithm. Figure S4. Admixture plots of European local pig breeds. The circular plot represents admixture plots for K = 24, K = 20, K = 15, K = 10, and K = 6 (from the outside to the inside of the circle). Figure S5. Comparison of SNP array and DNA-pool sequencing data. Left: allele frequencies estimated from SNP array genotypes vs DNA-pool sequencing data for each local breed. R are the correlation coefficients . Right: projection of the DNA-pool sequencing data  on the PCA established with individual genotypes on the SNP genotyping array. The x-axis is the average coordinate of individuals of each population on each of the PC of the genotyped-based PCA, and the y-axis is the coordinate of the allele frequencies of each population projected on each PCA. Figure S6. Principal component analysis showing the relationship between breeds (A) and the traits associated with growth (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds  are colored according to genetic similarity. Breeds  in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S7. Principal component analysis showing the relationship between breeds (A) and the traits associated with fatness (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds  are colored according to genetic similarity. Breeds  in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S8. Principal component analysis showing the relationship between breeds (A) and the traits associated with reproduction performance (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds  are colored according to genetic similarity. Breeds  in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque. Figure S9. Distribution of the European local pig breeds according to intramuscular fat content in longissimus dorsi (LD) muscle. Higher values on x-axis represent higher intramuscular fat content, while the lower values represent lower intramuscular fat content. Figure S10. Circular plot summarizing regions with candidate genes for phenotypic groups growth, stature, fatness and reproduction. Discovered regions associated with growth are colored in red, with stature are colored in blue, with fatness are colored in purple and with reproductive performance in green. Figure S11. Principal component analysis showing the relationship between breeds (A) and the traits associated with meat quality (B) and the corresponding phenotypic breed scores (C). Breeds (A) colored in grey are the breeds with more than 50% of missing variables, thus, their position on the PCA must be interpreted with care. The variables (B) are colored according to quality of the representation, which is measured by squared cosine between the vector originating from the element and its projection on the axis. The variables that contribute most to the separation of the trait into PC1 and PC2 are colored black. Breeds  are colored according to genetic similarity. Breeds  in green are genetically Landrace-like breeds, in purple are Large White-like breeds, in blue are Iberian-like breeds, in red are Duroc-like breeds and in light blue are Gascon and Basque.Additional file 3: Table S3. Mean values for phenotypic traits and information on the number of considered experimental units and animals. Table S4. Mean values for breed scoresAdditional file 4: Figure S12. The genetic structure of European local pig populations assessed from individual SNP genotyping data using principal component analysis.Additional file 5: Table S5. A list of detected genomic regions associated with phenotypic differentiation in production traits in European local pig breeds. Table S6. List of detected genomic regions associated with phenotypic differentiation in reproduction traits in European local pig breeds.


Peanut flavor is a complex and important trait affected by raw material and processing technology owing to its significant impact on consumer preference. In this research, principal component analysis (PCA) on 33 representative traits associated with flavor revealed that total sugars, sucrose, and total tocopherols provided more information related to peanut flavor. Genome-wide association studies (GWAS) using 102 U.S. peanut mini-core accessions were performed to study associations between 12,526 single nucleotide polymorphic (SNP) markers and the three traits. A total of 7 and 22 significant quantitative trait loci (QTLs) were identified to be significantly associated with total sugars and sucrose, respectively. Among these QTLs, four and eight candidate genes for the two traits were mined. In addition, two and five stable QTLs were identified for total sugars and sucrose in both years separately. No significant QTLs were detected for total tocopherols. The results from this research provide useful knowledge about the genetic control of peanut flavor, which will aid in clarifying the molecular mechanisms of flavor research in peanuts. Peanuts can be consumed in different formats, such as peanut butter, peanut oil, roasted peanuts, confections, and nutritional bars. The primary and popular means of consumption is roasted peanuts because of their unique, pleasant flavor and abundant nutrients although they can be amenable to various processes . The impThere are mainly two methods to check peanut flavor including descriptive sensory and analysis by instrument. Analysis by descriptive sensory, which is a forceful tool used to characterize food flavors, makes use of a panel trained in the use of lexicon to evaluate the various flavor characteristics , while iThe genetics and molecular foundation of flavor, especially volatiles are deficiently understood because of the complexity of flavor/aroma traits. High-throughput sequencing has paved the way for the QTL analysis of aroma components. Genome-wide association studies (GWAS), a powerful tool based on the linkage disequilibrium (LD) theory, are used to detect QTLs associated with complex agronomic traits . In GWASPrincipal component analysis (PCA) is an effective method to extract a few and major information from complex components and retain most of the variation in the dataset . PCA usePeanut breeders make the achievement of desirable flavor and quality characteristics as important breeding objective. Various traits can affect peanut flavor, but until now, little information is available on which trait has a bigger effect. Therefore, the aims of the present research were: 1) to explore correlations among moisture, oil content, tocopherols profile, sugars profile, and sensory attributes to identify the main traits that have a major effect on peanut flavor, 2) to determine genomic regions that are associated with main peanut flavor traits, and 3) to identify the candidate genes near the determined QTLs.22.12.1.12O % * 100)]. Seed water contents were estimated, and the mass of the estimation was changed over completely to a percentage of the total weight of each example. All examples were estimated in three replicates.In this experiment, 102 accessions primarily from the U.S. peanut mini-core collection  were use2.1.2A household coffee grinder  was used to grind the raw peanut samples into a fine meal. Cheesecloth was used to wrap approximately 200 grams of the meal before it was loaded into the X. A hydraulic press  was used to extract the oil. The normal phase HPLC was used to analyze tocopherols in the oil . In shor2.1.3A Soxhlet apparatus was used to boil hexane to completely defat the recovered pressed paste for tocopherols. The remaining meal was broken down for sugars analysis as previously depicted . In shor2.1.4After harvest, the crude peanuts were first dried and precisely shelled at the USDA, ARS Public Nut Research facility in Dawson, GA. For analysis, the mature peanuts, which had been dried and shelled, were sent to the USDA-ARS Market Quality and Handling Research Unit in Raleigh, North Carolina. Using a colorimeter , the raw peanut samples were roasted in a forced air oven  at 171°C until they reached a final color of L=48 ± 1 on the Hunter scale. A Robot Coupe Blixer 3 commercial food processor  was used to ground samples to a paste. Any variation between seeds was eliminated by this preparation .An eight-member panel that was trained in the descriptive evaluation of peanut flavor and maintained by the USDA-ARS Market Quality and Handling Research Unit in Raleigh, North Carolina, received the pastes. The Spectrum® Flavor Descriptive Analysis Technique was taught to the panel . Three-d2.1.5Using SPSS Statistics Version 24 , the phenotypic data were subjected to statistical analysis. SPSS factor dimension reduction was used in the PCA analysis. The analysis of variance components was assessed utilizing the restricted maximum likelihood (REML) method, and the standard GLM method was used to do the univariate variance analyses. The broad-sense heritability for every trait  across the two environment experiments was determined based on the estimated variance components with the accompanying equation: nt trial .2.2For DNA extraction, field-grown plant leaves were collected and stored at -80°C. The genomic DNA was extracted using a modified CTAB method . For theAt GeneSeek , the SNP array (Affymetrix 2) was used for the genotyping. No samples were excluded for low call rate (< 0.95) or low quality. After removing SNPs with genotyping error, a call rate of less than 0.95, or a minor allele frequency of less than 0.05, a total of 12,526 SNP markers were retained. STRUCTURE 2.2.3 was used to analyze the population structure of the 102 accessions . In a pr2.32) were figured. Based on the means of distances between SNP pairs and r2 values, LD decay plots were created.The filtered SNP data was used to calculate the linkage disequilibrium (LD) with the PopLDDecay program . Standar2.4To detect the association between SNPs and flavor-related traits in peanuts, statistical analyses were performed using three different methods: (1) TASSEL 5.0: the PCA (first five PCs) + GLM module  was utilized in the TASSEL software . (2) GAP2 threshold of 0.5, which generates 2,363 independent SNPs and LD blocks for this population. Based on the number of independent SNPs and LD blocks, the P-value threshold for significant and suggestive QTLs was calculated  = 4.67. Major or significant QTLs were defined as QTLs that met the significant threshold. In a genome-wide test, “suggestive association” permits one false-positive effect, indicating that the threshold p-value was 1/2363 = 4.23e−4 with − log10  = 3.37. Suggestive QTLs were defined as QTLs that were lower than the significant threshold but up to the suggestive threshold. Using qqman, Manhattan plots of the p-value results were created . The Tifrunner genome annotation file was used to annotate the identified genes. RNA-Seq data under BioProject PRJNA573070 were downloaded from the NCBI Sequence Read Archive (SRA) database and used for differential expressed genes (DEGs) analysis. Four seed developmental stages  of four genotypes  of F8 breeding lines were used for RNA-Seq. For details, see the paper by Genes within the ±80 kb region of the significant and suggestive SNPs associated with peanut flavor were detected from the Tifrunner genome sequences , sugars , and descriptive quality  were measured. Significant variability was identified for these traits among the accessions  against distances between each marker in the 102 U.S. mini-core collections. In this population, the LD decay was approximately 0.16 Mb and the r2 was 0.2  or poor quality. Population structure results indicated that the 102 mini-core accessions were grouped into primarily two subpopulations according to the delta K . Under K3.3The selected traits  displayed near-normal distribution for both years  plots suggested that the PCA + GLM model provided a better fit with the expected distribution  that encode UDP-glycosyltransferase and one gene (arahy.0B2YB3) that encodes acyl-CoA thioesterase. For sucrose, within the significant associated region, eight genes were found, including nucleotide-diphospho-sugar transferase superfamily protein (arahy.BXRJ0F), Gag-pol polyprotein n (arahy.0KX8XR), probable polygalacturonase (arahy.ETW0X5), acyl-CoA thioesterase (arahy.0B2YB3), UDP-glycosyltransferase (arahy.V3ZQ6H), UDP-Glycosyltransferase superfamily protein (arahy.94J2Q8), ATP-binding/protein serine/threonine kinase (arahy.8E8ZZ7), and lysosomal alpha-mannosidase-like (arahy.X2JSTS). The stable gene arahy.BXRJ0F related to sucrose was also detected in both years. In addition, four genes  near significant QTL regions were associated with both total sugars and sucrose, which includes one stable gene (arahy.BXRJ0F).44.1PCA is a useful method to extract main factors from complex traits that are highly correlated, which covers eigenvectors, standard deviation, and covariance . Correla4.2fastigiata, var. vulgaris, var. hypogaea, var.aequatoriana, var. peruviana, and var.hirsuta) , population structure and size, density of markers, and errors in phenotyping and genotyping data . At leashirsuta) . In thishirsuta) , leaf sphirsuta) , and yiehirsuta) ; however4.3Peanut flavor is a complex agronomic trait influenced by many factors. Peanut seed ingredients including free amino acids, reducing sugars, and amino acids released during protein denaturation are important precursors for flavor development through the Maillard reaction pathway. The scoring test and the hedonic scale method are the two most widely used sensory evaluation methods . An evalarahy.V3ZQ6H (120289043-120292411 bp) and arahy.94J2Q8 (120315438-120323907 bp), coding for UDP-Glycosyltransferase superfamily, were found to be located on chromosome A04, which catalyzes the covalent addition of sugars from nucleotide UDP-sugar donors to functional groups  on an expansive scope of lipophilic molecules (arahy.ETW0X5 plays a role in the biosynthesis of carbohydrates. Some other candidate genes including arahy.BXRJ0F (nucleotide-diphospho-sugar transferase superfamily), arahy.0KX8XR (Gag-pol polyprotein), arahy.0B2YB3 (acyl-CoA thioesterase), arahy.8E8ZZ7 (ATP-binding/protein serine/threonine kinase), and arahy.X2JSTS  were found participating in carbohydrate metabolism near significant QTLs regions. In addition, gene expression analysis indicated that most of these genes were involved in the carbohydrate metabolic process and were mainly expressed at the early seed development stages, such as genes arahy.X2JSTS, arahy.BXRJ0F, arahy.ETW0X5, and arahy.94J2Q8. There was no significant difference for gene arahy.0B2YB3 at different seed development stages in four peanut genotypes  were selected to have major effects on flavor than the other traits, and then GWAS was conducted for these three traits. The use of two-environment, two-year phenotypic data revealed QTL stability across environments. Seven significant QTLs associated with total sugars were identified including two stable QTLs and 22 significant QTLs related to sucrose were detected which also includes two stable QTLs. A total of four promising candidate genes, including The original contributions presented in the study are included in the article/HZ conducted the statistical analysis and prepared the manuscript. LD collected GWAS data and revised the manuscript. MW and PD prepared the samples and phenotypic data. ML was involved in experiment design and manuscript revising. CC supervised the whole study and provided assistance for manuscript preparation. All authors read and approved the final manuscript.


Multiple-trait genomic prediction enhanced the predictive ability for γ-decalactone to 0.7415 compared with the single-trait model value of 0.1017. One PTS1-SSR marker was designed to predict the linalool content, and the favorable genotype 187/187 was confirmed, mainly existing in the ‘Shanghai Shuimi’ landrace. Overall, our findings will be helpful in determining peach accessions with the ideal phenotype and show the potential of multiple-trait genomic prediction to improve accuracy for highly correlated genetic traits. The diagnostic marker will be valuable for the breeder to bridge the gap between quantitative trait loci and marker-assisted selection for developing strong-aroma cultivars.‘Chinese Cling’ is an important founder in peach breeding history due to the pleasant flavor. Genome-wide association studies (GWAS) combined with genomic selection are promising tools in fruit tree breeding, as there is a considerable time lapse between crossing and release of a cultivar. In this study, 242 peaches from Shanghai germplasm were genotyped with 145 456 single-nucleotide polymorphisms (SNPs). The six agronomic traits of fruit flesh color, fruit shape, fruit hairiness, flower type, pollen sterility, and soluble solids content, along with 14 key volatile odor compounds (VOCs), were recorded for multiple-statistical GWAS. Except the reported candidate genes, six novel genes were identified as associated with these traits. Thirty-nine significant SNPs were associated with eight VOCs. The putative candidate genes were confirmed for VOCs by RNA-seq, including three genes in the biosynthesis pathway found to be associated with linalool, soluble solids content, and  Peach is one of the most economically importantstone fruits and contains various flavor compounds. Due to the small genome size, it is recognized as a model fruit for other species in the Rosaceae family. The origin of peach and long domestication history of its cultivation in China has resulted in a high level of genetic diversity . Global Z)-3-hexenyl acetate and (E)-2-hexenyl acetate mainly contribute ‘fruity’ notes, while terpenoid compounds like linalool and β-ionone provide ‘floral’ notes. (Z)-3-hexenal and (E)-2-hexenal have been described as ‘green or grass’ notes . Sánchez et al. identified three loci on linkage groups 4, 5, and 6 that control monoterpene, lactone, and ester contents [et al. identified seven hotspots with more than four QTLs within a 1-Mb window, and found that the top end of chromosome 6 was related to benzaldehyde and δ-decalactone [LOX gene, PRUPE.2G005300, was identified, and the gene expression was negatively related to cis-3-hexenyl acetate. The role of the LOX gene in ester and lactone formation has been reported in several Rosaceae species [Nicotiana benthamiana) leaves via Agrobacterium-mediated transient transformation showed decreased E-2-hexanal compared with the empty vector control. Since cis-3-hexenyl acetate could not be detected in tobacco, we plan to repeat this experiment in tomato or strawberry in the future. The two polymorphic SSRs within this gene will be designed as new functional diagnostic markers to validate the allele effect in predicting phenotype accuracy.The main aim of our study was the genetic dissection of fruit aroma based on our germplasm, since it is one of the most direct traits that influence consumer preference. QTL mapping of VOCs has been reported by several research groups, based on biparental , 45 and alactone , 44. Conalactone , 44, we alactone . To a ce species –51. Valicis-3-hexenyl acetate) to 0.7415  by applying the multiple-trait GS model. The prediction value seems to be relatively lower than that from Biscarini et al.’s study on fruit weight (0.84) and titratable acidity (0.83) in peach [Developing a new peach cultivar is a time-consuming process because of the long juvenile stage, large plant size, and the multi-year recording of phenotypic performance . Nowadayin peach . Howeverin peach . Anotherin peach .Multiple GWAS models combined with transcriptomic profiling allowed us to accurately find novel association loci for complex aroma volatiles as well as traditional agronomic traits. The high genomic prediction accuracy based on GWAS-identified markers provides encouraging evidence to apply a multiple-trait genomic prediction model for genetically correlated traits. The high accuracy of diagnostic markers, particularly for linalool content, further bridges the gap between QTL detection and application of MAS. The newly designed KASP markers will be more suitable for high-throughput MAS implementation. Overall, these findings provide insight for candidate gene function validation, GS, and MAS.A total of 242 peach accessions were selected from the germplasm repository in Zhanghang experimental field of Shanghai Academy of Agricultural Sciences (SAAS), Shanghai, China. Of these, 195 accessions and their genome sequence have been reported in a previous study to uncover the genetic architecture for gummosis disease . All tre2. Ten microliters of 2-octanol (0.8 g/l) was added as an internal standard and the sample was vortexed for 1 minute. The vials were incubated for 20 minutes in a solid-phase microextraction (SPME) autosampler coupled to an Agilent 7890A gas chromatograph and a 5977B mass spectrometer. Volatiles were collected using 65 μm PDMS–DVB autosampler StableFlex™ SPME fibers , then desorbed at the injector for 5 minutes at 240°C. The initial oven temperature was kept at 40°C for 1 minute, then raised first to 100°C at a rate of 3°C min−1 then to 220°C at a rate of 5°C min−1, held for 5 minutes, then raised to 245°C at a rate of 25°C min−1, and held for 1 minute. The injection, transfer line, ion source and quad temperatures were 250, 250, 230, and 150°C, respectively. The energy was 70 eV in electron impact mode. The mass spectrometry data were acquired under SIM mode with the scan range from 20–400 mZ−1. Volatile absolute mass concentration was calculated based on the standard curves created by the reference value of the internal standard and authentic compounds from Sigma chemicals . To adapt multiple-trait analysis and GWAS to numeric trait values, all quantity traits were normalized , so the quantity trait values could be compared together. The qualitative traits values were coded as 0, 1 for binomial distribution traits, and −1, 0, and 1 for trinomial distribution traits.As published previously , five gret al. [P) value >7 as threshold for adjustment of significant markers that were associated with targeted traits [Leaf collection, total genomic DNA isolation, construction of the genome re-sequence libraries, and SNP filtration were performed according to Li et al. . The pheet al. , FarmCPUet al. , the mulet al.  using GAet al. . We usedet al. . The Bond traits .www.rosaceae.org) were identified by scanning the 100-kb flanking region on each side of the significant SNP. We used the RNA-seq data of the cultivars ‘Jin Chun’ (yellow-flesh peach) and ‘Hikawa Hakuho’ (white-flesh peach) during different developmental and ripening stages from our previous published studies  for related gene identification.Based on the linkage disequilibrium (LD) level in different subpopulations in our previous study on peach gummosis disease , candidahttps://github.com/jiabowang/Multiple-Traits-GWAS).GP was estimated by the ridge regression best linear unbiased prediction (rrBLUP) method with the rrBLUP package in R version 4.0.5. Here, we estimated the proportion of genotype-explained variance in the total phenotype variance as narrow-sense heritability . The varr) between the GEBVs estimated from the reference set and phenotypic values in the inference set was defined as the accuracy. The random selection of individuals from the whole population was named as one-time cross-validation. The loop was the prediction in each of the five subpopulations. In this study, using 30 replicates, the average correlation between real phenotype and GEBVs was considered the final accuracy.Based on the GWAS results, the significant markers above the Bonferroni threshold were used to predict individual GEBVs with rrBLUP. Prior to GWAS, the whole population was randomly separated into five subpopulations. In each loop of cross-validation, four of the subpopulations were used in GWAS to detect significant markers that would be further used in training populations. The phenotype values of the fifth subpopulation, as the inference population, were used to calculate prediction accuracy. The Pearson’s correlation coefficient , the pseudo-QTNs  (selected by GWAS) were used as the random effect in the rrBLUP model.cis-3-hexenyl acetate, linalool, β-ionone, γ-hexalactone, γ-octalactone, γ-decalactone, δ-decalactone, and γ-dodecalactone), the multiple QTNs (selected by GWAS in the relative traits) and the pseudo-QTNs (selected by GWAS in this trait) were used as the random effect in the rrBLUP model.For complex traits  (2+), 0.20 μl dNTP (10 mmol/l), 0.50 μl of each forward and reverse primer (10 mmol/l), and 0.10 μl Taq polymerase. PCR conditions were set as in the previous method [® .The second type of marker was an SSR marker designed for fruit linalool evaluation, with an AT motif repeat in the third intron of the GWAS-identified terpene synthesis gene (G030300) . Both fo μl dNTP 0 mmol/l,Web_Material_uhad117Click here for additional data file.


As one of the most widely grown crops in the world, rice is not only a staple food but also a source of calorie intake for more than half of the world’s population, occupying an important position in China’s agricultural production. Thus, determining the inner potential connections between the genetic mechanisms and phenotypes of rice using dynamic analyses with high-throughput, nondestructive, and accurate methods based on high-throughput crop phenotyping facilities associated with rice genetics and breeding research is of vital importance. In this work, we developed a strategy for acquiring and analyzing 58 image-based traits (i-traits) during the whole growth period of rice. Up to 84.8% of the phenotypic variance of the rice yield could be explained by these i-traits. A total of 285 putative quantitative trait loci (QTLs) were detected for the i-traits, and principal components analysis was applied on the basis of the i-traits in the temporal and organ dimensions, in combination with a genome-wide association study that also isolated QTLs. Moreover, the differences among the different population structures and breeding regions of rice with regard to its phenotypic traits demonstrated good environmental adaptability, and the crop growth and development model also showed high inosculation in terms of the breeding-region latitude. In summary, the strategy developed here for the acquisition and analysis of image-based rice phenomes can provide a new approach and a different thinking direction for the extraction and analysis of crop phenotypes across the whole growth period and can thus be useful for future genetic improvements in rice. Oryza sativa L.) is one of the most widely grown crops and is a staple food for over half the population throughout the world, with consumption increasing dramatically in recent years; rice crops have contributed importantly to food security [Rice  genotyping technologies have greatly propelled rice function genomics studies –8. With Recently, high-throughput plant phenotyping platforms –12 that It has been widely recognized that the dynamic plant growth and development processes can be evaluated on the basis of high-throughput phenotyping facilities. The approaches and standards for quantifying plant phenotypes have gradually attracted much attention from breeding researchers. In recent years, with the emergence of innovative artificial intelligence methods, such as machine learning, which has been widely applied in the image process of phenotyping analysis, remarkable achievements have been made because of the powerful adaptability and image processing capabilities of such methods –23. At pIn this study, we developed a strategy for the acquisition and analysis of i-traits in rice based on a high-throughput visible light imaging platform during the whole growth period. We extracted the rice phenotypic traits at the individual plant, panicle, culm, and grain levels of crops and further analyzed these traits at the temporal scale to obtain organ–time i-traits in rice during the whole growth period to explore the potential association between phenotype and genotype. We found that these i-traits could be used as good predictors of the final yield, and multiple differences among rice genotypes of different population structures and breeding regions of i-traits were also illustrated. Combined with genome-wide association studies (GWAS), we also demonstrate that further analyses performed in organ–time dimensions based on traits obtained by high-throughput phenotyping have potential as a feasible research direction in the fields of genomics and rice breeding.In our study, 93 rice accessions, including 32 indica and 61 japonica rice accessions with 4 replications, were planted in a greenhouse located at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences in Beijing. Specific information on the rice accessions is shown in Table The phenotyping facility used in this study  captured side-view RGB images of growing plants and culm images after the panicles were harvested Fig. A and B. R2), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) between the predicted and actual dry weights. A 5-fold cross-validation approach [R2, MAPE, RMSE, and MAE values. Statistical analyses of the 6 models were performed with LabVIEW 2015  and Python 3.8. The detailed statistical analysis results are shown in Tables The dry weights of the culm, panicle, and aboveground shoot parts were measured destructively after harvest. With the total projected areas of 3 different organs—culmTPA , PanicleTPA , and PlantTPA —in the last shoots before harvest, 6 models  were applied to obtain the predicted dry weight of the culm, panicle, and aboveground shoot parts. The model accuracy was evaluated by comparing the coefficient of determination , 6 models  were evaluated. Similarly, 2 panicle-related traits, including the PanicleTPA and the ratio of the yellow projected area to the PanicleTPA (PanicleYpar), were used to determine the best panicle growth model. The 6 models were implemented using LabVIEW 2015  and MATLAB 2018b , and related technical documentation is also shown in Note F, respectively, and related technical documentation is also shown in Note With all these i-traits considered in 6 different trait groups , pairwise multiple comparisons among the groups were further performed to locate the source of significance with the Wilcoxon test for at least one of the compared groups that differed from the others. The Kruskal–Wallis test and Wilcoxon test were implemented on the ggsignif R software package , and related technical documentation is also shown in Note To compare all the i-traits among the different rice population structures and breeding regions, the Kruskal–Wallis test was used to determine whether the independent groups based on different classification criteria were the same or different. When the value of the Kruskal–Wallis test was calculated as statistically significant  was performed using the correlation matrix of trait values in the pca R software packages  by setting “scale = TRUE.” Twenty-six traits, including 7 plant-related traits, 5 panicle-related traits, 6 culm-related traits, and 8 grain-related traits, were used to compute the PCs (Organ_PC). Furthermore, 32 traits, including 10 plant growth-related traits, 10 panicle growth-related traits, and 12 phenological traits, were used to compute the PCs (Time_PC). The first 2 Organ_PCs and the first 2 Time_PCs explained 63.5% and 58.9% of the total variance in the organ and temporal dimensions, respectively, and were retained for GWAS. GWAS was performed as described subsequently. The detailed statistical analysis results of PCA are shown in Tables Sequencing data of rice accessions were mapped to the rice reference genome Nipponbare (IRGSP 1.0)  with BWAAfter the side-view RGB image was acquired, the rice panicle was segmented with SegNet. A total of 154 panicle images were manually labeled and used as the training set, 40 images were used as the validation set, and 10 images were selected randomly as the testing set to evaluate the segmentation performance with 4 indicators, including the intersection over union (IoU), precision, recall, and F-measure. The detailed segmentation evaluation results of the 10 randomly selected images are listed in Table R2 value and lower values of other indices in the panicle dry weight modeling results, and the average R2 value between the predicted and manual dry weights was 0.72  were compared using the PanicleTPA in the last shoot before harvest. Compared to other models, the quadratic model had a higher .72 Fig. .R2 value. The average R2 values between the predicted dry weight and manually measured dry weight of the whole plant and culm were 0.86 , indicating that the final rice yield would be influenced by the total projected area. Positive correlations were also found between the yield and some growth-related traits, such as PanicleYpar_a (r = 0.522), which would reflect a changing panicle maturity pattern and, thus, influence the final yield. The correlations between all the i-traits are shown in Fig. It would be worthwhile for plant breeders to explore whether the existing traits obtained in i-traits can be used to predict the final rice yield. In this study, the rice yield was modeled using 6 groups of i-traits  [P < 0.001)  and between latitude and the plant total projected area . These results indicated that, among these 32 indica accessions, the higher-latitude rice accessions had smaller spikelet numbers and larger plant biomasses  , as did 01) Fig. B. These ses Fig. D.P < 0.05), while the pairwise comparison results among them differed. There were statistically significant differences between HS and PanicleYPA in different subspecies, but there were no significant differences between the Indica-2 group and the Japonica-1 group, potentially because of the similar genotypes of samples in these 2 groups . A total of 285 putative QTLs were detected for 33 traits  explained 34.5% and 24.3% of the total trait variance, respectively Fig. A. Time_PConfined to the limitations of traditional phenotyping involving the high labor cost, low efficiency, and high time consumption, most previous GWAS studies have usually focused on traditional phenotypes obtained manually at a specific point in time, generally at the mature stage, with characteristics of relative simplicity and the serious difficulty of large-number sampling; thus, with previous methods, it is almost impossible to perform vertical analyses in the temporal dimension. The growth of rice is a dynamic process, and the acquisition of phenotypes at the end time point is greatly affected by human activities, which are frequently obtained at the expense of integrity with destructive methods. With the development of high-throughput phenotype facilities, phenotype traits can be obtained precisely by ensuring high-throughput and nondestructive high-density time points that provide different research directions. In this study, we developed a strategy for the acquisition and analysis of image-based phenomes from the individual to organ level in rice during the whole growth period.On the basis of the i-trait phenomes obtained here, up to 84.8% of the phenotypic variance in the rice yield could be explained by 8 traits among all i-traits considered, and this traits could thus be applied to predict the final rice yield better than predictions performed with other trait groups. This finding indicates that the i-traits obtained here can comprehensively reflect the growth status of rice, with the exception of the final yield.To further explore the good environmental adaptability of the crop growth and development model, which would also show high inosculation with the latitude of the breeding region, the breeding region was divided into different latitude levels, and the correlations between all the image-based phenotypic traits of the elite rice cultivars and the regional latitude are shown in Fig. Combining the high-throughput rice phenotyping platform and GWAS to unlock the genetic information coded in rice, more than 255 putative QTLs for i-traits were identified, and the known identification of GSE5 and GS3 also most likely eliminated the limitation of the power of association analysis with a small population. The life span of rice showed a close relationship with plant senescence, which is also a basic problem in evolutionary ecology. PlantYpar was defined as the value of the ratio of the yellow projection area to the overall color distribution of the whole plant, regarded as an important quantitative index of plant senescence, and the detection of QTLs was thus of great significance. The indica rice displayed earlier senescence than japonica rice , and theFurthermore, PCA was also performed for the characteristics of the rice accessions in the organ–temporal dimension, and GWAS using PC scores was utilized to identify genetic factors. For organ dimension traits, Organ_PC1 captured 40.3% of the whole-trait variations in the organ dimension, whereas Organ_PC2 captured 23.3% of the variations in which Organ_PC1 primarily affected plant architecture and Organ_PC2 may have been related to panicle growth and maturity. Comparing the temporal dimension traits, Time_PC1 and Time_PC2 captured 34.5% and 24.3% of the total traits in the temporal dimension, respectively, which may have been related to the heading date and panicle growth-related traits. Among the PC values of the organ and temporal traits, a QTL on chromosome 3, with a lead SNP on chromosome 3: 24262598, was detected by both the organ and temporal PCs, indicating that this locus is deserving of further studies in the future involving confirmations with larger panels. Note that performing GWAS on the traits integrated by PCA is a double-edged sword, as it may not only help resolve the potential loci associated with complex traits but also cause more serious confounding effects, especially when using panels with strong population structures and traits with complex genetic control factors. As a proof of concept, this study demonstrates the potential value of this pathway in high-throughput phenomics studies and provides a reference for further similar studies in the future.In summary, in this work, on the basis of a high-throughput phenotyping tool, we extracted phenotypic traits by further analyzing the traits obtained across the whole growth stages of rice from the individual level to the organ level and identified trait-related QTLs with GWAS, thus providing a new research direction for heterosis breeding.


Wolbachia is a genus of maternally inherited endosymbionts that can affect reproduction of their hosts and influence metabolic processes. The pollinator, Valisia javana, is common in the male syconium of the dioecious fig Ficus hirta. Based on a high-quality chromosome-level V. javana genome with PacBio long-read and Illumina short-read sequencing, we discovered a sizeable proportion of Wolbachia sequences and used these to assemble two novel Wolbachia strains belonging to supergroup A. We explored its phylogenetic relationship with described Wolbachia strains based on MLST sequences and the possibility of induction of CI (cytoplasmic incompatibility) in this strain by examining the presence of cif genes known to be responsible for CI in other insects. We also identified mobile genetic elements including prophages and insertion sequences, genes related to biotin synthesis and metabolism. A total of two prophages and 256 insertion sequences were found. The prophage WOjav1 is cryptic (structure incomplete) and WOjav2 is relatively intact. IS5 is the dominant transposon family. At least three pairs of type I cif genes with three copies were found which may cause strong CI although this needs experimental verification; we also considered possible nutritional effects of the Wolbachia by identifying genes related to biotin production, absorption and metabolism. This study provides a resource for further studies of Wolbachia-pollinator-host plant interactions.The online version contains supplementary material available at 10.1186/s12864-023-09726-2. Wolbachia is a genus of maternally inherited intracellular endosymbiotic bacteria belonging to the order Rickettsiales and is estimated to be distributed in ca. 44% of arthropods, and 66% of insects, and also shows mutualistic symbiosis in nematodes  methyl ester and pimeloyl-[acyl carrier protein] methyl ester, fabF/G/Z/I, which are the key precursors for the synthesis of biotin, and the genes birA, which metabolize biotinyl-5 ‘- AMP and holo-[carboxylase] downstream, are present  controls the titer of Wolbachia in Nasonia body and inhibits adverse effects from Wolbachia due to endosymbiont overproliferation [Wolbachia in V. javana reflects a recent symbiotic relationship where control of the endosymbiont has not yet evolved, although there may also be a direct impact of diet composition of the pollinator that affects Wolbachia titers [The high proportion of feration . Perhapsa titers .Wolbachia from supergroup A appear to infect V. javana  Wolbachia have diverged after invasion among lineages and are then transferred horizontally between lineages, and (iii) the common ancestor of V. javana was multiply infected, and this situation persisted during further divergence [Wolbachia strains, but also through patterns of incompatibility produced through reproductive effects [Two strains of ana Fig. . Other srgroup A , 49. Thrvergence . A doubl effects . The pre effects , and the effects .cif genes with three copies suggests that wJav1 and wJav2 are likely to induce strong CI. The MLST analysis indicates that related species of V. javana infected with Wolbachia are known to cause CI . W). Wcif gy number . A straitrong CI . ExperimFicus hirta is a dioecious plant, with far more female trees in the field than male trees [V. javana spend their whole life cycle in the figs of male trees . The female wasps will fly out of gall figs after mating to spread Wolbachia and lay eggs in the gall flowers of other trees [Wolbachia through maternal transmission and the rate of spread will be enhanced by CI. When Wolbachia-uninfected females of V. javana lay eggs inside gall figs where Wolbachia-infected male pollinators are present, the offspring of the Wolbachia-uninfected females of V. javana will have a higher proportion of hatched males or eggs will fail to hatch. Studies have shown that both male and female pollinators of fig wasps can mate multiple times, although many females mate only once. In Ceratoolen solmsi marchali, females that multiply mate produce more female offspring, while the number of male offspring does not change significantly, resulting in a higher female sex ratio in offspring [Wolbachia, we speculate that after mating once, uninfected females will produce a male biased set of offspring . After multiple mating, the probability of females and male fig wasps uninfected by Wolbachia is high, resulting in an increase in the survival of females (oosperm). In addition, the high frequency of Wolbachia in populations may contribute to gender imbalance  of fig wasps [le trees . The winer trees –61. Femaffspring . Combiniig wasps . HoweverWolbachia from V. javana appears to have lost biotin-related genes, Wolbachia could still help the pollinators to absorb and metabolize biotin, which is synthesized by the host plant. As an essential vitamin for insects [While  insects , biotin  insects , 66.Wolbachia-pollinator-host plant system, we compared genome or transcriptome data from different fig tissues at the receptive phase  which hWolbachia genomes wJav1 and wJav2 of V. javana constituting two novel Wolbachia strains belonging to supergroup A. The MLST analysis helped to understand the relatedness of these Wolbachia to others. The annotation and analysis of prophages and insertion sequences shows some features that are different from other fig wasp endosymbionts, and highlights potential effects on genome evolution and the gene content of intracellular symbiosis, and these elements could be reused as gene manipulation tools. The discovery of at least three pairs of cifA-cifB genes from prophages belonging to type I suggests CI in the pollinators of F. hirta, and provides a basis for further studies on reproductive effects. The Wolbachia genome may help to provide a further understanding of nutritional aspects in this system.We have assembled the mixed V. javana from several figs of F. hirta in a single tree on Baiyun Mountain in Guangdong Province, China. The Wolbachia genomes were assembled based on whole genome sequencing data (CNCB accession number of GWHBDGE00000000 for V. javana).We collected nearly 500–1,000 female adult samples of Wolbachia genomes from different host species downloaded by NCBI were used as the reference genome  were aligned to the Wolbachia reference genome . Then Wolbachia PacBio sequencing reads in V. javana were assembled using Flye assembler v2.9-b1768 [165 assembled versions of me Table . The Pacme Table  using BL.9-b1768 , and the.9-b1768  with IllWolbachia genomic fragments in the sequence data of V. javana [V. javana genome (296.34 Mb). After using BLASR to set different identities and maxscore, different numbers of Wolbachia reads were obtained . SEED Viewer v2.0 was used to view the KEGG pathway [http://issaga.biotoul.fr/issaga_index.php) [Wolbachia genome were annotated using Phaster web server (http://phaster.ca), and the visualization of the positions of the prophage regions was automatically generated by the Phaster web server. Functions and potentially involved pathways of the coding genes in Wolbachia genome were further annotated using eggnog Mapper web server (http://eggnog-mapper.embl.de) [https://www.genome.jp/tools/kaas/) [The genome size and GC content of the assembled t v0.9.2 . Rapid g pathway  through dex.php) . The proembl.de)  and the s/kaas/) .Wolbachia strains in V. javana, five multilocus sequence typing genes (MLST) including gatB, fbpA, coxA, hcpA, and ftsZ in genomic data were blasted by BLASTN v2.9.0 according to the reference wMel strain of Drosophila melanogaster whose sequence type is typical ST1 (Table S2). The sequence type of the Wolbachia in V. javana was identified by aligning with PubMLST Wolbachia MLST database (http://www.pubmlst.org/Wolbachia/). In addition, ST sequences (sequences synthesized by housekeeping genes) of MLST in other Wolbachia genomes of A, B, D, F, and H supergroups were downloaded from PubMLST Wolbachia MLST database  a percentage of identity matches ≥ 50%. The obtained cif genes were used to construct the ML tree with the cif genes from other Wolbachia strains [cifA and cifB. Cif genes of wTri-2_pair1, wDacB_pair2, and wStri_pair1 were selected as outgroups (Table Based on the typical CifA and CifB protein sequences of type I-V Table , the cifs method  and slig strains . Model (ps Table .Below is the link to the electronic supplementary material.Supplementary Material 1


Flowering time and fruit yield are important traits in watermelon crop improvement. There is limited information on the inheritance and genomic loci underlying flowering time and yield performance, especially in citron watermelon. A total of 125 citron watermelon accessions were evaluated in field trials over two growing seasons for days to male and female flowers, fruit count, fruit weight, and fruit yield. The germplasm was genotyped with more than two million single-nucleotide polymorphism (SNP) markers generated via whole-genome resequencing. Trait mapping was conducted using a genome-wide association study (GWAS). Broad-sense heritability for all traits ranged from moderate to high, indicating that genetic improvement through breeding and selection is feasible. Significant marker-trait associations were uncovered for days to female flower , fruit count , fruit weight , and fruit yield on chromosomes Ca05, Ca07, and Ca09. The phenotypic variation explained by the significant SNPs ranged from 1.6 to 25.4, highlighting the complex genetic architecture of the evaluated traits. Candidate genes relevant to flowering time and fruit yield component traits were uncovered on chromosomes Ca02, Ca04, Ca05, Ca06, Ca09, and Ca11. These results lay a foundation for marker-assisted trait introgression of flowering time and fruit yield component traits in watermelons. Citrullus lanatus) is an economically important vegetable crop grown and consumed worldwide (Citrullus amarus (citron watermelon) displays significant genetic variation for traits important in the breeding and improvement of sweet-fleshed watermelons , polygenic associations for flowering time have been reported on chromosomes 6 and 7  molecular marker datasets in watermelon . These g3 cell size; T.O. Plastics Inc., Clearwater, MN, USA). To ensure optimum growth, citron watermelon seedlings were watered as needed, and a balanced water-soluble (NPK: 20:20:20) fertilizer was applied at a rate of 5 g/L . Greenhouse temperatures ranged from 18°C to 38°C with an average of 25°C. Seedlings were grown in the greenhouse for 4 weeks before being transplanted to the field plots.A total of 125 citron watermelon accessions were obtained from the United States Department of Agriculture (USDA) germplasm repository in Griffin, GA, USA  and second with the tractor-mounted Perfecta field cultivator . The two tillage methods were used in order to cut, loosen, and smoothen the soil as well as destroy any winter/spring vegetation before forming raised beds and laying the plastic mulch. The raised field beds were made using a Kennco Superbedder . The field beds were 121.9 m long, 91.4 cm wide, and 20.3 cm high. The distance between beds was 3.4 m. The beds were sprayed with herbicides and the next day covered with a black and white totally impermeable film (TIF) plastic mulch  using a Kennco Superbedder prior to transplanting.The day before transplanting, a tractor-mounted hole puncher was used to make planting holes (approx. 7.6 cm deep and 5.1 cm in diameter) for the watermelon seedlings. In both years, there were three plants per genotype replicated three times. Within the genotype, spacing was 1.8 m, while the space between genotypes was 2.7 m. Four-week-old watermelon seedlings were transplanted by hand to the research fields on May 16, 2019, and April 27, 2022. The experimental design in both the 2019 and 2022 field seasons was a randomized complete block design with three replications.®, Toro Agricultural Irrigation, El Cajon, CA, USA). In both the 2019 and 2022 field seasons, Possum’s 10-0-10 (N-P-K) PLUS fertilizer was utilized to ensure optimal plant growth . Fertilization was conducted via injection into the drip irrigation system with approximately 168 kg/ha of nitrogen applied over the entire growing season. Application of phosphorus fertilizer was not necessary for the fertigation program since soils at the research fields had high natural levels (> 210 kg/ha) of this nutrient based on the soil test results at the time of transplanting in both field seasons.To ensure optimum plant growth, sub-surface drip irrigation was applied using drip tapes placed centrally underneath the plastic mulch on all raised beds prior to transplanting. The drip tape specifications were 16-mm hose diameter, 0.20-mm wall thickness, and 30-cm emitter spacing, with an emitter flow rate of 1.0 L/h when the pressure regulator was set to 8 psi (Aqua-Traxx® (Gowan), Dual Magnum® (Syngenta), and Prowl H2O® (BASF). During the growing seasons, additional weeds were pulled by hand on a weekly basis. Fungicides to control foliar disease outbreaks during the two growing seasons were applied weekly, and in rotation, they included Inspire Super® (Syngenta), Quintec® (Gowan), Proline® (Bayer), and Initiate 720® (Loveland Products). Weather data  during the two growing seasons were retrieved from the Southeast Regional Climate Center .Biological pests in the research plots were controlled by physical and chemical means. Weeds were managed using a black and white TIF plastic mulch  and a single application before transplanting of broadleaf herbicides: SandeaA total of five adaptive and yield component traits were evaluated in this research. Days to male and female flowers was recorded as the number of days from seeding to when the two flower types were first observed while walking through the genotype plots. Within every genotype, fruits were declared to be physiologically ripe and mature (ready for harvest) upon detection of a brown and dry tendril at the node bearing the fruit, as well as a dull waxy fruit surface and light-colored ground spot on the fruit . Yield c2) were generated using the VARCOMP procedure in SAS v9.4 using restricted maximum likelihood estimation (REML) with all effects in the model treated as random  accessions were obtained using Illumina NovaSeq 6000 Sequencing Technology . Shotgun genomic libraries were sequenced on a single lane using the Illumina NovaSeq 6000 machine to generate 150-bp paired-end reads. Reads were filtered and trimmed as presented previously in C. amarus collection have been reported previously by prcomp function in R v4.2.0 (CMplot package (R2 statistic was used to compute the phenotypic variation explained (PVE) by the significant SNP markers for every trait . The search interval was chosen based on the linkage disequilibrium pattern present in this citron watermelon collection  and fruit weight (r = 0.29) but negatively correlated with fruit count (r = −0.37) (r = −0.46) but positively correlated to fruit weight (r = 0.39) (r = −0.77) and fruit yield (r = −0.41) (r = 0.71)  and were located on chromosomes Ca04, Ca05, Ca08, and Ca09  and were distributed on chromosomes Ca02, Ca03, and Ca05  was located on chromosome Ca05 and was uncovered by both the BLINK and FarmCPU models  and were located on chromosomes Ca02, Ca06, Ca08, Ca10, and Ca11  was uncovered by both the BLINK and FarmCPU models  and were distributed on chromosomes Ca05, Ca07, and Ca09  photoreceptor. Candidate gene CaU05G09420 was found at 53.5 kb downstream of SNP S5_6875362 on chromosome Ca05 and codes for a calmodulin protein. Candidate gene CaU09G15300 was located at 51.9 kb downstream of the SNP marker S9_17168406 on chromosome Ca09 and codes for a MYB transcription factor.For fruit count, candidate gene p- value = 1.5 × 10−5; p- value = 1.3 × 10−4)          compared to the “TT” allele  Figure 7Flowering time and fruit yield are crucial traits in watermelon breeding and cultivar development . FloweriFruit yield is a complex trait controlled by many loci and environmental factors. Detection of molecular markers associated with yield can help accelerate marker-assisted breeding. Fruit yield had a moderate heritability estimate, while yield component traits (fruit count and fruit weight) had moderately high heritability. These results are similar to earlier findings . A combiTrait correlation analysis was explored to help explain relationships among flowering time and yield component traits. Both days to male and female flowers were positively correlated, suggesting that it is feasible to select both phenotypes in a watermelon breeding program. Synchrony of male and female flowering times is very important, especially during the development of diploid pollenizers for triploid (seedless) watermelon breeding and production . Fruit cArabidopsis (Arabidopsis (PhyB) is a crucial photoreceptor that orchestrates multiple signaling pathways in plants to ensure optimal growth and development and transition to flowering  and internal/developmental factors such as hormonal status, transcription factors, sugars, and age-dependent signals . Three obidopsis . Both NAbidopsis . Seven cbidopsis . Phytochlowering . These cThis research characterized variation for flowering time , fruit yield, and fruit yield component traits. Moderate-to- high heritability estimates of these traits have been generated. Twenty SNP markers and several candidate genes associated with the evaluated traits across the citron watermelon chromosomes have been identified. These genomic regions and marker effects can help guide genomics-assisted breeding for phenological and fruit yield component traits in watermelon.http://cucurbitgenomics.org/; http://cucurbitgenomics.org/ftp/genome/watermelon/USVL246/; http://cucurbitgenomics.org/ftp/reseq/watermelon/Amarus_reseq/).The data presented in this study can be found in online repositories at the following cucurbit genomics websites: (DNK designed and implemented the project, performed the experiments, collected and analyzed the data, visualized and interpreted the results, and wrote the manuscript. AL designed and implemented the project. WW designed and implemented the project. All authors contributed to the article and approved the submitted version.