The results of genetic parameter analysis and association analysis showed that scaffold12-12716321, scaffold13-4787950, scaffold2-13687576 and scaffold290-11890 were four SNPs that met the requirement of polymorphic information content and conformed to the Hardy–Weinberg equilibrium. There were significant differences between their genotype and the phenotype of growth traits. The four SNP molecular markers developed in this research will lay a foundation for further exploration of molecular markers related to the growth traits of black porgy and will provide a scientific reference for the further study of its growth mechanisms. At the same time, these molecular markers can be applied to the production practices of black porgy, so as to realize selective breeding at the molecular level and speed up the breeding process.Black porgy is an important marine aquaculture fish species whose production is at the fifth position in all kinds of marine-cultured fishes in China. In this study, Illumina high-throughput sequencing technology was used to sequence the total RNA of black porgy. Sixty-one candidate SNPs (Single Nucleotide Polymorphism) were screened out and genotyped through GATK4  software and MALDI-TOF MS (Matrix-Assisted Laser Desorption/ Ionization Time of Flight Mass Spectrometry). The experimental results showed that a total of sixty SNPs were successfully genotyped, with a success rate of 98.36%. The results of principal component analysis and correlation analysis of growth traits showed that body weight was the first principal component, with a cumulative contribution rate of 74%. There were significant correlations ( Acanthopagrus schlegelii), belonging to Perciformes, Sparidae, which is a kind of nearshore and demersal fish with a wide tolerance of temperature and salinity, is mostly distributed in the coastal areas of China, Korea, Japan and Vietnam. Black porgy is one of the most popular and important commercial breeds in China and East Asian countries because of its high nutritional value, strong resistance to harsh environmental factors and fast growth [Black porgy , full length (FL), body length (BL) and body height (BH) were measured, numbered and recorded. The caudal fins were cut and stored in absolute ethanol for DNA extraction. The number of the caudal fin corresponds to the growth data for each individual.The black porgy population used in this experiment for SNP locus genotyping and association analysis was hatched in the same batch, cultivated in the same pond in the breeding base of black porgy in Lvsi  and were fed twice a day. The diet used in the whole process of the experiment is made by Tongwei Co., Ltd.  and contains 42% fish meal protein. During the cultivation period, the depth of the pond was 2 meters high, and the density of the population was about 60 ind·MThe extraction steps for black porgy DNA were carried out according to the instructions of the kit . The extracted DNA was tested for integrity, and concentration was estimated by 1% agarose gel electrophoresis and nucleic acid analyzer.According to the transcriptome data results from our previous study , on the According to the position of the screened candidate SNP sites and the sequence of about 200 bp before and after the corresponding position in the whole genome of black porgy, genotyping tools and MassARRAY assay design software  were used to design the PCR amplification primers and single base extension primer. The designed primers are in the range of 18–30 bp in length and the PCR product is about 80–200 bp. The Tm values of the primers were in the range of 55–65 °C and the annealing temperature was about 60 °C. The GC content of the primers was in the range of 40–70%. In addition, special care was taken to avoid the presence of primer dimers and non-specific amplification.On a 384-well plate, multiplex PCR technology was used for PCR amplification reactions. The PCR amplification reaction program was as follows: firstly 94 °C pre-denaturation for 3 min, then 94 °C denaturation for 30 s, 56 °C annealing for 25 s, and 72 °C extension for 30 s, a total of 40 cycles; finally, extend at 72 °C for 3 min.After the PCR reaction, the PCR product was subjected to SAP  treatment to remove dNTPs in the reaction. The formula of the SAP reaction solution is shown in the following table . The “SA−9 s) intense laser light and separated according to mass-to-charge ratio in a non-electric field drift region. Different genotypes can be distinguished according to the quality of the extended bases, which fly in the vacuum tube and arrive at the detector at different times [Sangon Bioengineering Co., Ltd.  was entrusted to perform genotyping of candidate SNP loci by MALDI-TOF MS. MALDI-TOF MS is a representative of medium-throughput SNP genotyping technology. It has the characteristics of high sensitivity, rapid analysis, easy to analyze spectra and small sample consumption. It is a powerful analytical tool in the field of molecular biology ,26. MALDnt times .Ho), heterozygosity (He), effective number of alleles (Ne) and polymorphism information content (PIC) parameters were calculated using Genepop32 software 4.2.2. Among them, the parameter of Ne is the reciprocal of gene homozygosity, which is an index to measure the genetic variation of the population. The more even the distribution of alleles in the population, the closer the Ne is to the actual detected allele frequencies. At the same time, the theoretical frequency of each genotype is calculated according to the allele frequency, so as to carry out the chi-squared test of the population Hardy–Weinberg equilibrium state [Gene frequency, genotype frequency, homozygosity  method was used for post-hoc multiple comparisons .DNAMAN software 9.0 was used to predict the amino acid sequence of the SNPs developed from this research.According to our previous study , the expThe 61 candidate SNPs were genotyped using MALDI-TOF MS and the verification population sample was the genomic DNA of 158 randomly selected 4-month-old black porgy from the same batch of hatchings. The results show that a total of 60 SNPs can be successfully genotyped and the genotyping success rate is 98.36%. Among them were scaffold2-3426830, scaffold3-16653046, scaffold5-12042944, scaffold19-709400 and scaffold305-32581; these 5 SNPs have only two genotypes. The genotyping results of some SNPs are shown in the following figures: Ho, He, Ne and PIC, and to carry out the chi-squared test (2χ) of the population Hardy–Weinberg equilibrium state. As the results show in PIC values of the SNPs ranged from 0.019 to 0.993. Among them, fifteen SNPs were low polymorphism sites (PIC < 0.25), such as scaffold10-3011393 and scaffold102-1223941. Twelve of them belong to moderate polymorphism SNPs (0.25 ≤ PIC < 0.5), such as scaffold12-12716321 and scaffold141-357706. Thirty-three SNPs were highly polymorphic sites (PIC ≥ 0.5), such as scaffold10-3011393 and scaffold11-8650275. The chi-squared test showed that 48 SNPs were in Hardy–Weinberg equilibrium , including scaffold10-3011393, scaffold12-12716321 and scaffold13-4787950. Eight SNPs, including scaffold102-1223941 and scaffold13-4805623, deviated significantly from Hardy–Weinberg equilibrium .Genepop32 software 4.2.2 was used to analyze the genetic parameters of sixty SNPs that were typed successfully, including gene frequency, genotype frequency, PIC and the Hardy–Weinberg equilibrium test, a total of thirty-seven moderately polymorphic or highly polymorphic SNPs that conform to the Hardy–Weinberg equilibrium (PIC ≥ 0.25 and χ2 < 5.99) were found, including scaffold10- 3011393, scaffold11-8650275 and scaffold12-12716321.To summarize the results of the p < 0.05). BW and FL, BW and BL, FL and BL, FL and BH, and BL and BH all show extremely significant correlations (p < 0.01). This result shows the significant correlation between the four growth traits, which meets the requirements of the experimental analysis and the conditions for further analysis. The dimensionality reduction and factor analysis were performed on the growth trait data of 158 black porgies. The results show that the BW accounted for 73.996% of the variance and the FL accounted for 24.977% of the variance, whereas the BL and BH accounted for less than 1%. According to the principle of principal component extraction, a factor with an eigenvalue greater than 1 is selected as the principal component. The initial eigenvalue of BW was 2.96, whereas the initial eigenvalues of FL, BL and BH were all less than 1. So, the BW was the first principal component of growth traits, with a cumulative variance of 73.996% . The average FL and BL for the AA genotype of scaffold168-178120 were significantly higher than for the GG genotype (p < 0.05). The TT genotype and CC genotype of scaffold12-12716321 and the AA genotype and CC genotype of scaffold13-4787950 had significant differences in BW, FL and BL (p < 0.05). The mean values for the TT genotype and CT genotype in scaffold290-11890 were significantly higher than those for the CC genotype on the four growth traits (p < 0.05). The mean values for the TT genotype of scaffold2-13687576 in all four growth traits were significantly higher than those for the AA genotype (p < 0.05). The calculated results for scaffold184-115262 and scaffold59-960994, which had less than two samples of partial genotypes, were not enough to explain the correlation between different genotypes and growth traits.Correlation analysis was carried out between the different genotypes of SNPs and the four growth traits, BW, FL, BL and BH. The SNPs involved in the calculation were thirty-seven moderately or highly polymorphic loci that passed the Hardy–Weinberg equilibrium test. The results show that the different genotypes of eight SNPs had significant differences in one or more growth traits . Among tHowever, BW was the first principal component trait, under the conditions that both the requirements of the PIC and the Hardy–Weinberg equilibrium are satisfied, only four SNPs had significant differences between different genotypes for BW . Combined with the results of the association analysis of other growth traits, scaffold12-12716321 and scaffold13-4787950 had significantly higher averages for FL and BL than those of the CC genotype. However, there was no significant difference between the different genotypes of these two SNPs on the growth trait of BH. Scaffold2-13687576 and scaffold290-11890 had significant differences in FL, BL and BH. The averages of the individuals with the dominant genotype were higher than that of the individuals with the non-dominant genotype.DNAMAN software 9.0 has been used to predict the amino acid sequences of the four SNPs developed from this research. The results show that scaffold13-4787950 was located at the 364th base from the 5’ end of the cDNA chain of cytochrome P450 3A (CYP3A40) and the base mutation type was A/C. Before mutation, the codon corresponding to this site was AAC and the translated amino acid was aspartic acid. The codon corresponding to this site was CAC after mutation and the translated amino acid becomes histidine . scaffolM. salmoides [C. idella [Megalobrama amblycephala [Cheilinus undulatus [Litopenaeus vannamei [Schizothorax prenanti [The RNA-Seq technique is a kind of molecular biology technology, which is deeply used in studying the signaling pathways and the molecular mechanisms of trait regulation of individual organisms at the transcriptional level. Moreover, with the continuous reduction of the cost of high-throughput sequencing, RNA-Seq technology has gradually played an important role in the screening of candidate functional genes and SNPs. However, the SNPs detected in transcriptome data often have a large number of false positive markers. Former studies have shown that in sequencing data with the whole genome sequence as a reference, although there are a large number of false positive SNPs that have been detected by the GATK software 4.0in the sequencing data, most of them can be removed by depth filtering . So, we almoides  (1/2kb) . idella  (1/3.73 ndulatus  (1/490bpvannamei  (1/486bpprenanti  (1/85bp)Scophthalmus maximus were genotyped using the HRM technique and 21 of them were successfully genotyped, with a success rate of 46.7% [M. salmoides using the SNaPshot method, with a success rate of 70% [Larimichthys crocea, of which, 9 were successfully typed, a success rate of 81.8% [Compared with RT-qPCR, SNaPshot, HRM and other genotyping techniques, MALDI-TOF MS has the characteristics of high throughput, low cost, and low requirements for sample quality due to the simultaneous detection of multiple samples and multiple sites. In addition, it less time-consuming, highly sensitivity and has stronger compatibility . In thisof 46.7% . Researce of 70% . Sanger of 81.8% . The higNe, He and PIC are all important genetic parameters in population genetics and variation analysis. He is the frequency of heterozygotes in the population at the tested locus, which can reflect the degree of genetic variation in the population. A decrease of He indicates that the degree of genetic variation of the population has become lower, so as to judge that the genetic diversity of the population is becoming lower [PIC is the reciprocal of genetic homozygosity, which can reflect the degree of mutual influence between alleles and is used as one of the evaluation parameters of population genetic diversity. According to the PIC’s judgment criteria [PIC ranges from 0.25 to 0.5, it is classified as moderately polymorphic, and when the PIC is higher than 0.5 or lower than 0.25, it is classified to be highly polymorphic or low polymorphic, respectively. In this study, the PIC values of our sixty SNPs ranged from 0.019 to 0.993. Among them, fifteen were low polymorphism sites (PIC < 0.25). Twelve of them belong to moderate polymorphism SNPs (0.25 ≤ PIC < 0.5) and thirty-three SNPs were highly polymorphic sites (PIC ≥ 0.5). The He of the black porgy population from those thirty-three highly polymorphic SNPs ranged from 0.096 to 0.499, with an average of 0.366. The average He of the eight SNP markers in the myosin heavy chain gene of M. salmoides was 0.41 [He of the four SNPs in the AMY gene of S. chuatsi was 0.45 [ng lower . PIC is criteria , when thwas 0.41 . The He was 0.45 . The abop < 0.01) and a significant correlation with BH . FL, BL and BH were all highly significantly correlated with each other (p < 0.01). Compared with the analysis results of related studies on other species, the total length, body length and body height of the 12-month-old Takifugu rubripes showed a very significant correlation with body weight (p < 0.01) [S. prenanti were all significantly correlated with the body weight, which was the first principal component trait, and the cumulative contribution rate was as high as 93.42% (p < 0.01) [In this study, the body size of black porgy when it grew to 4 months old was used as the standard for evaluating the growth rate. The results of principal component analysis showed that BW was the first principal component, with a cumulative contribution rate of 74%. The results of correlation analysis among the four growth traits showed that, as the first principal component, BW had a highly significant correlation with FL and BL ( < 0.01) . The ful < 0.01) . All thePIC and the Hardy–Weinberg equilibrium are satisfied, only four SNPs have significant differences on BW between different genotypes. On the growth trait of BW, the dominant genotype of scaffold12-12716321, scaffold13-4787950 and scaffold2-13687576 were TT, AA and TT, respectively. CT and TT were the dominant genotypes of scaffold290-11890. According to the growth-related research in other species, a marker-assisted BLUP method was used to verify a SNP marker that was significantly correlated with body weight of turbot [According to the results of association analysis, among the thirty-seven moderately or highly polymorphic SNPs that passed the Hardy–Weinberg equilibrium test, there were eight different SNP genotypes that were correlated with one or more traits of black porgy. However, BW was the first principal component trait, under the condition that both the requirements of the In addition, combined with the results of the association analysis of other growth traits, scaffold12-12716321, scaffold13-4787950, scaffold2-13687576 and scaffold290-11890 can be preliminarily confirmed as SNP molecular markers related to growth traits of black porgy, which can be used for the molecular breeding of black porgy to select individuals with a high growth rate.I. punctatus showed that 13 male-specific SNPs were identified in the coding region of the zbtb38 gene. Six of these substitutions resulted in changes in the amino acid coding of zbtb38 on the X and Y chromosomes [The sequence of bases in DNA in each organism has an important impact on the amino acid sequence it translates. The mutation of bases may lead to changes in codons, thereby changing the types of amino acids and ultimately affecting protein structure. DNA sequencing of omosomes . Four SNomosomes . Studiesomosomes ,47,48,49Salmo salar after infection by A. salmonicida, which demonstrating that the FGA may play an important role in the innate immune response to bacterial invasion in Atlantic salmon [scaffold2-13687576 was located at the 6070th base of the cDNA strand from the 5’ end of the complement component 4 gene (C4). Before and after the mutation, the resulting amino acid was changed from aspartic acid to tyrosine. Scaffold290-11890 was located at the 757th base on the fibrinogen beta chain (FGB). Before and after the mutation, the translated amino acid was changed from phenylalanine to leucine. Both C4 and FGB play important roles in the fish immune system. Fibrinogen is composed of three pairs of different polypeptide chains, α, β and γ. It is a glycoprotein synthesized and secreted by hepatocytes and is involved in the processes of coagulation and hemostasis. A large number of research results have shown that the damage, infection and immunity of organisms are related to extravascular fibrinogen. The results of research showed that the FGA gene is expressed in the liver tissue of c salmon . The FGBc salmon . In thisFour SNP molecular markers related to the growth traits of black porgy were identified in this study, which means that we can select and breed fine varieties of black porgy from the molecular biology level. We overcome the limitations of high cost, low efficiency and poor effect caused by artificial breeding. This is conducive to improving the accuracy of selection and can identify individuals with excellent traits at an early stage, thereby shortening the breeding cycle and speeding up the breeding process.


Establish a molecular breeding program involved assembling a diverse germplasm collection and generating accurate genotypes to characterize their genetic potential and associate them with agronomic traits. In this study, we acquired over eight hundred wheat accessions from international gene banks and assessed their genetic relatedness using high-quality SNP genotypes. Understanding the scope of genomic variation in this collection allows the breeders to utilize the genetic resources efficiently while improving wheat yield and quality.WAPO-A1, a gene that governs yield components.A wheat diversity panel comprising 39 durum wheat, 60 spelt wheat, and 765 bread wheat accessions was genotyped on iSelect 90 K wheat SNP arrays. A total of 57,398 SNP markers were mapped to IWGSC RefSeq v2.1 assembly, over 30,000 polymorphic SNPs in the A, B, D genomes were used to analyze population structure and diversity, the results revealed the separation of the three species and the differentiation of CIMMYT improved breeding lines and landraces or widely grown cultivars. In addition, several chromosomal regions under selection were detected. A subset of 280 bread wheat accessions was evaluated for grain traits, including grain length, width, surface area, and color. Genome-wide association studies (GWAS) revealed that several chromosomal regions were significantly linked to known quantitative trait loci (QTL) controlling grain-related traits. One of the SNP peaks at the end of chromosome 7A was in strong linkage disequilibrium (LD) with Here, the most updated and accurate physical positions of SNPs on 90 K genotyping array are provided for the first time. The diverse germplasm collection and associated genotypes are available for the wheat researchers to use in their molecular breeding program. We expect these resources to broaden the genetic basis of original breeding and pre-breeding materials and ultimately identify molecular markers associated with important agronomic traits which are evaluated in diverse environmental conditions.The online version contains supplementary material available at 10.1186/s12870-022-03844-x. Triticum aestivum . Cultivation and selection of wild emmer wheat led to the formation of the domesticated emmer wheat T. turgidum ssp. dicoccum , from which tetraploid durum wheat T. turgidum ssp. durum  evolved [T. aestivum Ssp. spelta; 2n = 6x = 42, AABBDD) has been debated; it is not known whether spelt wheat is the ancestral form of hexaploid wheat or is derived from the hybridization of free-threshing hexaploid wheat and emmer wheat [T. aestivum and emmer wheat [Wheat is the most widely grown cereal crop species in terms of cultivation area, ranking third in yield production and accounting for approximately 20% of the total daily calories and protein supply worldwide , 2. Wheaer wheat –9. However wheat , 6, 10.The sequencing of the high-quality reference genome of the bread wheat cultivar Chinese Spring   has enabTaGS5 [TaGW7 [TaGS3 [TaCYP78A3 [TaWTG1 [Since the Green Revolution, significant breeding efforts have been made to increase wheat yields. Grain size and grain weight are components that determine yield potential, and several genes controlling grain-related traits have been identified and isolated via comparative genomics methods, including TaGS5 , TaGW7 [5 [TaGW7 , TaGS3 [7 [TaGS3 , TaCYP78aCYP78A3 , and TaW [TaWTG1 . In rece [TaWTG1 , 32–34 b [TaWTG1 . Several [TaWTG1 , 36, 37.In this study, a diversity panel consisting of 765 bread wheat, 60 spelt wheat, and 39 durum wheat accessions was genotyped using the Illumina iSelect wheat 90 K SNP array. The physical positions of array probe sequences were mapped to IWGSC RefSeq v2.1, which was released in early 2021. SNP quality, including call rate and call score, was evaluated by several criteria and compared with the SNP quality of the variants detected by next-generation sequencing (NGS). The population structure of this diverse wheat panel was assessed using SNPs with updated genomic positions, and a series of GWASs were then conducted to identify chromosomal regions associated with grain-related traits of bread wheat.T. turgidum ssp. durum), 60 spelt wheat (T. aestivum ssp. spelta), and 765 bread wheat  accessions were mainly obtained from the Wheat Germplasm Bank of the International Maize and Wheat Improvement Center  and the Germplasm Resources Information Network (GRIN) of the USDA-ARS  and checked for quality. The purified DNA was subsequently hybridized to an Illumina iSelect wheat 90 K SNP array, and array processing and fluorescent signal detection were performed according to the manufacturer’s protocol .The Illumina iSelect wheat 90 K SNP array involves 81,587 functional assays . To obtahttps://wheat-urgi.versailles.inra.fr/Seq-Repository/Annotations).The flanking sequences of the unique markers were mapped against the IWGSC RefSeq v1.0  and IWGSThe image files of fluorescent signals, generated by the Illumina iSelect genotyping assays, were analyzed using the Polyploid Genotyping Module implemented in GenomeStudio v2.0.4 . For each SNP marker, allele clustering was performed on the selected samples using the parameters included with the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm. The most important parameters were “cluster distance” and “minimum number of points in the cluster”. To determine the best combination of parameters, five cluster distances  and three minimum numbers of points in clusters  were tested and evaluated for their performance. Because the wheat accessions used in this study had undergone several generations of selfing, the “inbred population” option was selected because only two allelic clusters representing homozygous AA or BB groups were considered.Determination of the best parameter combination was based on the sample call rate, sample p10 GC score , SNP call frequency, and SNP 10% GC score . The genotype calls of Chinese Spring in our diversity panel were also used to evaluate SNP clustering performance. For markers with assigned physical positions, the corresponding genotypes at the same position in the Chinese Spring reference genome were extracted. The genotypes of Chinese Spring from our clustering results and the genotypes extracted from the reference genome sequence were then compared. Genotype calls (AA and BB) were converted to International Union of Pure and Applied Chemistry (IUPAC) nucleotide codes based on the information in Supplementary Table Sr2 values. To investigate LD decay in our diversity panel, the r2 values were plotted against the physical distance between each pair of markers, and a trend line was fitted using the Hill and Weir expectation of r2 [r2 of 0.1 or 0.2 [r2 = 0.1 and 0.2 were reported.SNP markers with a minor allele frequency (MAF) smaller than 0.01 and a missing percentage greater than 10% were removed. PCA was conducted in TASSEL v5.2.26 . The sofon of r2 , which won of r2 . LD deca1 or 0.2 . For comST was calculated via GenoDive v3.04 to assess the genetic differentiation between subpopulations [The number of underlying subpopulations (K) in our diversity panel was determined using ADMIXTURE software . The K vulations . The meaulations . We furtulations  with theA subset consisting of 96 bread wheat lines from the diversity panel was subjected to GBS. The GBS library was prepared according to the protocol developed by Elshire et al. . Briefly2), perimeter (mm), grain length (mm), grain width (mm) and values representing three independent color channels. The value of each color channel was considered a proxy for the RGB color model. Pearson correlation coefficient was calculated between traits. Analysis of variance (ANOVA) for all the traits was performed using R. The broad-sense heritability was calculated as H2 = σ2G/(σ2G + σ2E); where σ2G was calculated as /2 and σ2E was MSresidual.The selected wheat accessions were grown over 2 years in the same experimental field. Within the same year, around 300 accessions were grown in the field with size around 0.1 ha, 18 individuals per accession were planted on one plot (plot size is 1 m × 1.2 m), two plots per accession were randomly arranged in the field. Mature grains of each accession were bulked, the dry and clean seeds (at least 100 grains and up to 600 grains per accession) harvested in two separate years (I and II) were scanned independently by an Epson Perfection V600 flatbed scanner at a 24-bit and 300 dpi resolution, a black cardboard box was used to cover the scanner to reduce internal reflection from the light emitted during scanning. The scanned image was saved as a JPG file for processing. Color calibration was performed by using the color Palette and histogram functions in Epson Perfection V600 Professional Mode. The grain size and color were measured from scanned images using GrainScan software developed by CSIRO . The meaMarker-trait associations were performed in TASSEL v5.2.56 . The genThe sequence of previously cloned rice genes controlling grain size traits (Supplementary Table S1 in ) was BLAThe Illumina iSelect wheat 90 K array includes 81,587 SNP detection assays corresponding to 517,587 hybridization sites in the wheat genome. The flanking sequences of each assay were obtained from a previous study , and we To determine accurate SNP genotypes for each wheat accession, we developed a customized SNP clustering pipeline based on Illumina GenomeStudio Software v2.0.4 Fig. . Within After removing SNPs with MAF < 0.01 and missing rate > 10%, a total of 28,836 polymorphic SNPs in the A and B genomes were used to analyze the population structure of 864 tetraploid and hexaploid wheat germplasms. The first PC separated tetraploid durum wheat from hexaploid wheat; bread wheat and spelt wheat were further differentiated by other PCs . Interestingly, the accessions developed at CIMMYT were grouped together and separated from others by PC1 based on 29,803 polymorphic SNPs  within the bread wheat group, individual accession was assigned to a specific subpopulation when the proportion of a single ancestry was larger than 0.8, a total of 259 individual accessions were assigned to subpopulation Pop1 (red dots), 354 genotypes belonged to subpopulation Pop2 (blue dots), and 152 accessions were considered admixtures  decayed from the initial value of ~ 0.45 to 0.2 were 1.76 Mb in bread wheat, 4.11 Mb in spelt wheat, 7.21 Mb in durum wheat  in spelt wheat  was highly correlated , the broad-sense heritability of each trait was then calculated, which ranged from 0.62 to 0.86  were quite similar between the two hexaploid wheat lines, where cessions , 74. Würcessions  suggesteid wheat , 75, 76.WAPO-A1 was shown to colocalize with the QTL region identified in this study and was identified as a candidate gene for a QTL responsible for spikelet number per spike [TaGW7, which regulates grain length and grain width [TaGL3-5A, which is associated with grain length and thousand-kernel weight [TaGW7 and TaGL3-5A were found to be orthologous to the rice GW7 [GL3.1 [R” [Tamyb10-B1” encoded R2R3-type MYB domain protein was further investigated for its allelic diversity and effect on grain color [Tamyb10-B1 gene was found to locate in the LD block calculated from the significant associated-SNPs detected in this study  LD decay at the whole-genome level. (b) LD decay of subgenome A. (c) LD decay of subgenome B. (d) LD decay of subgenome D. The physical distance (in megabases) is plotted against the LD estimate (r2) for pairs of markers.Additional file 6: Supplementary Fig. S6. Genome-wide association mapping for grain width. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and grain width.Additional file 7: Supplementary Fig. S7. Genome-wide association mapping for the grain perimeter. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and grain perimeter.Additional file 8: Supplementary Fig. S8. Genome-wide association mapping for grain area. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and grain area.Additional file 9: Supplementary Fig. S9. Genome-wide association mapping for color channel 1. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and color channel 1.Additional file 10: Supplementary Fig. S10. Genome-wide association mapping for color channel 2. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and color channel 2.Additional file 11: Supplementary Fig. S11. Genome-wide association mapping for color channel 3. Manhattan plots of the four models  and associated quantile-quantile (Q-Q) plots representing the statistical association between each SNP and color channel 3.Additional file 12: Supplementary Fig. S12. Phenotypic distribution of six grain traits and correlation analysis.Additional file 13: Supplementary Fig. S13. LD heatmap of the grain color locus. The pairwise LD of significant SNPs associated with three color channels on chromosome 3B was calculated and plotted. The position of significant SNPs and Tamyb10-B1 gene was labeled.Additional file 14.Additional file 15.Additional file 16.Additional file 17.Additional file 18.Additional file 19.Additional file 20.Additional file 21.


The trait is controlled by the recessive gene BADH2 was evaluated at the population, genetic, transcriptional, and metabolic levels to obtain insights into fragrance regulation in rice.Variation in Whole-genome resequencing of the Korean World Rice Collection of 475 rice accessions, including 421 breeding lines and 54 wild accessions, was performed. Transcriptome analyses of a subset of 279 accessions, proteome analyses of 64 accessions, and volatile profiling of 421 breeding lines were also performed.BADH2 coding region, including four novel haplotypes (one in cultivated and three in wild accessions). Tajima's D values suggested that BADH2 was under balancing selection in japonica rice. Furthermore, we identified 316 expression quantitative trait loci (eQTL), including 185 cis-eQTLs and 131 trans-eQTLs, involved in BADH2 regulation. A protein quantitative trait loci (pQTL) analysis revealed the presence of trans-pQTLs; 13 pQTLs were mapped 1 Mbp from the BADH2 region. Based on variable importance in projection (VIP) scores, 15 volatile compounds, including 2AP, discriminated haplotypes and were potential biomarkers for rice fragrance.We identified over 3.1 million high-quality single nucleotide polymorphisms (SNPs) in Korean rice collection. Most SNPs were present in intergenic regions (79%), and 190,148 SNPs (6%) were located in the coding sequence, of which 53% were nonsynonymous. In total, 38 haplotypes were identified in the BADH2 gene expression and protein accumulation are likely involved in the regulation of 2AP variation in fragrant rice. These data improve our understanding of fragrance and provide valuable information for rice breeding.We generated a catalog of haplotypes based on a resequencing analysis of a large number of rice accessions. eQTLs and pQTLs associated with  Rice taste is directly related to its fragrance betaine aldehyde dehydrogenase 2 (BADH2) gene [12]. Active BADH2 stimulates γ-aminobutyric acid (GABA) synthesis and inhibits 2AP accumulation in non-fragrant rice BADH2 fails to convert γ-aminobutyraldehyde (GABAld) into GABA, resulting in GABAld accumulation. Consequently GABAld spontaneously cyclizes to Δ1-pyrroline, leading to the synthesis of 2AP BADH2 and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and elevated transcript levels of triose phosphate isomerase (TPI) and Δ1-pyrroline-5-carboxylic acid synthetase (P5CS) increase 2AP accumulation Previous studies have identified hundreds of volatile compounds in fragrant rice, among which 2-acetyl-1-pyrroline (2AP) has been identified as a key compound responsible for a popcorn-like fragrance BADH2 mutations associated with fragrance in rice have been reported BADH2 allelic variation.Various With advances in high-throughput sequencing technologies, -omics-based studies of rice have progressed considerably, including analyses of genomes (genomics), RNA transcripts (transcriptomics), and metabolites (metabolomics) aimed at unraveling the functional variants and molecular mechanisms underlying traits for crop improvement BADH2 variation, and fragrance regulation. We conducted whole-genome resequencing of 475 accessions, including 54 wild rice accessions. Furthermore, we quantified BADH2 expression in a subset of 279 accessions by RNA-Seq, protein levels in a subset of 64 accessions by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the volatile profiles in a subset of 421 accessions by HS-solid-phase microextraction (SPME)/GC–MS. By this gene-to-metabolite approach, we identified four novel alleles in the BADH2 coding region, 316 eQTLs, and 13 pQTLs. Considering the importance of fragrance in the rice market, our data are expected to provide important information for rice breeding.We used multiple -omics approaches to study Table S1). This Korean World Rice Collection (475 accessions) was comprised of 421 cultivated accessions, including 305 japonica, 102 indica, 9 aus, 2 aromatic, and 3 admixture varieties.A set of 137 accessions were previously selected from 25,604 rice accessions of the Korean Genebank of the Rural Development Administration (RDA) using PowerCore http://rapdb.dna.affrc.go.jp/download/irgsp1.html) using Burrows-Wheeler Aligner (BWA) version 0.7.8 with default parameters Genomic DNA of each accession was extracted from the leaf using the DNeasy Plant Mini Kit  and subjected to whole-genome resequencing using the Illumina HiSeq 2500 sequencing platform , with an average coverage of approximately 15×. The raw sequences were aligned to the Nipponbare rice reference genome  was used to construct a dendrogram based on the neighbor-joining method with 1,000 bootstrap replications. The PCA was conducted using the high-quality SNPs in TASSEL 5.0 and plots were drawn using the R package ggplot2 version 3.6.2 K values ranging from 2 to 10. Nucleotide diversity (π), population differentiation (STF), and Tajima’s D values were calculated using VCFtools 0.1.13 with 500 bp sliding windows and 500 steps.SNPs  detected in the whole-genome resequencing data were used to a construct phylogenetic tree and were evaluated by a principal component analysis (PCA). FigTree version 1.4.3 .Genetic variation in the BADH2 gene  obtained from the DNA variant VCF file was imported into TASSEL 5.0 w), and Tajima’s D. Haplotypes were constructed by statistical parsimony using the TCS network with PopART  version 1.7 Genetic diversity of the Trichomonas vaginalis lactate dehydrogenase (TvLDH) using a Python script.A 3D model of dimeric BADH2 (where each monomer consisted of 504 amino acids) was built by homology modeling using MODELLER version 9.21 and by searching for structures related to a novel gene encoding Total RNA was extracted from 297 rice accessions grown in the paddy field at the Kongju National University, Korea. Panicles were collected at the milky stage (after 15 days of heading) and immediately transferred to liquid nitrogen. Total RNA was extracted from milky stage panicles using the RNeasy® Plant Mini Kit (Qiagen) as per the manufacturer’s instructions. The RNA quality was confirmed by 1.0% agarose gel electrophoresis and quantified using the NanoDrop ND-1000 . In addition, the RNA 6000 Pico Kit  was used to evaluate RNA integrity. Five micrograms of total RNA were used as input material for RNA sample preparation and sequencing libraries were generated using the TruSeq RNA Sample Preparation Kit (Illumina). Finally, the constructed cDNA libraries were sequenced on the Illumina HiSeq 2500 sequencing platform (Illumina). Trimmomatic was used to remove adapters and low-quality reads with default parameters. The clean reads were mapped to IRGSP 1.0 using the R Trinity package. Expression levels quantified in FPKM (fragments per kilobase of transcript per million mapped reads) were standardized using z-scores  et al. g. The supernatant was filtered through 5 μm and 0.45 μm membrane filters, sequentially . Protein extracts were precipitated overnight with trichloroacetic acid (20% v/v), washed with cold acetone three times, and fully dissolved in 8 M urea/Tris-HCl pH 8.5. The protein concentration was determined using the 2D-Protein Quant Kit .Proteins were extracted following the method described by Lee The proteins (500 μg) were reduced with 5 mM Tris (2-carboxyethyl) phosphine hydrochloride (TCEP) at room temperature for 30 min. Then, alkylation was performed with 10 mM iodoacetamide at room temperature in the dark for 30 min. Subsequently, samples were diluted with Tris-HCl  to reduce the urea concentration from 8 M to 2 M and digested with trypsin (5 μg) overnight at 37°C. Protein digestion was terminated with formic acid (5%). Then, digested samples were desalted the using SPEC PLUS PT C18 column (Agilent Technologies). SpeedVac was used to dry the solvent.m/z. MS/MS-ion scanning of the top five most intense parent ions was performed by collusion-induced dissociation.Samples were analyzed by A Nano LC connected to a Finnigan LTQ mass spectrometer . Biphasic columns were organized using 365-μm o.d. × 100-µm i.d. fused-silica capillaries . The desalted proteins were loaded onto the column. A binary buffer system with 0.1% formic acid (buffer A) and acetonitrile in 0.1% formic acid (buffer B) was used. A linear gradient from 3% buffer B to 50% buffer B at a flow rate of 0.200 µl/min was used for separation. An 11-step program with increasing concentrations of salt solution was used for peptide elution. The run time was 120 min for each step, and approximately 22 h for the 11 steps. Peptide eluent ionization was performed by electrospraying directly into the MS/MS system, and parent-ions were scanned in the range of 400–1600 BADH2 transcript levels and protein abundance. To detect genetic variants associated with BADH2 transcript levels, 279 accessions were subjected to an eQTL analysis using the mixed linear model implemented in the GAPIT R package BADH2 protein abundance as the phenotype. The R package qqman (https://cran.r-project.org/web/packages/qqman/index.html) was used to draw Manhattan plots. A SNP with a significant association with BADH2 transcript expression and protein abundance was defined as an eQTL and pQTL, respectively, a cut-off − log10 of < 4. A SNP mapped 1 Mb upstream or downstream of the target gene was considered as a cis-QTL, while the remaining SNPs were referred as trans-QTLs.The high-quality SNPs (MAF ≥ 5% and missing rate ≤ 20%) obtained by whole-genome resequencing were used in a genome wide analysis of associations with et al.Table S1) were placed in a headspace vial and spiked with 2,4,6-trimethylpyridine as an internal standard. The preheated volatiles were adsorbed onto a SPME fiber  and injected into a GC/MS instrument  equipped with an Rxi-5Sil MS capillary column . Volatile compounds were identified based on the n-alkane (C8 to C20)-based retention index for each peak in comparison with the NIST08  and FFNSC 2  mass spectral libraries. The peak area of the total ion chromatogram of each compound, including 2AP (m/z 83), was defined as the relative amount of compound and used for statistical analyses . A partial least squares discriminant analysis (PLS-DA) and variable importance in projection (VIP) were performed using MetaboAnalyst 4.0  Supplementary methods).The volatile profiles of rice panicles from the milky stage were analyzed according to Lee The whole-genome resequencing data for rice accessions are available in NCBI under the BioProjects PRJNA664261 and PRJNA564458.A total of 39,461,811,585 reads were resequenced with a depth of 20.81×, resulting in 24,919,662 SNPs . After rb) confirmed the results of the phylogenetic analysis. The indica accessions clustered with aus, aromatic, and admixture . The japonica group was closely related to the aromatic and wild rice groups in the PCA . The wild rice group showed overlap with the japonica group in both analysis types. Furthermore, we analyzed the population structure using K values ranging from 2 to 10 and found that a K value of 6 was optimal, with clear separation among groups .A phylogenetic tree was derived from the SNPs called from whole-genome resequencing data for the 475 accessions . This coFig. 1BADH2 region (non/ πsyn) ratio was higher than 1, indicating a high level of nucleotide polymorphism . The Tajima’s D value was the more negative in the wild group than in temperate-japonica, indica, and tropical-japonica (d). The fixation index (STF) revealed a very high level of gene flow (low STF) between the wild and admixture groups, with STF = −0.060, compared with higher estimates between the wild and aromatic groups (0.059), wild and aus groups (0.117), wild and tropical-japonica groups (0.245), wild and indica groups (0.327), and wild and temperate-japonica groups (0.580) (e). In addition, an analysis of novel alleles revealed that breeding between the temperate-japonica group and the aromatic group was markedly more frequent than that between the temperate-japonica and aus groups (f).Nucleotide diversity in the a groups a. The Tajaponica d. The fi (0.580) e. In adds groups f.Fig. 2GBADH2 did not experience significant positive selection between O. sativa and O. rufipogon or between O. sativa or O. rufipogon and other plants . Thirty-eight haplotypes were identified based on 26 SNPs within the coding region of BADH2 . The most common haplotype (Hap_1) was found in 384 rice accessions . One of the newly identified haplotypes, Hap_9, was distinguished by a substitution (C/A) at nucleotide position 476 at exon 2 (BADH2-E2-476C > A) in a cultivated accession (RWG-431). Interestingly, all of the 29 other newly identified haplotypes (Hap_10 to Hap_38) were found in wild rice accessions (Table S3). Three of the haplotypes were functional: Hap_16 with a SNP (G/A) at nucleotide position 4460 in exon 10 (BADH2-E10-4460G > A) found in an O. glumaepatula accession (RWG-459), Hap_17 with a SNP (A/T) at nucleotide position 5433 in exon 13 (BADH2-E13-5433A > T) detected in two accessions of O. glumaepatula (RWG-460 and RWG-461), and Hap_26 with a SNP (C/G) at nucleotide position 440 in exon 2 (BADH2-E2-440C > G) found in two accessions of O. meridionalis (RWG-475 and RWG-476) . Of note, more haplotypes were identified in wild rice accessions than in cultivated rice accessions (Table S3). Next, we performed cloning and sequencing of the coding region of BADH2 from accessions with the four novel SNPs and confirmed the SNPs by a sequence alignment against a reference  at 436th amino acid. In Hap_26, aspartic acid became glutamic acid (both polar) at 65th amino acid (a–d).We analyzed the three-dimensional structures of proteins with the four nonsynonymous SNPs and found a substitution in the α-helix active site of accessions, indicating a loss of BADH2 activity . In Hap_ino acid a–d.Fig. BADH2 gene and performed a genome-wise association study of BADH2 expression levels based on RNA-Seq data from a subset of 279 rice accessions. We found that the expression level of Os08g0424500 (BADH2) was regulated by 316 eQTLs with -log10P > 4 . Among these, 201 eQTLs were located on chromosome 8 and 185 eQTLs were within 1 Mbp of the BADH2 region at positions 20379823–20385975, suggesting that cis-SNPs had a greater contribution than trans-SNPS to BADH2 expression (Table S4). The flanking region about 20 kb upstream and about 12 kb downstream of BADH2 had loci with the most significant effects. A total of 63 eQTLs were located on 22 genes, such as dynamin family protein (Os08g0425100), eight hypothetical conserved proteins, cytokinin oxidase/dehydrogenase 10 (Os06g0572300), and carbonic anhydrase endoglucanase 21 (Os08g0424100). The remaining 253 eQTLs were in intergenic regions (Table S4). An eQTL (9_19892312 bp) from chromosome 9 located in intergenic region of two β-glucosidase genes (Os09g0511700 and Os09g0511600) had significant effects on BADH2 expression. Glucosidase enzymes hydrolyze glycosides and release aromatic components with a natural flavor Next, we quantified the expression levels of the −log10P > 4) on chromosomes 2, 7, 8, 10, and 12 . All five pQTLs from chromosome 8 were not within 1 Mbp of the BADH2 region. Chromosome 10:19402186 pQTL had the most significant P-value (4.57), while two pQTLs on chromosome 2 were located within Os02g0184800 and Os02g0184900, of which Os02g0184800 is a conserved hypothetical protein and Os02g0184900 is a cytochrome P450, CYP71D8L (Table S5).We further quantified the level of BADH2 protein expression and performed a genome-wide association study of BADH2 protein levels. We quantified the BADH2 protein level by LC-MS/MS in 64 accessions selected based on weak to strong fragrance for the pQTL analysis. We identified 13 significant pQTLs . We considered sample size ≥ 3, removed ‘N’ genotypes or missing values and selected five most significant SNP markers from eQTL and pQTL analysis. The Wilcoxon test showed that the four markers Chr08_20382857, Chr08_20374951, Chr08_20370280 and Chr08_20396752 had the most highly significant effect on the 2AP phenotype with p < 0.06 (c).We assessed evidence for an association between QTLs and 2AP accumulation using the unpaired two-samples Wilcoxon rank sum test on 2AP-producing accessions (accessions with mutant p < 0.06 c.Table S1). The aceession RWG-431 with a novel allele (badh2-E2-476C > A) showed a 2AP peak and was classified as moderately fragrant (Table S1). Five accessions of wild rice  with nonsynonymous substitutions were rated as slightly fragrant (Table S1).We carried out an aroma test with the 421 cultivated accessions from the core set. Based the panel assessment, samples were divided into four categories: nonfragrant, slightly fragrant, moderately fragrant, and strongly fragrant. In total, 50 accessions were categorized as fragrant, of which 25 accessions were slightly fragrant, 22 were moderately fragrant, and 3 were classified as strongly fragrant . We then identified the compounds that contribute to the variation observed in PLS-DA by extracting volatiles with a high VIP score (>1.0). The VIP score plots showed that 15 compounds were the main discriminants in the PLS-DA model. Among them, 2AP, 5,9-undecadien-2-one 6,10-dimethyl-(Z), benzaldehyde, n-decanal, hexanal, octanal, phenylacetaldehyde, gamma-nonalactone, 1-heptanol, n-hexanol, and oxepane-2,7-dione were significantly more abundant in haplotypes 7 and 6 than in other haplotypes. Haplotypes 7 and 6 were characterized by badh2-E13-5390C > T and an 8 bp deletion in exon 7, respectively. In addition to 2AP, benzaldehyde, n-decanal, hexanal, octanal, phenylacetaldehyde, and n-hexanol are known to contribute to rice aroma (b); among these four volatiles, toluene is known to confer a sweet and pungent aroma To examine the volatile profile variation within plotypes . Only halotypes , indica (-2.20), admixture (-1.54), aus (-1.171), and japonica (1.41) (Table S1). These results indicate that the aromatic, indica, admixture, and aus groups were under positive selection or experienced selective sweeps, while japonica  experienced population bottlenecks or balancing selection. A total of 37 synonymous and nonsynonymous substitutions, including 21 functional SNPs  were found in the BADH2 coding region of 543 accessions . Functional SNPs, badh2-E7-3035A > T, and 8 bp deletion in exon 7 characterized for the fragrant phenotype Tables S6, S8). Fourteen haplotypes were distinguished by 15 novel functional SNPs (14 nonsynonymous SNPs and one deletion) detected in 77 accessions (Table S8). The functional haplotypes identified from the Korean rice collection and 3 K-RGP data will provide important genetic resources for the development of new fragrant rice varieties.High demand for fragrant rice has prompted breeders to identify and characterize betaine aldehyde dehydrogenase homolog BADH1 (Os04g0464200) on Korean rice fragrance by association analyses  Fragrant rice varieties with the same mutant allele or haplotype showed considerable variation in 2AP accumulation, consistent with previous reports BADH2 region and fragrance, genetic variation at the whole-genome level associated with BADH2 gene expression levels, contributing to phenotypic variation, are lacking. In plants, most eQTL and pQTL studies are aimed at building a regulatory network to link genetic networks with phenotypic variation et al. granule-bound starch synthase I (GBSSI), and revealed cis-acting functionally relevant genetic variants influencing the glycaemic index and texture in rice. Most eQTLs detected in our study were located in intergenic regions. These regions function via transcription factor binding interactions and regulatory modules and as barriers in nucleosome positioning and organization, determining DNA accessibility trans-pQTLs, as 13 pQTLs were mapped on a different chromosome, 1 Mbp from the location of BADH2. The trans-pQTLs could be explained by many factors, like post-translational modifications, in addition to gene transcription in the regulation of protein expression GAPDH, TPI and P5CS) in eQTL or pQTL analysis, indicating that the correlations between these genes and 2AP accumulation reported earlier BADH2 expression in 2AP variation in fragrant rice.Despite studies of associations between the -genome resequencing, transcriptomics, and metabolomics analyses of 475 accessions in the Korean World Rice Collection. The novel functional haplotypes identified from the Korean rice collection and 3 K-RGP data provide important genetic resources for the development of new fragrant rice varieties. High-quality SNPs from whole-genome resequencing were used in association analyses with BADH2 transcript levels and protein abundance. An array of eQTLs and pQTLs associated with BADH2 expression and protein accumulation are likely regulators mediating 2AP variation in fragrant rice. Indeed, further studies are needed to validate the candidate eQTLs and their interactions. A multivariate analysis identified 15 volatile compounds along with AP as potential biomarkers for rice fragrance. Our study demonstrates the power of integrating genetic, gene expression, and phenotype variation to gain insights into rice fragrance.We performed wholeThis article does not contain any studies with human or animal subjects.Rungnapa Phitaktansakul: Methodology, Investigation, Validation, Data curation, Writing – original draft. Kyu-Won Kim: Methodology, Investigation, Software, Formal analysis, Visualization, Writing- review & editing. Kyaw Myo Aung: Investigation, Data curation. Thant Zin Maung: Investigation, Data curation. Myeong-Hyeon Min: Investigation, Software, Formal analysis. Aueangporn Somsri: Investigation, Data curation. Wondo Lee: Investigation. Data curation, Resources. Sang-Beom Lee: Investigation. Data curation. Jungrye Nam: Software, Formal analysis. Seung-Hyun Kim: Investigation, Data curation, Methodology. Joohyun Lee: Investigation, Data curation, Methodology. Soon-Wook Kwon: Investigation, Data curation, Methodology. Bhagwat Nawade: Formal analysis, Validation, Writing – review & editing. Sang-Ho Chu: Methodology, Project administration, Resources. Sang-Won Park: Investigation, Data curation, Methodology. Kwon Kyoo Kang: Investigation, Data curation, Methodology. Yoo-Hyun Cho: Investigation, Data curation, Resources. Young-Sang Lee: Investigation, Methodology, Data curation , Visualization. Ill-Min Chung: Conceptualization, Project administration, Fund acquisition , Writing – review & editing. Yong-Jin Park: Conceptualization, Project administration, Supervision, Fund acquisition, Supervision, Validation, Resources, Writing – review & editing.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Very little is known about factors influencing adolescent childbearing despite an upward trend in adolescent childbearing prevalence in Burundi, and its perceived implications on the rapid population growth and ill-health of young mothers and their babies. To adress this gap, this study aimed to examine the prevalence, trends and determinants of adolescent childbearing in Burundi.(BDHS) data were conducted using STATA. Weighted samples of 731 (1987 BDHS), 2359 (2010 BDHS) and 3859 (2016-17BDHS) adolescent girls aged 15–19 years old were used for descriptive and trend analyses. Both bivariable and multivariable two-level logistic regression analyses were performed to identify the main factors associated with adolescent childbearing using only the 2016–17 BDHS data.Secondary analyses of the 1987, 2010 and 2016–17 Burundi Demographic and Health Surveys p <  0.001), adolescent illiteracy , living in poor communities , early marriage , lack of knowledge of any contraceptive methods , and non-use of modern contraceptive methods , p <  0.001) were associated with higher odds of adolescent childbearing. While factors such as living in the richest household index , living in West region  or in South region  were associated with lower odds of adolescent childbearing.The prevalence of adolescent childbearing increased from 5.9% in 1987 to 8.3% in 2016/17. Factors such as adolescent girls aged 18–19 years old (aOR =5.85, 95% CI: 3.54–9.65, Our study found an upward trend in adolescent childbearing prevalence and there were significant variations in the odds of adolescent childbearing by some individual and community-level factors. School-and community-based intervention programs aimed at promoting girls’ education, improving socioeconomic status, knowledge and utilization of contraceptives and prevention of early marriage among adolescent girls is crucial to reduce adolescent childbearing in Burundi. The World Health Organization (WHO) and United Nations entities define an adolescent as an individual aged 10–19 years . AdolescThe Burundian population is characterized by its extreme youth, with 65% under the age of 25 and almost a quarter of this growing population (23%) are adolescents . In BuruSeveral studies were conducted to examine the factors influencing adolescent pregnancy and motherhood in various settings. The results of these studies showed that early marriage or sexual intercourse , 7, 9, iThis study used adolescent women (aged 15–19) data extracted from the three BDHS conducted in 1987 , 2010 2 and 2016The outcome variable of interest in this study is adolescent childbearing, which refers to the sum of the percentage of adolescents aged 15–19 who are already mothers (have had at least a live birth) and the percentage of adolescents who are pregnant with their first child at the time of the interview , 26. ThuBased on a prior literature review, our independent variables were classified into individual-level factors and community-level factors. The individual-level factors include: adolescent’s age, education, household wealth index, working status, religion, access to mass media, age at first marriage, knowledge of any contraceptive methods, and modern contraceptive use. Community-level factors include: place of residence, health regions, community-level education, and community-level poverty. It should be noted that of the four community-level variables, two variables  were created by aggregating individual-level factors  since these two variables are not directly found from the 2016–17 BDHS dataset.Created by combining the following three variables: frequencies of listening to radio, watching TV, and reading newspapers and coded as “yes” if the adolescent was exposed to at least one of the three media and “no” in the opposite case.This variable had eighteen categories corresponding to the eighteen current provinces of Burundi. To reduce its excessive number of categories, it was recoded into five regions such as North Region, Central-East Region, West Region, South Region and Bujumbura Mairie .< 50% of adolescents have at least primary education) and high (communities with ≥50% of adolescents have at least primary education) community-level of adolescent education.Aggregate values measured by the proportion of adolescents with a minimum of primary level education derived from data on an adolescent’s education. Then, it was categorized using national median value to values: low (communities with < 50% of adolescents living in poorest/poorer households) and high (communities with ≥50% of adolescents living in poorest/poorer households) community-level of adolescent poverty.Aggregate values measured by the proportion of adolescents living in households classified as poorest/poorer derived from data on household wealth index. Then, it was categorized using national median value to values: low - Dose Response program . A p-valp-value ≤0.2 in the bivariate analysis were included in the multivariable multilevel logistic regression analysis to assess the net effects of each independent variable on adolescent childbearing after adjusting for potential confounders. The fixed effects were reported in terms of adjusted odds ratios (aOR) with 95% confidence intervals (CI) and p-values. Variables with p-value < 0.05 were declared to be significantly associated with adolescent childbearing in the multivariate analysis.During the BDHS data collection, two-stage stratified cluster sampling procedures were used and therefore the data were hierarchical. To obtain correct estimates in inferential analyses, advanced statistical models such as multilevel modeling that considers independent variables measured at individual- and community-levels should be used to account for the clustering effect/dependency –31. ThusBefore performing these multilevel logistic regression analyses, an empty model was conducted to calculate the extent of variability in adolescent childbearing between clusters (between communities). The existence of this variability was assessed using the Intra-Class correlation Coefficient (ICC) and the Median Odds Ratio (MOR) –32. The The MOR is the Median values of the Odds Ratio of the cluster at high risk and cluster at lower risk of adolescent childbearing when randomly picking two adolescent women from two different clusters , 30 . ItThe deviance (or-2Log likelihood), Akaike Information Criteria (AIC) and Bayesian Information Criterion (BIC) were used to compare the fit to the data of the null model and that of the full model where we favored model with smaller values of these indices , 30, 33.The sociodemographic characteristics of the adolescents included in the three surveys are summarized in Table The percentage of adolescents who had their first sexual intercourse at age ≤ 14 years increased from 0.7% (1987 BDHS) to 2.6% (2016–17 BDHS). Similarly, the percentage of adolescents who had their first birth at age ≤ 17 years increased from 1.7% (1987 BDHS) to 3.3% (2016–17 BDHS). In contrast, the proportion of adolescents who had their first marriage at age ≤ 17 decreased slightly from 4% (1987 BDHS) to 3.8% (2016–17 BDHS). Similarly, 40.1% (1987 BDHS) of adolescents had knowledge of any contraceptive methods compared to 89.9% (2016–17 BDHS). The percentage of adolescents who do not intend to use contraception increased from 17.8% (2010 BHDS) to 24.8% (2016–17 BDHS). On the other hand, there was a reduction in the proportion of adolescents with unmet need for contraception, which decreased from 3.2% (2010 BDHS) to 2.5% (2016–17 BDHS). Regarding fertility preference, 5.8% (2010 BDHS) of adolescents wanted to have another pregnancy compared to 96.5% in the 2016–17 BDHS . Indeed, the prevalence of adolescents who have had at least one live birth increased from 3.2% (95% CI: 2.0–4.7) in 1987 to 6.7% (95% CI: 5.7–7.7) in 2010, and then decreased from 6.7 to 6.1% (95% CI: 5.3–6.8) in 2016/17. The trend analysis shows that there was an increase of 2.9% from 1987 to 2016/17 and this increase was statistically significant . On the other hand, the prevalence of adolescents who were pregnant with their first child increased from 2.7% (95% CI: 1.7–4.2) in 1987 to 2.9% (95% CI: 2.2–3.6) in 2010, and then decreased from 2.9 to 2.2% (95%CI: 1.7–2.7) in 2016/17. The trend analysis shows that there was a decrease of 0.5% from 1987 to 2016/17 but this decrease was not statistically significant .The prevalence and trends of adolescent childbearing were examined in its two components: prevalence and trend of adolescents who have had at least one live birth and prevalence and trend of those who were pregnant with their first child at the time of the survey see Fig. . Thus, tBivariable and multivariable multilevel logistic regression analyses were conducted to identify individual and community-level factors associated with adolescent childbearing by using only the most recent (2016–17) BDHS data. First, an empty model was performed to calculate the extent of variability in adolescent childbearing between clusters by using the ICC and the MOR indicators. The deviance, AIC, and BIC were also used to select the model that best fit the data. The results of bivariable and multivariable analyses, random effect model and model fitness are summarized in Table p <  0.001) lower values of deviance (905.70), AIC (955.71), and BIC (1112.16) compared to those of the empty model. In the bivariable analysis, factors like adolescent’s age, education, working status, household wealth index, religion, access to mass media, age at first marriage, knowledge of any contraceptive methods, modern contraceptive use, health regions and community-level poverty met the minimum criteria (p ≤ 0.2) to be included in the multivariable analysis.According to the findings in Table p <  0.001) of childbearing than those aged 15–17 years. The odds of childbearing among adolescents who had no education was about 4 times higher , and those who had only a primary education was about 2 times higher  than adolescents who had a secondary or high education. The adolescents in the richest household quintile had 48% lower odds  of childbearing compared to those in the poorest household quintile.In the multivariable analysis, only factors such as adolescent’s age, adolescent’s education, household wealth index, age at first marriage, knowledge of any contraceptive methods, modern contraceptive use, health regions, and community-level poverty remained significantly associated with adolescent childbearing. Indeed, adolescents aged 18–19 years had about 6 times higher odds  than those who got married at the age between 18 and 19. Moreover, the adolescents who didn’t have knowledge of any contraceptive methods had about 5 times higher odds  of childbearing than those who had knowledge of any contraceptive methods. Similarly, the odds of childbearing among adolescents who were not using modern contraceptive methods was about 24 times higher , p <  0.001) than those who were using modern contraceptive methods. Also, the odds of childbearing among adolescents living in West, and those in South were about 74% , p = 0.027) and 69%  times lower respectively than those living in Bujumbura Mairie. Finally, the odds of childbearing among adolescents living in high community-level poverty was about 2 times higher  than those living in low community-level poverty.Similarly, the odds of childbearing among adolescents who got married at ≤17 years old was about 9 times higher   and skilOur findings also revealed that household poverty or living in poor communities is associated with higher odds of adolescent childbearing. In the Burundian context, this association could be explained by the fact that Burundian society was highly affected economically by the civil war of 1993–2005 , 37. ConUnexpectedly, Bujumbura Mairie, which is generally considered less poor than other regions and where more youth have access to education , was fouThis study also found that the adolescent early marriage is associated with a higher odd of childbearing. This link between early marriage and higher risk of adolescent childbearing could be justified by the fact that early marriage implies early sexual debut and therefore a major risk of early pregnancy and childbearing , 9, 46. Similarly, our findings showed that both the lack of knowledge of any contraceptive methods and the non-use of modern contraceptive methods were associated with higher odds of adolescent childbearing. The positive influence of good knowledge and use of family planning services in preventing or reducing the rate of unintended pregnancies among adolescent girls has been widely reported in the scientific literature , 42, 46.The prevalence of adolescent childbearing increased from 5.9% in 1987 to 8.3% in 2016/17 although this increase was not statistically significant. There were variations in the odds of adolescent childbearing by some individual and community-level factors. Factors such as late adolescent age, adolescent illiteracy, household poverty or high community-level poverty, early marriage, lack of knowledge of any contraceptive methods, non-use of modern contraceptive methods, and living in Bujumbura Mairie were associated with higher odds of adolescent childbearing. School- and community- based intervention programs aimed at promoting girls’ education and improving socioeconomic status, knowledge and utilization of contraceptives and prevention of early marriage among adolescent girls is crucial to reduce adolescent childbearing in Burundi.


Arachis hypogaea L.) is a grain legume crop that originated from South America and is now grown around the world. Peanut growth habit affects the variety’s adaptability, planting patterns, mechanized harvesting, disease resistance, and yield. The objective of this study was to map the quantitative trait locus (QTL) associated with peanut growth habit-related traits by combining the genome-wide association analysis (GWAS) and bulked segregant analysis sequencing (BSA-seq) methods.Peanut  in 103 accessions of the U.S. mini core collection genotyped using an Affymetrix version 2.0 SNP array. With a total of 12,342 high-quality polymorphic SNPs, the 90 suggestive and significant SNPs associated with lateral branch angle (LBA), main stem height (MSH), lateral branch height (LBL), extent radius (ER), and the index of plant type (IOPT) were identified. These SNPs were distributed among 15 chromosomes. A total of 597 associated candidate genes may have important roles in biological processes, hormone signaling, growth, and development. BSA-seq coupled with specific length amplified fragment sequencing (SLAF-seq) method was used to find the association with LBA, an important trait of the peanut growth habit. A 4.08 Mb genomic region on B05 was associated with LBA. Based on the linkage disequilibrium (LD) decay distance, we narrowed down and confirmed the region within the 160 kb region  on B05. Four candidate genes in this region were involved in plant growth. The expression levels of Araip.E64SW, Araip.YG1LK, and Araip.JJ6RA) and one candidate gene related to PPP family proteins (Araip.YU281) may be involved in plant growth and development in this genome region. The expression analysis revealed that Araip.E64SW was involved in peanut growth habits. These candidate genes will provide molecular targets in marker-assisted selection for peanut growth habits.In this study, the SNP  associated with LBA by GWAS was overlapped with the results in BSA-seq through combined analysis of GWAS and BSA-seq. Based on LD decay distance, the genome range related to LBA on B05 was shortened to 144,193,467–144,513,467. Three candidate genes related to F-box family proteins (The online version contains supplementary material available at 10.1186/s12864-022-08640-3. Arachis hypogaea L.) is a grain legume crop that originated from South America and is grown around the world    , 70 and  = 4.09] , 71. The = 4.09] .2 populations (35 with prostrate growth habit and 35 with erect growth habit), which were used for BSA-seq [1 pool from the 20 ‘Jihua 5’ plants, the P2 pool from the 20 ‘M130’ plants, the prostrate pool (P-pool) from the 35 extreme prostrate plants, and the erect pool (E-pool) from the 35 extremely erect plants of the F2 generation. DNA from these four pools was digested to completion with HaeIII and RsaI . A single-nucleotide A overhang was added to the digested fragments with Klenow Fragment (3′- 5′ exo-)  and dATP at 37℃. The duplex Tag-labeled sequencing adapters  were ligated to the A-tailed DNA with T4 DNA ligase. Polymerase chain reaction (PCR) was performed using diluted shearing-ligation DNA samples, dNTP, Q5® High-Fidelity DNA Polymerase, and PCR primers. The PCR products were then purified using Agencourt AMPure XP beads . Fragments ranging from 300 to 500 base pairs (with barcodes and adaptors) in size were excised and purified using a QIAquick gel extraction kit . Gel-purified products were then diluted. Paired-end sequencing with read lengths of 125 bp was performed using an Illumina HiSeq 2500 system  according to the manufacturer’s recommendations at Beijing Biomarker Technologies Corporation (http://www.biomarker.com.cn).Genomic DNA was extracted using the modified CTAB method from fresh leaves of the ‘Jihua 5’, ‘M130’, and F BSA-seq . Four DNA. duranensis and A. ipaensis genome sequence using SOAP software [The barcodes and the terminal 5-bp positions were trimmed from each high-quality read, and clean reads from the same sample were mapped onto the software . SNP andsoftware . To discsoftware . The SNPR/(PR + MR), SNP-index(E) = ME/(PE + ME), Δ(SNP-index) = SNP-index(R) – SNP-index(E), where MR is the depth of the R population derived from M, and PR is the depth of the R population derived from P; ME indicates the depth of the E population derived from M, and PE indicates the depth of the E population derived from P.SNP-index(P) = MA. duranensis and A. ipaensis, were downloaded from the PeanutBase (https://peanutbase.org/home).Based on LD decay, the predicted genes around the suggestive and significant SNPs within the 160 kb and annotations of diploid ancestors of cultivated peanut, Araip.E64SW, was selected to perform the quantitative real-time PCR (qRT-PCR) analysis. The ‘Jihua 5’ (erect) and ‘M130’ (prostrate) were used for this study. Fresh first pair of lateral branch were collected at 9th, 19th, 29th, and 39th day after planting. The procedure of total RNA extraction, cDNA synthesis, qRT-RCR amplification, and candidate genes expression analysis were used as previously described [To validate the expression levels of candidate genes between prostrate and erect accessions, the identified candidate gene, escribed , in whicAdditional file 1: Analysis of variance for five traits in U.S. mini-core collectionunder two environments.Additional file 2: Correlation analysis between growth habit-related traits in two environment. A. Correlation analysis for five traits in Qingyuan satation. B. Correlation analysis for five traits in Dawson. LBA, Lateral Branch Angle; MSH, Main Stem Height; LBL, Lateral Branch Length; ER, Extent Radius; IOPT, the Index of Plant type. ** representing significance at P < 0.01 level (two-tailed). *representing significance at P < 0.05 level(two-tailed). Additional file 3: One hundred and three genotypes mainly coming from the peanut mini-core collection used for GWAS analysis related with growth habit-related traits. Additional file 4: The distribution frequency of each subgroup within each botanical variety (A), and each botanical variety within each subpopulation (B).Additional file 5: The value of genome-wide average LD decay. The x-axis indicates the inter-marker genetic distance and the y-axis indicates the r2 value.Additional file 6: The result of marker SNP index associated with LBA. The x-axis indicates the position of the chromosome and the y-axis indicates the value of the ΔSNP index. Black lines indicated that the result of all Della SNP index after fitting. The dotted line showed that the threshold value of the ΔSNP index. Additional file 7: Manhattan plots along with QQ plots showing the GWAS for growth habit-related traits by GLM in Qingyuan station. The dashed horizontal line represents the significance threshold (P < 1×12342−1) and suggestive line (P< 1.0 × 10−3).Additional file 8: Manhattan plots along with QQ plots showing the GWAS for growth habit-related traits by GLM in Dawson station. The dashed horizontal line represents the significance threshold (P < 1×12342−1) and suggestive line (P< 1.0 × 10−3).Additional file 9: The summary of SNPs associated with growthhabit-related traits.Additional file 10: List of 597 candidate genes with growth habit-related traits in peanuts.Additional file 11: List of  transcription factors mediating plant growth and developmental processes.Additional file 12: Primers used for qRT-PCR.


Panthera leo) have improved our insight into the distribution of genetic variation, as well as a revised taxonomy which now recognizes a northern (Panthera leo leo) and a southern (Panthera leo melanochaita) subspecies. However, existing whole range phylogeographic studies on lions either consist of very limited numbers of samples, or are focused on mitochondrial DNA and/or a limited set of microsatellites. The geographic extent of genetic lineages and their phylogenetic relationships remain uncertain, clouded by massive sampling gaps, sex-biased dispersal and incomplete lineage sorting.Previous phylogeographic studies of the lion . Phylogenetic analyses revealed the same basal split between northern and southern populations, as well as four population clusters on a more local scale. Further, we designed a SNP panel, including 125 autosomal and 14 mitochondrial SNPs, which was tested on >200 lions from across their range. Results allow us to assign individuals to one of these four major clades  and delineate these clades in more detail.The results presented here, particularly the validated SNP panel, have important applications, not only for studying populations on a local geographic scale, but also for tracing samples of unknown origin for forensic purposes, and for guiding conservation management of ex situ populations. Thus, these genomic resources not only contribute to our understanding of the evolutionary history of the lion, but may also play a crucial role in conservation efforts aimed at protecting the species in its full diversity.The online version contains supplementary material available at 10.1186/s12864-022-08510-y. Panthera leo). As a result, both the evolutionary history of a species and population histories can be reconstructed based on vastly expanded datasets [Recent developments in next generation sequencing (NGS) techniques allow for the application of massive parallel sequencing to non-model organisms , 2, suchdatasets , 4. In adatasets , 6. Thisdatasets –11. The ‘Critically Endangered’ , 13. HavP. leo) has been the subject of several phylogeographic studies which have provided insights into the evolution and distribution of genetic variation in the African populations (formerly subspecies P. leo leo) and its connection to the Indian population (formerly subspecies P. leo persica), located in and around the Gir National Park, Gujarat state, India. Although this Africa-Asia split has long been used to inform management (e.g. there is a separate studbook for Asiatic lions in zoos), this taxonomic distinction has since been overhauled. Phylogeographic studies played an important part in this, by providing improved understanding of the evolutionary history and relationships between populations. These studies included data from mitochondrial DNA (mtDNA) [Ple) [The lion ( (mtDNA) –23, auto (mtDNA) , 22–24 aA) [Ple) . StudiesA) [Ple) , 18, 21.P. leo leo) and a southern subspecies (P. leo melanochaita) [Two studies have included Single Nucleotide Polymorphisms, although both have included only two sampling localities in West and Central Africa and exact sampling localities were often unknown, hence calling for improved sampling in the region , 24. Theochaita) .Ple prevalence is geographically restricted. Additional data from genome-wide markers, and covering more lion populations, are necessary to overcome these shortcomings. This will improve our understanding of the spatial distribution of variation in the lion, as well as help guide future conservation efforts that seek to preserve the species’ genetic diversity. As patterns of instra-specific variation are often shared across co-distributed taxa [However, each of the approaches used in the studies mentioned above have limitations. Although mtDNA is regarded as a useful genetic marker for gaining insight into phylogeographic patterns, partly because of its shorter coalescence time compared to nuclear markers, it may lead to ‘over-splitting’, reflecting fully coalesced groups based on mtDNA data, but incomplete lineage sorting of nuclear DNA (nuDNA) alleles. Moreover, mtDNA cannot identify admixture and represents only a single locus, potentially misrepresenting phylogenetic relationships due to the stochastic nature of the coalescent , 27. In ted taxa , 30, a dN = 125) of the discovered SNPs, which was then used for genotyping > 200 samples from 14 countries, representing almost the entire current distribution of the lion. This resolves phylogeographic breaks at a finer spatial resolution, and may serve as a reference dataset for future studies. Finally, we discuss the applications and future directions of high-throughput genotyping for wildlife research and conservation with recommendations on how they may contribute to future studies on conservation genomics.Here, we describe the discovery and phylogeographic analysis of genome-wide SNPs based on whole genomes and complete mitogenomes of ten lions, providing an overview of the intraspecific genomic variation. We further developed a SNP panel consisting of a subset  were retained for subsequent alignment , India) and the southern ones , Namibia) . STRUCTURE plots including the entire lion dataset (N = 211) are available in Supplemental Figure A total of 211 samples from across the entire range of the lion were genotyped for 125 autosomal SNPs and 14 mitochondrial SNPs. Missing data for the autosomal SNPs ranged from to 0 to 115 with a median value of 11; for the mitochondrial data, these ranged from 0 to 10 with a median value of 0 , presenBased on the ten whole genomes, we generated a SNP panel of 125 autosomal and 14 mitochondrial SNPs which allows cost-effective genotyping of larger numbers of samples.P. leo leo), East Africa, Southern Africa   Fig. C. Main rta) Fig. C, and wata) Fig. . The varta) Fig. C. After ta) Fig. C. The in. 2005; Barnett et al. 2006a; Barnett et al. 2006b; Bertola et al. 2011) and gene flow may be reduced in that region, co-occurrence of strongly diverged haplotypes and admixture detected in microsatellite and SNP data in Ethiopia indicates that the Rift valley does not represent an impenetrable barrier for lion dispersal [In order to put the phylogenetic patterns of the lion into an evolutionary perspective, it is worthwhile to explore current and historical barriers to lion dispersal. Paleoclimatic data show that cyclical contraction and expansion of vegetation zones, e.g. rain forest and desert, may have acted as temporal barriers to lion dispersal . The disispersal .., https://onlinelibrary.wiley.com/doi/full/10.1111/eva.13318). Human-mediated movement of wildlife, regarding lions and other species, only rarely take genetic considerations into account. Providing a baseline of the distribution of genetic variation and a tool to screen potential candidates for translocations, can help support these initiatives with scientific data. To assess the suitability of this SNP panel on a smaller geographic scale, we are currently genotyping > 200 lion samples from across Kenya. The aim is to provide a robust tool for wildlife managers seeking genetic support for management decisions. Secondly, it enables tracing of lion samples of unknown origin, such as material confiscated from illegal trade chains or through anti-poaching efforts. Illegal trade in wildlife products is currently estimated to be the fifth largest illegal industry globally, and a major concern for conservationists [The design of a SNP panel and a reference dataset of > 200 lions from 14 lion range states has important applications for wildlife research and conservation. First, it allows us to distinguish phylogeographic breaks and overlap between lineages which can be used to study the evolutionary history of the species in more detail, e.g. at the national level. This may have important conservation implications. For example, genomic data can be used to prioritize lion management at the population level, including providing key information for decisions regarding translocations of individuals within and among lion range states. Based on CITES documentation, > 1000 live lions have been imported into lion range states since the 1980s with (future) potential to interbreed with resident lions , obtained from Planckendael Zoo , was included as an outgroup. The Amur tiger  genome (816 scaffolds: Genbank Accession numbers KE721553-KE722368) [Blood or tissue samples of ten lions, representing the main phylogeographic groups as identified in previous studies , 19–21 E722368) , supplem68 , suppAfter mapping of the reads to the reference genome  minimum coverage of 20 for all lion samples combined, across 50 bp upstream and downstream of the SNP position, 2) maximum of one variable position in these 50 bp flanking regions, 3) high quality mapping of the flanking regions as identified by eye using IGV Genome Browser , 80, 4) N = 211) and a dataset excluding samples with > 25% missing data (N = 171). MtDNA SNPs were used to assign a specific haplotype to each individual, matching with previously described lineages  and the reduced dataset excluding samples with > 25% missing data (N = 177). Because the PCA is disproportionately affected by the very low genetic diversity of Indian lions, we repeatedthe analysis excluding this population, thereby allowing a more detailed picture from the African populations to emerge.A PCA was performed in Genalex , using tPatterns of connective zones and barriers were investigated using Estimated Effective Migration Surfaces (EEMS) , 93 for The level of observed heterozygosity was assessed for each individual for which the whole genome had been sequenced, expressed as the proportion of heterozygote positions compared to the total number of SNPs, excluding positions with ambiguous nucleotides . This was done on the individual level for all SNPs, and on the populations level for all SNP panel SNPs which were successfully called in five or more individuals from the same population. Results were then compared to known levels of heterozygosity based on earlier studies using microsatellite data from the same populations , 20.Additional file 1:Supplemental Information S1. Details on laboratory protocols, sequencing, mapping, SNP calling and quality control .Additional file 2:Supplemental Information S2. Validation of phylogenetic inferences and populations structure from whole genome sequencing and SNP panel data .Additional file 3:Supplemental Table S1. Lion and leopard samples included for whole genome sequencing and results from sequencing runs. Supplemental Table S2. Number of discovered lion SNPs per chromosome and estimated chromosome size in tiger ). Supplemental Table S3. Coordinates of autosomal and mitochondrial SNPs in lion SNP panel. Supplemental Table S4. Genotype calls for all individuals included for genotyping with the SNP panel . Supplemental Table S5. Assignment values from SNPpanel SNPs. STRUCTURE analysis was done on 125 nuclear SNPs with the complete dataset (N = 211) and a reduced dataset excluding samples with >25% missing data . Haplogroups were infered from callings of 14 mtDNA SNPs which were included in the SNP panel  for reference of haplogroups). If 40% or more of the mtDNA SNPs failed, the field for the haplogroup is marked with '?'. Haplogroups marked with '?' after the assignment indicate that 60%-85% of the included mtDNA SNPs were succesfully called. Supplemental Table S6. Observed heterozygosity for lions, based on SNP data from either whole genome sequencing  or based on results of the SNP panel (populations), and comparison with observed heterozygosity based on microsatellite data (populations). Shading indicates the ranking from low heterozygosity (red) to high heterozygosity (green). It must be noted that due to low coverage in the sample from Benin, heterozygosity may be underestimated. For the SNP panel, only results from the same samples which had previously been analyzed for the microsatellites were included. The left scatter plot shows the correlation between observed heterozygosity called based on all SNPs and based on SNPs covered in 5 or more lions. The right scatter plot shows the correlation between observed heterozygosity based on all SNPs and based on the SNP panel data (grey), and observed heterozygosity based on previously published microsatellite data (red). The bar plot shows observed heterozygosity for each dataset and per individual/population. Supplemental Table S7. Genbank entries to filter bacterial reads in contaminated lion samples Benin and RSA. Supplemental Table S8. Tiger scaffolds from Cho et al. (2013) identified as potentially of Y-chromosomal origin.


NUDT9 altered the structure and affinity of the binding pocket. The results suggested that SNP11 is an SNP related to goose broodiness. In the future, we will use the cage breeding method to sample the same half-sib families to accurately identify SNP markers of growth and reproductive traits.The broodiness traits of domestic geese are a bottleneck that prevents the rapid development of the goose industry. To reduce the broodiness of the Zhedong goose and thus improve it, this study hybridized it with the Zi goose, which has almost no broody behavior. Genome resequencing was performed for the purebred Zhedong goose, as well as the F2 and F3 hybrids. The results showed that the F1 hybrids displayed significant heterosis in growth traits, and their body weight was significantly greater than those of the other groups. The F2 hybrids showed significant heterosis in egg-laying traits, and the number of eggs laid was significantly greater than those of the other groups. A total of 7,979,421 single-nucleotide polymorphisms (SNPs) were obtained, and three SNPs were screened. Molecular docking results showed that SNP11 located in the gene  China’s domestic geese output ranks first in the world, suggesting its great potential for the development of the goose industry. However, the broodiness of domesticated geese has always been a key problem limiting the development of goose industrialization . Broody growth differentiation factor 9 (GDF9) gene is homozygous AA in wild type ewe populations, and the mutant heterozygous Aa can increase the litter size. Interestingly, the homozygous SNP mutation aa type leads to infertility [GDF9, and thus we implemented whole genome resequencing to identify the SNP markers of growth and reproduction traits in the Zhedong and Zi crossbred geese. The purposes of this study were to verify whether there were SNPs that affected goose reproductive traits and to provide guidance for the commercial breeding of hybrid geese. These results can provide a reference for goose breeding research.One SNP of the ertility . Modern ertility ,16, cowsertility , sheep [ertility , goats [ertility , and layertility . SNPs reertility ,12. CurrThe study was approved by the Committee for Animal Welfare of the Institute of Animal Husbandry of Heilongjiang Academy of Agricultural Sciences, China (No. NKY-20140506), Ministry of Science and Technology.The experimental geese were sourced from two farms; one was located at the Qiqihar Animal Husbandry and Veterinary Research Institute , and the other was at the Shuangyashan Friendship Farm . The two localities are very close; the monthly average local temperature ranges from −1 °C to 10 °C, and the minimum average temperature at night in January is −14 °C. The highest daytime average temperature in June is 37 °C. A total of 1200 Zhedong geese and 600 offspring were used as the experimental base population. In 2016 and 2019, 600 Zhedong geese were introduced from Xiangshan City, Zhejiang Province, to Heilongjiang Province. After at least one year of local domestication, they were used for hybridization experiments. The local domestication process gradually changed from the feed formula in Zhejiang Province to the feed formula used in this study. All geese were raised under natural light and temperature conditions. The geese engaged in free activities in the grounds during the day and entered their house at night. They had free access to feed and water daily. The male-to-female ratio was 1:4–5. The method of hybridization is shown in The egg-laying data included a random selection of experimental mother geese from a large population. The ratio of males to females was 1:4–5, matching the male geese to form a test group. The number of eggs laid was recorded daily. The first egg was laid and recorded on 21 February 2017; thereafter February 21 each year was assigned as week 0. The bodyweight data were randomly collected from newly hatched goslings that were then marked on the neck with a dye representing a number. For example, red, yellow, blue, and green from top to bottom represented 0001. The body weight of each goose was tracked, measured, and recorded every two weeks until 12 weeks. The body weight was recorded when the goose entered and exited the goose house in order to minimize stress.Blood samples were collected for DNA extraction during the non-egg-laying stage. Five geese were randomly selected in each group to avoid as much stress as possible. Vacuum tubes containing ethylenediaminetetraacetic acid were used to collect wing blood. The blood samples were sent to Harbin Botai Gene Company for testing, and the quantity of clean data of each sample was not less than 11 GB, ensuring a Q30 value ≥ 80% .We used SnpEff to annotate the mutated sites and to determine the corresponding gene information, synonymous and non-synonymous mutations, and the impact on amino acids of the mutated sites. This study applied the classical genome-wide association study (GWAS) analysis method but failed. There were four methods: (1) ADMIXTURE (Version 1.3.0) was used to analyze the population structure; (2) The genetic relationships analysis and principal component analysis of SNPs were performed using TASSEL 5.0; (3) The PopLDdecay software(Version 3.41) was used to analyze linkage disequilibrium (LD) decay; (4) The SNP sequences were employed using Mega X to construct an evolutionary tree via the neighbor-joining (NJ) method. The 15 samples could not be clustered, and thus the analysis failed.Finally, the filtered SNPs were further screened based on the background of the project. For the screening principle, the parents were homozygous, and the two groups of offspring samples were either the heterozygous or homozygous Zhedong goose genotype (not the homozygous Zi goose genotype). To be specific, if the primary generation was homozygous for Zhedong goose site AA, then AB and AA could appear in the F2 and F3 generations. However, BB was not possible. See NUP37 (XP_013036270.1) and NUDT9 (XP_013045959.1) were found. These two candidate genes include SNP3, SNP4 and SNP11. For the candidate genes, we searched for protein changes caused by SNP transformation. The 3D structures of the target proteins containing SNPs were produced via SWISS-MODEL (https://swissmodel.expasy.org). The NUDT9 protein and adenosine diphosphate ribose (ADPR) were combined. AutoDock Vina software was used for docking analysis, and the results were visualized using Discovery Studio.NCBI was searched for reports of candidate SNP genes in recent years. Candidate genes for The egg-laying data are presented in The sequences obtained in this project were compared with the reference genome, and 7,979,421 SNPs were obtained. The SNPs obtained were filtered using VCF tools, and finally 68,376 SNPs were obtained. To further narrow the scope, it was necessary to link the SNP data with the grouped experimental data. (1) See p-values. These included DNA integration , the DNA metabolic process , and the nitrogen compound metabolic process  terms. The enrichment analysis results of level two GO terms are shown in Based on the background of this project, it was assumed that the parents were homozygous, and the two groups of offspring samples were heterozygous or homozygous for Zhedong goose sites. The offspring were grouped according to original purebred Zhedong geese, Zhedong and Zi crossbred geese, and F2, and F3 hybrid groups. A total of 3,370 candidate SNP loci were obtained. The GO enrichment analysis results are shown in The results of SNP3 and SNP4 affecting the NUP37 protein are shown in The broodiness trait of domestic geese is a bottleneck preventing the rapid development of the goose industry . The advBased on the breeding season, geese of different varieties can be divided into three types: type one and type two are long sunshine geese that inhabit the high-latitude or middle-latitude 30–40° N) temperate zone. Type three refers to the short sunshine geese distributed in subtropical regions 0–40° N t. A transThe egg production of domestic geese is a quantitative trait regulated by multiple genes and is affected by environmental factors . The eggThe broodiness of the Zhedong goose is strong, and as such the goose is an ideal animal model for studying the mechanism of broody behavior ,4,26. InBefore 2019, geese were generally raised on the ground . This reNUP37 is a biomarker in breast cancer [NUP37 regulates the YAP/TEAD signaling pathway [t cancer . NUP37 r pathway . The Yes pathway , cell di pathway , and regNUDT9, a gene that can regulate the menstrual cycle [NUDT9 plays a role in breast cancer [The newly discovered SNP11 in this study is located in al cycle . This sut cancer . The NUDt cancer . We simuNUDT9, is an SNP related to goose breeding. Moving from Zhejiang Province to Heilongjiang Province caused the egg-laying performance of the Zhedong goose to change under different lighting and temperature conditions. In the future, we will use the cage breeding method to sample the same half-sib family to accurately identify the SNP markers of growth and reproduction traits.In terms of growth traits, the F1 hybrids of Zhedong and Zi crossbred geese showed significant heterosis, and the body weight was significantly greater than in other groups. The F2 hybrids showed significant heterosis in egg-laying traits, and the number of eggs laid was significantly greater than those of the other groups. SNP11, located in