ROH) to that of pedigree-based F (FPED) coefficients within the breed.Since the 1950s, the Norwegian–Swedish Coldblooded trotter (NSCT) has been intensively selected for harness racing performance. As a result, the racing performance of the NSCT has improved remarkably; however, this improved racing performance has also been accompanied by a gradual increase in inbreeding level. Inbreeding in NSCT has historically been monitored by using traditional methods that are based on pedigree analysis, but with recent advancements in genomics, the NSCT industry has shown interest in adopting molecular approaches for the selection and maintenance of this breed. Consequently, the aims of the current study were to estimate genomic-based inbreeding coefficients, i.e. the proportion of runs of homozygosity (ROH), for a sample of NSCT individuals using high-density genotyping array data, and subsequently to compare the resulting rate of genomic-based F (FROH ranged from 1.78 to 13.95%. Correlations between FROH and FPED were significant (P < 0.001) and ranged from 0.27 to 0.56, with FPED and FROH from 2000 to 2009 increasing by 1.48 and 3.15%, respectively. Comparisons of ROH between individuals yielded 1403 regions that were present in at least 95% of the sampled horses. The average percentage of a single chromosome covered in ROH ranged from 9.84 to 18.82% with chromosome 31 and 18 showing, respectively, the largest and smallest amount of homozygosity.A total of 566 raced NSCT were available for analyses. Average FGenomic inbreeding coefficients were higher than pedigree inbreeding coefficients with both methods showing a gradual increase in inbreeding level in the NSCT breed between 2000 and 2009. Opportunities exist for the NSCT industry to develop programs that provide breeders with easily interpretable feedback on regions of the genome that are suboptimal from the perspective of genetic merit or that are sensitive to inbreeding within the population. The use of molecular data to identify genomic regions that may contribute to inbreeding depression in the NSCT will likely prove to be a valuable tool for the preservation of its genetic diversity in the long term.The online version of this article (10.1186/s12711-019-0465-7) contains supplementary material, which is available to authorized users. Since tROH) for a sample of NSCT using a high-density genotyping array and to compare the rate of FROH to that of classical pedigree-based F (FPED) within the breed. Common ROH within the breed were also assessed for overlaps with previously characterized quantitative trait loci (QTL) for health and reproduction traits in the horse, thus providing a first look at genomic regions and traits that may warrant industry intervention in the future.Consequently, the NSCT industry has actively supported a shift towards using genomic data for F calculations in the breed, thus allowing for diversity across the entire genome as well as at specific regions to be evaluated and monitored, and providing not only a more accurate assessment of inbreeding within the breed, but also a much more detailed assessment. As such, the aims of the current study were to provide genomic-based F coefficient estimates . The pedigree consisted of 112,195 individuals with a median pedigree depth of 15 generations.In total, 566 individuals born between 1 January 2000 and 31 December 2009 were selected for this study based on the following criteria: (1) each horse had to have participated in at least one race during its lifetime; this restriction was implemented to allow for a broader use of the data in future analyses that will explore racing performance traits within the breed; (2) hair and/or blood samples had to be readily accessible from the pedigree registration authorities in either Norway  or Sweden ; and (3) a sufficient amount of sample material had to be available to ensure high DNA quality standards.DNA was extracted from hair roots using a standard procedure of hair preparation. Briefly, 186 μL of Chelex 100 Resin  and 14 μL of proteinase K  were added to each sample. This mix was incubated at 56 °C for 2 h and proteinase K was inactivated for 10 min at 95 °C. For DNA preparation from blood, DNA from 350-μL blood samples were extracted by using the Qiasymphony instrument and the Qiasymphony DSP DNA mini kit .p > 0.0001), and genotyping rate (> 0.95) with data that did not conform to these criteria and individuals with missing genotypes (> 15%) being removed. Descriptive data for the sample of horses used in the analyses are in Table Prior to quality control (QC), the dataset consisted of individuals that were genotyped with the 670K Axiom equine genotyping array (n = 473) and the 670K+ Axiom equine genotyping array (n = 93). Data from the two arrays were subsequently merged based on SNP name, chromosome number and position, which yielded a combined SNP dataset of 611,888 SNPs for 566 horses (SNPs located on chromosomes X and Y were excluded during this process). Then, QC was performed with the PLINK v1.07 software. SNPs were screened based on minor allele frequency (MAF > 0.01), Hardy–Weinberg equilibrium (PED) were calculated based on the complete pedigree of the breed using the Contribution, Inbreeding (F), Coancestry v1.0 software, which uses a modified algorithm of Sargolzaei et al. [PED calculations.Inbreeding coefficients  were estimated for each threshold setting by dividing the summed length of all ROH  by the length of the genome  covered with SNPs. Pearson correlation coefficients between FPED and all FROH were determined using the statistical software R [ROH were also performed.Inconsistency between ROH-defining criteria in various industries and breeds has been shown to convolute the comparison of studies over time and across population samples –6, 17–22ftware R . Paired PED and FROH. These scripts were used to determine which regions of the genome were shared in at least 95% of individuals in the sample [PED and FROH since not only did it allow the capture of longer ROH that would subsequently be more beneficial when evaluating previously associated QTL, but it also yielded a more conservative estimate of inbreeding within the breed (i.e. an estimate that was more likely to be skewed upwards than downwards). Homozygous regions that were present in at least 95% of the sampled NSCT were then compared to previously reported QTL for reproduction and health traits in the horse  using batabase; .PED are in Table PED and FROH of Norwegian born horses were higher than those of Swedish born horses, although the highest FPED estimate was found for a Swedish born horse. Median FPED and FROH for the entire cohort of sampled horses, stratified by year, are shown in Fig. PED and FROH estimates, respectively. Average FROH (%) ranged from 1.78 to 13.95%  and ranged from 0.27 to 0.56  was obtained from the analysis of the pruned data with the same threshold settings except that the minimum number SNPs that a run must have to be called as a ROH was set to 50 SNPs.Whereas the above settings resulted in the highest correlation between Ft test between all FROH yielded significant differences for most of the FROH threshold settings with only 35 (1.49%) comparisons resulting in no significant difference  1).By applying the threshold settings that resulted in the highest correlation between F2% Table  Column D2% Table . The lenROH in the NSCT population tended to be slightly higher than the FPED estimates [PED and FROH, and drastically altered the ability to capture longer ROH. Since size and frequency of ROH provide evidence for relatedness within and between populations, as well as details on distant and recent ancestry, the ability to capture consistently long ROH is essential for the integration of genomic data into breeding evaluation and preservation protocols for the NSCT breed [As expected based on previous studies in other species, the realized Fstimates , 17, 26.CT breed –6, 18–20ROH in the NSCT breed appears to have steadily increased between 2000 and 2009. While the overall inbreeding level within the breed is slightly underestimated based on classical metrics, the upward trend of inbreeding level revealed by the FPED calculations is clearly supported by the FROH estimates and likely warrants additional exploration by the NSCT breeding industry—particularly in relation to the difference in inbreeding levels between Norwegian born horses and Swedish born horses  for breeders and owners to use as part of their criteria for determining sire/dam pairing [Although NSCT is not currently considered an at risk breed, it represents unique Norwegian and Swedish genetic resources and is present on the department of agriculture’s list of horse breeds that should be preserved . The NSC pairing , 29. WhiAs with other species and breeds, opportunities exist for the NSCT industry to develop software programs that provide breeders with easily interpretable feedback on regions of the genome that are suboptimal from the perspective of genetic merit or that are sensitive to inbreeding within the population. Overall, 1403 common ROH regions were identified within the sample of raced horses used here. There were few overlaps with known QTL for health and reproduction traits, which indicates that perhaps only a small percentage of these regions may warrant concern, at this time . WhereasIncreased inbreeding within a population also tends to impact fertility traits unfavorably; however, only one of the common ROH regions overlapped with a known QTL related to reproduction , which sPED and FROH were calculated for a sample of raced NSCT with FROH resulting in higher inbreeding coefficients, and both methods showing a gradual increase in inbreeding between 2000 and 2009. Stricter ROH threshold criteria regarding the number of heterozygous SNPs and missing calls allowed in a ROH significantly reduced correlations between FPED and FROH and noticeably altered the chances of capturing long ROH. While the exact reasons behind this decrease in correlations are not known with certainty, the established associations between classical F estimates and recent inbreeding within a pedigree (characterized by long ROH) in other species provide some insight. Since retaining genetic variation is important to allow populations to adapt to changing environments, the integration of genomic data into their EBV and the use of molecular data to identify both genomic regions contributing to inbreeding depression and pedigree errors will likely prove invaluable as the NSCT industry moves forward in its conservation and selection efforts.In the current study, both FAdditional file 1: Table S1. Threshold settings used to define runs of homozygosity in PLINK and the corresponding average FROH for a sample of raced Norwegian-Swedish Coldblooded trotters born between 1 January 2000 and 31 December 2009.Additional file 2: Table S2. Correlation matrix between FPED and all FROH estimates.Additional file 3: Table S3. Results of the paired t-test  between FPED and all FROH estimates.Additional file 4: Figure S1. Histograms for run of homozygosity (ROH) lengths based on four different threshold combinations in PLINK v 1.07. A = 50snp_500kb _100snp_0_0; B = 50snp_500kb _100snp_0_2; C = 50snp_500kb _100snp_1_2; D = 50snp_500kb _100snp_1_5 (FORMAT: “sliding window size”_”minimum length (kb) to be called as homozygous”_”minimum number of SNPs to be called as homozygous”_”number of heterozygotes allowed”_”number of missing calls allowed”).Additional file 5: Figure S2. Histograms for run of homozygosity (ROH) lengths based on varying window size thresholds in PLINK v 1.07. A = 50snp_500kb _15snp_0_1; B = 50snp_500kb _50snp_0_1; C = 500snp_500kb _50snp_0_1; D = pruned_50snp_500kb _50snp_0_1 (FORMAT: “sliding window size”_”minimum length (kb) for a run to be called as a ROH”_”minimum number of SNPs for a run to be called as a ROH”_”number of heterozygous SNPs allowed in a ROH”_”number of missing calls allowed in a ROH”).Additional file 6: Table S4. Homozygous regions of the genome that are shared by at least 95% of the sample of Norwegian-Swedish Coldblooded Trotters (n = 566).


Citrullus lanatus) is one of the most important food crops in the Cucurbitaceae family. Diversification after domestication has led cultivated watermelons to exhibit diverse fruit flesh colors, including red, yellow, and orange. Recently, there has been increased interest in red-fleshed watermelons because they contain the antioxidant cis-isomeric lycopene. We performed whole genome resequencing (WGRS) of 24 watermelons with different flesh colors to identify single-nucleotide polymorphisms (SNPs) related to high lycopene content. The resequencing data revealed 203,894–279,412 SNPs from read mapping between inbred lines and the 97103 reference genome. In total, 295,065 filtered SNPs were identified, which had an average polymorphism information content of 0.297. Most of these SNPs were intergenic (90.1%) and possessed a transversion (Tv) rate of 31.64%. Overall, 2,369 SNPs were chosen at 0.5 Mb physical intervals to analyze genetic diversity across the 24 inbred lines. A neighbor-joining dendrogram and principal coordinate analysis (PCA) based on the 2,369 SNPs revealed that the 24 inbred lines could be grouped into high and low lycopene-type watermelons. In addition, we analyzed SNPs that could discriminate high lycopene content, red-fleshed watermelon from low lycopene, yellow or orange watermelon inbred lines. For validation, 19 SNPs (designated as WMHL1–19) were chosen randomly, and cleavage amplified polymorphic sequence (CAPS) markers were designed. Genotyping of the above 24 lines and 12 additional commercial cultivars using WMHL1–19 CAPS markers resulted in match rates of over 0.92 for most validated markers in correlation with the flesh color phenotypes. Our results provide valuable genomic information regarding the high lycopene content phenotype of red-fleshed cultivated watermelons, and the identified SNPs will be useful for the development of molecular markers in the marker-assisted breeding of watermelons with high lycopene content.Cultivated watermelon ( Citrullus lanatus) is one of the most important horticultural crops in the Cucurbitaceae family. Watermelon originated from north Africa more than 4000 years ago and is now grown throughout the world for its fleshy and sweet fruit [Watermelon , it is now possible to identify potential molecular markers from the informed selection of genes.During the 1980s , marker-Guo et al.  developeThe carotenoid biosynthesis pathway and its involvement in fruit color has been well characterized in tomato and citrus , 16. Carc and C (dominant to c), respectively. Henderson [Y (dominant to y and yo), y (yellow), and yo (dominant to y), respectively. Furthermore, an interaction between the C and I loci was found to result in red flesh, where the C locus is either allele C or c, which is inhibited when the I locus is in the homozygous recessive allelic (i) form [lycopene beta-cyclase (LCYB) gene, and its expression between red and canary yellow watermelon, and developed the Clcyb.600 marker, which perfectly co-segregated with flesh color phenotypes. Recently, Zhu et al. [Poole  reportedenderson  reported(i) form . Althoug(i) form , 29, 30 u et al.  ruled ouSeveral QTLs associated with flesh color have been detected by various researchers. Two QTLs have been identified for red flesh color on chromosomes 2 and 8 . RecentlIn total, 24 watermelon inbred lines were obtained from two domestic seed companies in Korea (Hyundai and Asia Seed Company) for WGRS analysis. The collected inbred lines possessed diverse fruit characteristics, including shape (circular and elliptic), fruit ground color , stripes , and flesh color , as indicated in As listed in Statistical analysis for color parameters were conducted using an SPSS 19.0 statistical software package. Significance was analyzed using one-way analysis of variance (ANOVA) followed by Tukey's multiple comparison test at the P < 0.05 level. The carotenoid contents among lines in the red, yellow, and orange flesh color groups were compared by calculating the standard error of the mean.http://tgsol.seeders.co.kr/, Korea). The generated Illumina paired-end sequence reads for each inbred line were represented approximately 20× genome coverage considering the reference watermelon (97103) genome size (350 Mb), as previously described [WGRS of inbred watermelon lines was performed through NGS using the Hiseq 2000 and Nextseq  systems at Seeders company  or intergenic (other genomic) SNP variants. Genic SNP variants were further categorized into coding sequences of exon and intron. The polymorphism information content (PIC), an index of polymorphism, was calculated for SNP variants from the 24 watermelon lines . The abor) in MEGA 7.0.14 software [http://www.circos.ca/). The physical location of major QTLs for various fruit trait-related characteristics, including flesh color and lycopene content, were obtained from previous reports [To elucidate the genetic relationship between 24 inbred watermelon lines, a principal component analysis (PCA) was performed based on a subset of the total SNPs using the package SNPRelate in R . Pairwissoftware . SNPs, i reports , 42, 43 AccI, AluI, ApeKI, AvaI, BamHI, BbsI, BsrI, Bsu36I, DpnI, EcoNI, EcoRI, Hind-III, MluI, MspI, NdeI, NruI, PstI, SacI, SalI, SpeI, XbaI, and XhoI). Following prediction of the enzyme site, primers were designed for candidate CAPS sequences using Primer3Plus software with the following criteria: oligo length of 18–24 bp, GC content of 40–60%, Tm of 55–60°C, and amplicon length of 100–500 bp.Based on the preliminary visual assessment and a cross-validation by estimation of color coordinates, the 24 inbred lines were classified according to the color of their fruit flesh . Among d2O to a 12 μL volume. Amplifications were conducted with an initial denaturation step of 95°C for 3 min, which was followed by 40 cycles of 30 s at 95°C, 30 s at 58°C, 30 s at 72°C; and a final cycle of 10 min at 72°C. For the enzyme digestion of PCR amplicons, a 15 μL reaction mixture was used containing: 12 μL of PCR product, 1.5 μL of 10× enzyme buffer, 1 U of restriction enzyme, and ddH20 to a final volume of 15 μL, which was then incubated at 37°C for 2–4 h. Digested products were analyzed by electrophoresis in a 3% metaphor agarose gel  containing 1× Tris-Borate-EDTA (TBE) buffer, followed by fragment separation at 180 V for 25 min. After the digestion of PCR products and subsequent separation of fragments in agarose gel, the match rate between SNP information and CAPS genotyping was analyzed, as described in a previous study [For experimental validation of CAPS markers, each PCR was carried out in a 12 μL reaction volume containing 100 ng of template DNA, 1 μL of 100 μM primers, 1.2 μL of 10× PCR buffer, 1 μL of 10 mM dNTP, and 0.08 μL 5 U/μL Tag, and ddHus study .Cross hybridization between different inbred lines results in genetic populations with different carotenoid profiles, and consequently, the flesh color variations often seen in the pericarp tissues of watermelon fruit . To inveStatistically significant (P < 0.05) differences were observed for all color parameters . All theab) values and carotenoid contents revealed substantial variation among red, yellow, and orange inbred lines  to 41.9 × 106 (line 842), with an average length of 101 bp per inbred line. The total length of reads ranged between 3,518,799,499 bp (line 834) and 4,236,980,805 bp (line 842) (NGS-based WGRS strategies have been widely applied for genetic studies of important traits in many crops . To undeine 842) .http://www.ncbi.nlm. nih.gov/bioproject/ PRJNA516776).After the trimming of raw reads, the ratio of the total length of raw reads over the trimmed reads  was calculated, and varied from 59.8% (54) to 66.46% (843) , wherebySNP markers are co-dominant, highly informative, and polymorphic. These attributes make SNPs effective DNA-based molecular markers for studying genome association and genetic diversity in crop breeding . After tThe rates of transitions (Ts) and transversions (Tv) were 68.36% and 31.64%, respectively. The SNP PIC values ranged from 0.1 to 0.38, with an average value of 0.297. Most SNPs were in intergenic regions (90.1%), with the remaining (9.9%) located within genic regions. Most (70.7%) SNPs within genic regions were in introns rather than exons. To determine the possible linked functions of SNPs located within genic regions, we searched the corresponding transcripts of genic SNPs. Accordingly, we identified 7712 non-redundant transcripts, and Gene Ontology (GO) annotations were simultaneously performed. Based on GO analysis, these transcripts were classified into “cellular components (79%)”, “molecular functions (73%)”, and “biological processes (72%)”, and were annotated to “primarily located in intracellular components, nucleus, and cytoplasmic part”; “proteins involved in enzyme binding, protein binding, and DNA binding”; and “involved in cellular and metabolic processes and protein metabolism”, respectively .PIC indicates the usefulness of a marker for detecting polymorphism. Multi-allelic molecular markers, such as RAPD, and AFLP, usually have PIC values that range between 0.5 and 1.0. In contrast, SNPs are bi-allelic in nature, possess a co-dominant inheritance pattern, and have lower PIC values, which can range between 0 and 0.5 . SNPs wiTo determine the genetic relationships between the 24 different flesh-colored lines, a NJ tree was constructed using 2369 selected SNPs . AccordiY, y, yo, C, and I) [O), two , or three  alleles, respectively. Thus, genetic relationships within watermelon inbred lines, as inferred from genome-wide SNPs  , 28, sug, and I) , skin co, and I) , or stri, and I) , have beide SNPs , would nide SNPs . With noide SNPs : clusteride SNPs . These rWe also determined the genome-wide distribution frequency of SNPs in three typical inbred lines with red (803: Cluster-IA), yellow (835: Cluster-IIA), and orange (840: Cluster-IIB) colors, grouped separately in both the PCA and NJ tree . As showThe analysis of genetic diversity between 24 inbred lines revealed that the SNPs were able to generally distinguish between red flesh type with high lycopene content, and non-red flesh type, such as yellow or orange, with low lycopene cultivars. To validate the SNPs detected during resequencing , we randWe also noted some discrepancies between SNP information (WMHL11 and WMHL17), which can be explained by a failure of restriction digestion or technical errors during resequencing, as observed in a previous study on watermelons . With thin silico expression profiling of these genes was performed using comparative watermelon transcript data (BioProject: PRJNA338036) from the same genes available in various fruit developmental stages of red-fleshed (LSW177) and pale-yellow-fleshed (COS) cultivated watermelons. The results revealed that these SNP-carrying genes presented preferential and stage-specific expression between LSW177 and COS. At 10 DPA, these genes were generally upregulated in pale yellow fleshed COS compared with LSW177. At the 26 DPA ripening stage, almost all genes displayed robust, uniform upregulated expression of their transcripts in both COS and LSW177, relative to other developmental stages. At the 18, 34, and 42 DPA stages, almost all SNP-carrying genes were significantly upregulated in the red flesh of LSW177 compared to that in COS , stripe pattern (wsb6-11), and flesh color (Clcyb.600 and Lcyb) , 30, 52.lycopene β-cyclase (LCYB) gene (Cycl.600 and Lcyb) were identified [LCYB gene , where all SNPs from these genes were found to be cosegregated with the flesh color and high-lycopene phenotypes of the inbred lines used in this study. As listed in LCYB at 8,721,803 (Cla005007: Unknown Protein) and 8,923,655 bp (Cla005012: Kinesin-like protein), with PICs of 0.37 and 0.38, respectively. The obtained SNP from kinesin-like protein  was confirmed to be cosegregated with the flesh color and high lycopene content upon validation  of flesh color (FC3.1) . A QTL oIn the present study, genome resequencing revealed significant genetic variations among different flesh-colored inbred lines in watermelons. By utilizing resequencing data, we identified numerous SNPs associated with flesh-color characteristics among all the inbred lines, which will be useful for the rapid development of diagnostic markers for MAS and fine mapping. Analysis of genetic diversity using 2369 SNPs mined on a genome-wide scale demonstrated practical significance, whereby two groups were found in the 24 inbred lines. Upon validation, the selected SNP/CAPS were able to distinguish between inbred lines with high (red) and low (yellow/orange) lycopene contents, and the results were correlated with the flesh color phenotype of each individual. Overall, for the first time, polymorphic SNPs were discovered in watermelons with high lycopene content. The SNPs identified from the current WGRS approach in watermelon may be utilized in population genetics, QTL mapping, and genomics in order to assist in the breeding of watermelons with high lycopene content.S1 FigPhotographs of longitudinal cross-sections of representative individuals for the flesh color categories of 24 inbred lines used in WGRS (A) and 12 commercial cultivars used in CAPS validation (B).(PDF)Click here for additional data file.S2 FigIdentified transcripts are annotated into functional categories of cellular component, molecular function and biological process as indicated in the figure.(PDF)Click here for additional data file.S3 FigA) Determination of the optimal K-value  based on five independent runs with burn-in period of 10,000 iterations followed by 10,000 Monte Carlo Markov Chain (MCMC) iterations according to a previous study [B) Population structure for the 24 watermelon inbred lines when K = 3. Each individual inbred line is indicated by a narrow vertical bar, which is partitioned into red, blue and green zones in proportion to the association coefficiencies to the 3 populations, such as the high lycopene content of cluster I comprising two other sub-clusters (A and B) and the low lycopene content of cluster II and III populations. The color and shape schemes are the same as those for the neighbor-joining (NJ) analysis in (us study . The rat(TIF)Click here for additional data file.S4 Figgreen asterisk”.The name of validated CAPS markers (WMHL) are shown below their corresponding gel pictures. Numbers in lanes represents the DNA sample names of the inbred lines  and comm(PDF)Click here for additional data file.S5 Figin silico expression profiling are indicated at the top and bottom of the heat map, respectively. A combined table showing the gene ID, marker name and gene description. The color scale at the top indicate up (red) or down (green) regulated expression.DPA and watermelon lines used for (TIF)Click here for additional data file.S6 FigLCYB gene (Cycl.600 and Lcyb) by Bang et al. [Upper panel shows the position of SNP hotspots of different chromosomes. Lower panel describes the schematic representation of genetic linkage map for markers on chromosome 4 (WII04E08-38 to WII04EBsaHI-6) and Chromosome 3 (BVWS00048 to WII03E09-92) linked to flesh color (FC4.1 and FC3.1) or lycopene content (LCY4.1) according to previous studies . A CAPS g et al. , 30 mark(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.


Continuous storage root formation and bulking (CSRFAB) in sweetpotato is an important trait from agronomic and biological perspectives. Information about the molecular mechanisms underlying CSRFAB traits is lacking.p-value < 5 × 10− 7), we identified 34 unique SNPs that were significantly associated with the complex trait of CSRFAB at 150 days after planting (DAP) and seven unique SNPs associated with discontinuous storage root formation and bulking (DCSRFAB) at 90 DAP. Importantly, most of the loci associated with these identified SNPs were located within genomic regions (using Ipomoea trifida reference genome) previously reported for quantitative trait loci (QTL) controlling similar traits. Based on these trait-associated SNPs, 12 and seven candidate genes were respectively annotated for CSRFAB and DCSRFAB traits. Congruent with the contrasting and inverse relationship between discontinuous and continuous storage root formation and bulking, a DCSRFAB-associated candidate gene regulates redox signaling, involved in auxin-mediated lateral root formation, while CSRFAB is enriched for genes controlling growth and senescence.Here, as a first step toward understanding the genetic basis of CSRFAB in sweetpotato, we performed a genome-wide association study (GWAS) using phenotypic data from four distinct developmental stages and 33,068 single nucleotide polymorphism (SNP) and insertion-deletion (indel) markers. Based on Bonferroni threshold (Candidate genes identified in this study have potential roles in cell wall remodeling, plant growth, senescence, stress, root development and redox signaling. These findings provide valuable insights into understanding the functional networks to develop strategies for sweetpotato yield improvement. The markers as well as candidate genes identified in this pioneering research for CSRFAB provide important genomic resources for sweetpotato and other root crops. Perennial plants, species that live for more than 2 years, account for 13% of food crops and provide advantages over annual crops because they increase the carbon of storage organs . CSRFAB Sweetpotato is an important food crop in sub-Saharan Africa (SSA). Its production continues to increase in east and central Africa due to recent interests in the crop for its unsurpassed healthy promoting values and being staple food crop in the region. High production in the region is recorded in Uganda and Tanzania , howeverThrough various research networks, sweetpotato breeders across countries and continents have shown outstanding consistency in the general areas of priority for breeding, most of which point to the need for increased yield potential and resistance to biotic and abiotic stresses. Frequently mentioned breeding objectives, targeting increased sweetpotato adoption and wide utilization, revolve around improvement of fresh storage root yield, storage root dry matter content (DMC), resistance to principal local pests and diseases, tolerance to adverse soil and climatic conditions, good plant habit, ornamental quality and other quality traits . ConsideSeveral reports have described the anatomy and physiological processes of storage root formation and development in sweetpotato under controlled conditions –15 and fIpomoea trifida and I. triloba [DNA-based genetic markers can provide great potential to assist plant breeders in the identification of genes of interest and detecting markers tightly linked to traits for the development of new cultivars. Tanaka et al. 2016)  reviewed16  revie triloba  and the In this study, we evaluated a diverse set of 358 sweetpotato breeding accessions for CSRFAB and DCSRFAB under field conditions. We genotyped this diversity panel and analyzed it for quantitative trait nucleotides (QTNs) using SNP and indel markers to identify genomic regions and polymorphisms associated with CSRFAB and DCSRFAB traits.Pair-wise correlations provide information on whether two traits are related. This information about the magnitude and direction (negative or positive) of the relationship can assist in breeding selection decisions. Thus, CSRFAB was highly and positively correlated with storage root number (SRN), storage root yield (SRY) and harvest index (HI) Fig. . CSRFAB The distribution of genotypic performance across the four-harvest times (HT) was visualized using a boxplot Fig. . On averWe previously  reportedI. trifida and I. triloba) in order to determine sequences with 2x, 4x, and 6x doses that correspond to diploid, tetraploid, and hexaploid genotypes in the hexaploid sweetpotato genome. Based on the distribution of read depth for each individual in the population  values were calculated using the diploidized genotypic data. Overall, based on the I. trifida genome assembly size of 526.4 Mb [The pattern and extent of genome-wide linkage disequilibrium (LD), which is the non-random association of alleles at different loci, is presented in a boxplot that shows the distribution of LD within a range of distances between marker pairs Fig. . All pai526.4 Mb  and a to526.4 Mb . Accordi526.4 Mb  rapid LDOut of 33,068 SNPs analyzed in this GWAS, based on Bonferroni correction-based threshold, 34 and seven SNPs were significantly associated with CSRFAB 150 DAP) and DCSRFAB (90 DAP), respectively. The number of significant SNPs associated with the other CSRFAB-related traits  are shown in Table 50 DAP anAmong the 57 SNPs associated with CSRFAB and DCSRFAB at different storage root development stages, some SNPs were shared between 150 DAP, AUGPC and 180 DAP, while other SNPs were simultaneously detected between 90 DAP, 120 DAP and slope. The largest number of associated SNP markers was detected on chromosome 1 and the largest number of significant associations was recorded for 150 DAP followed by the slope  ranging from 8.11E-43 to 1.79E-03, implying that the type I error with false positive SNPs was minimized. The top four most confident SNPs discoveries were Chr04_6057850, Chr12_903336, Chr01_30468732, and Chr09_6258404. Most significant SNPs that passed the stringent significance threshold of 5% Bonferroni were also associated with CSRFAB variations at 150 DAP and R2 were greater than 9% and the majority explained more than 15% of the variation. In the previous study, 150 DAP and 90 DAP were proposed to predict CSRFAB and DCSRFAB traits and further investigation in this study will be only focused on these two times [Thirty-four SNPs (around 60% of the significant SNPs) were associated with CSRFAB scores collected at 150 DAP and were concentrated on chromosome 1, 6 and 8. All the minor allele frequencies (MAFs) of the significant SNPs at Bonferroni correction rate of 5% were above 10%, equivalent to recommended strict threshold for MAFs. All the significant SNPs passing Bonferroni threshold of 5% scored low adjusted P value for each of the SNPs in the genome , when tested for differences in frequency between trait and markers. The line shows the threshold for genome-wide significance . Each dot is a SNP laid out across the sweetpotato chromosomes from left to right, and the heights correspond to the strength of the association to traits under study . Chromosomes 1, 3, 4, 6, 8 and 12 had a high number of the associations over the four harvest times forming clusters where multiple traits were associated due to probably a set of linked genes. These regions include: chromosome 1 (six traits), chromosome 3 (4 traits), chromosome 4 (4 traits), and chromosome 12 (four traits). On a multiple plot, we placed vertical lines at specific positions along the x-axis to identify consistently associated QTNs  plots . The ManI. trifida reference genome (http://sweetpotato.plantbiology.msu.edu/). Candidate genes that are co-located with the top significant SNPs are listed in Table GWAS are useful for identifying genomic locations related to a trait of interest, however, the information provided does not capture the function of genes under the region of interest. It is therefore of great importance that putative causal gene functions be determined and investigated through different functional genomic approaches. It is well recognized that few of associated markers involve genes previously reported to be related to the trait of interest, and some are found in genomic locations harboring no known genes and sometimes regulatory elements . TherefoCandidate genes that might underlie variation in CSRFAB were identified based on proximity of the associated SNPs with predicted genes Table . The strThe associated Chr01_30468732 SNP is co-localized with P-LNPHP gene encoding a P-loop containing nucleoside triphosphate hydrolases super family protein involved in regulation of growth and hormonal signaling in plants. The gene HVA22 was found to be associated with Chr02_16003392 locus encoding basic helix-loop-helix bHLH-DNA-binding superfamily protein and was previously reported to regulate leaf senescence, cell death and abscisic acid (ABA) biosynthesis . AnnotatCandidate gene TRO-Z was associated with Chr03_18599676 SNP marker and this gene encodes thioredoxin z previously reported as a facilitator of plants to cope with fluctuating environments by integrating energy transduction, metabolism, gene expression, growth and development . The genP-values for the slope, indicating that slope is not a good predictor of responses at 90 DAP. Five genome wide SNPs hit common chromosome region at 150 DAP and AUGPC . This indicates that AUGPC and 150 DAP tightly affect the same phenotype. In a previous study [In this study, we showed that CSRFAB, SRN, SRY and HI were highly correlated, suggesting that their genetic basis is controlled, in part, pleiotropically . This implies that we can expect positive changes in the SRN and SRY for varieties whose CSRFAB expression is increased. In breeding, this means that when we select for CSRFAB we can get a correlated response in the SRN and SRY. Further detailed analyses on the CSRFAB trait responses over the four harvest times and their respective predictor (AUGPC and slope) revealed that AUGPC and 150 DAP were highly and positively correlated . In a previous study  these haP-values  suggesting the presence of some major to moderate QTL particularly underlying CSRFAB (the wild type or predominant habit of sweetpotato), while DCSRFAB (the habit bred for in cultivated sweetpotato) seems to be controlled by minor alleles. The rapid decay of LD in the diversity panel breaks the genome in small LD blocks and provides us with the ability to fine map QTL, oftentimes to the gene level [We used genome-wide association (GWA) analysis to identify SNPs associated with CSRFAB and DCSRFAB in sweetpotato. GWA analysis provides much more precise physical positioning than the method of QTL mapping in biparental mapping populations that has been previously used for complex growth traits in plants. We observed particularly high ne level . In thisne level . We deteI. trifida genome sequence to identify candidate genes encompassing or adjacent to these SNPs. Several of the candidate genes that we identified play a role in the plant growth pathways. Candidate genes can also be identified based on their positions on quantitative trait locus (QTL) maps or patterns of gene expression [2+ dependent kinase) and stress signaling pathways whereas the cluster of genes associated with CSRFAB involved mostly growth hormone signaling such as auxin, ABA, gibberellins . The former modulate growth that is declining due to senescence, aging, and drought stress. ABA is reported to be involved in the repression of germination, drought response, and promotion of leaf senescence [We used the publicly available pression . Identifpression . Suzuki pression  argued tpression  classifipression . In thispression  increasepression  by maintpression . The clunescence .This situation makes continuous storage root growth regulation a highly complicated process and it seems to be controlled at many different levels by complex actions of gene networks in both time and space. For instance, SNPs as well as genes involved in storage root formation and bulking at 90 DAP were completely different from genes found at 150 DAP implying that the regulation mechanism of root formation and bulking is time dependent.A key contribution of this study is the multipurpose characteristic of the discovered SNPs that can be validated for early maturing and continuous storage root formation for commercial storage root production, piecemeal harvesting and biomass production for animal feed. At the top of significant and upregulated genes, P-loop containing nucleoside triphosphate hydrolases super family protein and DHHC-type zinc finger family protein were associated with CSRFAB in this study and these genes generally have a meristem-specific mRNA expression pattern and KNOX proteins playing central roles in shoot development by maintaining the apical meristem activity . CelluloWe genotyped 358 sweetpotato genotypes and run the first GWAS in sweetpotato for CSRFAB. The study identified 40 unique SNPs significantly associated with CSRFAB traits and 12 unique SNPs significantly associated with DCSRFAB. Novel genes including 12 promising genes for CSRFAB and seven genes for DCSRFAB were identified in this study. These genes can be used for sweetpotato genetic improvement of CSRFAB using maker assisted breeding approaches after marker validation. To validate these associations and candidate genes, additional studies will be required . The discovery of the candidate genes has increased our understanding of the molecular causal mechanisms of CSRFAB in sweetpotato and may contribute to the basic knowledge in breeding for CSRFAB in sweetpotato. The validation of genotypes with excellent haplotypes will provide valuable breeding materials to improve sweetpotato for CSRFAB through marker-assisted selection in future breeding efforts.Parental genotypes were obtained from the National Crops Resources Research institute (NaCRRI) in Uganda. The study involved a collection of 358 sweetpotato genotypes including; a set of 130 genetically diverse genotypes from two contrasting genepools previously characterized with 31 simple sequence repeat (SSR) markers  and a seThe experimental design was based on a randomized complete block design, with a two-row experimental plot containing 20 plants. A total of 2 replications were sampled even though 4 replicates were planted. Measurements were conducted at one-month intervals starting from 3 months after planting (MAP).To identify the patterns of storage root formation and bulking, storage roots for each plant genotype were destructively sampled at 3, 4, 5, and 6 MAP. At each sampling point, data were collected from four plant stands using above- and below-ground parts. The following sweetpotato storage root traits were measured for each of the four harvest times to allow correlation analysis of CSRFAB and yield component traits and these included: (i) number of harvested plants (NPH), allowing calculation of average values, (ii) total storage root number (SRN), (iii) total root weight (TRW), and (v) vine weight (VW). CSRFAB was estimated using a previously developed scale of 1 to 9, where 1 = no visible storage root (SR) initiation and no visible bulking; 2 = No visible SR initiation but bulking is detectable; 3 = No visible SR initiation; 4 = Distinct SR initiation, with 2 bulking roots; 5 = Distinct SR initiation and 3 bulking roots; 6 = Distinct SR initiation and 4 bulking roots; 7 = Distinct SR initiation and 5 bulking roots; 8 = Distinct SR initiation and 6 bulking roots; 9 = Distinct SR initiation and 7 bulking roots [Most growth curves in living organisms follow a logarithmic or exponential growth trend. The logarithmic growth curves increase quickly in the beginning, but the gains decrease and become slower as time goes by, while the exponential growth curves increase slowly in the beginning, but the gains increase rapidly and become larger as time goes . The incg time i .Slope: m = f ‘(x) = constant .Yijk = μ + Gi + Tj + Ek + GTij + GEik + GTEijk + Rlk + eijkl; where μ is the total mean, Gi is the effect of the ith genotype, Tj is the effect of the jth harvesting time, GTij is the effect of the interaction between the ith genotype and the jth harvesting time, GTEijk is the effect of the interaction between the ith genotype and the jth harvesting time in the kth environment, Ek is the effect of the kth environment, GEik is the interaction effect between the ith genotype and the kth environment, Rik/ is the effect of the Lth block within the kth environment, and eijk is a random error .The analysis of variance was performed for CSRFAB scores, slope and AUGCP predictors that take into account all the time harvests at once. R package lme4  was usedCorrelation analyses between CSRFAB traits were performed using the cor function and Pearson method in R software.Boxplots  were useA total of 5 mg of young leaf tissue was sampled for each genotype using a polythene sampling bag and then kept cool under ice. Extraction buffer was prepared  and placed in a water bath at 65 °C. Using a FastPrep-24™ 5G tissue homogenizer, leaf tissue samples in 2 ml tubes were ground for 5 min in liquid nitrogen using sterile 4 mm stainless steel ball bearings. To obtain high molecular weight DNA, 1 ml of prewarmed (65 °C) CTAB buffer  was added to the ground samples and vortexed at 3000 rpm for 30 s. Tubes were heated in a water bath at 65 °C for 30 min and mixed gently at 10 min interval during incubation. Samples were cooled on ice, 500 μl chloroform:isoamyl alcohol (24:1) was added, and mixed by inverting the tubes 20 to 30 times. Samples were then centrifuged at 15,000 round per minute (rpm) for 15 min and the top layer recovered into a new tube. This step  was repeated to ensure purity of extracted DNA. DNA was precipitated with 1/5 volume of 5 M NaOAC and 2.5 volume of cold absolute ethanol (stored at − 20 °C). The samples were gently inverted to mix and incubated at − 20 °C for 60 min. Samples were centrifuged and DNA pellet recovered by decanting the supernatant. The DNA pellet was washed twice with 500 μl of cold (− 20 °C) 70% (v/v) ethanol and air dried. The DNA pellet was resuspended in 100 μl low-EDAT TE buffer  containing 400 μg RNase-A. The DNA concentration and purity were determined using a NanoDrop spectrophotometer.I. trifida and I. triloba [DNA samples (40 μl each) were sent to the integrated genotyping services and support (IGSS) at the Biosciences eastern and central Africa - International Livestock Research Institute (BecA-ILRI) Hub for sequencing based on the DArTseq technology. The raw Fastq files were processed within the GBSapp pipeline for pre-processing fastq files, variant and dosage calling, and variant filtering. The pipeline integrates various software, including GATK v3.7 , optimiz triloba  were use2), and only P-values < = 0.01 for each pair of loci were considered significant. The LD decays were computed for LD-based genome-wide association analysis.Linkage disequilibrium analysis was performed using GAPIT  and implN = 358). The first three principal components of the SNP data were included in the GWAS model. The Bonferroni threshold for P values was calculated based on the number of markers  according to the method described in Li et al., (2013) [To minimize false positive rates and increase statistical power, the population structure Q and kinship (K) matrix were estimated. A compressed mixed linear model (CMLM) was used, with the kinship or relatedness (K) matrix as a random effect to account for population structure and reduce spurious associations. The analysis was performed using the R package for Genome Association Prediction Tool (GAPIT) version 3 . Varianc, (2013) .I. trifida (http://sweetpotato.plantbiology.msu.edu/) was used as the reference genome for identifying candidate genes. The putative function candidate genes that co-localized with associated SNPs were annotated based on similarity to known annotated genes in other species, particularly, Arabidopsis thaliana. Independent analyses were performed using the Basic Local Alignment Search Tool from the National Center for Biotechnology Information Basic Local Alignment Search Tool (NCBI BLAST) and the conserved domains database (CDD) resources [Based on the significant trait-associated SNPs, the physical genome assembly of the diploid esources  for anno


Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection. Maize is the major source of food security and economic development in the major developing countries in sub-Saharan Africa, Latin America and Asia . DroughtWhile genetic gain for grain yield  under DS in sub-Saharan Africa is similar to other regions of the world, absolute yields in farmers’ fields remain low ,b. BreedThe genetic studies for understanding the genetic architecture of DS tolerance in maize have been conducted on grain yield (GY) and secondary traits with strong genetic correlation with GY over a wide range of genetic mapping populations with different population sizes, marker types, and marker densities, where the phenotypic data were collected from the inbred lines and their testcross hybrids . These s2 population consisted of 234 individuals, two consensus QTL controlling GY and the secondary traits were identified by The quantitative trait loci (QTL) conferring tolerance to DS in maize were investigated in several linkage mapping studies to reveal the genetic architecture of the GY and the secondary traits . Lu et aGenome-wide association study (GWAS) in a panel consisted of 350 tropical and subtropical maize inbred lines using 57647 SNPs revealed 33 candidate genes associated with GY and the secondary traits for WW and DS conditions. ThirunaCandidate genes identified from the genetic studies are possible to be validated by expression profiling analysis with RNA-seq data. Genomic selection, also named as genomic prediction (GP), incorporates all available marker information into a prediction model to predict the genomic estimated breeding values of the unknown-phenotype breeding materials for selection, where all the alleles with both major effects and minor effects are captured simultaneously . TherefoIn contrast to intensive genetic studies on DS tolerance in maize, relatively less effort has been made for HS tolerance, and the tolerance to combined drought and heat stress . Most ofThe DTMA panel consisted of 300 tropical and subtropical maize inbred lines was used in this study for association mapping and GP analyses . The teshttp://hdl.handle.net/11529/10548156. The Pearson’s correlation coefficients were calculated by using R function cor.test  and the pair-wise correlations were visualized with function pairs .1The target traits in the current study are GY, AD, and ASI. In total, 12 trait-environment combinations between the three target traits and the four evaluation conditions were investigated. For each of the target trait evaluated under the different conditions, the estimated best linear unbiased prediction values  were app2 was used to anchor reads to the B73 reference genome , the SUPER method not only retains the computational advantage, but also increases statistical power  . In ordeal power .p-value with a threshold of 1 × 10-4 was used to declare the significant associations. The quantile–quantile plot and Manhattan plot generated with customized R scripts were used to visualize the observed P  of SNP-trait associations. The candidate genes were identified as the genes that the significant SNPs located in or adjacent to (<3kb) against B73 AGPV2 (MaizeGDB)3. The significant SNPs and candidate genes were visualized using R package “VennDiagram” for comparison. The significantly associated SNPs were then grouped in genomic regions with sliding window method (window size = 20 Mb) from the first significant SNPs on each chromosome for each trait. The hotspot regions were identified as the top 5% quantile of density of significant SNPs for each trait.A moderate 4, where the principal component and the kinship matrix calculated with the filtered SNP dataset above were incorporated into the MLM model. A moderate p-value with a threshold of 1 × 10-4 was used to declare the significant.The gene-based-haplotype method was applied to perform association analysis as well, where each gene was regarded as a window and the SNPs within each gene were used to construct the haplotypes . Only th5 and Phytozome6 to search the best match. The summarized flowering time related genes and the reported loci underlying related traits were collected in this study to evaluate the association results  (2fold-change| > 1) and q-value ≤ 0.01 (1% FDR)  or DS responsive genes. The raw RNA sequencing data is available from the following repository in the NCBI Sequence Read Archive under accession number PRJNA294848 (SRP063383): https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294848.Expression profiling analysis was conducted by using the transcriptomic sequencing data of four tropical maize inbred lines as previously described . The fou States) . A cutof(1% FDR)  of the rMGr to assess the prediction accuracy.Genomic prediction was performed by using rrBLUP model, implemented in R with rrBLUP package for all the traits evaluated under different conditions . PredictFor each of the target traits, the range of phenotypic distribution was large under each management, indicating that the broader diversity in the panel . For eacThe phenotypic correlations differed among the same trait evaluated under the different management conditions . The pheThe phenotypic correlations also differed among the different target traits evaluated under the same condition. In general, the GY was negatively and significantly correlated with ASI significantly under each evaluation condition with a range of -0.32 to -0.67, this is consistent with the previous observation . However-4 for all the 12 trait-environment combinations. The average PVE for 1661 associations was 4.33% and 589 of them had a PVE value greater than 5% , 83 associations in total were identified for all the 12 trait-environment combinations, except for ASI-HS and ASI-DH. Number of significantly associated SNPs identified above the p-value threshold of 2.6 × 10-6 ranged from 2 for GY-DH and AD-DH to 32 for AD-DS , 311 haplotypes associated for all the target trait-environment combinations, except for the three traits under HS condition, i.e., GY-HS, AD-HS, ASI-HS were detected. Number of significantly associated haplotypes for each trait-environment combination ranged from 3 for ASI-WW to 26 for both AD-DS and GY-WW. The average PVE values was 10.56% with a range from 5.63 to 19.71%, which was higher than that in the SNP-based association mapping analysis.In total, 301,897 haplotypes formed from the 19,674 annotated genes were used to perform the haplotype-based association mapping. Compared with the SNP-based association mapping method, the haplotype-based associated mapping method detected fewer number of significantly associated SNPs with higher PVE values . At the In the SNP-based association mapping analysis, 120 significant associations had a pleiotropy effects for at least two trait-environment combinations, 1038 significant associations were in the genic region of 673 candidate genes, and the average number of SNPs per candidate gene was 1.36, i.e., (1038-120)/673. The max number of SNPs per candidate gene, i.e., 13, observed for GRMZM2G700686 associated with trait-environment combination of GY-HS , 4. NumbFew candidate genes overlapped for the same traits evaluated under the different conditions, indicating the genetic divergence between the individual stress tolerance and tolerance for DHS. Two candidate genes were overlapped for GY evaluated under the different conditions, i.e., one common candidate gene between WW and DS conditions, and one common candidate gene between WW and HS conditions. Only one overlap candidate gene was observed for ASI evaluated under WW and DS conditions. Number of the overlap genes for AD evaluated under the different conditions ranged from zero to 14, with the maximum number of genes overlapped for AD evaluated between WW and DS conditions.In the haplotype-based association mapping analysis, 19 candidate genes were identified for the 12 trait-environment combinations, and 156 SNPs were in the genic region of these candidate genes, 87 SNPs were used to build the haplotypes of the candidate genes, the haplotypes of each candidate gene was built with two to five SNPs in the genic region. The number of the candidate genes for all the trait-environment combinations ranged from zero to five, and the total number of candidate genes detected for the target trait across all evaluation conditions was 5, 7, and 8 for GY, ASI, and AD, respectively.Four candidate genes, i.e., GRMZM2G329229, GRMZM2G313009, GRMZM2G043764, and GRMZM2G10 9651, overlapped in both the SNP-based and the haplotype-based association mapping analyses . Three oet al.The results of gene ontology-based functional enrichment analysis shown that the 688 candidate genes were significantly (FDR < 0.05) enriched to 15 GO terms related with different biological processes . Two tervs. WW conditions (log2foldchange > 1 and FDR < 0.01) in at least one line  was consistently lower than that of the less complex trait (AD). For the same trait evaluated under the different management conditions, the MGr mean of the target trait evaluated under the WW condition was consistently higher than that evaluated under different stress conditions, i.e., DS, HS, and DHS.GP for all the 12 trait-environment combinations were shown in MGr value obtained from the trait associated SNPs was 0.75, 0.43, 0.69, and 0.58 for the GY evaluated under WW, DS, HS, and DHS condition, respectively. The corresponding MGr mean value obtained from the whole genome wide high-quality SNPs was 0.59, 0.50, 0.38, and 0.35 for the GY evaluated under WW, DS, HS, and DHS condition, respectively. Similar trend was also observed for AD and ASI evaluated under the different conditions. This result indicated that the trait associated SNPs corresponding to the target trait is more effective than the whole genome wide randomly selected SNPs for GP.The number of trait associated SNPs used for different trait-environment combination were ranged from 8 to 339, whereas for random genome-wide markers we used 10,108 high-quality SNPs. Interestingly, the mean accuracy was higher when the prediction was based on trait linked markers obtained through GWAS than the random whole genome wide high-quality SNPs for all 12 trait-environment combinations except for GY-DS. The average DS, HS, and DHS have been recognized as the major abiotic constraints to maize yields in the main production regions. Previous studies indicated that maize is highly susceptible to abiotic stresses during flowering time, secondary traits including AD and ASI, with strong genetic correlation with GY, are potential to be included in the breeding program to facilitate the effective selection on GY . The infp-value threshold of 1 × 10-4. The average PVE of all the significant SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These observations indicate that the GY and secondary trait of flowering time under optimal and stress conditions are complex in nature, controlled by multiple minor QTL with small effects distributing across the maize genome. It also shown the difficulty of improving maize GY and the secondary trait of flowering time under the stress conditions by using marker assisted selection due to the genetic architecture complexity of the target traits. In addition, the previous phenotypic analysis also observed that the tolerance to DHS in maize was genetically distinct from tolerance to individual stresses, and tolerance to either stress alone did not confer tolerance to DHS , and effects the flowering time.Some candidate genes reported in the previous studies were also observed in the current study. In total, eight candidate genes related with GY , two can control . In the  control . In the  control . For the studies . This gep-value. GRMZM2G068294, encoding phytochrome A-associated F-box protein, was associated with AD-WW, AD-DS and AD-HS simultaneously. ZmGR2c, associated with AD-DS, is a gibberellin responsive gene. Seven SNPs harbored by GRMZM5G866432 were associated with ASI-DS with average PVE value of 5.17%. WRKY DNA-binding protein gene GRMZM2G076657 was associated with ASI-DH.Besides the previous reported candidate genes, new candidate genes with different functions were also identified in the current study. In total, nine new candidate genes related with GY, 10 new candidate genes related with AD, and seven new candidate genes related with ASI, were highlighted in GMr compared to the other two traits, which is consistent with the nature of complexity : https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA294848.The phenotypic data of the estimated best linear unbiased prediction values and the SNP data is available from the following repository: RB, MG, XZ, and MO coordinated the genotyping. JC, DM, CM, FSV, and BP coordinated the phenotyping. YY and YaL performed the RNA-seq. YY, AZ, YuL, NW, ZH, YaL, and XZ carried out the data analysis and wrote this manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


TaRPP13L1 was associated with flour colour. Wheat cultivars or lines with TaRPP13L1‐B1a showed extremely significantly higher flour redness and lower yellowness than those with TaRPP13L1‐B1b in the Chinese wheat natural population and the doubled haploid (DH) population. Two tetraploid wheat lines with premature stop codons of the TaRPP13L1 gene mutagenized by ethyl methanesulfonate (EMS) showed extremely significantly higher flour redness and lower yellowness than wild type. Our data suggest that the TaRPP13L1 gene plays an important role in modulating wheat flour colour. This study provides useful information for further dissection of the genetic basis of flour colour and also provides valuable genes or genetic loci for marker‐assisted selection to improve the process of breeding quality wheat in China.Flour colour, kernel hardness, grain protein content and wet gluten content are important quality properties that determine end use in bread wheat. Here, a wheat 90K genotyping assay was used for a genome‐wide association study (GWAS) of the six quality‐related traits in Chinese wheat cultivars in eight environments over four years. A total of 846 significant single nucleotide polymorphisms (SNPs) were identified, explaining approximately 30% of the phenotypic variation on average, and 103 multienvironment‐significant SNPs were detected in more than four environments. Quantitative trait loci (QTL) mapping in the biparent population confirmed some important SNP loci. Moreover, it was determined that some important genes were associated with the six quality traits, including some known functional genes and annotated unknown functional genes. Of the annotated unknown functional genes, it was verified that  The Yellow and Huai River Valley is the most important and largest wheat‐producing region, with 60%–70% of both total harvested area and wheat production in China (Chen Pina and Pinb) on 5DS, which form the molecular basis of wheat kernel hardness  contributing to hardness on almost all 21 wheat chromosomes, especially on chromosomes 1B, 2A, 4B, 5A, 5B, 5D and 7D  is often used to measure the nutritional value of food. Wheat is considered as a highly nutritious crop due to the 12%–14% protein content of the grain Shewry, . Wheat pet al., in silico cloning or biparent mapping, for example the phytoene synthase (Psy1) gene on the chromosomes of group 7, which were considered to be the most critical chromosomes affecting colour  gene on the chromosomes of group 2  gene on the chromosomes of groups 4 and 5  gene on the chromosomes of group 3 , but was significantly and negatively correlated with FL* (r = −0.834**). GPC was significantly and positively correlated with WGC (r = 0.895**) and Fa* (r = 0.306**). GPC, WGC and Fa* were significantly and positively correlated between any two of the three traits . Among three flour colour parameters, Fb* was significantly and negatively correlated with FL* (r = −0.638**) and Fa* (r = −0.808**). Coefficients of correlation among eight environments showed that the six quality traits possessed stable phenotypes (Table The grain protein content (GPC), wet gluten content (WGC), flour lightness (FL*), flour redness (Fa*) and flour yellowness (Fb*) were nearly symmetrically distributed in the cultivars surveyed Figures , S1. HowP < 1.0e‐3) in the eight environments  identified 846 significant SNPs  was most significantly associated with FL* in all 8 environments, which explained the 26.6% phenotypic variation on average and showed a negative effect to FL*. On 5AS, BS00029347_51 (with PVE of 15.3%), BS00041219_51 (with PVE of 15.1%) and wsnp_Ra_c24707_34262900 (with PVE of 15.1%) were all significantly associated with FL* in the same 7 environments, and BS00029347_51 and BS00041219_51 showed a positive effect to FL*, while wsnp_Ra_c24707_34262900 exhibited a negative effect to FL*. Additionally, IAAV7827 (with PVE of 15.6%) on 5AL was detected in 5 environments and showed a positive effect to FL*.Seventy‐five SNPs, mainly distributing on chromosomes 1AL, 5AS, 5AL, 5BL, 5DS and 6AS, were significantly associated with FL*, and PVE of each SNP ranged from 12.7% to 29.0% Tables d, S5. OfPsy) genes  on 5DS (with PVE of 45.5%), which was the most significant SNP with Fb*, was identified in all 8 environments and showed a positive effect to Fb*. On 7AL, Kukri_rep_c68371_1242 (with PVE of 39.4%) was significantly associated with Fb* in 5 environments and showed a positive effect to Fb*. On 7BL, Kukri_c4352_194 (with PVE of 40.2%), RAC875_c38693_499 (with PVE of 40.2%), Excalibur_c5851_1661 (with PVE of 39.2%) and tplb0026f23_1134 (i.e. Psy‐7B with PVE of 39.3%) were significantly associated with Fb* in 8, 7, 6 and 5 environments, respectively. Of them, Kukri_c4352_194 and tplb0026f23_1134 showed a positive effect to Fb*, while RAC875_c38693_499 and Excalibur_c5851_1661 exhibited a negative effect to Fb*. On 7DL, Excalibur_rep_c92684_578 (with PVE of 41.6%) was significantly associated with Fb* in all 8 environments and showed a positive effect to Fb*.One hundred and fifty‐two SNPs, mainly distributing on chromosomes 1AL, 2AS, 2AL, 2BL, 3B, 4AL, 5BL, 5DS, 6BS, 7AL, 7BL and 7DL, were significantly associated with Fb*, and the PVE of each SNP ranged from 32.6% to 52.1% Tables . Of all t‐test (Table P < 0.01).The 58 multienvironment‐significant SNPs were verified to be significantly associated with the 6 quality traits in the natural population by st Table , and thet‐test indicated that cultivars with allele TT at BS00000020_51 (i.e. Pinb) locus showed significantly higher HI, Fb* and lower FL*, whereas cultivars with allele GG at IAAV7827 showed lower HI and higher FL*. Cultivars with allele GG at BS00046964_51, AG at IAAV5188 and CT at RAC875_c3187_873 showed higher GPC and WGC. Cultivars with allele TT at Ex_c16529_304, Kukri_rep_c110544_52, Excalibur_c20309_539, RAC875_c38693_499 and Excalibur_c5851_1661 loci, GA at Excalibur_c42979_666, CT at BS00104177_51 and AA at Kukri_c4352_194, tplb0026f23_1134 and Excalibur_rep_c92684_578 loci showed significantly higher Fa* and lower Fb*  were in a 20‐kb block, and there were two main haplotypes for this block. The haplotype CATCAT possessed extremely significantly higher flour redness than haplotype TGGTGC in all 8 environments  gene, which was predicted from Arabidopsis thaliana. Excalibur_c5938_1846, Excalibur_c5938_1703 and BobWhite_c10975_60 on 7BL were located in this gene . The mutant line Kronos3570 showed a G to A mutation at the 3051‐bp position of the TaRPP13L1‐A1 gene, and thus, it resulted in a tryptophan stop codon at amino acid position 1016. The mutant line Kronos4546 showed a G to A mutation at the 3039‐bp position of the TaRPP13L1‐B1 gene, and thus, it resulted in a tryptophan stop codon at amino acid position 1012. These mutations both caused premature stop codons in the TaRPP13L1 gene. Determination of flour colour parameters of Kronos3570 and Kronos4546 indicated that both mutant lines Kronos4546 and Kronos3570 possessed significantly higher Fa*  and lower Fb*  than the wild cultivar  in the natural population Table . As showFor GPC, 12 cultivars containing the alleles with a relatively high GPC at the 11 loci  showed an average of 14.3% (ranging from 13.3% to 15.8%), whereas 3 cultivars without any alleles showing relatively high GPC exhibited an average of 12.4% (ranging from 12.0% to 13.0%).For WGC, 18 cultivars containing the alleles with a relatively high WGC at the three loci  showed an average of 31.0% (ranging from 28.4% to 34.1%), whereas 61 cultivars containing the alleles with a relatively low WGC exhibited an average of 28.9% (ranging from 25.9% to 32.4%).For FL*, 31 cultivars containing all alleles with relatively high flour lightness at five loci  showed an average of 96.0 (ranging from 94.2 to 96.6), whereas 13 cultivars containing all alleles showing relatively low flour lightness exhibited an average of 94.9 (ranging from 94.3 to 95.6).For Fa*, 32 cultivars containing all alleles with relatively high flour redness at the 20 loci  showed an average of −0.38 (ranging from −0.86 to −0.01), whereas the 2 cultivars containing all alleles with relatively low flour redness exhibited −1.17.For Fb*, cultivar Jingshuang 16 with all alleles showing relatively high flour yellowness at 14 loci  exhibited 11.8, whereas 24 cultivars containing all alleles with relatively low flour yellowness showed an average of 7.3 (ranging from 5.9 cm to 9.1). These data suggest that pyramiding multiple above‐mentioned SNPs loci for each quality trait is more effective for improvement of wheat quality in Chinese wheat breeding programmes.TaRPP13L1‐B1a and TaRPP13L1‐B1b alleles, a dCAPS marker was developed, and cultivars with an 183‐bp fragment possessed TaRPP13L1‐B1a allele by the digestion of restriction enzyme SphI  and WGC (30.3) than cultivars with BS00046964_51_AG  showed significantly higher HI (64.5) and Fb* (9.42), and lower FL* (94.9) than cultivars with BS00000020_51_CC ’, most areas focus on producing high‐gluten, high‐quality wheat, and some areas along the Huai River south of Henan focus on planting soft low‐gluten, high‐quality wheat. Therefore, revealing the genetic basis of quality traits and improving quality are of great significance in this wheat region.Pina and Pinb genes on chromosome 5DS in the Ha locus mainly control grain hardness in bread wheat  of Pinb‐D1b is significantly higher than that of Pinb‐D1a, which is consistent with the results of previous studies  are important quality indicators for wheat. In the current study, 7 multienvironment‐significant SNPs on 6AS were significantly associated with GPC, and 2 of them were also significantly associated with WGC. Previous studies and our QTL mapping also detected some QTLs on 6A for GPC (Bordes Pinb) on 5DS was extremely significantly detected in all 8 of the environments. The FL* of Pinb‐D1a is significantly higher than the FL* of Pinb‐D1b, which is consistent with a previous study  was also detected in all 8 environments and was significantly associated with HI and FL*, which is consistent with a previous study  was also significantly associated with flour yellowness. In addition, Excalibur_c42979_666 (it may be linked to PPO gene) on 2AL was detected in all 8 environments and extremely significantly associated with Fa* and Fb*.SNPs significantly associated with Fb* were mainly observed on chromosomes 1AL, 2AS, 2AL, 2BL, 3B, 4AL, 5BL, 5DS, 6BS, 7AL, 7BL and 7DL. On 5DS, the SNP BS00000020_51  and was further used to select for white endosperm mutants in recombinant lines carrying a mutant of the Psy‐E1 gene. The results showed an association of GYPC with allelic variations of Psy‐1 gene in hexaploidy wheat. However, a white endosperm mutant mapped to chromosome arm 7EL showed no mutations in Psy‐E1, suggesting the existence of additional gene(s) affecting GYPC in 7EL. This hypothesis was further supported by the mapping of QTL for GYPC on 7AL proximal to Psy‐1 in a cross of durum wheat UC1113 and Kofa. Therefore, the authors speculated that Psy‐1 and at least one additional gene in the distal region of the long arm of homologous group 7 were associated with flour colour. Roncallo et al. . In summary, we concluded that there were at least two important genes affecting flour colour on the 7 homologous chromosomes.Zhang and Dubcovsky  reportedo et al.  also detPsy genes, which had been verified to sharply affect flour colour  and cultivated areas, a total of 163 bread wheat cultivars from the Yellow and Huai Valley of China, including Henan, Hebei, Shaanxi, Shanxi and Shandong Provinces, were selected for the GWAS (Chen 10 recombinant inbred lines (RIL) derived from the UC1110/PI610750 (UP) cross was planted at Zhengzhou during the 2015–2016 cropping season. A doubled haploid (DH) population of Bainong 64/Jingshuang 16 (BJ) comprising 143 lines was planted at the Yuanyang Scientific Research and Education Center of Henan Agricultural University  during the 2017–2018 cropping season. Each population was planted in a randomized complete block design with two replications. The two populations grew well, and no lodging occurred in field.A biparental population with 187 FIn order to obtain a sufficient quantity of seeds for accurate assessment of wheat quality, kernels of two replicates for each cultivar were fully mixed together. The kernel hardness index (HI) was measured by the Perten Single Kernel Characterization System (SKCS) 4100 using 300 randomly selected seeds. The grain protein content (GPC) and wet gluten content (WGC) were tested by near‐infrared spectroscopy (NIRS) using a Perten DA 7200 instrument  under 12% moisture content. Each sample was measured twice, and the average was used for further analysis.et al.  according to the method of Chen et al. . Flour cet al. . The colhttp://www.bjcompass.com/), using the wheat 90K genotyping assay  was given. After the GWAS, significant SNPs with minor allele frequency (MAF) > 0.05 were used for further analysis.The GWASs were implemented with the GAPIT packages . Inclusive composite interval mapping of the additive genetic model (ICIM‐ADD) was selected as the mapping model, and other parameters referred to the default design. The threshold for declaring the presence of QTL was fixed at a limit of detection (LOD) value of 2.5. Haplotype analysis was performed using software Haploview 4.2.A genetic linkage map of the UP population was constructed using 558 unique marker loci extracted from 1494 polymorphic probes , the entire genome database of Chinese Spring (http://plants.ensembl.org/Triticum_aestivum/Info/Index) and Aikang 58 (unpublished data) to identify putative genes. The most significant SNP was used to identify candidate genes in its flanking 2 Mb range in the genomes of Chinese Spring and Aikang 58. The dCAPS markers were designed by dCAPS Finder 2.0 (http://helix.wustl.edu/dcaps/dcaps.html).The flanking sequences of significant SNPs were blasted in the National Center for Biotechnology Information (NCBI) database . All of the EMS mutant lines of Kronos were sequenced using exome capture and next‐generation sequencing  and each (B) values among quality traitsClick here for additional data file.Table S2 GWAS result for hardness index in 8 environmentsClick here for additional data file.Table S3 GWAS result for grain protein contents in 8 environmentsClick here for additional data file.Table S4 GWAS result for wet gluten content in 8 environmentsClick here for additional data file.Table S5 GWAS result for flour lightness in 8 environmentsClick here for additional data file.Table S6 GWAS result for flour redness in 8 environmentsClick here for additional data file.Table S7 GWAS result for flour yellowness in 8 environmentsClick here for additional data file.Table S8 T‐test analysis for multienvironment‐significant SNP in different traitsClick here for additional data file.Table S9 Comparison of flour redness for TaRPP13L1 and Psy alleles in natural populationClick here for additional data file.Table S10 Development of molecular markers for six quality traitsClick here for additional data file.