Malus × domestica). In this study, we qualify and quantify the compatibility of SNP calling, defined as SNP calls that are both accurate and concordant, across both arrays by two approaches. First, the concordance of SNP calls was evaluated using a set of 417 duplicate individuals genotyped on both arrays starting from a set of 10,295 robust SNPs on the Infinium array. Next, the accuracy of the SNP calls was evaluated on additional germplasm (n = 3141) from both arrays using Mendelian inconsistent and consistent errors across thousands of pedigree links. While performing this work, we took the opportunity to evaluate reasons for probe failure and observed discordant SNP calls.Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. As new arrays are developed to take advantage of new technology and of improved probe design using new genome sequence and panel data, a need to integrate data from different arrays and array platforms has arisen. This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple  that were further curated, leading to a final set of 8412 (81.7%) SNPs that were deemed compatible. Compatibility was highly influenced by the presence of alternate probe binding locations and secondary polymorphisms. The impact of the latter was highly influenced by their number and proximity to the 3′ end of the probe.The Infinium and Axiom SNP array data were mostly compatible. However, data integration required intense data filtering and curation. This work resulted in a workflow and information that may be of use in other data integration efforts. Such an in-depth analysis of array concordance and accuracy as ours has not been previously described in the literature and will be useful in future work on SNP array data integration and interpretation, and in probe/platform development.The online version contains supplementary material available at 10.1186/s12864-021-07565-7. Single nucleotide polymorphism (SNP) array technology has been increasingly used to generate large quantities of SNP data for use in genetic studies. Over time, next generation arrays are developed that use new sequence data and/or new genome drafts to either refine or expand upon the set of SNPs used in previous arrays. Additionally, different SNP array technologies have been developed, resulting in different array platforms, creating a need for data harmonization and integration .Malus × domestica), where a large amount of SNP array data has been generated using the Infinium® IRSC 8 K [This need has been faced in apple , pomological textbooks , histoks . Thus, individuals with an alternate allele(s) at these secondary polymorphisms have distinct clustering patterns . As individuals may differ for their secondary polymorphism, this gives raise to multiple sub-clusters for the heterozygous genotypes. The location of the Ab and aB cluster moves towards the AA and BB homozygous clusters, respectively, with decreasing intensity of the a and b alleles, which may impact cluster separation and calling accuracy. Secondary polymorphisms may also lead to so called null alleles, where probes completely or nearly completely fail to bind to some of the target genomic sequences [Mendelian inconsistent and consistent errors in SNP array data can result from the presence of secondary polymorphism(s) on probe sites and/or the presence of duplicated or paralogous sequences. Secondary polymorphisms are sequence differences between the probe and intended target genomic sequence. They may impact the affinity by which a probe binds to a genomic sequence, resulting in distinct signal intensities for the same marker allele . While creating the integrated and highly curated dataset, we took the opportunity to thoroughly evaluate observed discordances and inaccurate SNP calls. Thus, the goals of this study were i) to qualify and quantify the compatibility, defined as SNP calls that are both accurate and concordant, of SNP calls across both arrays and ii) to evaluate reasons for observed probe failures and discordant SNP calls in order to improve SNP array data interpretation and probe/platform development. Towards these goals, this study included classical concordance evaluations across individuals genotyped on both platforms, as well as accuracy evaluations by detecting and evaluating Mendelian inconsistent and consistent errors across pedigrees on a mixed dataset. We hereby updated the apple integrated genetic linkage (iGL) map [This study was undertaken in view of our need for an integrated high-quality dataset of Illumina Infinium® 20 K and Affymetrix Axiom® 480 K SNP array data in apple (iGL) map  and usedThere were 10,295 SNPs that passed the Infinium SNP data curation steps and thus were included in the genetic map. Of these, 94.1% (9685) were SNPs retained from the iGL map , and when evaluating only 8412 SNPs that were also concordant in Axiom data (Subset 2), respectively  required no additional adjustments, whereas 1995 (23.7%) did. The most common adjustments needed were to set errant Axiom SNP calls between clusters to missing data (class B), to reassign clusters in Axiom data (class C), usually heterozygous, and to set to missing data discordant SNP calls when there were only one or two of these (class D). These were followed by four less common classes. Examples of each class and some further descriptions of each can be found in Additional file There were 1883 SNPs where Axiom data was deemed incompatible with included Infinium data. They were classified into nine different classes (J-R)  of the 10,295 included SNPs and 7175 (92.9%) of the 7724 excluded SNPs  at probe sites negatively impacted SNP inclusion of Infinium data and SNP call compatibility of included SNPs in Axiom data. Increasing numbers of secondary polymorphisms were correlated with reduced SNP inclusion and compatibility rates  between the Infinium® 20 K SNP array and the Affymetrix Axiom® Apple 480 K SNP array across 10,295 SNPs we deemed robust in Infinium data Table . Our ini.7% betweAB sub-clusters and/or null alleles, which made them highly informative for the purpose of linkage map creation using full-sib families [A set of 10,295 SNPs passed our rigorous data curation step  [Medicago sativa (100%) [Populus nigra (100%) [Glycine max (> 99%) [Cicer arietinum (> 99%) [Juglans regia (99.2%) [Overall, average within-platform repeatability of genetic duplicates was high, depending on the SNP subset used Table . This hi9.7076%) , Medicaga (100%) , and Popa (100%) , and for (> 99%) , Cicer a (> 99%) , and Jug (99.2%) . AlthougJ. regia 700 K SNP array [It should be noted that a very minor level of discordancy could be due to minute mutation differences between clones held in different collections. However, the chance that a SNP coincides with a mutation between clones is thought to be rare because mutation rates are usually low, and regions of mutation are usually small. Therefore, we assumed discordant SNP calls to have been entirely due to cluster position variance within each platform. Some level of discordancy could be attributed to laboratory or sampling issues. Low quality DNA has been the reason for high levels of discordancy among technical replicates in the Axiom NP array .The results of this study should not be used to determine whether the two platforms differ in their overall performance, meaning that the results and interpretations are not meant to be an endorsement or a repudiation of either platform. This is because the SNPs on the Infinium 20 K apple array had all been consciously included in the Axiom array to maximize the potential for cross-platform compatibility, even when Axiom’s selection criteria for array inclusion or calling performance were violated . The cluHowever, despite this high level of concordance, 1995 SNPs showed significant clustering differences between the platforms, resulting in the need for additional work to maximize compatibility. Of the 10,295 SNPs included from the Infinium array, 19% required adjustments to make them compatible and 18% were deemed incompatible. Researchers using data from both arrays could try to avoid issues by only using the 6417 SNPs that did not require any additional adjustments, though this would result in either a smaller number of SNPs available or a higher level of missing data present in the combined dataset, which this study sought to avoid. However, manual reclustering of the 1995 cluster plots was very time consuming. The most common manual curation performed was to set some percentage of individuals to missing data in the Axiom data due to their errant positions between clusters in cluster plots  similarity between probes and intended genomic sequences on SNP inclusion and compatibility was examined. Hereto, all the SNPs’ BLAST hits that met the various E-value thresholds were compared together regardless of where sequence differences occurred. This was a conscious decision, as the nature of the differences between intended and non-intended targets was often complex and involved multiple mismatches, making it difficult to quantitatively classify the differences in affinity at a more detailed level.As much as 94.1% of the probe sequences had a full match to the GDDH13 WGS. This high proportion was not surprising, considering the full genetic relatedness between GDDH13 and ‘Golden Delicious’ , the souMalus [Another factor that influenced exclusion and incompatibility rates was the number of paralogous binding sites as estimated by the number of BLAST hits. Probes with multiple BLAST hits were more likely to be excluded Fig.  because Malus . Other SMalus , which pThough SNPs whose probes had more BLAST hits were more likely to be removed during the SNP data curation steps and to be deemed discordant, many were both included and deemed compatible in Infinium and Axiom data, respectively  to the target SNP between probes and their target and non-target sequences was identified as a major factor in SNP inclusion and compatibility. However, secondary polymorphisms identified roughly on the final 40 (of 50) positions on the Infinium array and the final seven positions on the Axiom array did not impact SNP inclusion and compatibility. The results could be used to help guide further SNP filtering for array construction and SNP array data interpretation.Illumina Infinium 20 K SNP array data came from previous , 20, 27 Malus UNiQue genotype code (MUNQ) value as described in Muranty et al. [Each accession used for SNP curation, the analysis of duplicates, and the analysis of SNP call concordance and accuracy in this study is listed in Additional file y et al. .Five general groups of germplasm were distinguished, which partly overlapped. The first group was comprised of 1566 Infinium genotyped cultivars/accessions, some of which were included more than once as different accessions from different institutions. The second group was comprised of 1466 individuals genotyped on the Axiom array. The third group was comprised of 30 full-sib families totalling 2422 seedlings genotyped on the Infinium array and evaluated in various previous genetic linkage mapping and QTL discovery studies , 22, 27 Malus floribunda 821 from both platforms and rounding down to the closest whole percentage point, as Malus floribunda 821 was most prone to atypical clustering with each of the arrays and showed a relatively high level of discordant SNP calls compared to other expected genetic duplicates, due to its wild origin compared to the domesticated pool for which both arrays were initially built.Germplasm groups 1 and 2  were used to identify genetic duplicates. SNP calls for the first group were generated using default settings in GenomeStudio® v2.03 . SNP calls for the second group were obtained from Muranty et al. . IndividWe used a revised version of the iGL map  to ensurNew cM position estimates were made for SNPs where rearrangements resulted in conflicts with the original cM positions. This was accomplished by using a linear algebra approach using the relative physical distances to the nearest flanking SNPs with concordant positions. Following this step, SNPs that were originally not included in the 20 K iGL map were placed into the genetic map. Their genetic positions were estimated using the same algebraic method as previously described. For rearranged SNPs that required new cM positions that had no blast hits, a cM position was assigned that was equidistant between the genetically flanking SNPs. Occasional SNPs that required rearrangement and new cM positions to address spurious double recombinations (due to incorrect SNP order) had BLAST hits that were inconsistent with flanking SNPs. If one of these adjacent SNPs was inconsistent with the SNP that needed a new cM position and its other flanking SNP, another adjacent SNP that had a consistent BLAST hit was used for cM position imputation using linear algebra. If this was not possible, a cM position that was equidistant between the adjacent SNPs was assigned instead. The ordering of SNPs for the first ~ 6 Mb of LG1, was based on unpublished data made available by Eric van de Weg.AA, AB, and BB clusters. SNPs with null alleles had individuals with cluster positions of lower intensity, or in other words, a lower “Norm R” value, in GenomeStudio cluster plots compared to true homozygous clusters. These were identified via the individuals with those lower intensity cluster positions also having a parent or an offspring with a cluster position of lower intensity located below the opposite homozygous cluster. In such a parent-offspring relationship, GenomeStudio assigns one of the individuals in this relationship a SNP call of “AA” and the other individual a SNP call of “BB”, when in reality the two individuals share a third allele, termed “Null” . These null alleles may be due to either indels or additional polymorphisms on probe sequences resulting in calling problems obscuring the true alleles at the intended locus [After the construction of this preliminary updated map, all Infinium genotyped individuals that had direct parent-child relationships  and using different subsets of SNPs. These subsets were 1) the SNPs that passed the Infinium data curation steps in this study, 2) SNPs considered concordant and accurate between both arrays, 3) SNPs in the Axiom array previously deemed robust , 4) SNPsDuring the above-described identification and evaluation of discordant SNP in duplicated individuals, SNPs deemed compatible, which we defined as having concordant and accurate SNP calls between both platforms, were classified into nine groups according to the type and prevalence of the applied manual cluster adjustments to achieve compatibility . This metric was used instead of the difference between the minimum and maximum Theta values to account for occasional outliers.We also assessed how secondary polymorphisms at the probe site were influencing SNP inclusion rates of Infinium data and SNP call compatibility rates of included SNPs in Axiom data. To accomplish this, we first identified secondary polymorphisms among 53 individuals from the 480 K Axiom array SNP discovery panel   and/or gaps (G).Additional file 10. Families used for the improvement of the 20 K integrated genetic linkage map.


Os10g0469000-GC) to 15 (Os06g18720-AC). Superior haplotypes for RS and PGI, the candidate gene Os06g11100 (H4-3.28% for high RS) and Os08g12590 (H13-62.52 as intermediate PGI). The identified superior donors possessing superior haplotype combinations may be utilized in Haplotype-based breeding to developing next-generation tailor-made high quality healthier rice varieties suiting consumer preference and market demand.Increasing trends in the occurrence of diabetes underline the need to develop low glycemic index (GI) rice with preferred grain quality. In the current study, a diverse set of 3 K sub-panel of rice consisting of 150 accessions was evaluated for resistant starch and predicted glycemic index, including nine other quality traits under transplanted situation. Significant variations were noticed among the accessions for the traits evaluated. Trait associations had shown that amylose content possess significant positive and negative association with resistant starch and predicted glycemic index. Genome-wide association studies with 500 K SNPs based on MLM model resulted in a total of 41 marker-trait associations (MTAs), which were further confirmed and validated with mrMLM multi-locus model. We have also determined the allelic effect of identified MTAs for 11 targeted traits and found favorable SNPs for 8 traits. A total of 11 genes were selected for haplo-pheno analysis to identify the superior haplotypes for the target traits where haplotypes ranges from 2 ( Oryza sativa L.) is a major source for carbohydrate of more than 50% of the global population. In recent times, an increase in living standards has created awareness among the peoples all over the world for consumption of superior quality rice to improve their health status1. Thus, the presence of premium grain quality in rice is one of the crucial determinant factors for all the stakeholders including breeders, producers and consumers2. Currently, more efforts have been put forth to improve the rice quality traits by utilizing the key allelic variants from the germplasm variation to meet quality rice demand of consumers and potential markets. But, what are the key grain quality determinants? They are broadly classified in to, milling, appearance, cooking/eating parameters and nutritional quality. Among these, the later two are associated with the end-users. The physicochemical characters which determine the cooking and eating quality are amylose content (AC), gel consistency (GC) and gelatinization temperature (GT) or alkali spreading value (ASV). Generally, rice with intermediate amylose, GT and soft to medium gel consistency are preferred by consumers in South Asia3. Apart from quality, these days people have more concerns about their health, and prefer low glycemic index rice varieties which could be safe for diabetics and obesity. Hence, grain quality improvement of rice together with the lower glycemic index are of vital importance for the rice breeders in the current scenario of increasing diabetic population all over the world, especially in the Asia. Low GI diets effectively prevent type II diabetes5 and consumption of high amylose class rice varieties with soft-textured on cooking might be an alternative for intermediate amylose rice varieties especially for those who are suffering from type II diabetes6. The most critical factor responsible for breeding low glycemic index (GI) rice is resistant starch (RS). It is the starch portion resistant to enzyme hydrolysis that escapes the small intestine and enters the large intestine where it gets fermented and slowly releases glucose to the bloodstream. Generally, it has been reported that increased level of RS content in rice grains lowers the GI value ensuring a negative correlation with amylose content7. Recently low-to-intermediate RS phenotypic variations in rice panel were identified, resulting novel RS associations to numerous genes associated with amylopectin biosynthesis and degradation7.Rice  as on of the prominent techniques to identify marker-trait associations (MTAs). Research on the glycemic index identified a novel association of candidate loci Os05g03600 reporting intermediate to high GI variations9. Another hotspot on chromosome 6 was found to include GBSSI, hydrolase genes and genes involved in signaling and chromatin modification with differential methylation patterns in GI6.1 region. Alternative splicing of GBSSI promoter region resulted in intermediate to high GI variations. Novel SNP associations on chromosomes namley 2, 5, 6 and 11 has been reported and these SNPs influences the final viscosity variations but no significant association with GI9. Besides, the predicted glycemic index (PGI) is estimated by in-vitro enzymatic action of starch digestion leads to hydrolysis and the glycemic index (GI) estimated in-vivo, which requires human clinical evaluation of the two to three hours of blood glucose response after food intake. Thus, it is more time consuming and resource demanding. The in-vitro digestion methods have been developed to measure the starch hydrolysis index and it is used to calculate PGI using the formula developed10. Significant positive association (r = 0.946) between pGI and GI of rice samples by using bread as the reference11.In any crop species, genetic diversity plays important role in breeding elite varieties. Identification of favorable alleles12. Nine MTAs were identified for seven QTLs namely GS3, TUD, qGRL7.1, qPGWC7, qGL3.4, qGW1.1 and qGW10.2 controlling quality traits13. Numerous genes/QTLs were identified for grain number and grain length viz., GW2, GIF1, qSW5, GS3, GS5, qGL3, GW8, GS6, GS2, GL7/GW7, OsMA PK6, GLW7 and GAD126. QTLs controlling grain size have also been detected via genetic mapping and association studies32. Starch is made upof two components namely amylose and amylopectin. Amylose, an important parameter affecting cooking and eating quality of rice directly involved in changing the grain texture by absorbing water on cooking. However, the difference in the amylopectin structure of similar AC rice explained variations in the textural qualities of cooking. It is a well-established fact that the waxy (Wx) gene, granule-bound starch synthase (GBSSI) located on chromosome 6 and genomic regions surrounding the gene are highly diverse33. The Alk locus carrying SSIIa (starch synthase IIa) mapped on chromosome 6 confers alkali spreading to determine the gelatinization temperature of milled rice34. There are several other starch synthesis-related genes involved namely, starch debranching enzymes, starch branching enzyme soluble starch synthase with a predominant role in controlling eating and cooking properties of rice35. Two QTLs for resistant starch in rice qRS7-1 and qRS7-2 (chromosome 7), explaining phenotypic variance from 7.6 to 17.3%, have been reported36.Recently, 11 candidate loci controlling grain quality traits has been identified that are involved in the starch biosynthesishaplotype-based breeding.Rice grain quality is one of the major determinants in selecting parents for any breeding program and the genetic constitution of the genotypes determine the effect of prevailing environmental conditions on the developmental process involved in seed formation and maturation. The good quality grain is an important factor influencing its acceptance by consumers and thus, is one of the major traits in rice breeding to withstand marketability in trade and commerce. However, one of the significant challenges to date has been the lack of extensive knowledge of the genetic and molecular basis of several grain quality traits including GI. The availability of 3000 rice genomes (3 K-RGP) offers opportunities for harnessing haplotype diversity for GI along with other critical quality-related genes and enhances the possibility for the identification of ideal haplotypes for deployment in rice breeding. The current study was undertaken with the objective to identify the candidate genes and their superior haplotypes associated with RS, PGI and nine major grain quality traits across the 3 K-RGP subset under transplanted (TPR) situation Fig.  and explThe subset of 3 K RGP was extensively phenotyped to study the variations for RS, PGI along with nine other grain quality-related traits. The results revealed significant variations among the accessions Table . The RS p-value-0.048) and negative correlation  with RS and PGI, respectively whereas; it showed a negative significant relationship with ASV and GC. KL had a significant positive correlation with LBR and KLAC whereas it was negatively correlated with KB  > 5). In general, modification of bonferroni correction significant threshold value to control the false positive rate in a single-locus GWAS are so conservative that some associated SNPs may be eliminated. To avoid this problem multi-locus GWAS of mrMLM was also conducted to confirm the MTAs with the LOD score of greater than 3. A set of 41 causatives MTAs were detected for all the investigated quality traits in MLM (Table Single locus GWAS (MLM) was performed using the phenotypic data with approximately more than 0.5 million (500 K) SNPs of concerned accessions and the peak associations were predicted based on considering the value of PVE (phenotypic variance explained) with suggestive significant LM Table . For KL,LM Table . For KB,LM Table . LBR recLM Table . For ASVLM Table . Three MLM Table . For KBALM Table . For LERLM Table .Table 1G37, FastmrMLM38 and FASTmrEMMA39 by adopting the critical threshold of significance for SNP-trait association was set at a LOD score of greater than 3. On comparing both MLM and mrMLM, we found that all the MTAs identified by MLM were also detected by any one method of mrMLM namely mrMLM, FastmrMLM and FASTmrEMMA with significant LOD score  was also conducted with three models mrMLMre Table a. In mrMAssociated MTAs of RS, PGI and nine quality traits were subjected for the identification of favorable SNP alleles Table b. In thiAll the MTAs associated with the quality-related traits were subjected to the identification of candidate loci using RAP database Table b. Out ofOs08g0276000, Os02g0135800 and Os10g09430 for AC, Os05g09510 for KB and LBR, Os05g0566200 and Os12g0484375 for KLAC, Os09g0469400 for LER, Os02g0594100, Os02g0191000, Os11g20120 and Os11g20130 for RS and Os08g0520400 for PGI had registered one haplotype, respectively. The loci namely, Os06g18710 and Os06g18720 for AC, Os05g0187500 for KB and LBR, Os06g12380 for ASV, Os08g01010 for KBAC, Os06g16080 and Os02g16970 for RS and Os07g0675300 for PGI were found to be recorded 15 haplotypes explaining maximum diversity. On the other hand, 14 loci had 2 haplotypes, 7 loci had 3 haplotypes, 4 loci had 4 haplotypes, 3 loci had 5, 1 locus had 8, 10 and 14 haplotypes, respectively , Os06g18710  and Os06g18720  had shown positive allelic effect with the superior haplotype of increasing in amylose content. For RS and PGI, the candidate loci Os06g11100  and Os08g12590 (H13-62.52 as intermediate PGI)  and H7 (5.67 mm), Os11g0706500 had H2 (6.41 mm) as a desirable haplotype for development of long and medium slender grain rice varieties . Os05g0187500 and Os03g35870 were associated with KB had H7 (2.37 mm) and H1 (2.42 mm) as superior haplotypes for the corresponding loci. For LBR, Os05g0187500 had H9 as desirable haplotype having genotypes recorded higher length-breadth ratio (2.89) possessing slender grain rice lines. Os06g0229000 and Os06g12380 associated with ASV showed H4 (3.67 of mean score) and H5 (4.48 of mean score) as anticipated one since intermediate ASV is desirable to breed good quality rice varieties. The other loci for RS Os06g16080 registered H11 (5.37%) for high and H7 (2.95%) as desirable haplotypes for intermediate RS content, respectively. Other loci, Os02g16970 linked with RS reported H14 (4.49%) as a superior haplotype for improving RS content  plays a key role in amylose biosynthesis and the formation of long-chain amylopectin43. Another gene Starch synthase IIa (SSIIa) modifies amylopectin structure and improving grain quality45. Likewise, several other genes namely SSI, SSIIIa, isoamylase1 (ISA1), pullulanase (PUL), branching enzyme (BEI and BEIIb)50, have also been reported to control starch structure formation and modifies the physicochemical properties of rice grains. Therefore, amylopectin content and structure alteration have a significant impression on the modification of starch granules and affect cooking properties in rice.Rice, one of the essential staple food crop species is consumed by half of the global population especially in Asian continents. The ever-increasing population of the world demands more than 50% additional rice production to be attained by 2030 to meet future needs51. Currently, breeders and geneticists are making efforts to develop high RS rice varieties. Cultivars rich in amylose content of rice, maize and barley developed by either mutational breeding or biotechnological approaches have been found to possess high RS content54.In recent times, non-infectious diseases cause several health-related issues like cardiovascular problems, obesity and diabetics due to the high consumption of calories and improper physical exercises. Recent study reported that high amylose starch-rich RS decreases the glycemic value of starch, insulin response in human metabolism and reduce the risk of type II diabetes mellitus and other health-related issueshaplo-pheno analysis was carried out to ascertain superior or appropriate haplotype for the improvement of grain quality in rice breeding programs.In the present investigation, diverse panel of rice accessions (3 K-RGP sub-set) were not only phenotyped for grain quality-related traits but also phenotyped for the most important health/well-being related traits such as RS and PGI to map candidate SNPs, functional annotations and to unravel the haplotype diversity of identified candidate loci across the 3 K-RGP. Then, the 7. We identified nine strong MTAs for RS with significant PVE on chromosomes 1, 2, 6, 8 and 11 (Table sbe3-rs on chromosome 256 and SSIIIa (Os08g0191433) on chromosome 845 involved in the production of RS content and it may be associated with expression of Waxy gene as well. In our study, we report two MTAs in proximity to the candidate regions mentioned above of chromosome 2 (Os02g0594100) and chromosome 8 (Os08g0335500) with not much significant haplotype diversity but significant variations in the trait value was observed among the accessions57. We also identified a locus on chromosome 6 (Os06g11100) near (1 Mb away) to the position of starch synthase IIa  gene, significantly affecting GC, degree of starch crystallinity by increasing the fractional amount of A chains in amylopectin58. All the identified MTAs and their corresponding candidate loci exhibit a significant number of variable haplotypes for the traits studied. In recent past, the discovery of mutation/variants each in GBSSI, SSI, SSIIa, and SSIIIa with a potential to increase RS content and hydrolysis index in rice has been reported60. For PGI, we identified a total of 9 associations on chromosome 1 (Os01g0548000), 3 (Os03g38070), 7 (Os07g0675200), 8 (Os08g12580 and Os08g0520300), 9 (Os09g13060 and Os09g29090) and 11 (Os11g10500 and Os11g22020) , Chr 3 (snp_03_22422723), Chr 9 (snp_09_21456056 and snp_09_21523496) and Chr 11 (snp_11_334055 and snp_11_28758733). Similar is the case for GC; our results match with “qAC7” associated with RM8261 in the physical position Chr7: 25,866,58161. Here, we identified a candidate Os07g0597400 on chromosome 7 associated with GC. A QTL qRS7-2 on chromosome 7 associated with RS content between RM3404  and RM478  was identified and reported36.RS, a valuable trait was found to have a significant positive correlation with amylose content and was negatively correlated with glycemic index as suggested by several previous reports and thus a suitable parameter for rice consumption to improve the health status of those who are suffering from diabetics, colon cancer, and obesity62, by several other loci63 and even by unidentified non-waxy genes64. Our study reported nine associations for AC 67. Several other loci were identified on Chromosome 2, Os02g0135800 encoding for WD domain, G-beta repeat domain-containing protein, expressed and Os02g05180  encrypting for retrotransposon protein, putative Ty3-gypsy subclass. Starch branching enzyme 3 (SBEIIb- Os02g32660) is essential for amylopectin synthesis in the endosperm69. Several reports found that high amylose rice and maize reveals in amylose extender (ae) phenotype due to the inactivation of SBEIIb46. These results clearly confirmed that a network of genes are involved in the biosynthesis of amylose. Our association analysis study provides a stronger picture of how genetic network of starch synthesizing genes involved in cooking and eating quality improvement in rice. We also identified an association on chromosome 11, S11_28809145 on Os11g0704000 with the function of selT/selW/selH selenoprotein domain-containing protein, expressed slightly closer to Os11g3133070.Over several decades, quality traits like AC, GC, ASV, LER, etc., of rice grains have been extensively studied and it has been reported that AC is governed by waxy gene AC Fig.  and the 13, who reported quite a lot of QTLs for grain length, grain width and grain length–width ratio on chromosome 3, 5 and 7 by utilizing 3 k panel sub-set. To date, several hundred QTLs associated with grain size have been identified and functionally characterized viz., GS3, GW2, qSW5/GW5, GS5 and qGL3/qGL3.174. Among the above said QTLs GS3 and qSW5 showed consistent and sturdy effect on grain size variations reported in our investigation also 77. For instance, the locus Os05g0187500 identified having large haplotype diversity responsible for the putative function of IQ calmodulin-binding motif family protein exhibits peak association close to the SNP position of S5_5361877 reported for KB and LBR78.Kernel length, kernel breadth, and LBR are the important traits to assess the grain size and dimensions for selecting appropriate breeding lines for their consumer’s preference and market acceptability. Medium slender rice is more preferable than bold rice varieties. For kernel length, an MTA was identified on chromosome 11 (S11_28909306) with 9 haplotypes. For kernel breadth, one MTA was observed on chromosome 5, two on chromosome 3 and one on chromosome 2. LBR determines the grain size and is controlled by a complex network of genes and also affected by environmental factors. Our study reported similar findings ofOs06g0229000 and Os06g12380) with worthy haplotype diversity in the range of 4 to 15 haplotypes, respectively. The same QTL on chromosome 6 at the position of 6,726,252 associated with ALK gene was reported 78. A study conducted with 258 accessions of 3 k panel also reported the same genic location (S6_6752888) for GT and a candidate of ALK gene encoding SSIIa affecting the chain-length distribution of amylopectin causing alkali disintegration of rice grains79. For gel consistency, we identified two candidates on chromosome 7 (Os07g0597400) and 10 (Os10g0469000) with minimum haplotype variations of 8 and 2. Previously, a qGC6 was reported between close proximity to WAXY gene13. More importantly, a new novel association for GC is identified on chromosome 7 had near vicinity and it was not previously reported. Linear elongation ratio is an important cooking quality trait and we identified an associations for LER on chromosomes 9. A QTL for LER was reported on chromosome 4 with the marker interval of C933 to C946 using RIL population of indica x indica hybrids80.ASV is an indicator for gelatinization temperature, we identified a strong and peak association on chromosome 6 at the position of S6_6711302  had maximum haplotype diversity with the superior haplotype of H3 (Os06g18710 – 25.50%) possessing high amylose class, LBR (Os05g0187500) owns H9 (2.81) as superior one among 15 haplotypes, RS (Os06g11100) holds H4 (3.28%) as topmost one from all the 4 haplotypes.On a whole, amylose and amylopectin production is a complex phenomenon governed by several networks of genes and their coordinating expression during grain filling and seed maturation affecting the grain textural properties and even prevailing environmental conditions also. Noteworthy, it is not an easy breeding protocol to improve the quality aspects of rice varieties by selecting a notable gene that may be either superior or inferior for a specific trait and introgression by markers assisted pyramiding and selection. In this, we identified several superior haplotypes by studying haplotype diversity and linking haplotype variations with the trait value. Assembling superior haplotype combinations for most of the grain quality traits viz., AC, ASV, GC, LBR and RS coupled with GI in to one background to maximize the textural properties of grains, yield and most of morpho-agronomic traits to develop tailor-made new generation rice with enhanced genetic gain compared to available popular mega rice varietieshaplotype-based breeding program to develop elite lines with low glycemic index value with desirable quality traits by assembling superior haplotypes suited for different situations (Table 9 reported as low GI (< 55). Apart from this we also selected different classes of glycemic index lines from the 3 K sub-panel grown in 2019WS and different quality-related traits were phenotyped and result showed that four lines namely BAIANG 6::IRGC 6129–1 from Indonesia, MAKRO::IRGC 74,763–1 from India, AUS 329::IRGC 29,116–1 from Bangladesh and KOTTEYARAN::IRGC 47,383–1 from Srilanka possesses medium slender grains with good elongation ratio and medium to soft gel consistency after cooking  during dry season 2017 (DS2017) and Wet season 2018 (WS2018) Table . These a84 and the mean was calculated. Amylose content of the grains was estimated by the rapid protocol of cut grain dip method85. For cooking-related parameters, 20 kernels from each entry were soaked in 5 ml of distilled water in 15 ml test tubes for 20 min and cooked in a boiling water bath at 100 °C intended for the time period of 8 min to determine the KLAC, KBAC and LER. Gel consistency was determined by the method formulated by86, where known amount of rice flour was placed in the culture tubes and wetted with 0.2 ml 95% ethanol containing 0.03% thymol blue and 2 ml 0.2 N KOH added and mixed with a Vortex Genie mixer set at specified speed. Tubes were covered with glass marbles and heated in a vigorously boiling water bath for 8 min. Then, the tubes were removed from the water bath and kept at room temperature for 5 min, cooled in an ice-water bath for 20 min, and laid flat on a laboratory table and length of the blue-colored gel was measured in millimeters. The method separated different classes of GC into soft (61–100 mm), medium (41–60 mm) and hard gel consistency (25–40 mm) based on SES, 2013. GT was estimated based on alkali spreading value (ASV) of milled rice by87. Six whole-grain, milled rice samples were placed in duplicate Petri plates containing 10 ml of 1.7% KOH. The Petri plates were covered and incubated for 23 h at 30 °C. The appearance and disintegration of grains were visually observed after incubation based on the scale given by SES, 2013.Traits namely, KL, KB and LBR were measured with 10 kernels using Standard Evaluation System of Rice (SES)88. Fine powder of polished rice flour (10 mg) was taken in 2 ml eppendorf tube and it was digested with 400 μl of enzymatic mixture-containing 10 mg/ml of pancreatic α-amylase and 3.0U/ml of amyloglucosidase (AMG) and the mixture was incubated at 370C for 16 h with continuous shaking (200 strokes/minute). After incubation, 400 μl of 99% ethanol was added to the mixture and vortexed to stop the reaction. Then, the samples were centrifuged for 10 min at 12,000 rpm and the supernatant was collected and the pellets were washed repeatedly with 200 μl (vortexed) and 600 μl of 50% ethanol. Washing with 50% ethanol was made twice to remove all the non-resistant starch in the samples and supernatants were pooled for the measurement of non-resistant starch. Then, the pellets were air-dried for 30 min to remove moisture content in the residue. Around, 200 μl of 2 M KOH was added to the residue and mixed properly to avoid the formation of clumps and incubated at 50C for 1 h in a shaker (200 strokes/minute). Then, 800 μl of 1.2 M sodium acetate buffer and 10 μl of AMG , vortexed and tubes were placed in a water bath for 30 min at 500C with intermittent mixing with the help of vortex mixer for every 10 min. After incubation, samples were cooled and centrifuged at 12,000 rpm for 10 min. Then, 100 μl of sample aliquot was taken in a fresh test tube (15 ml), 3 ml of GOPOD reagent  and incubated in a water bath at 500C for 30 min. The absorbance was determined with the help of UV-1800  at 510 nm against a blank containing buffer and GOPOD reagent.RS content of the rice samples was determined by using a resistant starch assay kit  by ten-fold downscaling of sample and reagents with slight modifications10 with little modifications. Fine powder of polished rice flour (50 mg) was cooked in 5 ml of water for 30 min and 10 ml of HCl-KCl buffer (pH = 1.5) was added to the sample. Then, 0.2 ml of a solution containing 1 g of pepsin in 10 ml of HCl-KCl buffer was added to each sample and incubated at 40 °C for 1 h in a shaking water bath. After incubation, the volume was adjusted to 25 ml with Tris maleate buffer (pH = 6.9). Then, 5 ml of α–amylase (2.6 units) in Tris maleate buffer (pH 6.9) was added and samples were incubated at 37 °C in a shaking water bath. After incubation, 1 ml of the sample aliquot was collected serially at intervals of 30 min for 3hrs . The enzyme activity in the aliquot was inactivated by heating at 100 °C for 5 min and refrigerated until the end of the incubation period. 3 ml of 0.4 M Sodium acetate buffer (pH 4.75) and 60 µl amyloglucosidase was added to hydrolyze the digested starch to glucose. The samples were incubated at 60 °C for 45mins in a water bath and glucose content in each aliquot was estimated using the GOPOD kit . Glucose was converted into starch by multiplying with 0.9. Kinetics of starch digestion was estimated by non-linear first-order equation; C = Cα (1-e-kt). Where, C = Concentration of starch hydrolyzed at the time (t) , Cα = equilibrium concentration  and k = kinetic constant. The area under the hydrolysis curve (AUC) was calculated using the equation; AUC = Cα (tf-t0)—Cα/k (1-e-k (tf – to)). Where, C∞ corresponds to the concentration at equilibrium (t180), tf is the final time (180 min), t0 is the initial time (0 min) and k is the kinetic constant. A hydrolysis index (HI) was calculated by comparison with the AUC of a reference food (white bread). The predicted glycemic index (PGI) was estimated using the formula PGI = 39.7 + 0.548 (HI).The glycemic index was determined by the protocol given byhttps://CRAN.R-project.org/), R based approach considering both kinship values  and population structure (Q-matrix)89. Of the several methods suggested to correct false positive in association analysis even keeping stringent p-value benchmark, the most stringent correction method called “Bonferroni Correction” was used in the present analysis.The bonferroni threshold calculated by the formula 1/m where ‘m’ is the number of test performed which resulted in –log10 (1/m) = 5.69. As it is too conservative and suggestive value of p = 0.00001 (− log10 (p) > 5)90 was used for the identification of peak associations (MTAs) with the targeted grain quality traits. Multi-locus GWAS (mrMLM) was also conducted to validate the MTAs identified by single locus model of MLM by adopting the significant LOD score of 391. The phenotypic allele effect (ai) was determined by the formula given by29 and the favorable alleles of each trait were subsequently identified according to the breeding objective. Strongly associated SNPs with the targeted traits were utilized to find the causative QTLs/genes by the RAP database. In-built tool of SNP seek database89 was utilized to conduct haplotype analysis for entire candidate locus, by employing default parameters with Calinski criteria for k-group determination. Nipponbare was used as the reference genome. All of the 150 lines belonging to 8 sub-populations namely, aro, aus, admix, ind1A, ind1B, ind2, ind3, and indx was considered for the haplotype analysis. We utilized the ‘3kfiltered’ SNP set present in the SNP seek database for the entire analysis. The filtered was obtained from the Base SNP set by applying the following filtering criteria: (1) alternative allele frequency at least 0.01, (2) proportion of missing calls per SNP at most 0.292 (http://snp-seek.irri.org/_download.zul) and this SNP set was already available in the SNP seek database which was directly utilized in this study. Haplotype analysis for candidate locus have been carried out considering only the nonsynonymous SNPs and indels in the exon region that results in amino-acid change. The information regarding haplotype and their diversity was obtained from the SNP-seek database (http://snp-seek.irri.org/_download.zul) to detect the superior haplotype by categorizing haplotypes by using phenotyping data of concerned genotype trait means for the associated genes.Mean data was used for GWAS analyses using approximately more than 500 K SNP data. The analysis was carried out by using GAPIT (MLM model) (https://CRAN.R-project.org/). Significant difference among the haplotypes  was studied with the help of F-statistic and Duncan’s test with the significance level of p < 0.05.Mean, range and standard deviation were calculated with the help of a standard excel program. Duncan’s multiple range tests were carried out using XL Stat 2019. Correlation analysis among all the component traits related to quality was conducted by SPSS ver. 20. Boxplot and correlogram were constructed with the help of R-package (Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.


Grain size is a key agronomic trait that contributes to grain yield in hexaploid wheat. Grain length and width were evaluated in an international collection of 157 wheat accessions. These accessions were genetically characterized using a genotyping-by-sequencing (GBS) protocol that produced 73,784 single nucleotide polymorphism (SNP) markers. GBS-derived genotype calls obtained on Chinese Spring proved extremely accurate when compared to the reference (> 99.9%) and showed > 95% agreement with calls made at SNP loci shared with the 90 K SNP array on a subset of 71 Canadian wheat accessions for which both types of data were available. This indicates that GBS can yield a large amount of highly accurate SNP data in hexaploid wheat. The genetic diversity analysis performed using this set of SNP markers revealed the presence of six distinct groups within this collection. A GWAS was conducted to uncover genomic regions controlling variation for grain length and width. In total, seven SNPs were found to be associated with one or both traits, identifying three quantitative trait loci (QTLs) located on chromosomes 1D, 2D and 4A. In the vicinity of the peak SNP on chromosome 2D, we found a promising candidate gene (TraesCS2D01G331100), whose rice ortholog (D11) had previously been reported to be involved in the regulation of grain size. These markers will be useful in breeding for enhanced wheat productivity. During the domestication process from ancestral (Einkorn) to common wheat (Triticum aestivum L.) going through tetraploid species, wheat abruptly changed, from a grain with greater variability in size and shape to grain with higher width and lower length3. However, grain yield is determined by two components namely, the number of grains per square meter and grain weight. Following, grain weight is estimated by grain length, width, and area, which are components showing higher heritability than mainly yield in wheat4.The grain size, which is associated with yield and milling quality, is one of the essential traits that have been subject to selection during domestication and breeding in hexaploid wheat5. Geometric models have indicated that changes in grain size and shape could result in increases in flour yield of up to 5%6. Consequently, quantitative trait loci (QTLs) or genes governing grain shape and size are of interest for domestication and breeding purposes8. Many genetic mapping studies have reported QTLs for grain size and shape in wheat cultivars10 and some studies have revealed that the D genome of common wheat, derived from Aegilops tauschii, contains important traits of interest for wheat breeding12.Larger grains may have a positive effect on seedling vigor and contribute to increased yield13 performed QTL analyses for grain size and shape-related traits using four synthetic wheat F2 populations to identify the genetic loci responsible for grain size and shape variation in hexaploid wheat and found QTLs for grain length and width on chromosomes 1D and 2D. This is particularly interesting as the tenacious glume gene Tg-D1 on chromosome 2D is a well-known locus that has been recruited for the domestication of wheat grain size and shape. During allohexaploid wheat speciation, a dramatic change in grain shape occurred due to a mutation in the Tg-D1 gene14. Furthermore, Yan et al.15 reported a genomic region associated with grain size on chromosome 2D.At the genomic level, Okamoto et al.16. Furthermore, Genotyping-by-sequencing (GBS) is a Next-Generation Sequencing (NGS) technology for high-throughput and cost-effective genotyping, that provides a great potential for applying GWAS to reveal the genetic bases of agronomic traits in wheat17. Arora et al.18 conducted GWAS in a collection of Ae. tauschii accessions for grain traits, using SNP markers based on GBS. They identified a total of 17 SNPs associated with granulometric characteristics distributed over all seven chromosomes, with chromosomes 2D, 5D, and 6D harboring the most important marker-trait associations. On the other hand, most studies on germplasm of hexaploid wheat have focused on understanding the genetic and morphological diversity of this species. No studies have used GWAS based on GBS for economically important and essential grain yield components traits such as grain length and width in an international collection of hexaploid wheat. The present investigation aimed to identify QTLs and candidate genes governing grain length and width in an international collection of hexaploid wheat using a GBS-GWAS approach.New advances in genomics technologies has revolutionized research in plants by developing new high throughput genotyping methods to increase knowledge of the genetic basis of diversity in large core collection of genetic materials through genome-wide association studies (GWAS). Based on such high-density SNP markers, GWAS can be used for the description and high-resolution mapping of genetic variance from collections of genetic ressources that have derived from several historical recombination cyclesp < 0.01) and also between grain length and grain width . Also, significant positive correlations were identified between grain yield and grain length  and between grain yield and grain width . Interestingly, a bimodal distribution was observed for grain length and width , grain width (Gwi), 1000-grain weight (Gwe) and grain yield (Gyi) over two years at two sites. Those phenotypes are referring only to the international panel of wheat and do not include the Canadian accessions. As shown in Table In examining the relationship between 1000-grain weight and grain length/width using bagplots on the collection of 159 accessions, no outliers were found when considering the relationship between grain weight and width. In contrast, two accessions  were indeed detected as outliers when comparing grain weight and length  GBS approach to genotype this collection. Overall, 77,124 and 73,784 SNPs were discovered for the set of 71 Canadian wheat accessions and 157 exotics wheat accessions, respectively.To assess the reproducibility and accuracy of genotypes called via the GBS approach, we genotyped 12 different plants of CS , which were added to the set of 288 wheat samples for SNP calling and bioinformatics analysis. Sequence reads of the full set of 300 wheat samples obtained from GBS were analyzed following the standard steps of SNP calling and bioinformatics analysis described below. This yielded a total of 129,940 loci that were used for the assessment of accuracy and reproducibility of SNP calls. For each individual plant of CS, the GBS calls were compared between replicates and with the Chinese Spring reference genome (at the corresponding positions).On the non-imputed data, we detected a very high level of concordance (99.9%) between the genotypes of each CS individual and the reference alleles for the 1,196,184 called genotypes  and A  sub-genomes compared to the D  sub-genome  and 73,784 SNPs. As seen in Fig. p-value = 4.34E−05) for grain width.The most significant association was observed on chromosome 2D and contributed to both grain length and grain width Table , Fig. 3.2 = 0.90). For this reason, we considered these to define one quantitative trait locus (QTL) on chromosome 2D  displaying association with grain traits. In addition, almost all accessions which have the major allele on chromosome 1D are the same which have the major allele on chromosome 2D. Thus, the combined impact of these two loci could explain the observed bimodal distribution.On chromosome 1D, the SNP marker chr1D:166874041 defined a QTL for both grain length and width. The percentage of phenotypic variation explained by this marker for grain length and width was 11% and 6% respectively, with a MAF of 0.30 and allelic effects of 0.76 and 0.33 mm for grain length and width, respectively. Furthermore, a high degree of interchromosomal LD was observed among the peak SNPs between chromosomes 1D and 2D  included a total of 315 high-confidence genes of which 66 genes are expressed during embryogenesis and grain development in wheat. On chromosomes 1D and 4A, the linkage blocks harboring SNP markers chr1D:166874041 and chr4A:713365388, each defining a QTL, did not include high-confidence genes. Upon examination of the annotations and gene expression profile for the candidate genes, the most promising appears to be the TraesCS2D01G331100 gene in the QTL on chromosome 2D, which is most highly expressed in the developing embryo during embryogenesis and grain development in wheat  than those obtained for the other haplotypes. As shown in Fig. To provide a useful breeding tool for the main QTL identified in this research, we defined SNP haplotypes around our candidate gene. Using HaplotypeMiner, we identified two SNPs  in addition to grain yield. A statistical analysis revealed that the genotype was a major source of variance for all traits and that these exhibited a high heritability. In agreement with Arora et al.20 reported that the accuracy of SNP calls was 99% for non-imputed and 89% for imputed SNPs dataset in Barley. In our study, 76.7% of genotypes were called initially, and only 23.3% were imputed. Thus, we conclude that the imputed data are of lower reliability.To assess the reproducibility and accuracy of genotypes called via the GBS approach, we genotyped 12 different plants of Chinese Spring , which were added to the set of 288 wheat samples for SNP calling and bioinformatics analysis, which yielded a total of 129,940 loci. Among the 12 biological replicates of CS, we found a very high reproducibility (~ 100%) in our genotype calls. Firstly, we verified the quality of our SNP data by investigating the reproducibility and accuracy of GBS-derived SNPs calls, and found that GBS-derived genotypes were in agreement with the reference genome in 99.9% of cases in over 1 M comparisons for non-imputed data and 93.8% after imputation of the missing genotype calls. Recently, Abed et Belzile22. The fact that the vast majority of GBS-derived SNPs are located in non-coding regions makes it easier to distinguish between homoeologues21. This likely contributed to the very high accuracy of GBS-derived calls described above. We conclude that GBS can yield genotypic data that are at least as good as those derived from the 90 K SNP array. This is consistent with the findings of Elbasyoni et al.23 as these authors concluded that “GBS-scored SNPs are comparable to or better than array-scored SNPs” in wheat genotyping. Likewise, Chu et al.24 observed an ascertainment bias for wheat caused by array-based SNP markers, which was not the case with GBS.As a further examination of data quality, we compared the genotypes called by GBS and a 90 K SNP array on a subset of 71 Canadian wheat accessions. Among the 9,585 calls available for comparison, 95.1% of calls were in agreement. It is likely that both genotyping methods contributed to cases of discordance. It is known, however, that the calling of SNPs using the 90 K array is challenging because of the presence of three genomes in wheat and the fact that most SNPs on this array are located in genic regions that tend to be typically more highly conserved, thus allowing for hybridization of homoeologous sequences to the same element on the arrayAe. tauschii, was the main source of genetic variability for grain size traits in hexaploid wheat12. This is also consistent with the findings of Yan et al.15 who performed QTL mapping in a biparental population and identified a major QTL for grain length that overlaps with the one reported here. In a recent GWAS on a collection of Ae. tauschii accessions, Arora et al.18 reported a QTL on chromosome 2DS for grain length and width, but it was located in a different chromosomal region than the one we report here. With a view to develop useful breeding markers to improve grain yield in wheat, SNP markers associated to QTL located on chromosome 2D appear as the most promising.Confident that the GBS-derived SNPs provided high-quality genotypic information, we performed a GWAS to identify which genomic regions control grain size traits. A total of three QTLs located on chromosomes 1D, 2D and 4A were discovered. Under these QTLs, seven SNPs were found to be significantly associated with grain length and/or grain width. Five SNPs were associated to both traits and two SNPs were associated to one of these traits. The QTL located on chromosome 2D shows a maximum association with both traits. Interestingly, previous studies have reported that the sub-genome D, originating from It is worth noting, however, that another genomic region of interest was also located on the D subgenome. Interestingly, the peak SNP on 1D exhibited a very high degree of LD with the peak SNP on 2D. This may reflect that, when selecting for large seed size, favorable alleles at both QTLs tend to be captured. In biparental progeny segregating for both loci, it would be interesting to assess if there are any epistatic effects between these QTLs leading to both loci being required to achieve the full phenotypic effect.TraesCS2D01G331100 gene seems like a highly promising candidate as it is most highly expressed in the developing embryo during embryogenesis and grain development in wheat. As well, it is expressed at the corresponding endosperm and pericarp, and was found to encode the cytochrome P450 (CYP724B1), which showed homology to enzymes involved in brassinosteroid biosynthesis, indicating the mechanism by which grain size is regulated in wheat. Furthermore, this gene has been well conserved during the domestication process from ancestral (Einkorn) to common wheat (Triticum aestivum L.) going through tetraploid species25. It is an orthologous to the rice CYP724B1 gene, commonly known as the D11 gene. The latter has been reported as involved in the regulation of internode elongation and seed development due to his role in brassinosteroid synthesis26. Brassinosteroids are a group of plant hormones and are key regulators of plant growth and development (including seeds) that promote cell expansion and elongation27.To identify a candidate gene contributing to grain length and width, we examined the genes residing in the same linkage block as the peak SNP for each QTL. In the genomic interval spanned by the QTL contributing the most to the phenotypic variation for grain size (2D_40.4–45.1 Mb), a total of 66 high-confidence genes expressed during embryogenesis and grain development were observed. The TraesCS2D01G331100 gene and grain width and length, we defined SNP haplotypes. An analysis of haplotypes surrounding this gene identified three distinct haplotypes, and we observed that, for all grain size traits, the phenotypes corresponding to haplotype AT displayed significantly higher values than those of other haplotypes. We therefore suggest that SNP markers flanking TraesCS2D01G331100 could provide a useful tool in marker-assisted breeding programs to improve wheat productivity by selecting alleles leading to larger grain size and higher yield. In the longer term, it would be interesting to define more precisely the exact nature of the alleles at this gene through targeted re-sequencing of this gene in a broader collection of accessions.To further refine the association between the Triticum aestivum L.) varieties were used in our study. These accessions comprised two groups. A first group of 71 Canadian accessions was used to validate the accuracy of GBS in wheat. The second group of 157 accessions was used for genome-wide association analyses. Indeed, accessions were collected from many wheat breeding programs. Canadian accessions were collected from the University of Guelph Wheat Breeding Program and accessions from the second group were collected from South Africa through the Agricultural Research Council (ARC), Stellenbosch University’s Plant Breeding Laboratory (SU-PBL) and SENSAKO’s breeding programs, East Africa and Mexico via the International Maize and Wheat Improvement Center (CIMMYT), Central Africa by the Institute of Agricultural Research for Development (IRAD) and from farmers28, and North Africa per the International Center for Agricultural Research in the Dry Areas (ICARDA). With the latter accessions, field trials were conducted in two different trial sites in the bimodal humid forest zone of Cameroon, during the 2015–2016 wheat-growing seasons in Mbankolo (1057 m above sea level) and during 2016–2017 in Nkolbisson (650 m a. s. l.). In Mbankolo, the average temperature is 18–20 °C, bimodal rainfall with an annual average of 1600 mm. In Nkolbisson, the annual average temperature is 23.5 °C, the rainfall is bimodal with an annual average of 1560 mm. At each trial site, an incomplete alpha-lattice design with two replications was used. Each accession was planted in five-row plots, in 3-m rows with 5 cm between plants and 25 cm between rows. Then, fields trials were managed in accordance with the technical recommendations and standard agricultural practices for wheat29. Grain length (Gle), grain width (Gwi), 1000-grain weight (Gwe) and grain yield (Gyi) were recorded for each accession. Gle and Gwi were measured by a digital Vernier caliper on 20 seeds per variety randomly picked from a pool of grains from each harvested area18.A total of 228 hexaploid wheat (2) for each trait using the following formula: G: genetic variance; VGE: genetic × environment variance and Ve: error variance.The analysis of variance for each trait was performed using PROC MIXED in SAS 9.4. Each cultivar was considered as a fixed effect, whereas replications and environments were considered as random effects. Pearson correlation coefficients between pairs of phenotypic traits were computed using Pearson’s correlation in SPSS 20.0. We estimated the broad-sense heritability  and the concentrations were normalized to 20 ng/μl for library preparation. Our 228 DNA samples were part of a larger set of 288 wheat samples on which GBS analysis was performed simultaneously . To allow an assessment of the quality of GBS-derived SNP calls, 12 independent samples of Chinese Spring (CS) DNA (each from a different plant) were used to produce a single (12-plex) PstI/MspI library that was sequenced on one PI chip.Genomic DNA was extracted from dried young leaf tissue ~ 5 mg) for all accessions using a CTAB DNA isolation method mg for a33 to align reads on the wheat reference genome (Chinese Spring v1.0) and to call SNPs. Fast-GBS results were first filtered to (i) keep only SNPs having the label “PASS” and SNPs positioned on chromosomes (i.e. not on scaffolds), (ii) remove indels and multiallelic SNPs, (iii) convert all heterozygous calls with genotype quality (GQ) < 30 to missing data, (iv) keep only SNPs with a minor allele count (MAC) ≥ 4, (v) remove accessions with more than 80% of missing data, (vi) exclude SNPs with more than 10% heterozygotes and (vii) exclude SNPs with missing data (N) > 80%. Finally, missing data were imputed using BEAGLE v534 with the parameters described in Torkamaneh and Belzile35. Imputed genotypes were also filtered to keep only SNPs with a minor allele count (MAC) ≥ 4. After these standard filtration steps, three subsets of accessions were extracted from the full dataset for specific goals: (1) establish the accuracy and reproducibility of GBS-derived SNP calls on 12 replicates of cv. Chinese Spring; (2) compare SNP genotypes obtained via GBS and the 90 K array on a subset of 71 Canadian accessions and (3) perform GWAS for grain size on a diversity panel of 157 accessions. Additional filtration steps were performed on these subsets before these analyses. The imputed genotypes of the subset of 71 wheat accessions were filtered to keep only SNPs with a minor allele count (MAC) ≥ 4 and exclude SNPs with more than 10% heterozygotes, while those from the collection of 157 wheat accessions were filtered to keep only SNPs with a minor allele frequency (MAF) ≥ 0.05  of wheat samples obtained from GBS were analyzed using the Fast-GBS pipeline.05 Fig. .Figure 636. For the 135 SNPs called in common using both methods, genotype calls were compared using an in-house script.The SNP genotypes for 12 different cv. Chinese Spring plants were used to assess the accuracy and reproducibility of GBS-derived SNP calls. Before and after imputation of missing data, we measured both the degree of agreement in SNP calls between replicates and the agreement between the GBS-derived SNP calls and the Chinese Spring reference genome V1.0 using an in-house script. To compare the accuracy of GBS-based and array-based genotype calls, we used a set of 71 Canadian wheat accessions for which genotypic data for 51,649 SNPs had been obtained previously using the 90 K SNP Infinium iSelect array37 on SNP markers filtered at MAF ≥ 0.05 as recommended by Sobota et al.38. Population structure was evaluated using the filtered set of SNP markers using a simple prior and 1,000 iterations for K ranging from 1 to 12. The optimal range of K was determined based on model complexity using the marginal likelihood method using the fastSTRUCTURE script chooseK.py, as well as on visualization of the log marginal likelihood, and population visualization using Distruct version 1.139.An analysis of population structure was performed on the collection of 157 wheat accessions (excluding the two accessions considered to be outliers) using fastSTRUCTURE version 1.040, via the Gabriel method41. This method is based on a confidence interval and a normalized measure of D′. The pattern and distribution of intrachromosomal LD were visualized with LD plots generated using Haploview version 4.242 to investigate the average LD decay along chromosomes. The smoothed second-degree LOESS curve was fitted to determine the critical D′ and r2 between loci.Genome-wide linkage disequilibrium (LD) analysis was performed using PLINK version 1.943. This approach, based on associations between the estimated genotypic values (BLUEs) for each trait and individual SNP markers46 was conducted with a compressed mixed linear model45. A matrix of genomic relationships among individuals , and the intercept; u is a vector of random effects including additive genetic effects as matrix of relatedness between individuals (the kinship matrix), u ~ N, where σa2 is the unknown additive genetic variance and K is the kinship matrix; X and Z are the design matrices of β and u, respectively; and ε is the vector of residuals, ε ~ N, where σe2 is the unknown residual variance and I is the identity matrix. Association analysis was performed while correcting for both population structure and relationships among individuals with a combination of either the Q + K matrices; K matrix was computed using the Van Raden method43. The p value threshold of significance of the genome-wide association was based on false discovery rate (FDR-adjusted p < 0.05).GWAS for grain traits was performed on the subset of 157 wheat accessions via the Genomic Association and Prediction Integrated Tool (GAPIT) version 2International Wheat Genome Sequencing Consortium (IWGSC) website (https://urgi.versailles.inra.fr/jbrowseiwgsc/gmod_jbrowse), and the annotated genes within each interval were screened based on their confidence and functional annotation thanks to the annotated and ordered reference genome sequence in place by IWGSC et al.47. Candidate genes potentially involved in grain size traits were further investigated by analyzing gene structure and crossing-referenced them against genes reported as controlling grain size in other Triticeae as well as orthologous search in other grass species50. Furthermore, the selected genes were further evaluated for their likely function based on publicly available genomic annotation. The function of these genes was also inferred by a BLAST of their sequences to the UniProt reference protein database (http://www.uniprot.org/blast/). To further provide more information about potential candidate genes, we used RNA-seq data of Ramírez-González et al.48, based on the electronic fluorescent pictograph (eFP) at bar.utoronto.ca/eplant  to identify in what tissues and at which developmental stages candidate genes were expressed in wheat.To identify candidate genes affecting grain size in wheat, we defined haplotype blocks containing the peak SNP. Each region was visually explored for its LD structure and for genes known to reside in such regions. The associated markers located in the same LD block as the peak SNP were searched and positioned on the wheat reference genome v1.0 on the 52 to identify SNPs flanking the TraesCS2D01G331100 gene. For each haplotype, we calculated the trait mean  for lines sharing the same haplotype using the R ggpubr program53.To better define the possible alleles in a strong candidate gene, we used HaplotypeMinerExperiments on wheat were carried out in accordance with national, international guidelines.Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.


Oryza AA genome group, presents major challenges and often results in cases of misidentification. Recent work by this group identified diagnostic single nucleotide polymorphic (SNP) markers specific for several rice species and subspecies based on DArTseq next-generation sequencing technology (“DArTseq”). These SNPs can be used for quality control (QC) analysis in rice breeding and germplasm maintenance programs. Here, we present the DArTseq-based diagnostic SNPs converted into Kompetitive allele-specific PCR (KASPar or KASP) assays and validation data for a subset of them; these can be used for low-cost routine genotyping quality control (QC) analysis. Of the 224 species/subspecies’ diagnostic SNPs tested, 158 of them produced working KASP assays, a conversion success rate of 70%. Two validation experiments were run with 87 of the 158 SNP markers to ensure that the assays amplified, were polymorphic, and distinguished the five species/subspecies tested. Based on these validation test results, we recommend a panel of 36 SNP markers that clearly delineate O. barthii, O. glaberrima, O. longistaminata, O. sativa spp. indica and japonica. The KASP assays provide a flexible, rapid turnaround and cost-effective tool to facilitate germplasm curation and management of these four Oryza AA genome species across multiple genebanks.Morphological identification of closely related rice species, particularly those in the  Oryza, which consists of 27 species and 11 genome types1, of which, eight species, representing five genomes, are found in Africa. This includes the AA ; BB (O. punctata); CC (O. eichingeri); FF (O. brachyantha); and BBCC (O. schweinfurthiana) genome species2. The four AA genome Oryza species account for nearly 99% of the ~ 21,300 rice accessions conserved at the AfricaRice genebank (https://www.genesys-pgr.org/). O. glaberrima is taxonomically differentiated from O. barthii using phenotypic characters, such as growth habit, spikelet shattering, and hairiness of awn and spikelets. However, it is often difficult to clearly separate these two indigenous African species based on phenotype for a few different reasons: (i) O. glaberrima is thought to have evolved from O. barthii through selection4 and both species are highly similar even at the molecular level5; (ii) the phenotypic traits used to differentiate the two species are not conclusive across diverse accessions and are highly affected by environment and/or ecology; and (iii) there is a wide range of intermediate types between O. barthii and O. glaberrima that are much more challenging to differentiate from the two species4.Rice belongs to the genus O. sativa subspecies, indica and japonica, are also taxonomically classified based on their morphological traits  in combination with other traits 6. However, the phenotypic attributes used to differentiate indica from japonica have low resolution to clearly separate many collections, are laborious to measure and are highly affected by environmental conditions. Since the advent of molecular marker technology, the phylogenetic relationships among the Oryza species have been determined using both low-density and high-density molecular markers8. Recently, our team at the AfricaRice reported 339 diagnostic single nucleotide polymorphic (SNP) markers that can be used for accurate taxonomic classification of the O. barthii, O. glaberrima, O. longistaminata and the two O. sativa subspecies9. Validation of potential use of these markers for breeding and germplasm maintenance in these species and subspecies forms the basis of the current study.The two 10. Genebanks were established as repositories to safeguard plant genetic resources for food and agriculture (PGRFA) against loss, and serve as a critical source of genes for improving agricultural crops11. Currently, there are nearly 7.4 million plant germplasm entries (accessions) conserved by numerous institutions12 of which, the CGIAR System Organization (CGIAR) genebanks hold 21% of the global ex situ rice collections (https://www.genesys-pgr.org/). AfricaRice is one of the 11 CGIAR institutions that have genebanks intended for the conservation of a wide range of germplasm types . The CGIAR genebanks make these PGRFA available under the terms of the International Treaty on Plant Genetic Resources for Food and Agriculture and distribute them to the global community free of charge.The conservation of plant genetic resources began as a response to the rapid loss of agricultural biodiversity mainly due to continuous replacement of traditional varieties (landraces) by modern varieties, changes in eating habits, and rampant development, which has threatened local landraces and crop wild relatives13 include taxonomic misclassification (misidentification or misnaming) and mislabeling, which lead to errors in various genebank operations15. These errors negatively affect effective conservation, dissemination and use of germplasm17. Most taxonomic misclassification and mislabeling are due to human error during planting of material, characterization of accessions for phenotypic traits, and mislabeling or misreading of the germplasm names18 during the various genebank operations. Such errors have been reported in various crop species and error rates vary from 3 to 28%22. Recently, the impact of taxonomic misnaming was highlighted in a study of the genus Citrullus (watermelon) in two major databases: Genesys PGR (https://www.genesys-pgr.org/) and the European Search Catalogue for Plant Genetic Resources . Just 3% of the Citrullus accessions for which information is stored in these two databases was found to have been correctly named, and 28% of the collections showed major taxonomic errors. As a consequence, over a quarter of the Citrullus collection has never been used in research, breeding, cultivation, and reintroduction projects. These types of errors can best be avoided (or at least minimized) by implementing routine genotyping quality control (QC) methods using low cost, high throughput and user-friendly SNP markers.Some of the major challenges facing genebank managers and plant genetic resource scientists24. Next generation sequencing technologies-based platforms, such as GBS25 and Diversity Array technology-based sequencing (DArTseq)26 have been found to be cost-effective and rapid for applications that require high-density markers, such as germplasm characterization, gene discovery and genomic selection. Other applications such as marker-assisted selection (MAS) and QC analysis require genotyping of a large number of samples with a small number of SNPs, which can more economically be achieved through uniplex (single-plex) genotyping platforms27. Currently, Kompetitive allele-specific PCR (KASP) is one of the most widely used uniplex genotyping platforms for applications requiring from one to a moderate number of markers, and offers reasonable cost, efficiency and flexibility28. KASP genotyping can be carried out in 96-, 384- and 1536‐well PCR plates and genotyping costs go down as number of wells go up24. Once SNPs of interest have been identified through the different approaches such as GBS and DArTseq, they must be converted for use as KASP assays and validated.The numerous SNP genotyping platforms may be divided into (a) genotyping-by-sequencing (GBS) methods based on next generation sequencing technologies; (b) those platforms that use fixed SNP assays; and (c) SNP assays that run one marker at a time (uniplex), usually PCR basedO. barthii, O. glaberrima, O. longistaminata and the two O. sativa subspecies29. These diagnostic markers require conversion for use in KASP assays and validation prior to use for quick and low-cost QC analysis. The present study presents the custom KASP assays created from the DArTseq-based Oryza species and subspecies diagnostic SNPs and the results of their validation. The aim was to recommend a smaller set of the best diagnostic SNPs for routine use in taxonomic curation of the four rice species and two subspecies conserved at different genebanks.In rice, our team recently reported 339 diagnostic SNPs that can be used in correcting taxonomic misclassification of 5 and seven newly identified DArT SNPs9, we submitted position and context sequence (the 50 base pairs (bp) on either side of the SNP) for 224 SNPs to the LGC Biosearch Technologies service laboratory, Hoddesdon, UK (https://www.biosearchtech.com/). Context sequence was found by BLASTing each 64-bp diagnostic DArTseq SNP sequence minus the common 5-bp adaptor/promoter against the Oryza sativa japonica reference genome in Gramene (http://www.gramene.org/). We copied 100–120-bp of sequence and marked the position of each SNP of the top BLAST hit for each of the 224 chosen DArTseq SNPs (Supplementary Table Based on the 332 DArTseq-based SNPs that were diagnostic for rice species/subspecies categorization reported in our previous studyO. glaberrima (18), O. barthii (18), O. longistaminata (9), O. sativa spp. indica (20) and japonica (15). All 158 KASP SNPs successfully produced genotype calls of which; (a) 20 SNPs were monomorphic across the 80 DNA samples; (b) 51 SNPs either generated too much missing data, too many heterozygotes, very low MAF and/or PIC, or were not taxonomically diagnostic; (c) 87 SNPs appeared to be diagnostic of which (d) 22 SNPs did not show a consistent pattern per species/subspecies (more details are provided in the results). After removing these 22, the remaining 65 were validated using 625 DNA samples representing O. barthii (88 samples), O. glaberrima (169), O. longistaminata (69), O. sativa spp. indica (178) and O. sativa spp. japonica (121)  and gene diversity (GD) of a locus, also known as expected Heterozygosity (He) were computed using PowerMarker v3.2531. Based on 625 genotypes, a QC subset of SNP markers that were highly diagnostic for the four rice species  and the two subspecies (lowland O. sativa spp. indica and upland O. sativa spp. Japonica) was selected. The SNPs were selected by comparing the total number of accessions per species/subspecies genotyped and those that were successfully grouped by the markers used, focusing on the minor allele33. Because mismatches among positive controls genotyped in the same plate using KASP genotyping may account for up to 2.9%23 of the polymorphisms, we decided to select the best diagnostic markers which were SNPs that successfully clustered more than 97% of accessions per species/subspecies tested.Statistical values including Minor Allele Frequency (MAF) and heterozygous marker ratio for each SNP from the 80 genotypes were estimated using TASSEL v.5.2.5934 was used to perform the analysis of molecular variance (AMOVA) to detect the genetic variance within and among populations using the PhiPT value  with 999 permutations. Each data set was converted to phylo “interleaved” format and imported in R v4.0.335 using the ape package version 5.4-136. Neighbor-joining (NJ) trees were built with a maximum likelihood approach and bootstrap analysis with 200 replicates using R/phangorn package version 2.5.537. “Newick” format of Trees were exported and further refined using FigTree 1.4.3 program (http://tree.bio.ed.ac.uk/software/figtree/). We ran Principal Component Analysis (PCA) in TASSEL v.5.2.43. The first three principal components (PCs) were plotted for visual examination using R/scatterplot3d package version 0.3–4138 with species/ecotype as a categorical variable. The power of the selected diagnostic markers to correctly assign each individual to a distinct population was investigated performing a multivariate DAPC  method using R/adegenet package version 2.1.340. For that analysis, three methods were used, as follows: (i) the number of clusters was assessed using the function fnd.clusters (assuming 20 as the maximum); (ii) the optimal number of clusters and/or groups in our data set were estimated using the Bayesian information criterion (BIC) and Silhouette Method using cluster package version 2.1.141 in R; (iii) the DAPC result was presented as a barplot42.GenAlEx software version 6.503Experimental research and field studies on plants (either cultivated or wild), including the collection of plant material, has complied with relevant institutional, national, and international guidelines and legislation. Seed samples were obtained from the AfricaRice genebank, Cote d'Ivoire. All accessions/genotypes used in this study are public goods and freely available for non-commercial purposes.O. barthii) and cultivated (O. glaberrima) African species ; (ii) SNPs that distinguish complex (O. barthii/O. glaberrima) and complex (O. sativa and O. longistaminata) ; (iii) SNPs that distinguish Asian species O. sativa from indigenous African species complex (O. barthii/O. glaberrima/O. longistaminata) ; (iv) SNPs that distinguish O. longistaminata and complex (O. barthii/O. glaberrima and O. sativa) ; (v) SNPs that distinguish lowland O. sativa spp. indica and complex (upland O. sativa spp. Japonica/O. barthii/O. glaberrima/O. longistaminata) ..KASP primers for 158 of 224 sequences of DArTseq-based SNP species/subspecies diagnostic markers were converted and successfully produced genotype calls. This represented 70% of the 224 DArTseq-based SNP markers selected and mapped to the 12 rice chromosomes  the cluster II contained the 18 accessions of O. barthii; (c) cluster III contained the 18 accessions of O. glaberrima; (d) cluster IV contained the 20 accessions of Asian rice, O. sativa spp. indica groups adapted to the lowland and finally (e) the cluster V contained 15 accessions of the Asian rice, O. sativa spp japonica to the upland ecologies. For routine QC and taxonomic classification of genebank materials, however, 65 markers is considered too many to run.Use of the 65 diagnostic KASP markers to seek subpopulations within the 80 rice accessions using PCA and NJ phylogenetic tree demonstrated their suitability to properly classify all accessions in this group. Three first three principal components accounted for 73%, 13% and 6% of the total molecular variance in the genotype data , with within species diversity accounting for only 3% of the observed genetic diversity   and explained discernible population differentiation, ranging from 0.81 to 0.99 between different groups  cluster II that contained accessions of the wild rice species, O. barthii; (c) cluster III that contained accessions of African rice, O. glaberrima; (d) cluster IV that contained accessions of Asian rice, O. sativa spp. indica groups adapted to the lowland and finally; (e) cluster V that represented Asian rice, O. sativa spp. japonica to the upland ecologies. As indicated in Fig. O. barthii accessions were grouped with African rice (O. glaberrima) in cluster III.Genetic variance partitioning through the AMOVA test based on ty Table . The PhiO. barthii species were very close to O. glaberrima species in the PCA plot (cluster II and III). Genetic distinctness of these rice species/subspecies was evaluated with the 36 diagnostic markers using DAPC. Bayesian information criterion (BIC) and Silhouette score value revealed K = 5 as the optimal number of clusters, consistent with partitioned variance between these groups of accessions 5 and the International Maize and Wheat Improvement Center (CIMMYT)44.Genebank managers are tasked with ensuring the accurate identification of species and maintenance of genetic integrity of the accessions in their ex situ collections by, as much as possible, preventing human errors during routine genebank operations. These errors are mainly caused by mislabelling and physical as well as genetic admixture during various gene banking activities. Many genebanks do not have the resources and capacity to assess their collections in order to verify the correctness of their passport (origin) data and to undertake correct species identification45, which is what is needed for marker-assisted recurrent selection and marker-assisted backcrossing46, and routine QC analysis. QC analysis can use up to a few dozen markers, depending on the range and partitioning of variation in a group of taxa23. In our study, species discriminating KASP markers were developed for four rice species  and two subspecies (lowland O. sativa spp. indica and upland O. sativa spp. japonica), offering a new tool for routine QC genotyping of these rice species for conservation in genebanks. As we demonstrated in our previous study, no DArT SNPs from the 332 diagnostic SNPs selected could differentiate O. barthii from O. glaberrima accessions29. The present study, however, permitted us to select 7 new SNPs that could be used to distinguish them9. This was possible because of the larger number of markers used in the present study compared to the previous one . Not all SNP sequences can be converted to KASP assays, as nearby polymorphisms and high GC content in the sequence around the SNP hinder primers from proper annealing and thus stop assays from proper amplification. In this study, KASP markers were successfully developed for 158 of 224 DArTseq-based SNPs sites submitted for conversion 23, we chose KASP assays that had a low missing data rate, low heterogeneity, and very high (97%) scoring success rate in our panels, which contained a high number of lines to ensure diversity within the taxa were represented. This appeared to be a good approach to validate diagnostic KASP markers, and has been followed by others as well44. Most of the KASP markers selected for broad quality control in the present study generated less than 11% missing values, except one marker (ARC-00336 = 21% missing data)  Minor allele frequency (MAF), which helps to differentiate the common and rare SNPs in the population49. As presented in the Supplementary Table Two diversity indices were estimated using both DArT and KASP SNPs markers: (i) the Polymorphism Information Content (PIC) value, which is often used to measure the informativeness of a genetic marker for linkage studies50, and is a good indicator that the species are indeed distinct, and that these KASP markers can properly measure the differentiation. Also, one reason the genetic diversity was much higher between populations vs within populations is attributable to the ascertainment bias of the set of 36 SNPs, which had been specifically selected to have higher polymorphism between populations. The PhiPT calculations provide an estimation of the structure of genetic variation among populations, groups or species, with < 0.05 indicating little, 0.05–0.15 moderate, 0.15–0.25 great, and > 0.25 very great genetic differentiation51. All pairwise population PhiPT values in this study were high for the species/subspecies compared, and ranged from 0.811 (O. barthii vs O. glaberrima) to 0.988 (O. glaberrima vs O. longistaminata) , which then partitions genetic variation to maximize differences between clusters while minimizing within-cluster variation42. We used this method to access the ability of the diagnostic markers to correctly identify clusters of individuals known to be evolutionarily related.In germplasm characterization, phylogenetic trees are primarily used to define groups, and measure the divergence between them. This can be useful for understanding the broader pattern of phylogenetic and evolutionary relationships, and more practically, to identify potential duplicates and to select a subset of accessions that capture the genetic variation of a given groupO. longistaminata species is morphologically distinct from all other AA-genome species in Oryza, and here, all grouped tightly together into cluster I in the PCAs and N-J trees. As expected, O. barthii and O. glaberrima  are very similar genetically56 and also have a close evolutionary relationship, as the African wild species O. barthii is the direct and recent ancestor of cultivated O. glaberrimai59   clustered with the African cultivated rice O. glaberrima accessions 5 and was higher than that found as diagnostic for the remaining group-specific categories of germplasm (between 1 to 9 SNPs). However, they allowed the correct discrimination of the four species and two subspecies considered in this study via their haplotype pattern, PCA, cluster analysis, and population structure analysis. To increase this small number of diagnostic SNPs specific per germplasm group, we suggest that more SNPs can be converted from DArT to KASP assay. However, for all other groups, fewer SNPs may be sufficient; in fact, a subset of 24 out of the 36 KASP SNP markers yielded the same results  in developing countries, which have agreements with KASP genotyping service providers. The cost per sample is reduced with higher numbers of data points generated, and service includes both DNA extraction and SNP genotyping63. The use of this service will help the AfricaRice genebank team and other genebanks to correct/update traditional passport data of their germplasm collections focusing on the four species , and to identify and track cases of misclassification/mislabeling and physical germplasm contamination during repetitive genebank operations. We propose that subsets of 24 and 36 KASP-SNPs markers be employed for “rapid” and “broad” diagnostic activities, respectively, for rice germplasm. These markers constitute an important genomic resource to support the conservation and use of rice genetic resources.In the present study we successfully developed robust and user-friendly, routine KASP genotyping assays for rice species conserved in various genebanks including the AfricaRice genebank. The KASP assays are based on previous DarTseq SNPs markers selected as diagnostic between various Supplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.