Receiver total efficiency using Li2CO3-K2CO3 salt was compared with that using solar salt nanofluid. Spectral properties of the base fluid and nanoparticles were modeled as wavelength-dependent and the absorption of the solar radiation was modeled as a volumetric heat release in the flowing heat transfer fluid. Initial results show that the receiver efficiency increases with increasing solar concentration, decreasing nanoparticle volume fraction, and decreasing receiver length. It was also found that the Carnot efficiency increases with increasing receiver length and nanoparticle volume fraction, and decreasing solar concentration and inlet velocity. Comparative study shows that solar salt HTF could provide higher total efficiency. However, a higher operating temperature of Li2CO3-K2CO3 will allow for a greater amount of thermal energy storage for a smaller volume of liquid.Considered to be the next generation of heat transfer fluids (HTFs), nanofluids have been receiving a growing interest over the past decade. Molten salt nanofluids have been shown to have great potential as an HTF for use in high temperature applications such as direct absorption solar collector (DAC) system. Very few studies using molten salt nanofluids as the HTF in a DAC receiver can be found in the open literature. This study aimed to develop a 3D computational fluid dynamics model of the receiver of a DAC using graphite-nanoparticle-dispersed Li Heat transfer fluids (HTFs) are critical to concentrating solar power (CSP) plants, and their selection is paramount to the overall efficiency of the system ,4,5,6,7.Nanofluids-suspension of nano-sized solid particles in a liquid, since its coining in 1995 by Choi, has been emerging as a new alternative HTF . Unlike Concentrated solar radiation can be focused onto a black or spectrally selective coated high-absorptive receiver of a conventional solar thermal collector, which can then be conducted to an HTF to be used in a thermodynamic cycle . These cInstead, a direct absorption collector (DAC), conceptually somewhat similar to small particle collectors, can directly absorb solar radiation into the HTF, resulting in a more uniform heat distribution and low temperature gradient between the absorber and fluid ,19,20,21The nanoparticles dominate the optical properties of the nanofluid, which allows us to tune the performance of the receiver by altering the size, shape, concentration, and material type of the particles .As the nanofluid directly absorbs the solar radiation, DACs do not require a surface absorption plate .Nanofluids possess superior thermophysical properties, including an enhanced thermal conductivity, heat transfer coefficient, and, in some cases, specific heat capacity ,24,25,26Optically selective nanofluids allow for high absorption in the solar range and low emittance in the infrared. Therefore, a volumetric receiver can be employed in place of a selective surface receiver of higher emissive losses resulting from a poorer temperature profile .Using a nanofluid receiver in place of a conventional surface type receiver, an efficiency improvement in the order of 5–10% is possible . With thWhile direct sunlight is incident on a thin flowing film of water/aluminum nanofluid, a 2D finite difference heat transfer model assuming Rayleigh scattering, developed by Tyagi et al., shows that approximately 10% efficiency can be increased using a DAC receiver over a conventional flat-plate solar collector . However2O3) nanoparticle doping [BETI], [C4 mim][NTf2], [C4 mmim][NTf2], and [C4 mpyrr][NTf2], it was found that the specific heat and the thermal conductivity were increased by 9–32% and by 3–6%, respectively. However, the disperse had an adverse effect on the HTFs’ viscosity, which was reported to increase by 20% on average and, in some cases, up to 70% [4 mim][BF4], [C4 mim][CF3SO3], [C6 mim][NTf2], [C8 mim][NTf2] and [HMIM]BF4. In these cases, though the thermal conductivity was found to be increased (ranging between 4 and 35.5%) as expected, the specific heat capacity was observed to be decreased slightly (ranging between 0.2 and 2.4%). However, the viscosity of [HMIM]BF4 nanofluids was observed to be decreased unexpectedly with the addition of graphene nanoparticles and multi-walled carbon nanotubes compared to the base fluid. The recorded decrease was up to 19% in some cases [Another potential HTF is a nanoparticle-enhanced ionic fluid that consists of organic-based compounds with discrete charges resulting in a significantly lower vapor pressure. Being a type of molten salt, several ionic fluids can be found to have low freezing points (below 0 °C), are non-volatile at atmospheric pressure, possess relatively high heat capacities, and are approachable to high temperature (about 400 °C) . Many rep to 70% ,45,46,47me cases ,49,50.The upper temperature limit and the thermophysical properties of nanofluids are two of the most important factors to consider for concentrating solar thermal collector systems. The upper temperature limit is important as it determines the maximum Carnot efficiency of the total system and directly influences the efficiency of any thermal storage that might be included in the system. The thermophysical properties are important as they affect how the heat flows, directly influencing the efficiency of the solar receiver and the thermal storage.3–KNO3, commonly known as solar salt) and lithium carbonate–potassium carbonate (Li2CO3–K2CO3). The nanoparticles used with the solar-salt-based fluids were SiO2 [2O3, TiO2, SiO2–Al2O3 [2 [2O3 [Apart from ionic fluid, molten salt nanofluids have also been investigated experimentally as base fluids dispersing different nanoparticles for high temperature applications. The molten salts mostly used have been sodium nitrate–potassium nitrate salts  engineering software package, we investigated the energy performance of graphite-doped solar salts nanofluids for high temperature applications, as published earlier . This st2CO3–K2CO3 molten salt nanofluid as an HTF is flowing between two parallel, horizontal, and no-slip flat plates of height h. It is assumed that the concentrated sun is incident normally and uniformly on the HTF through the top transparent wall of the DAC receiver, the bottom wall is perfectly insulated , the receiver is volumetrically heated, the HTF flow is fully developed, and the flow and energy variation across the width of the receiver is negligible. The side walls of the receiver were modeled as planes of symmetry. Therefore, though the model is 3D to account for a volumetric heat source, it is 2D in nature. Heat losses are included, namely, convection and radiation from the top surface to the ambient, as shown in the figure, and thermal re-emission from within the fluid. The bottom of the receiver is insulated and modeled as an adiabatic black wall, and reflective losses are not considered.The model setup for the DAC receiver is shown in The adiabatic surface, symmetry, and outflow boundary conditions were simulated asThe opaque surface is simulated asThe temperature boundary condition was used to simulate the inlet temperature of the receiver.Heat flux is calculated using2CO3–K2CO3 at a mole fraction of 62:38, respectively. Graphite, a commonly occurring dopant in the literature, is used as a nanoparticle with this molten salt to produce the HTF. Graphite nanoparticles exhibit high absorptivity in the solar range. The operating temperature range of the HTF is an important factor for a power generation cycle for maximizing its Carnot efficiency. Graphite-doped Li2CO3–K2CO3 nanofluid is very well suited to high-temperature DAC systems. Li2CO3–K2CO3 has a melting point of 761 K and an upper temperature limit of 1071 K; above this temperature, the salt becomes unstable [pC, of the HTF for this temperature limit are employed as shown by Equations (7) to (9) [pC is set to 1600 J/kgK. Because of a very low volume concentration, the influence of the nanoparticles on the thermophysical properties of the nanofluid is neglected for simplicity. The nanofluid is considered to be a Newtonian fluid in nature [This study considers a mixture of Liunstable . Therefo) to (9) . Cp is sn nature ,59,60:.α, is employed to quantify the size effect of the nanoparticle as per Equation (11).Particle shape, size, material type, and volume fraction of the dopant have a significant influence on the optical properties of the nanofluid. As no reliable theory exists to describe the impact of varying particle shapes, except for the spherical ones on the optical properties of the nanofluid , it is aA combination of refractive and absorptive indexes is used to quantify the complex refractive index of different material .(12)snpThe Mie theory can be used to account for the absorption and scattering of spheres, ,37. HoweDue to an extremely small size ratio,  ignored ,32. Agai ignored . TherefoAs can be seen in Equation (11), this simplification is only valid for sufficiently uniform small particles for the fraction of incident light that is scattered ,36.(15)Since the base fluid is not completely transparent, the total extinction coefficient of the nanofluid under the assumption that no aggregation occurs can be represented by Equation (16) .(16)κ=κThe concentrated solar radiation can be approximated using Planck’s black body distribution.In order to account for both fluid dynamics and heat transfer into the DAC receiver, two physics modules in COMSOL Multiphysics are incorporated. A fully developed velocity profile at the inlet is assumed considering that the nanoparticles are suspended in the fluid. For the mixture model, the governing equations are from Equation (18) to Equation (27).The heat transfer can be defined by Equation (27).y is the distance from the top of the receiver, and As the Reynolds number of a volumetric receiver generally varies between 10 and 1000 ,39, a fuStefan–Boltzmann’s law is applied to account for the radiative heat loss from the receiver top surface to the ambient.nfh is the convection coefficient between the nanofluid and the top surface, and tpk is the heat conductivity of the top plate.An overall heat transfer coefficient is calculated using Equation (30) in order to calculate convection heat loss.−6 to 1.354 × 10−5 [The top plate of the receiver is modeled as a fused quartz with a 1 cm thickness for good thermal insulation, as its thermal conductivity is only 1.3 W/mK. Moreover, the material is stable at high temperatures with an annealing point of 1140 °C, has an extremely low coefficient of thermal expansion that reduces the effects of thermal shock, and, most importantly, is virtually transparent in the solar range with an absorptive index ranging from 1.72 × 104 × 10−5 .h, can be calculated.The average Nusselt number of the nanofluid is given by Equation (31), from which the heat transfer coefficient, Solar radiation that reaches the bottom surface of the receiver, being an adiabatic black wall (as assumed), is completely absorbed and released as heat energy as per Equation (33).Finally, the receiver efficiency is calculated as the ratio of usable thermal energy to the incident solar energy.−5, and the default absolute tolerance, 1 × 10−4, was used for all dependent variables. To make this model converge quickly, the tolerance for the pressure was set to 1 × 10−3. However, at the end of the solution, the residual values were found to be much smaller than the set value.The solution method of the governing equations using heat transfer with radiation in participating media, and the mixture model, Laminar flow physics in COMSOL, is shown in The work of Veeraragavan et al.  is choseA grid independence test was performed recording variation in the maximum static temperature (K) from the model for eight different grid systems as shown in Veeraragavan et al.  presenteTrend of the receiver efficiency, the Carnot efficiency, and the total efficiency of a DAC receiver with its length was investigated and compared with those found by Veeraragavan et al. , as presAs the figure shows, the investigated trend is almost similar to that of the literature. However, a slight under-prediction in the current study can be seen, possibly because of the governing equations used in this study in contrast to an analytical model using simple governing equations and solving combining homogeneous and particular integral solutions in the literature. Another reason may be the values used for the specific heat, density, and viscosity of Therminol in the current study. The values were extracted from , and areDue to the acceptable level of agreement between results of the current study and Veeraragavan’s model , this mo−5, an inlet velocity of 0.0021 m/s, and a solar concentration of 100×. The inlet velocity was set such that at the maximum receiver length of 1 m the peak temperature of the nanofluid did not exceed 1071 K. A high solar concentration was initially chosen to ensure that the temperature of the heat transfer fluid did not drop below its melting point. This can be an issue with low concentrations  because the nanofluid is initially at a temperature significantly higher than that of the ambient temperature, and the concentrated radiation is not great enough to maintain that temperature near the inlet of the receiver. To determine the overall performance, receiver efficiency, Carnot efficiency, and total efficiency were considered. The sweep was conducted over a receiver length range from 0.01 to 1 m with a step of 0.0495 m, as shown in To run a parametric sweep of the receiver length, several parameters had to be arbitrarily set, those being a volume fraction of 1 × 10It can be seen from the plot that an increase in the receiver’s length results in a decrease in the receiver and total efficiencies, and an increase in the Carnot efficiency. The decrease in the receiver efficiency is due to an increasing surface area of the receiver and increasing fluid temperature with increasing length. This results in higher losses, as the losses are dominated by surface to ambient radiation, which is dependent on the surface area and the difference in receiver and ambient temperature to the power of four. Even though the rise in temperature results in an increase in the Carnot efficiency, it is not significant enough to counteract the decrease in receiver efficiency, resulting in a drop in the total efficiency. However, the Carnot efficiency does not pay enough attention to the rise in temperature achieved, which then implies that the most efficient receiver design is such that a negligible temperature rise occurs. This is not the case, as the entire objective of the receiver is to achieve a temperature rise. Hence, an adjusted Carnot efficiency (using the inlet temperature as the low temperature instead of the ambient temperature) is used to give a better indication of the trade-off between decreasing receiver efficiency and average temperature rise. This is illustrated in From the plot, it can be seen that the adjusted efficiency continually increases with the receiver length. This reflects that the best trade-off between receiver efficiency and temperature rise occurs at the maximum receiver length. This implies that the longer the receiver is, the better the trade-off is (to a certain extent), which contradicts what the normal total efficiency implies. The trend of the plot is seen to be curved and not linear, which implies that, at a certain receiver length, it will reach a maximum and the efficiency will then decrease with increasing length. This point indicates where the trade-off between the receiver efficiency and the rise in temperature is at a maximum; if the length is further increased, the rise in average temperature will not be enough to offset the decrease in receiver efficiency.2CO3–K2CO3 is at an even higher temperature, it is expected that the inlet velocity will have an even smaller effect on the total efficiency than it did with the solar salt. To investigate this, the velocity and receiver length were both varied to achieve a constant exposure time, chosen as 463 s. This time was selected from determining at what velocity for a receiver length of 0.5 m the peak temperature of the nanofluid would be equal to its upper temperature limit if 1071 K. The exposure time is given simply as the receiver length divided by the inlet velocity. This exposure time was then held constant for receiver lengths ranging from 0.5 to 6 m with a step of 0.5 m. The receiver lengths and their associated inlet velocities are summarized in −5, and a solar concentration of 100×.Another factor that affects the performance of the receiver is the inlet velocity of the heat transfer fluid. An increase in velocity allows for longer receiver lengths but also increases the Nusselt number and by extension the convective heat transfer coefficient, resulting in higher losses. However, as the heat transfer fluid in question is of such a high temperature, the thermal losses are dominated by the radiative losses; as such, the increase in the convective heat transfer coefficient results in only a small drop in efficiency. Given that LiThe receiver efficiency, Carnot efficiency, and total efficiency are illustrated in −5 m/s. The results are illustrated in The effects of the inlet velocity were also investigated by keeping the receiver length constant and then increasing the velocity. The receiver was set with the same parameters and the length was arbitrarily chosen as 1 m. For this length, the lowest velocity will be such that the peak temperature of the nanofluid is approximately the upper temperature limit of 1071 K. This corresponds to a velocity of 0.0021 m/s; the velocity is then ranged from this value to 0.003 m/s with a step of 4.5 × 10The results depicted are somewhat controversial, as it shows that, by increasing the inlet velocity, the overall efficiency of the receiver is actually increased. This effect can again be attributed to the dominating nature of the surface to ambient radiation losses; with an increase in the velocity, the exposure time is reduced, resulting in a decrease in the temperature rise across the receiver. Even though an increase in the velocity increases the convective losses of the receiver, the drop in the temperature rise results in a much larger drop in the overall thermal losses, causing the receiver to become more efficient. However, when considering the adjusted efficiency as depicted in Therefore, while for a fixed receiver length an increase in velocity results in a decrease in the adjusted total efficiency of the receiver, it has a negligible effect on the receiver if the length and velocity are adjusted such that the peak temperature of the nanofluid is the same as the upper temperature limit.The volume fraction of the nanoparticles suspended in the base fluid is another important factor that has to be considered because it directly influences the absorptivity of the nanofluid. It was seen that an increase in the volume fraction of nanoparticles significantly increased the absorption coefficient of the nanofluid, resulting in a shallower receiver required to absorb 99% of the radiation . An inte−5. After this point, an optimal receiver length becomes apparent and decreases with increasing volume fraction. This point indicates the receiver length that will provide the best trade-off between reduced receiver efficiency and temperature rise, after which point the drop in receiver efficiency becomes too great to justify the rise in temperature.To directly compare the effects that the volume fraction has on the overall performance of the receiver, the results of each volume fraction at the maximum receiver length, 1 m, and peak temperature, 1071 K, are compared and presented in −5 to 1 × 10−4 instead of showing a linear relationship. For volume fractions of 1 × 10−4 and 5 × 10−4, the optimal length is not equal to the maximum length. Therefore, instead of using the maximum receiver length as the point of reference, the peak adjusted total efficiency should instead be the point of reference. These values are depicted in the same table, −4 and 5 × 10−4 are significantly more efficient. The total efficiency declines sharply initially with increasing volume fraction, levels off, and then begins to increase. The adjusted efficiency also has an almost linear relationship with the volume fraction, with a steep increase initially.By considering the peak adjusted efficiency as the point of reference, it can be seen that the volume fractions 1 × 10The final parameter investigated is the solar concentration. To do this, the receiver height, volume fraction, and receiver length were set at 0.0142 m, 0.0001, and 1 m, respectively. The inlet velocity was set such that, at the maximum length of 1 m, the peak temperature of the nanofluid was equal to the upper temperature limit of the base fluid. This corresponded to an inlet velocity of 0.0248 m/s. The overall performance of the receiver is illustrated in From the plot, it can be seen that, at low solar concentrations (<25×), the receiver efficiency is negative because the receiver is losing more energy than it is gaining from the concentrated solar radiation. This is due to the significantly high initial temperature of the nanofluid, as high solar concentrations are required to just maintain this temperature. Furthermore, from the plot, it can be seen that the receiver and Carnot efficiencies both increase with the solar concentration, implying that increasing the solar concentration does not have an adverse effect on the receiver. The adjusted total efficiency is also considered to verify whether this is the case. From However, when comparing the average and peak temperatures of the receiver at different solar concentrations, an interesting trend becomes apparent. As shown in This trend therefore implies that, if the peak temperature were kept constant, the Carnot efficiency would actually decrease with increasing solar concentration, which in turn implies that there would be an optimal solar concentration that provides the best trade-off between Carnot efficiency and receiver efficiency. This was investigated using the same parameters as already stated and varying the inlet velocity to obtain a constant peak temperature for a range of solar concentrations. The results are summarized in The plot does in fact reflect what is expected, as it can be seen that the Carnot efficiency decreases. However, the rise in receiver efficiency is great enough to overcome this decrease and result in a net increase in the total efficiency with increasing solar concentration. It should be noted that the solar concentration starts at 80×, since, for any concentrations less than this value, the peak temperature of 1071 K could not be achieved. This plot does not give an accurate representation of the overall performance of the receiver because it does not place enough emphasis on the temperature rise of the receiver. When considering the adjusted Carnot efficiency, a different trend is observed.−4 and a length of 1 m, the optimal solar concentration is approximately 150×.From 2CO3–K2CO3 molten salt nanofluid on the total efficiency and the adjusted total efficiency of the receiver in comparison to that of solar salts was investigated, as presented in The effect of Li3–K2CO3 operates at higher temperature and is therefore subject to higher thermal losses. The reason, therefore, for choosing LiCO3–K2CO3 over solar salt is that the higher the average temperature it can achieve, the more desirable for thermal storage it will be, as a greater amount of energy can be stored for a smaller volume of liquid.The plots show that both molten salts, even though solar salt is more efficient, follow similar trends. The difference in efficiencies is expected as LiCO3–K2CO3 base fluid, was studied computationally for the receiver of a DAC using a validated CFD model. Several parameters including the receiver length, inlet velocity, volume fraction, and the concentration of the incident solar radiation were investigated for the nanofluid to explore their effects on the overall energy performance of the receiver. The results are discussed extensively in this paper. The following conclusions can be drawn from the study:⮚Investigation into the receiver length and nanofluid volume concentration shows that, at high operating temperature, the receiver efficiency (from ≈0.75 to 0.6) and total efficiency (from ≈0.48 to 0.4) are decreasing, while the Carnot efficiency (from ≈0.615 to 0.68) is increasing slightly with receiver length. An adjusted Carnot efficiency (inlet temperature as the low temperature instead of the ambient temperature) shows that the rise in average temperature of the HTF is exponential with receiver length to a certain maximum receiver length, beyond which the temperature rise is not enough to offset the receiver efficiency decrease.⮚Using the normal Carnot efficiency, it was shown that an increase in heat transfer fluid (HTF) velocity resulted in no apparent effect on the overall efficiency (⮚−6 to 5 × 10−4) of nanoparticles was investigated over a range of different receiver lengths with initial results indicating that the most efficient receiver  and the total efficiency (from −0.55 to 0.2), and the adjusted Carnot efficiency and the adjusted total efficiency, are gradually increased with an increase in the solar concentration (from 10× to 100×). An interesting trend was observed, however, where the difference between the average and peak temperatures of the receiver actually increased by 5–41 K by increasing solar concentration from 30× to 100×. This discovery implies that, if the peak temperature were kept constant by balancing the receiver length and inlet velocity, the average temperature would actually decrease at some point with increasing solar concentration, resulting in a decrease in the Carnot efficiency and a drop in the overall efficiency of the receiver, which contradicts the initial results. By keeping the peak temperature constant, an optimal solar concentration was indeed discovered when considering the adjusted Carnot efficiency. For a receiver with a volume fraction of 1 × 10⮚3–K2CO3 will be more desirable for thermal storage, as a greater amount of energy can be stored for a smaller volume of liquid at higher operating temperature.A comparative study shows that solar salt is superior to the lithium carbonate salt because of the higher total efficiency of the collector. However, LiCOA molten salt nanofluid, with the composition of graphite as the nanoparticles in LiCO


Phaseolus vulgaris L.) worldwide. Since the 1980s, the commercial Carioca variety has been the most consumed in Brazil, followed by Black and Special beans. The present study evaluates genetic diversity and population structure of 185 Brazilian common bean cultivars using 2827 high-quality single-nucleotide polymorphisms (SNPs). The Andean allelic introgression in the Mesoamerican accessions was investigated, and a Carioca panel was tested using an association mapping approach. The results distinguish the Mesoamerican from the Andean accessions, with a prevalence of Mesoamerican accessions (94.6%). When considering the commercial classes, low levels of genetic differentiation were seen, and the Carioca group showed the lowest genetic diversity. However, gain in gene diversity and allelic richness was seen for the modern Carioca cultivars. A set of 1060 ‘diagnostic SNPs’ that show alternative alleles between the pure Mesoamerican and Andean accessions were identified, which allowed the identification of Andean allelic introgression events and shows that there are putative introgression segments in regions enriched with resistance genes. Finally, genome-wide association studies revealed SNPs significantly associated with flowering time, pod maturation, and growth habit, showing that the Carioca Association Panel represents a powerful tool for crop improvements.Brazil is the largest consumer and third highest producer of common beans ( Phaseolus vulgaris L.) belongs to the Fabaceae family, subfamily Faboideae, and genus Phaseolus L. This genus has approximately 70 species, of which only five are cultivated. One of these, the common bean, was one of the most successful American crops, and it spread worldwide after the travels of Columbus [The common bean , with a record of 30.4 million tons in 2018 [The common bean originated on the American continent ,9, from In Brazil, the common bean varieties that predominated in the consumer market until the 1970s were those with single-colored grain, such as the ‘Roxinho’, ‘Rosinha’, ‘Bolinha’, ‘Preto’, ‘Jalo’, and mainly ‘Manteigão’ types of Andean origin , the sceP. vulgaris species have great genetic variability, mainly for morphological characters such as grain shape, size, and color [The nd color . Howevernd color . Thus, knd color .Several studies that have analyzed genetic diversity have already been carried out, mainly for morphological ,28,29,30In the current study, a total of 2827 high-quality single-nucleotide polymorphisms (SNPs) that are distributed over the 11 chromosomes of this species were genotyped using SNP Assay technology  with a For the present study, a total of 185 accessions of commercial cultivars, advanced lines, and some landraces of common bean were selected from the germplasm bank of the IAC, to represent the genetic diversity of the Brazilian common bean. The accessions were selected based on genetic characterization that was carried out by Perseguini et al. .The BDP includes accessions that date from 1963 to 2018 that were released by 11 different genetic breeding institutions in Brazil, both private and governmental. Altogether, 131 accessions belong to the Carioca commercial class, 29 to the Black class, and 25 to the Special class. These include ‘Pinto’, ‘Cranberry’ and ‘Dark Red Kidney’ beans, which were developed for export, and ‘Jalo’, ‘Rosinha’, ‘Alubia’, ‘Mulatinho’, ‘Roxinho’, and ‘Bico de Ouro’ beans, which were marketed before the launch of the Carioca variety. This collection also includes the first Carioca variety that was launched in 1971, Carioquinha (Carioca Comum), which revolutionized the history of beans in Brazil  was inclThe Carioca accessions were used as a sub-panel for validation of the GWAS. For the analysis of genetic diversity, these were divided into three groups based on their years of release. The release year of the cultivar BRS-Pérola (1994) was used as a reference, with the first group consisting of cultivars launched from 1971 to 1994 (old Carioca), the second group of cultivars was launched from 1995 to 2018 (modern Carioca), and the third group of lines had unknown release dates (Carioca lines). For association mapping, 10 further Carioca accessions that were not part of the BDP and were previously genotyped were used to increase the size of the GWAS panel, for a total of 141 Carioca accessions. These accessions are not of Brazilian origin or do not have known information, and for that reason, they were not used to the other genetic diversity analyzes. All of the information about the accessions is given in the Centro Internacional de Mejoramiento de Maíz y Trigo  [Call Frequency and GenTrainScore < 0.6.The DNA of each sample of the BDP was extracted from young leaves  collected from five plants of each accession, using the cetyltrimethylammonium bromide (CTAB) protocol described by  CIMMYT) . The qua CIMMYT) . The qualoci was performed using the Beagle 5.0 software [The SNP calling was performed using the TASSEL 5.0 software , for whisoftware .To validate the subset of Carioca cultivars for GWAS, a phenotypic evaluation of 141 Carioca accessions was carried out for days to flowering (DTF), days to pod maturation (DTM), and growth habit (HAB). For this purpose, the accessions were planted at the experimental station of the IAC during the irrigated period . Each experimental plot consisted of a 1-m line with 10 plants, each spaced at 50 cm. The experimental design was as randomized blocks with four replications, and for phenotypic evaluation, only the six central plants of each plot were considered.For the HAB assessment, four phenotypic classes were considered as proposed by Schoonhove and Pastor-Corrales , where: GenAlEx 6.5  was usedPrincipal component analysis (PCA) was carried out using the ADE 4 package  and visuThe population structure was evaluated using the Bayesian clustering approach, implemented in the STRUCTURE v2.3.1 software , and usiloadingplot function was used to identify alleles that contribute most to the genetic separation of the three commercial classes.Discriminant analysis of the principal component (DAPC) proposed by Jombart et al.  and implPhaseolus vulgaris v 2.1; Schmutz et al. [To understand the dynamics of the Andean introgression into the Brazilian Mesoamerican cultivars, Bayesian analysis (STRUCTURE) was performed using the parameters previously described, at K = 2, and the analysis was performed separately for each chromosome. Cultivars with 100% Mesoamerican membership coefficients for all of the chromosomes  were compared to the 10 Andean cultivars using genotype contrast analysis. This allowed the identification of SNPs with contrasting and diagnostic alleles between the Mesoamerican and Andean cultivar sets. These SNPs are called the ‘diagnostic SNPs’, and are plotted using the MapChart 2.32 software . Using tz et al. ), using z et al. .locus mixed model, and performs the analysis in two interactive steps: a fixed-effect model is applied first, followed by a random-effect model, so that both are repeated interactively until no significant SNP is detected. The phenotypic matrix composed for the least squares means of each accession was obtained by restricted maximum likelihood using the JMP 7 software (SAS Institute). To avoid type I errors , the structuring matrix was tested using the Bayesian Information Criterion (BIC) test according to Schwarz [p-value threshold of each SNP was determined by the resampling method using the function FarmCPU.P.Threshold. Each trait was permuted 300 times to break the relationship with the genotypes, and then the random association between all of the SNPs to the phenotype was estimated. The minimum p-values obtained among all of the SNPs for the 300 repetitions were recorded, and then the 95% quantile from all of the minimum p-values was defined as the p-value threshold [For association mapping, the FarmCPU  model in Schwarz , for a r Schwarz  with thehreshold . Bonferrhreshold  thresholTo determine the genome window interval used in the BlastN analysis against the reference genome of the SNPs with Andean allelic frequency > 20% and significant SNPs in the GWAS, LD decay was estimated using squared allele-frequency correlation intrachromosomal pairs, with the R package LDcorSV , when acOverall, 2827 high-quality SNPs with polymorphism rates > 3% and well distributed over the 11 chromosomes of the species were filtered from the initial set of BeadChip  and were used for the BDP analysis.As expected, the population structure analysis  than the Andean accessions (Rs = 1.36). The same was confirmed using the Shannon’s I and uHe . AlthougTo investigate the relationships between the accessions that make up the BDP, the Nei  genetic The PCA and the dendrogram (not shown) indicated the great distance between the Andean and Mesoamerican accessions, although due to the small number of Andean accessions, this was estimated only for the Mesoamerican accessions. Considering the commercial classes, there was no clear genetic differentiation according to the dendrogram shown in the The accession Oito-Nove, which has a black tegument, showed the greatest genetic distance from the others, while seven landraces  were identical to the first Carioca cultivar released (Carioca Comum). The genetic distance analysis confirmed the pedigree of several cultivars, such as cultivar IAC-Polaco, which grouped close to cultivars Branquinho and IAC-Imperador, which were used as the parental lines for its development, and cultivar IAC-Pyatã, which was closest to cultivar IAC-Akytã, both of which were derived from the same crossing .For the genetic diversity among the commercial classes, the highest was seen for the Special commercial group, for all of the evaluated parameters , with an emphasis on uHe (0.40), which was around three times that of the Carioca (0.11) and Black (0.15) groups . Althougcompoplot function (loading function. Polymorphisms at the SNPs ss715650213 (Pv04), ss715645828 (Pv08), and ss715644007 (Pv08) were the most important for distinguishing between the groups , with the highest variance restricted between accessions within each class , with only 8% (p < 0.0001) for within accessions, as expected for an autogamous species  to decrease as the distance between SNPs increased, for both sets analyzed (2 = 0.046) was considerably greater than for the BDP (2 = 0.11). The chromosomes Pv01 (mean r2 = 0.12), Pv03 (mean r2 = 0.12) and Pv11 (mean r2 = 0.11) of the BDP, and Pv06 (mean r2 = 0.08) of the Carioca panel had the highest mean r2 values. When considering the standard LD decay threshold (r2 = 0.2), the mean distances for all the chromosomes of the BDP and the Carioca panel were 2.00 Mb and 0.59 Mb, respectively.As expected, the LD decay curve showed a clear tendency for mean LD values  and the distance of 0.59 Mb for the decay of LD to 0.2 for the Carioca panel, the minimum number of SNPs required for good genome coverage and satisfactory GWAS data was 995, just over half of the number used in the present study (1927 SNPs).STRUCTURE analyses were performed for each of the chromosomes individually . These d2 > 0.8), thus showing a large Andean allele segment in this region .Using the set of diagnostic SNPs, it was possible to identify the Andean alleles that showed the highest frequencies across all the Mesoamerican cultivars. Considering only the SNPs with an Andean frequency >10%, introgression was observed for all the chromosomes (89 SNPs). For the SNPs that showed introgression >20% , seven SNPs were identified on chromosome Pv05, two on Pv07, and only one on Pv02, Pv06, Pv08, and Pv10. This thus showed the greatest introgression levels for chromosome Pv05, which agreed with the STRUCTURE results . Five SNThe BlastN analysis of the SNPs with the highest Andean allele occurrence in the reference genome of the species revealed two SNPs located within a genic sequence. In general, most of the SNPs were in regions enriched with genes, such as ss715645658 on chromosome Pv06, located inside a 2-Mb window containing 261 genes. Genetic annotation of the genes showed distinctive functions, such as a zinc finger protein (Phvul.002G182400), a transcription factor (Phvul.005G119400), an RNA helicase protein (Phvul.006G179000), a growth-regulating factor (Phvul.007G222300), a vacuolar iron transporter protein (Phvul.010G021600), and others. However, genes related to disease resistance were the most common among these, such as those encoding the leucine-rich repeat protein kinase family protein LLR , the auxin-responsive GH3 protein , a pectin lyase-like superfamily protein (Phvul.002G188700), and others. SNP ss715648423 on Pv10 was associated with the largest number of resistance genes .The genes that were located within 2 Mb windows centered on the SNPs with the highest Andean allele occurrence are given in the 2) was considered high for all the traits. Shapiro-Wilk tests revealed nonnormality just for HAB, mainly because the Carioca accessions are elite germplasm bank genotypes that have undergone several selection cycles, such that none of the evaluated accessions has growth habit type 4 . As expected, Pearson’s correlation showed a significant positive correlation between DTF and DTM (r = 0.50), which showed that the plants that flower first tend to be earlier for pod maturation. A significant positive correlation was also seen between HAB and DTF (r = 0.17) and HAB and DTM (r = 0.20), indicating that erect accessions tend to be more precocious.After quality control and SNP calling, 1927 high-quality SNPs were selected for association mapping on the panel of 141 Carioca accessions that were phenotypically evaluated for DTF, DTM, and HAB. The ANOVA results showed significance differences for cultivars, and repetition for all traits, except for DTF . The coeTo determine whether correction for population structure should be applied to the GWAS model, PCA was performed for the genetic matrix, and the BIC test was performed on the first five components, which together explained 27.6% of the variance. According to Schwarz (1978), the highest BIC reveals the best number of covariates for the model. For all traits, no PCs were required (not shown). This was confirmed by the PCA , where n2 = 1) with SNP ss715646076 was the only one significantly associated with DTF  showed a significant marker phenotype association for HAB a and DTMwith DTF b. The diwith DTF d. Two otFor the SNP associated with HAB, the accessions with two copies of the reference allele “C” tended to be more erect than the accessions with two copies of the alternative allele “T”, or the heterozygote “C/T” e. A simiTerminal Flower 1 gene . The other SNP on Pv01 (ss715639272) is located between genes Phvul.001G193600 (upstream) and Phvul.001G193700 (downstream) at ~100 kb from each of these genes, both without functional annotation. SNP ss715649644 had the lowest p-value for DTM and is located ~299 kb upstream of Phvul.002G062000 . The third SNP significantly associated with DTM, ss715647383, is located at ~1.9 kb from Phvul.010G150400 .The BlastN analysis of SNP ss715646076 against the Andean common bean reference genome  showed Our study shows the potential usefulness in crop genetics studies of the Brazilian panel, which comprises 185 accessions of common bean (dried beans) that were selected tof represent the genetic diversity of commercial cultivars, advanced lines, and landraces in Brazil. It is a panel that includes several commercial varieties that harbor high molecular diversity. Here, we have characterized both the molecular and the phenotypic diversity of the BDP panel based on 2827 SNPs and on three phenotypic traits of the Carioca commercial type, which represents a subset of 141 accessions that was useful to highlight the structure of the diversity of the Brazilian genotypes and their potential for association studies.We showed two contrasting genetic groups that correspond to the Andean and Mesoamerican gene pools, with the Mesoamerican predominant in the BDP panel. This result was expected for Brazil, where most of the germplasm has a Mesoamerican origin ,76. ThisThe Andean accessions show lower levels of genetic diversity  than the Mesoamerican accessions  for all of the parameters calculated. Several other studies have reported higher genetic diversity of the Mesoamerican gene pool, when comparing both improved commercial accessions ,62,82 anloci showing introgression from the Andean into the Mesoamerican gene pool. This might be due to the use of Andean accessions as donor parents in some Brazilian breeding programs. The pedigree data support this, where some Andean parents were included in the crosses in the development of Mesoamerican cultivars, such as IAC-Alvorada, BRS-Talismã, IAC-Milênio, IPA9, IAC-Tigre, and IAC-Galante [Unlike other studies ,38,76,84-Galante ,86,87,88-Galante . The dis-Galante  that occ-Galante . AND 277-Galante , powdery-Galante , anthrac-Galante , and rus-Galante , and it -Galante . The useloci of resistance to various diseases close to these diagnostic SNPs. Oblessuc et al. [UC on Pv10 as the quantitative trait loci (QTL) with the greatest angular leaf spot resistance. Oblessuc et al. [At least one resistance-related gene was found in the genetic annotation for the diagnostic SNPs selected, with the R genes the most frequent, such as for SNP ss715648423 on chromosome Pv10, in a region where 41 R genes are located. The R genes are involved in resistance to several diseases, including those of bacterial, viral, and fungal origin, and also in resistance to insects and nematodes. Most of the R genes encode intracellular proteins with a conserved central nucleotide-binding domain , and a more variable C-terminal leucine-rich repeat domain ,95. In ac et al.  identific et al.  performec et al.  identifiDifferent from other countries, in Brazil, 85% of common bean consumption is of only two commercial varieties: Carioca and Black beans . Both belong to the Mesoamerican gene pool, as also demonstrated in the present study. However, the greatest genetic diversity and allelic richness were observed for the Special group , which can be explained by the presence of both Andean and Mesoamerican accessions in this group. For the Carioca  and Black  groups, both had a relatively low genetic diversity, and very similar values, although the genetic diversity of the Black group was a little higher. Similar genetic diversity was demonstrated by Delfini et al.  and CardFst (0.24) and the high genetic variance among the accessions (67.5%) showed that the genetic differentiation is not well defined. This was supported by the DAPC, in which overlap was seen among the commercial groups. In the case of the varieties such as Carioca beans, for which the seed coat appearance and color are extremely important for the consumer market, the use of nonCarioca accessions in the breeding process is particularly challenging due to the ease with which the grain can lose its ideotype. This might explain the higher proportions of nonCariocas accessions with the Carioca background in the compoplot illustrated in In the present study, the Black group also had a genetic structure that was clearly differentiated from the other groups, as based on the Bayesian analysis and in relation to the restricted genetic variance among the commercial classes (23.8%), with this value very similar to that reported by Delfini et al.   when compared to the allelic diversity (Rs) for cultivars launched after 1994 shows that the variety improvement process is still recent and that the expected heterozygosity has been the main source of variation for the selection process over this short time. Indeed, the number of alleles is more sensitive to changes in the population size and bottlenecks than to the expected heterozygosity ,102,103.PvTFL1y gene (Phvul.001G189200), which controls the determinate HAB in common bean [fin locus on chromosome Pv01 that was previously identified by Koinange et al. [Genome-wide association studies were performed for HAB, DTF, and DTM in the sub-panel of the 141 Carioca common bean accessions. This showed two SNPs  in LD and ~89.9 kb apart that were significantly associated with all the traits evaluated. Among the 1927 SNPs that we used from the BARCBean6K_3 chip, the significant SNPs ss715646076 and ss715639272 were the closest to the mon bean , and whie et al. .Cichy et al.  mapped tThis is the first study on genetic diversity, population structure, and gain in genetic variability that examines a large number of Brazilian accessions. The 2827 molecular markers genotyped here has provided insights into the levels of diversity of the three main commercial classes of beans in Brazil. A strong population structure was identified for the Black bean group and a high degree of genetic and allelic diversity for the Special bean group. Among the commercial classes, Carioca beans showed the lowest diversity. However, over almost half a century of Brazilian breeding programs since the release of Carioca, a significant increase in genetic diversity has been developed. It was also possible to identify the introgression of Andean alleles into the Brazilian Mesoamerican background, which resulted from the selection of novel sources of resistance to the main crop diseases. The Carioca bean panel is shown to be a powerful tool for selection scans and GWAS, which underlines the value of the Carioca association panel for crop improvement.


Soil-borne wheat mosaic virus (SBWMV) causes a serious viral disease that can significantly reduce grain yield in winter wheat worldwide. Using resistant cultivars is the only feasible strategy to reduce the losses caused by SBWMV. To fine map the resistance gene Sbwm1, 205 wheat accessions was genotyped using wheat Infinium iSelect Beadchips with 90 K SNPs. Association analysis identified 35 SNPs in 12 wheat genes and one intergenic SNP in the Sbwm1 region that showed a significant association with SBWMV resistance. Those SNPs were converted into Kompetitive Allele-Specific Polymerase assays (KASP) and analyzed in two F6-derived recombinant inbred line (RIL) populations derived from the crosses between two resistant cultivars ‘Wesley’ and ‘Deliver’ and a susceptible line ‘OK03825-5403-6’. Linkage analysis mapped this gene on chromosome 5D at intervals of 5.1 cM and 3.4 cM in the two populations, respectively. The two flanking markers in both populations delimited the gene to a 620 kb region where 19 genes were annotated. Comparative analysis identified a syntenic region of 660 kb in Ae. tauschii with 18 annotated genes and a syntenic region in chromosome 1 of B. distachyon. The candidate region includes several disease resistance related genes and we identified a PTI1-like tyrosine-protein kinase 1 gene as a putative candidate gene for Sbwm1. The two flanking SNPs for Sbwm1 can effectively separate the resistant and susceptible lines in a new diversity panel of 159 wheat germplasm. The results from this study lay a solid foundation for the cloning, functional characterization and marker-assisted selection of Sbwm1. Soil-borne wheat mosaic virus (SBWMV) was first appeared in the Great Plains of the U.S.A. in 19191 and has become an important disease in most winter wheat growing regions globally7. SBWMV infects wheat roots in wet soil during germination at 15 °C to 20 °C with an optimum temperature at 17 °C. Soil water is critical for the swimming zoospore to reach host roots9. Virus movement from the site of infection on roots to the leaves occurs at temperatures lower than 20 °C. Higher temperatures limit virus movement into leaves and further infection in developing leaves will not occur if temperatures rise to more than 20 °C after some leaves are infected9.Soil-borne wheat mosaic (SBWM) disease, caused by Polymyxa graminis, the vector of SBWMV10. In wet soil climates, these patches may present anywhere in an infected field. SBWMV symptoms stop developing on leaves when the average temperature exceeds 20 °C, but can be reinitiated on newly emerged leaves given conductive environmental conditions10. Reported yield losses caused by SBWMV ranged from 10% to 30% and can reach up to 80% in SBWMV severely occured fields in the U.S.A. and 50% in Brazil13.The SBWMV symptom usually first appears in early spring when the crop begins to green up. Low spring temperatures is usually conducive to the occurrence of SBWMV and could promote the development of the symptoms. Irregular chlorotic patches in a field indicate SBWMV infection. In dry environments, patches of symptomatic SBWMV-infected plants usually present in low-lying, wet regions of a field that are favorable for infection by the swimming zoospores of Polymyxa graminis is one of the endoparasitic slime molds (Plasmodiophoromycota), which can produce resting spores that harbor viral RNA and movement protein, and can be easily distributed by wind, water and machinery. Those resting virus-containing spores can remain dormant and invasive in soil for up to 30 years, and then germinate in an environment favorable for infection when host are available. Thus, it is very difficult to control this disease once it happened in a field. The most practical SBWM control strategy is to grow resistant cultivars15.14. However, a cultivar ‘Embrapa 16’ from Brazil was reported to carry two genes for SBWMV resistance2. With the rapid development and wide use of molecular markers in wheat genetic studies, quantitative trait locus (QTL) mapping approach has been used to localize SBWMV resistance genes using linkage maps. The QTL on 5DL has been reported by several independent studies on winter wheat ‘Karl 92’16, ‘Pioneer 26R61’, ‘AGS 2020’ and ‘Heyne’17. A breeding line KS96WGRC40 derived from Aegilops tauschii also carries this gene18. This gene was designated as Sbwm117. Interestingly, a gene conferring resistance to Soil-borne cereal mosaic virus (SBCMV) in ‘Cadenza’, ‘Tremie’ and ‘Claire’, designated as Sbm1, was also mapped in the region of Sbwm120. Because they tightly linked to the marker Xgwm469-5D, Sbm1 for SBCMV resistance is either a tightly linked or the same gene as Sbwm1 for SBWMV resistance.Inheritance studies have shown that resistance to SBWMV is controlled by major genes. Winter wheat cultivars such as Shawnee, Centurk and KS73256 were reported to carry a dominant gene for SBWMV resistance. Newton might contain a gene for resistance different from that in ‘Arthur 71’, ‘Homestead’, and ‘Tascosa’21. Fixed SNP chips have been released and widely used for various genetic and breeding studies including genome-wide association analysis and genomic selection in many crop species23. In wheat, SNP chips with various number of SNPs including Wheat 9 K iSelect, Wheat 90 K iSelect and Wheat 660 K Axiom SNP chips have been developed and widely used to map genes or QTLs for different traits28.Single nucleotide polymorphisms (SNPs) are the most widely distributed DNA sequence variations in a genome. During the past decade, next-generation sequencing (NGS) technologies have become the cheapest and fastest technology for genome-wide SNPs discoveryXgwm469, that was associated with the SBWMV resistance on chromosome 5DL has been identified29. Further association mapping analysis using Wheat 9 K iSelect chip identified six SNPs from two genes that were highly associated with SBWMV resistance, and the identified SNPs were further mapped on 5DL using a F6 RIL population ‘Heyne’/‘Trego’17. However, those SNPs were mapped on one side of Sbwm1 and the marker flanking on the other side of Sbwm1 is about 20 cM a way. Therefore, a tightly linked flanking marker is urgently needed for effective marker-assisted transfer of Sbwm1 to new wheat cultivars and cloning Sbwm1.In a former study, a SSR marker Sbwm1. Two new RIL populations were used to map the SBWMV resistance genes in the two resistant cultivars Wesley and Deliver and to verify the linkage among the newly identified SNP markers Sbwm1. We also pinpointed the candidate region for Sbwm1 using the Chinese Spring reference genome, developed KASP assays for the linked SNPs to Sbwm1 for marker-assisted selection (MAS), and identified a putative candidate gene for Sbwm1 by gene expression analysis.In this study, the panel of 205 wheat accessions previously genotyped with 9 K SNP chips were further genotyped using Infinium 90 K wheat SNP chips to identify more flanking SNPs for 29. All these accessions were genotyped using Infinium iSelect 90 K wheat SNP chips25. A second panel of 159 wheat cultivars and breeding lines were used to validate the mapped KASP assays (Supplementary Table The association mapping panel includes 205 wheat accessions with 137 hard winter wheat (HWW) and 68 soft winter wheat (SWW) from six 2008 HWW and SWW nurseries (Supplementary Table 6 RIL populations developed by single-seed descent from the crosses ‘Wesley’ x OK03825-5403-6 (180 RILs) and ‘Deliver’ x OK03825-5403-6 (260 RILs) were used to map the SBWMV resistance gene. ‘Wesley’ and ‘Deliver’ are SBWMV-resistant HWW cultivars, whereas OK03825-5403-6 is a highly SBWMV-susceptible HWW breeding line.Two Fet al.30. An additional panel of 159 accessions were evaluated for SBWMV resistance in 2010–2011 and 2011–2012 wheat growing seasons in the same field nursery for marker validation. The two RIL populations were evaluated for SBWMV resistance in the same field in 2013–2014 and 2014–2015 growing seasons using the same rating scale described for association mapping. All experiments used random complete block design with two replicates. In all experiments, each accession or RIL was sowed at 1.5 m -long rows with planting density of 40 seeds. After planting, the nursery was sprinkler irrigated everyday for 7 days till the seedling emergence to maintain high moisture in the soil to promote SBWMV infection.The association panel of 205 wheat accessions were evaluated for SBWMV resistance in the SBWMV-infested field at the Rocky Ford Research Farm of Kansas State University, Manhattan, KS in 2009–2010 and 2010–2011 wheat growing seasons. The nursery has shown consistent and severe SBWMV infection on susceptible wheat cultivars since 2006. Field experimental design, planting, and disease damage evaluation followed Zhang et al.25. SNPs with less than 5% minor allele frequency (MAF) or with more than 15% missing data were removed31. A total of 21,600 SNPs were scored and used in the final analysis. Sequences that harbored significant SNPs were blasted against the Chinese Spring Wheat SeqVer1.0 reference sequence32 to assign their putative chromosome positions and were converted to Kompetitive Allele Specific Polymerase Chain Reaction (KASP) assays for screening the two RIL populations  was screened across the two RIL populations  was estimated using the formulag2 is the variance among RILs, бge2 is the variance for G x E; бe2 is the variance of environments, n is the number of environments, and r is the number of replicates34.Data for SBWMV phenotypes were analyzed using two-way GLM in SAS for Windows v9  to determine the effects of genotypes (g), environments (e), and G x E interactions. Heritability  was set as 1 to 10 with variable length of burn-in period and number of iterations at 20,000–250,000. For each trait, Bayesian information criterion (BIC) was applied to determine the optimum number of subpopulations31. Kinship was calculated using the same set of 1,500 SNPs used for structure analysis and SPAGeDi package36.Population structure was assessed by Structure 2.3.4Genome-wide association analysis used the generalized linear model (GLM) with the Q matrix as fixed effects and the mixed linear model (MLM) with a Q matrix as fixed effects and a kinship matrix as random effects. The models were applied to each experiment for SBWMV resistance, and model fitness was determined based on the Bayesian information criterion value.37. A threshold of p < 10−5 (1/number of markers) was set to claim significant marker-trait associations. Linkage disequilibrium and haplotype analyses of the significant SNPs were performed using HAPLOVIEW v.4.2 .Association analysis of SNP data was conducted using the genome association and prediction integrated tool (GAPIT) implemented in a R package38. Recombination fractions were converted into centiMorgans (cM) using the Kosambi function39. The threshold of logarithm of odd (LOD) score was set at 3.0 to claim linkage among markers with 0.4 as a maximum fraction of recombination. The goodness-of-fit between observed and expected segregation ratios between two alleles was analyzed for each marker locus using a chi-square-test.Genotypes with disease rating of 1–2 were classified as resistant and 3–4 as susceptible for SBWMV resistance in the two RIL populations. SNP linkage maps were constructed for the two RIL populations using JoinMap ver. 4.0Ae. tauschii reference genome sequence and B. distachyon genome sequence using BLASTN41. A significant match was declared when the queried sequences showed at least 99% nucleotide identity with an e-value lower than e−20.Sequences that contain significant SNPs associated with SBWMV resistance were used as queries to blast the IWGSC Chinese Spring reference genome sequence RefSeqv1.0, 2O, 2.4 μL 10 × buffer, 2.4 μL 50 mM MgCl2, 1 μL of 10 mM dNTPs, 1 μL each primer at 20 μM, 2.5 U Taq polymerase, and 1 μL cDNA. Cycle conditions were 95 °C for 1 min, annealing at 55–60 °C for 1 min for different primers, and extension at 72 °C for 1 min. A final extension at 72 °C was performed for 10 min. Annealing temperatures and cycle conditions for each primer pair were optimized such that each PCR was in the linear range of amplification. PCR primers were listed in Supplementary Table Leaf tissues were collected from seedlings of Wesley and OK03825-5403-6 grown in the SBWMV field nursery for RNA extraction in fall 2015 and spring 2016 when the SBWMV symptom appeared. RNA was extracted and purified using the RNeasy Plant Kit with on-column DNaseI treatment . Complementary DNA from reverse-transcription reaction using a SuperScriptTraesCS5D01G531200 was further quantified with three replications by quantitative real-time PCR performed on an ABI7900HT instrument using SYBR Green (Thermo Fisher Scientific). Gene expression level was analyzed using the 2−ΔΔCt method. Actin gene was used as the endogenous control with ACCTTCAGTTGCCCAGCAAT as the forward primer, CAGAGTCGAGCACAATACCAGTTG as the reverse primer.The identified candidate gene p < 0.01), the two RIL populations , and the association mapping population 17, suggesting a high repeatability of SBWMV resistance data between the field experiments. Analysis of variance showed highly significant genotypic effects and very high heritabilities (from 84.9% to 98.2%) in both the RIL populations and the validation population   on chromosome 5D. SNP Excalibur_c22724_85 was from an annotated low confidence gene TraesCS5D01G625100LC, and BS00013935_51 was from an intergenic region. Using the Chinese Spring reference genome (IWGSC RefSeq v1.0), those SNPs can be located in a 69 Mb interval on 5D. However, when SNP BS00013935_51, the SNP with the lowest association among those SNPs (P = 1.44E-07) was excluded, the interval can be delimited to a 1.18 Mb region from 546,086,597 to 547,273,657 bp on the chromosome 5D.Genotyping the association mapping population with the 90 K SNP chips identified 21,600 polymorphic SNPs. Using the MLM model, thirty-five SNPs were identified significantly associated with SBWMV resistance in both years 17. TraesCS5D01G532100 carries five SNPs with four new SNPs. All the remaining SNPs were distributed in nine other genes.Among the 11 high confidence genes that carry significantly associated SNPs for SBWMV resistance, wsnp_JD_c4438_5568170 from the gene TraesCS5D01G530600 and wsnp_CAP11_c209_198467 from the gene TraesCS5D01G532100  in a 660 kb interval on the chromosome 5D of Ae. tauschii  present in Ae. tauschii, but absent in wheat, and one gene (TraesCS5D01G530500) present in wheat, but absent in Ae. tauschii  on chromosome 1. However, this synteny in B. distachyon was interrupted by four non-syntenic regions , a poly (ADP-ribose) glycohydrolase activity proteins (TraesCS5D01G530500 and TraesCS5D01G530900), a protein kinase family protein (TraesCS5D01G530600), a potassium channel activity protein (TraesCS5D01G530700), a S-adenosylmethionine-dependent methyltransferase (TraesCS5D01G530800), a pentatricopeptide repeat domain family protein (TraesCS5D01G531000), a zinc ion binding protein and zinc finger containing proteins (TraesCS5D01G531100 and TraesCS5D01G531400), a pto-interacting protein 1 (PTI1) (TraesCS5D01G531200), a protein with serine/threonine phosphatase activity (TraesCS5D01G531300), an albino-like protein (TraesCS5D01G531600), a neutral amino acid transmembrane transporter activity protein (TraesCS5D01G531700), a KDEL sequence binding protein (TraesCS5D01G531800), a major pollen allergen-like protein (TraesCS5D01G531900), and a gene encoding a protein with unknown function (TraesCS5D01G531500) , whereas nine expressed but showed the same expression levels between the resistant and susceptible parents  before and after SBWMV symptoms appeared. Only one gene, TraesCS5D01G531200 showed significant differential expression between the resistant and susceptible parents when SBWMV symptoms appeared in the leaf tissues  in this region encoding proteins that may be involved in disease resistance, we believed TraesCS5D01G531200 might play an important role in the SBWMV resistance.Expression test of these genes in leaves showed that eight genes did not express . Among these accessions, 104 were resistant and 55 were susceptible. The two SNPs identified four haplotypes across these accessions . Among the polymorphic SNPs, 35 SNPs were identified to be highly associated with Sbwm1. More trait associated SNPs identified in this study is mainly due to the high SNP density (10 times) in the 90 K SNP chip than the 9 K SNP chip25. Among those Sbwm1-associated SNPs, wsnp_JD_c4438_5568170, c4438_5567972, wsnp_JD_c4438_5567834 and wsnp_CAP11_c209_198671 were the same as previously identified from the 9 K SNP chips, and the other 31 were newly identified SNPs from the new 90 K chips in this study, indicating that increasing marker density facilitate identification of more trait-associated SNPs. Those common SNPs identified in both chips were owing to that the most probes in the 9 K chip were included in the 90 K chip25.Our previous association mapping study identified an SSR marker, Ae. tauschii-derived breeding line18. Those QTLs most likely underline the same gene, Sbwm1 as reported by Liu et al.17. A gene for SBCMV resistance, designated as Sbm119, was also mapped in the similar location as Sbwm1 in wheat and they may be either tightly linked or the same gene.Several independent mapping studies have identified a major locus for SBWMV resistance in the similar location on the long arm of chromosome 5D from different wheat cultivars and an BS00079676_51 and wsnp_CAP11_c209_198467 with BS00079676_51 as the closest marker in the two populations, respectively. Since wsnp_CAP11_c209_198467 has been identified as the closest marker to Sbwm1 in cultivar Heyne17, the resistance gene in Wesley and Deliver is in the same region of Sbwm1 and they are most likely the same gene. Therefore, this study defined Sbwm1 region to a 3.4 cM region in the Deliver/OK03825-5403-6 population and identified closely linked flanking markers for Sbwm1, which laid solid ground for map-based cloning and marker-assisted breeding of Sbwm1. Using association mapping together with linkage mapping can effectively determine the marker-trait association.In the linkage maps from Wesley/OK03825-5403-6 and Deliver/OK03825-5403-6 populations, those significant SNPs identified from the AM panel were all mapped near the SBWMV resistance gene in the same linkage group corresponding to the distal end of chromosome 5DL  phenotypes. However, six accessions carry the two SNPs in repulsion phase and inconsistent phenotypes were found for each recombinant haplotype. When allele A  at wsnp_CAP11_c209_198467 and allele G  at BS00079676_51 were together, all accessions with this haplotype showed SBWMV resistance; whereas when accessions carry G  at wsnp_CAP11_c209_198467 and A at BS00079676_51 , their reactions to SBWMV infection were mixed (Table Sbwm1 located between the two SNPs, and the two SNP together can separate the resistant and susceptible genotypes effectively and can be widely used in marker-assisted breeding to improve SBWMV resistance.The effectiveness of the two flanking KASP assays ed Table , indicatSupplementary information


SNP-CRISPR can be used to design sgRNAs based on public variant data sets or user-identified variants. In addition, the tool computes efficiency and specificity scores for sgRNA designs targeting both the variant and the reference. Moreover, SNP-CRISPR provides the option to upload multiple SNPs and target single or multiple nearby base changes simultaneously with a single sgRNA design. Given these capabilities, SNP-CRISPR has a wide range of potential research applications in model systems and for design of sgRNAs for disease-associated variant correction.CRISPR-Cas9 is a powerful genome editing technology in which a single guide RNA (sgRNA) confers target site specificity to achieve Cas9-mediated genome editing. Numerous sgRNA design tools have been developed based on reference genomes for humans and model organisms. However, existing resources are not optimal as genetic mutations or single nucleotide polymorphisms (SNPs) within the targeting region affect the efficiency of CRISPR-based approaches by interfering with guide-target complementarity. To facilitate identification of sgRNAs (1) in non-reference genomes, (2) across varying genetic backgrounds, or (3) for specific targeting of SNP-containing alleles, for example, disease relevant mutations, we developed a web tool, SNP-CRISPR ( Streptococcus pyogenes Cas9 nuclease is directed to a target site or sites in the genome that have a unique 20 nt sequence followed by a 3 bp sequence conforming to NGG known as the protospacer adjacent motif (PAM). A double-strand break (DSB) induced by Cas9 nuclease recruits the cellular machinery, which can repair the break either through the error-prone non-homologous end-joining (NHEJ) pathway or through homology directed repair (HDR). NHEJ often results in insertions and/or deletions (indels), which can result in frameshift mutations. HDR allows researchers to introduce or ‘knock in’ specific DNA sequences, such as precise nucleotide changes or reporter cassettes.The CRISPR-Cas9 system, a repurposed bacterial adaptive immune system, is a powerful programmable genome editing tool for research, including in eukaryotic systems, that also has potential for gene therapy . With thIn addition, catalytically dead forms of Cas9 have been fused with different effector proteins to manipulate DNA or gene expression . For exaSingle-nucleotide polymorphisms (SNPs) can be defined as single-nucleotide differences from reference genomes. The targeting efficiency of Cas9 has been examined using data from genome-wide studies combined with machine learning  gene symbols or genome coordinates and (2) sequences. Resources that support the former typically precompute sgRNAs based on annotated reference genome information. Moreover, these sgRNA sequences are designed based on a single wild-type reference sequence without considering variants  is located at the web site of the Drosophila RNAi Screening Center (DRSC). The back-end is written in PHP using the Symfony framework and the front end HTML pages take advantage of the Twig template engine. The JQuery JavaScript library with the DataTables plugin is used for handling Ajax calls and displaying table views. The Bootstrap framework and some custom CSS is also used on the user interface. Hosting by Harvard Medical School Research Computing makes it possible to provide a web-facing user interface to run the SNP-CRISPR core pipeline on Harvard Medical School’s “O2” high-performance computing cluster. When jobs are submitted from the website, the form parameters and uploaded input file path are passed to a bash script controlling the pipeline, which is then run as a cluster job. When the job is complete, an E-mail is sent to the user with a URL that contains a unique ID used to retrieve the corresponding results.The SNP-CRISPR web tool  or a csv file in the provided format, and then upload this file on the SNP-CRISPR homepage. The acceptable variants include single nucleotide changes, small insertions and small deletions. The user then chooses the species and whether to create designs that target each input variant individually or to target all SNPs within each potential sgRNA sequence. When a user submits input, the web logic starts a job on the Harvard Medical School “O2” high-performance computing cluster, using the uploaded file and parameters as input for the pipeline. After the pipeline finishes running, an automated E-mail is sent to the user with a link to a webpage at which the user can view and export results. For a couple of variants, the design pipeline usually takes up to a few minutes and with an input of 2,000 human SNP variants, it takes about half an hour for users to receive the results by E-mail. The result page shows the wild-type and variant designs with corresponding scores in a tabular view that can be sorted by one or more columns. The output table also lists the genome targeting position of each sgRNA and the position of the variant within the sgRNA sequence relevant to the PAM sequence. The variant is shown in lowercase, which can be easily spotted by users. Using the checkboxes in the left-most column, users can opt to export all or only selected rows to an Excel or csv file. Currently, SNP-CRISPR supports reference genomes from human, mouse, rat, fly and zebrafish .vs. SNP-CRISPR. After validation, SNP-CRISPR then re-constructs the template sequence, swapping the reference nucleotide with the variant nucleotide for SNPs, while inserting or deleting the corresponding fragment for indel type variants. Second, SNP-CRISPR computes potential variant-targeting sgRNAs based on availability of PAM sequences in the neighboring region since the presence of a PAM sequence (NGG or NAG) is one of the few requirements for binding. Third, sgRNA designs that contain four or more consecutive thymine residues, which can result in termination of RNA transcription by RNA polymerase III, are filtered out  using the command line version of the pipeline and the designs can be found at https://github.com/jrodiger/snp_crispr/tree/master/results.The web tool supports up to 2,000 variants per batch while the command line version has no limit with the number of variants and can be used for any annotated genome. The command line version also provides better performance on large inputs when run multi-threaded. For example, a multi-threaded test run was able to process over 1,000 human SNPs per minute on Harvard Medical School’s “O2” high-performance computing cluster. We pre-computed sgRNA designs (NGG-PAM) for all clinically associated SNPs annotated at the Ensembl genome browser (SNP-CRISPR is a unique web tool that designs sgRNAs targeting specific SNPs or indels. SNP-CRISPR is user-friendly and provides all possible CRISPR-Cas9 target sites in a given genomic region with required parameters, allowing users to select an optimal sgRNA. SNP-CRISPR provides not only efficiency scores but also off-target information for sgRNAs targeting sequences with and without SNPs and/or indels of interest in the same genomic region. SNP-CRISPR supports the human reference genome and genomes from major model organisms; namely, mouse, rat, fly and zebrafish. Conveniently, SNP-CRISPR displays the positions of variant nucleotides in each sgRNA region as part of the design output. Moreover, SNP-CRIPSR accepts up to 2,000 inputs per batch for design of large-scale experiments at the website. The command line version has no limit as to the number of variants and can be used for any genome that has been properly annotated. Altogether, SNP-CRISPR improves the ability of researchers to edit SNP or indel-containing loci by facilitating the design of sgRNAs that target specific variants. As such, SNP-CRISPR provides a valuable new resource to the genome editing technology field.Drosophila melanogaster, millions of novel sequence variants have been identified (More and more variant data has become available in recent years, and much current research focuses on the biological impact of variants (


Rice rooting ability is a complex agronomical trait that displays heterosis and plays an important role in rice growth and production. Only a few quantitative trait loci (QTLs) have been identified by bi-parental population. More genes or QTLs are required to dissect the genetic architecture of rice rooting ability.indica and japonica. We measured four traits for evaluating rice rooting ability, namely root growth ability (RGA), maximum root length (MRL), root length (RL), and root number (RN). Using the association study in three panels consisting of one for the full population, one for indica, and one for japonica, 24 SNPs associated with rooting ability-related traits were identified. Through comparison of the relative expression levels and DNA sequences between germplasm with extreme phenotypes, results showed that LOC_Os05g11810 had non-synonymous variations at the coding region, which may cause differences in root number, and that the expression levels of LOC_Os04g09900 and LOC_Os04g10060 are closely associated with root length variation.To characterize the genetic basis for rice rooting ability, we used a natural rice population, genotyped by a 90 K single nucleotide polymorphism (SNP) array, to identify the loci associated with rooting-related traits through the genome-wide association study (GWAS). Population structure analysis divided the natural population into two subgroups: Through evaluation of the rice rooting ability-related traits and the association mapping, we provided useful information for understanding the genetic basis of rice rooting ability and also identified some candidate genes and molecular markers for rice root breeding. Rice is the staple food for most regions in Asia and feeds more than half of the world’s population  encodes a protein with a LATERAL ORGAN BUONDARIES (LOB) domain, which is involved in auxin-mediated cell dedifferentiation and controlled the initiation of adventitious root primordia in rice. SHORT-ROOT5 (SRT5/ Oscyt-inv1) encodes a putative cytosolic Inv-N that cleaves sucrose at pH 7.0 ~ pH 8.0 and is the key isoform of Inv-Ns required for carbon and energy supply during early root development  encodes a novel NAC-domain transcription factor and overexpressing this gene significantly enhances drought resistance in transgenic rice and transgenic lines exhibited altered root architecture involving an enlarged stele and aerenchyma  encodes a novel transcription factor with helix-loop-helix domain. Overexpression of OsPTF1 transgenic rice plants showed significantly higher total root length and root surface area and enhanced the tolerance to Pi starvation in rice.Most research on rice root has concentrated on investigating seedling root morphological traits and physiological and biochemical indexes under different stress conditions  have been used successfully in rice to analyze the genetic basis of agronomic traits. Based on phenotype evaluation and genotyping, GWAS is an effective way to isolate the gene related to the target trait. For instance, researches using GWAS found that In our research, 145 accessions were used to conduct the GWAS. The goals were (1) to assess the natural variation for the traits related to rice seedling rooting ability in the diverse rice panel, (2) to identify the loci and candidate genes related to rice rooting ability, and (3) to find varieties with superior rooting ability to be used in rice breeding.indica (Pop1) and japonica (Pop2), were present in our natural population  value between the two subpopulations was 0.71, indicating that a high level of subpopulation differentiation existed in the 145 accessions. Finally, we analyzed the linkage disequilibrium (LD) rate for three populations using the SNP data. The genome-wide LD decay rates of Pop1, Pop2, and the full population were estimated to be about 200 kb, 300 kb, and 600 kb, respectively . RN was moderately correlated with RGA (r = 0.68) and weakly correlated with RL and MRL. These results may indicate that RN is a relatively independent trait for evaluating rooting ability compared with the other traits in our research. The four traits’ phenotypic variation explained by population structure (RQ2) were significant (p < 0.01), but all RQ2 values were relatively low. The RGA had the lowest RQ2 value at 0.08 (Table p = 0.0436), RL (p = 0.0118), and RN (p = 0.0133), with the exception of RGA (p = 0.9266) , maximum root length (MRL), root length (RL), and root number (RN) were all close to normal distribution and had abundant variations Fig. . RN had t p < 0.0, but allt p < 0.0, but allp < 1.8 × 10− 5), 23 (p < 2.1 × 10− 5), and 43 (p < 3.2 × 10− 5) suggestive SNPs were detected in the full  was used for GWAS in the full population. MRL-related SNPs were detected most commonly , followed by RL, RGA, and RN. The SNP seq4_15403602 had the lowest p-value for RL, MRL, and RGA and explained 39.33%, 47.56%, and 37.50% of phenotypic variation, respectively. Only one region containing clustering SNPs was detected for MRL, and the leading SNP seq8_18343904 could explain 43.66% of MRL variation.A total of 23 significant SNPs were detected in the Pop1, and most of them were related to MRL, followed by RL and RGA. No significant SNP was detected for RN  plots of the GWAS results. We then focused on the SNPs distributed in clusters and detected by more than one panel to avoid false positives if the Q-Q plot was not well-fitted. Based on this, a total of 24 SNPs (Table http://www.ricedata.cn/gene/) and the Rice Genome Annotation Project (http://rice.plantbiology.msu.edu/index.shtml). We mainly selected genes with GO information related to hormone metabolism processes or stress responses or genes which have the high expression level at seedling stage. As a result, some known genes and QTLs reported previously were obtained (Table OsCDPK13) was located at about 99 kb upstream of the SNP seq3_1727891. It has been found to negatively regulate the expression level of the enzymes essential for gibberellin biosynthesis and prevent drought stress injuries in the seedling development stage  was mapped at about 4 kb upstream of the SNP seq6_29405495. Its transcript’s level is up-regulated upon wounding (by cut), hormones, and heavy metals, et al.  participated in the cytokine signaling pathway of rice, and seminal and crown roots of the mutant were short and thin  was identified at about 142 kb downstream of the SNP seq6_12537542. Its expression level is specifically induced by Pi starvation  was located at 24 kb upstream of the nearest SNP seq9_15520580. And osgl1–1 mutants showed increased sensitivity to drought  and SHORT LATERAL ROOT LENGTH1 (SLL1) directly influence root architecture and were found at 257 kb downstream of the SNP seq7_5182658 and 348 kb upstream of the SNP seq4_18740406, respectively. RNAi of the SQS plants grown in plates with Yoshida’s nutrient solution with 1.2% agar showed increased root length and an enhanced number of lateral roots . The primers used for the qRT-PCR are listed in the supplementary materials  synthase (OsCyc1) protein, which is responsible for phytoalexin biosynthesis  was conducted by qRT-PCR. LOC_Os04g09900 had a higher expression level in the group with longer root length (LRL group) than the group containing varieties with shorter root length (SRL group) (p = 0.0330) , is a syn-copalyl diphosphate specific 9β-pimara-7,15-diene synthase gene. Previous qRT-PCR results  and LOC_Os05g12260 (OsPYL), were identified in qRN-5. LOC_Os05g11810 encodes the gibberellin 2-oxidases (GA2oxs), which can regulate plant growth by inactivating endogenous bioactive gibberellin (GA) . Additionally, both two groups had low transcripts which may cause that although hap2 group had a higher mean value of relative expression level than hap1, it was not statistically significant (p = 0.3727).One candidate region 30) Fig. a. AnotheA vigorous root system is the basis of vigorous growth in rice at early stage and grain filling at later stage. Furthermore, rooting ability was confirmed to be inherited and showed significant heterosis , phosphorus absorption pathways (OsPT9), and the mechanism of both root morphology (SQS and SLL1) and drought-tolerance (OsGL1–1) may regulate rice rooting ability. These results may be illustrated by the fact that cutting roots causes nutrient and water deficiency and new roots are needed to absorb both nutrients and water. The results could also suggest that endogenous hormone metabolism could regulate the growth of new roots, which is consistent with previous research reported that the content of endogenous hormones in seedlings can affect the RGA of seedling rice. Specifically, endogenous hormone abscisic acid in roots can inhibit RGA, while endogenous hormone gibberellin can promote RGA  / lnN, where H′ represents the diversity index, Pi is the percentage of varieties in the ith grade of one trait in the total number of varieties in one period, and N is the total number of varieties in the period. The percentage of phenotypic variation explained by the population structure (RQ2) was analyzed using a one-way analysis of variance (ANOVA) in the SAS 9.4 system. Correlation analysis of traits was conducted by the package “corrplot” in R (3.6.0). ANOVA conducted in the EXCEL 2010 was separately used to test significant phenotypic differences between the two subpopulations. Additionally, “two sample t-test for means” in the SAS system was used to analyze the phenotypic or relative expression level differences between the varieties with the contrasting phenotype or haplotype.Mean value, standard deviation, and diversity index of the H′ value were all calculated in EXCEL 2010. The mean value and standard deviation were calculated using the AVERAGE  and STDEVP . Based on the mean value and standard deviation of each trait, we divided 145 accessions into 10 levels, from the first level (Xi<X-2σ) to the tenth level (Xi>X + 2σ). Every 0.5σ was a level. According to these grading results, the modified Shannon-Wiener diversity index was used to calculate the diversity index of different traits. The calculation formula is: H′ = (−∑2) went down to the half of the maximum . For clustered significant SNPs within the LD decay distance of the whole genome, we chose the SNP with lowest p value as the lead SNP. LD block was calculated using the Haploview 4.2 to obtain the candidate regions.Population structure analysis was conducted using the STRUCTURE software (2.3.4), and the Nei’s genetic distance’s calculation and neighbor-joining (NJ) tree construction were both conducted by PowerMarker version 3.25. Both the principle component analysis (PCA) and genome linkage disequilibrium (LD) analysis were conducted by the Tassel (5.2.51). The LD decay rate was measured as the physical distance between SNPs at which the average pairwise correlation coefficient . The 50 μl total PCR reaction volume contained 25 μl KOD FX PCR buffer, 10 μl dNTPs, 1.5 μl each of 10 pmol/μl primer, 5 μl template DNA, 6 μl PCR grade water, and 1 μl KOD FX. The PCR cycle conditions were as follows: initial incubation at 94 °C for 2 min, 35 cycles of 98 °C for 10 s, 55 °C for 30 s, and 68 °C at 1 kb/1 min, followed by a final extension at 68 °C for 8 min.Total RNA extraction from the plant’s seedling roots used the Total RNA extraction kit. First-strand cDNA was synthesized using PrimerScript RT Master Mix. Then, quantitative real-time PCR (qRT-PCR) was performed in a two-step reaction using PowerUp SYBRGreen Master Mix on an Applied Biosystems 7500 Real-Time PCR system. The rice ubiquitin gene was used as the internal control. Each measurement was performed on three replicates of each of three biological samples. The sequences of the candidate genes were downloaded from the Rice Genome Annotation Project  Proportion of the 56,456 SNPs categorized by the distance to adjacent SNPs. ‘d’ represents the distance between two adjacent SNPs. (b) Boxplots of root growth ability and related traits in different subgroups. Figure S2. Quantile-quantile plots for 4 rooting ability-related traits in three association panels. (a) MRL in full panel. (b) RGA in full panel. (c) RL in full panel. (d) RN in full panel. (e) MRL in Pop1 panel. (f) RGA in Pop1 panel. (g) RL in Pop1 panel. (h) RN in Pop1 panel. (i) MRL in Pop2 panel. (j) RGA in Pop2 panel. (k) RL in Pop2 panel. (l) RN in Pop2 panel. Red lines represent the quantile-quantile plots of the GWAS by GLM and blue lines represent the quantile-quantile plots of GWAS by MLM. Figure S3. GWAS for rooting ability-related traits in the full association panel. Manhattan plots and quantile-quantile plots for MRL(a), RGA(b), RL(c), RN(d). Figure S4. The distribution of the significant loci on 12 chromosomes. Pink, yellow, green, blue, black, and red lines represent the loci identified from full, Pop1, Pop2, both full and Pop1, both full and Pop2, all full, and Pop1 and Pop2 association panels.Additional file 2: Table S1. Summary of 145 rice accessions and their phenotypic traits related to root growth ability. Table S2. Significant loci detected in the full population. Table S3. Significant loci detected in the indica population. Table S4. Significant loci detected in the japonica population. Table S5. The primers of genes for qRT-PCR and DNA sequencing analysis. Table S6. The results of relative expression level of candidate genes.


OsNRAMP1, where cultivars with a deletion upstream of the gene had higher concentrations of cadmium compared to the cultivars that did not have the deletion. The distribution of alleles within the BAAP suggest this QTL is easily detected in this population because it is composed of aus cultivars. Local genome cluster analysis suggest high Cd alleles are uncommon, but should be avoided in breeding.It has been estimated that up to 90% of human exposure to cadmium is through food, and that cadmium within rice grains can be a major contributor to that dietary source. In this study genome wide association mapping was conducted on the Bengal and Assam Aus Panel (BAAP) of rice to identify quantitative trait loci and candidate genes for lowering grain cadmium. Field experiments were conducted over two years under two different irrigation systems: continually flooded and alternate wetting and drying (AWD). There was significant effects of water treatment, genotype, and genotype by water treatment interaction. Importantly, AWD increased grain cadmium, on average, by 49.6% and 108.8% in year 1 and 2 respectively. There was between 4.6 and 28 fold variation in cadmium concentration. A total of 58 QTLs were detected but no loci are clearly specific to one water regime despite approximately 20% of variation attributable to genotype by water regime interaction. A number of QTLs were consistent across most water treatments and years. These included QTLs on chromosome 7 , chromosome 5 (8.66–8.72 Mbp), and chromosome 9 (11.46–11.64 Mbp). Further analysis of the loci on chromosome 7 (8.93–9.04 Mbp), identified the candidate gene The online version contains supplementary material available at (10.1007/s10681-020-02752-1). Cadmium in rice grains has been highlighted as an issue, specifically for people that consume large quantities of rice, with a proportion of people in Bangladesh and Sri Lanka consuming rice with unsafe amounts of cadmium   1 and 2 , the Low Affinity Cation Transporter OsLCT 1  for cadmium concentration in rice plants. These studies have found numerous QTLs for cadmium concentration in the roots, shoots, and grains of rice plants. Recent advances in genetic mapping in plants have meant that genome wide association (GWA) studies are feasible in rice , which has 2 million SNPs for GWA mapping, has been specifically created to assess the genetic diversity within the This study utilises the Bangladesh and Assam Aus Panel (BAAP)   of 2013 and 2014. The population was screened under both AWD and continuously flooded (CF). Full details of the field screening is given in Norton et al.  and Nort−1 indium as the internal standard.Grain cadmium concentration was determined as described in Norton et al. . Brieflyhttps://github.com/tony-travis/PIQUE) to pre-process genotype and phenotype data and then run association analysis for each phenotype in parallel using EMMAX  was employed only to keep the number of detected QTLs manageable. For every SNP with P < 0.000001, pairwise r2-values were calculated between surrounding SNPs that fell within 243 kb; any two SNPs meeting this criteria that also shared an r2 ≥ 0.3 were clumped into bins. Binning was done for each experiment. Clumps with less than two significant SNPs (P < 0.00001) were discarded. The LD (Linkage Disequilibrium) decay value of 243 kb was selected as this is the average LD decay for the population  using the PLINK  and SigmaPlot v.13  and significance reported at alpha < 0.05. Analysis of variance across treatments in each year was only conducted on those cultivars present in both treatments (n = 218 in year 1 and 207 in year 2).The mean grain cadmium concentrations recorded for each cultivar in this study are reported in supplementary Table 1. Grain cadmium concentration was significantly different between the plants grown under AWD and CF in both years Fig. . Across It has been widely reported that AWD alters the accumulation of a range of elements in rice grains, including cadmium  and CF  in year 2 , accounting for 7.2% and 18.1% of the observed variation. In year 1 there was no significant difference in grain cadmium for the accessions belonging to the different subgroups. Generally, aus subgroup 3 has lower grain cadmium concentrations while subgroups 2 and 4 have higher concentrations.The population used in this study is made up of rice accessions from the P < 1 × 10–6) which will probably introduce type II error. A probable example can be seen at the bottom of chromosome 7 where all traits have a peak but it is called only for three traits because for CF2 the P value was 9.4 × 10–5 (for SNP at 29.15 Mbp) and therefore not above the threshold used. Even some of the single trait QTLs contained large numbers of SNPs with P < 0.0001, (up to 2694 SNPs for AWD2 at 8.17 on chromosome 5). AWD2 was notable for having a very large number of single trait QTLs, while CF2 was notable for having by far the fewest QTLs, only 5 of which were common to another trait, and none common to two other traits. Examining Fig. SNP-trait associations were detected for grain cadmium content across the two years of the experiment Fig. . Clump wOsZIP8   on chromosome 7 between 7.23 and 7.61 Mbp (as defined by Clump). This region revealed QTLs in the studies of Zhao et al.  and Tan OsHMA3 was the first cloned gene involved in cadmium accumulation in rice , a QTL for cadmium concentration was mapped to a 1.9 Mbp region on chromosome 7  , which has a predicted amino acid mutation at the 380th position from Ser to Arg. The haplotype had no cadmium transport activity when the gene was expressed in yeast, and the allele did not complement a known non-functional allele of OsHMA3 .A second loss-of-function allele for n et al. , who onln et al. , with aln et al.  but thataus accessions in this population do not have any of the known loss-of-function alleles, OsHMA3 is still a good candidate gene for this association as both the studies that previously identified the loss-of-function alleles  and these SNPs can be used to classify the accessions into four haplotypes . Further exploration of the sequence read depth around OsNRAMP1 (see methods) revealed that this deletion is present in all cultivars from haplotypes 1–3, while cultivars from haplotype 4 do not have the deletion. When the phenotype data is analysed based on the presence or absence of the deletion, the cultivars that do not have the deletion have significantly reduced grain cadmium  with a small percentage of aus (1.5%) and japonica (3.6%), haplotype 3 is only present in indica and aus rice, and haplotype 4 is most common in japonica rice (77.8%) and aus (12.4%). These results demonstrate that these haplotypes are predominantly separated based on the japonica (haplotype 4) and indica (haplotypes 1–3) split in rice, however aus cultivars have representation in all haplotypes.This observation of balanced allelic variation only in aus highlights why aus-based populations like BAAP have the potential to reveal some QTLs that are not revealed in other studies.A QTL was detected for grain cadmium in three out of the four experiments Fig. , Zhao etium Fig.  was examOsNRAMP1 revealed it was predominantly expressed in the roots  than a low cadmium-accumulating cultivar (Sasanishiki) regardless of the presence of cadmium, and the amino acid sequence of OsNRAMP1 showed 100% identity between Sasanishiki and Habataki . It has been demonstrated that mutational disruption of OsNRAMP5 dramatically reduces cadmium in rice grains  in combination with a water management technique resulted in rice with both reduced arsenic and cadmium. Future work should focus on applying this same approach but utilising natural allelic variation.For grain cadmium concentration five QTLs were detected in three of the four experiments. When exploring the contribution of the different genetic clusters around QTLs to the observed phenotype, it must be noted that in a majority of cases the rare cluster contained the accession with high grain cadmium, meaning that a majority of the tested accessions already had the low grain cadmium alleles. Therefore, for breeding, the important consideration will be to ensure that the high cadmium alleles are selected out. Two of these QTLs are at genes known to be involved in cadmium accumulation. While neither of the previously characterised non-functional alleles of a et al.  where a Supplementary Figure 1Grain cadmium data for the population (JPG 1782 kb)Supplementary Figure 2QTLs based on clump (JPG 248 kb)Grain cadmium QTLs in other studies (XLSX 27 kb)Supplementary file4 (XLSX 19 kb)Supplementary file5 (XLSX 13 kb)Below is the link to the electronic supplementary material.