MY O 1A), feed conversion rate (RDH5), and reproduction (AMDHD1). A total of 1,799 HERs were identified in the Purunã breed with 92.3% of them classified within the 0.5–1 Mb length group, and 19 HER islands were identified in the autosomal genome. These HER islands harbor genes involved in growth pathways, carcass weight (SDCBP), meat and carcass quality (MT2A), and marbling deposition (CISH). Despite the genetic relationship between Purunã and the founder breeds, a multi-breed genomic evaluation is likely not feasible due to their population structure and low consistency of the gametic phase among them.Purunã is a composite beef cattle breed, developed in Southern Brazil by crossing the Angus, Charolais, Canchim, and Caracu breeds. The goal of this study was to perform the first genetic characterization of the Purunã breed, based on both pedigree and genomic information. For this, 100 randomly selected animals were genotyped, and 11,205 animals born from 1997 to 2019 had pedigree information. The genetic analyses performed were principal component analysis, admixture, phylogenic tree, pedigree and genomic inbreeding, linkage disequilibrium (LD), effective population size (Ne), consistency of the gametic phase, runs of homozygosity (ROH), heterozygosity-enriched regions (HERs), and functional analyses of the ROH and HER regions identified. Our findings indicate that Purunã is more genetically related to the Charolais, Canchim, and Angus breeds than Caracu or Nellore. The levels of inbreeding were shown to be small based on all the metrics evaluated and ranged from −0.009 to 0.029. A low (−0.12–0.31) correlation of the pedigree-based inbreeding compared to all the genomic inbreeding coefficients evaluated was observed. The LD average was 0.031 (±0.0517), and the consistency of the gametic phase was shown to be low for all the breed pairs, ranging from 0.42 to 0.27 to the distance of 20 Mb. The Ne values based on pedigree and genomic information were 158 and 115, respectively. A total of 1,839 ROHs were found, and the majority of them are of small length (<4 Mb). An important homozygous region was identified on BTA5 with pathways related to behavioral traits , as well as candidate genes related to heat tolerance ( The characterization of the population structure and genetic diversity is essential for the understanding of the genetic background of environmental adaptation and conservation of cattle genetic resources . Such chThe Purunã breed is a composite population developed in Southern Brazil by crossing Angus, Charolais, Canchim, and Caracu, in identical proportions. This was performed to improve key traits of interest and exploit the complementarity among the breeds , especiaBased on the first results, IAPAR researchers conducted a second mating to generate another set of animals using the progenies resulting from the previous F1 population. The goal at that point was to combine all favorable characteristics in a composite that presented a heavyweight and produce a high-quality carcass. The hypothesis for using the breeds mentioned earlier was to capture a particular contribution from each breed to create a composite population with higher productive performance and adapted to the tropical and subtropical regions of Brazil. The Angus breed provided traits related to precocity, more docile temperament, and high meat quality ; CharolaSome studies in Brazil have evaluated the performance of the Purunã breed for carcass traits , meat prGenetic diversity studies are crucial in the initial phase, called pre-breeding, in which it is possible to regenerate, characterize, explore, and promote the conservation of variability of the population . The par–6) parameters were also used to filter out SNPs.One hundred animals of the Purunã breed were randomly sampled and genotyped using the GGP Bovine 100K array  containiFor the pedigree database, information from 11,205 animals born between 1997 and 2019 was considered, where 5,224 were males and 5,981 females, and the base population was formed by 3,999 animals. These data were used to create the pedigree database including information on individual animals, sire, dam, sex, and birth date.G) where the covariance of each SNP was divided by the respective variance, using only the SNPs in common for all breeds (after the QC), as the following equation proposed by  by PLINK v1.9 software . The genposed by :G= depending on the fraction qik of i’s ancestry attributable to population k and on the frequency fkj of allele 1 in the population k. The matrices Q = (qik) and F = (fkj).The admixture analysis was performed using the ADMIXTURE software  to assesK, which represents the number of ancestral populations. We evaluated K equal to 1 until 20, but only K = 2 and 3 were chosen to be shown here, which have more biological interpretation and K = 3 had the smallest cross-validation error. The “pong” package , using the ENDOG v4.8 software (t) was calculated as follows:t and Ft-1 represent the average inbreeding estimates for the current and the previous generation , calculated as follows (The second method was based on the homozygous genotypes observed and expected (F follows :FHOM1=HGRM), using the following model  using the following model The aforementioned models are all dependent on the genotype allele frequency, and for this reason, a fifth model was a test based on the correlation between uniting gametes (FROH) using the following equation  is the length of chromosome jth, and A is the number of autosomal chromosomes (A = 29). Still, for each class of ROH , inbreeding estimates were obtained by dividing the total sum of ROH segments by the total length of the cattle autosomal genome covered by SNPs. All the genomic inbreeding coefficients were calculated using the PLINK v1.9 software  was estimated by PLINK v1.9 software. To observe the r2 decrease along with the increase in the marker distance, we used the binning approach estimating the r2 average of each distance from 10 to 100 kb in each 10 kb, and after the distance of 100 kb in each 100 kb until the distance of 1,000 kb (1 Mb). As a preliminary analysis, we defined that the bins reported in this study were required to have at least 50 pairwise markers to estimate the binned average of r2.The linkage disequilibrium . The first method used pedigree information through the following equation:ectively . The estectively .tth generation, ct is the recombination rate for the physical distance between the markers, α is the probability for the occurrence of mutation, and r2 is the LD value.The second method was performed using genomic information, and investigated with the relationship method between LD variances and Ne through the following formula :Ne(T)=, as:p(a) is the frequency of the haplotype-a, p(b) is the frequency of the haplotype-b, and pab is the haplotype frequency with allele a on the first locus and allele b on the second locus. The CGP was assumed as the Person correlation between each founder breed and Purunã using the signed-squared-root values. To estimate the CGP, only the SNPs in common  between each breed pair were used to estimate the CGP based on the same distance and bin described in the LD section.The consistency of the gametic phase (CGP) was taken by the square root of The proportion of polymorphic SNPs, after QC, was calculated based on the MAF. The distributions of SNPs were calculated on 10 MAF ranges from 0 to 0.5 defined every 0.05 points in MAF.• One heterozygous and one missing SNP were allowed;• The window of the threshold used was 0.05;• The gap between consecutive SNPs could not be higher than 1,000 kb;• The minimum length of an ROH was 500 kb;• The minimum number of consecutive SNPs that create an ROH must be equal to or greater than 30;• The density of 1 SNP used in at least 50 kb;• A sliding genomic window was used with 50 SNPs.The PLINK v1.9 software was used for the ROH identification based on the following criteria:ROHs were classified in the following classes: <2 Mb, 2–4 Mb, 4–8 Mb, 4–16 Mb, and >16 Mb . A regio• a minimum number of 20 consecutive SNPs constitutes an HER;• a minimum length of 500 kb;• a minimum of two homozygous and one missing SNP is allowed; and• the maximum gap between consecutive SNPs could not be higher than 1,000 kb.The detectRUNS package  was usedThe genomic regions that showed at least 10% of the animals with HER were included in the subsequent functional analyses and phylogenetic tree.Bos taurus from the Ensembl database (www.ensembl.org/Bos_taurus/Info/Index), version ARS-UCD1.2  were used when developing the Purunã breed. In average, Angus had 99.2% and 0.9%, Canchim 60.6% and 39.4%, Charolais 89.6% and 10.4%, Nellore 99.8% and 0.2%, and Purunã had 80.8% and 19.2% from ancestral population 1 and 2, respectively. For K = 3, three groups were observed to affect the admixture analysis for the population in the study. This result (K = 3) indicates more contribution from Charolais and Canchim in the Purunã breed, following the Angus breed and a small proportion of the Nellore breed.The averages of inbreeding coefficients are presented in PED2) was 0.002. The methods FHOM1, FHOM2, FUNI, and FGRM were the methods showing the lowest average values (−0.009), while the highest inbreeding coefficient average was obtained by the FROH metric (0.029). The correlations among the inbreeding coefficients method are presented in The average for the inbreeding coefficient estimated based on pedigree for all Purunã individuals (FHOM1-FHOM2 (0.97), FHOM1-FROH (0.93), FHOM1-F>16MB (0.85), FHOM1-F>8MB (0.89), FHOM2-FROH (0.90), FHOM2-F>16MB (0.79), and FHOM2-F>8MB (0.85). Low values were found for all correlations among FPED and the other methods. The FGRM method shows a very weak correlation for almost all the methods, except for the FUNI, when the correlation was classified as moderate (0.74). Negative correlations were found for the methods: FGRM-FHOM2 (−0.21), FGRM-F<2MB (−0.24), FGRM-F2–4MB (−0.08), FGRM-F4–8MB (−0.01), FGRM-F8–16MB (−0.21), FGRM-F<8MB (−0.11), and FGRM-FPED (−0.12).Strong correlations were found between the methods: FThe average LD ranged from 0.43 to 0.04, with a distance between two markers of 10 to 1,000 kb, respectively. The general average of LD was 0.031 (±0.0517) at the average distribution of the markers 4.856 (±2.8890) Mb. The decrease in LD with an increase in the marker distance can be observed in th generation back, respectively. For the SNeP, in the 13th generation, the Ne was 229, while for the PLINK, the result for the same generation was 207. The Ne estimated for the 5th generation on PLINK was equal to 115.The effective population size based on pedigree was 158 for the current generation. On the other hand, the genomic-based Ne differed based on the generation and software used. The SNeP software  and the The consistency of the gametic phase between Purunã and Angus, Canchim, Charolais, and Nellore is presented in The highest correlation, at 20 kb between SNP pairs, between Purunã and the other breeds was found with Charolais (0.43), followed by Canchim (0.42), Angus (0.40), and Nellore (0.27). The distance of 10 kb showed a lower number of pairwise markers than the threshold (<50) used as a criterion. Therefore, these results were not presented.0.00-0.05 3,232 (3.67%); MAF0.05-0.10 2,967 (3.37%); MAF0.10-0.15 4,242 (4.82%); MAF0.15-0.20 5,792 (6.59%); MAF0.20-0.25 7,255 (8.25%); MAF0.25-0.30 9,314 (10.59%); MAF0.30-0.35 11,287 (12.83%); MAF0.35-0.40 13,283 (15.10%); MAF0.40-0.45 14,932 (16.98%); and MAF0.45-0.50 15,652 (17.80%).The proportion of polymorphic SNPs based on the MAF category were as follows: MAFA total of 1,839 ROHs were found for the Purunã breed. The distribution along all autosomal genomes can be observed in In total, 1,799 HERs were found in the Purunã breed. The HER pattern distribution along with all autosomal genomes is shown in p < 0.05) in which these genes are part of are presented in With the ROH analysis, we found a common region in homozygosity present in 36% of the animals, despite the fact that Purunã is a recently-developed composite breed. This region is located on BTA5 between 54, 304, 681 bp and 62, 031, 799 bp, and has a length of 7.73 Mb, where 131 SNPs are present in this region. This region is responsible for coding 220 genes, with 181 protein-coding genes, seven pseudogenes, five long non-coding RNA, nine microRNA, ten miscellaneous RNA, six small nucleolar RNA, and two small nuclear RNA. The list of all the genes found in this region is presented in Ten biological processes, three molecular functions, and six cellular components were identified in the significant pathways. Interestingly, pathways linked to animal behavior were found in this region, including sensory perception (GO:0007600), detection of stimulus (GO:0051606), response to extracellular stimulus (GO:0009991), olfactory receptor activity (GO:0004984), and others. To track the origin of this homozygous region in Purunã, we performed a phylogenetic tree analysis, using only the SNPs allocated in this region. For the HER analysis, the regions identified in at least 10% of the animals were considered as HER islands and used to verify the candidate genes and pathways. We found 19 HERs distributed in 17 chromosomes, where the BTA5 and BTA14 presented two HERs in each chromosome. The most frequent HER (27% of the population) was found in BTA23. The longest HER was found in BTA22 with a length size of 3.67 Mb, and the smallest HER was found in the BTA15 at 0.60 Mb.p < 0.05) and their related genes are presented in All these regions are responsible for coding 413 genes, including 363 protein-coding, six pseudogenes, 13 long non-coding RNAs, seven microRNAs, two miscellaneous RNAs, one small nucleolar RNA, 15 small nuclear RNAs, three processed pseudogenes, and three ribosomal RNAs. The list of all the genes found in these regions is presented in PPIB (peptidylprolyl isomerase B), SDCBP (syndecan binding protein), MT2A and MT3 components of metallothionein, RAI1 (retinoic acid-induced 1), FLCN (folliculin), DCAF1 (DNA damage-binding protein 1), CISH (cytokine-inducible SH2), HYAL1 and HAYAL2 components of hyaluronidase, SEMA3B and SEMA3F components of semaforin, ARIH2 (ariadne RBR E3 ubiquitin protein ligase 2), and IP6K2 (inositol hexakisphosphate kinase 2). To track the origin of these heterozygous regions in Purunã, we used a phylogenetic tree analysis. In total, we found 17 significant GO terms involved in biological processes, eight in molecular functions, and six in cellular components. Interesting regions related to the growth pathways  were found in the heterozygous-enriched regions in BTA10, BTA14, BTA18, BTA19, and BTA20, where 14 genes are acting in higher variability in the population. The genes are Our main goal in this study was to genetically characterize the Purunã breed by estimating genetic diversity and population structure parameters based on both genomic and pedigree information. This breed was developed by crossing, in the same proportion, Charolais, Canchim, Angus, and Caracu breeds, while Canchim is also a composite breed that has Charolais and Nellore as the main founder breeds. Therefore, the average genetic proportion for Purunã is 13/32 Charolais, 8/32 Caracu, 8/32 Angus, and 3/32 Nellore.Bos taurus taurus than Bos taurus indicus breeds. This was expected due to the greater contribution of taurine breeds in the formation of the Purunã breed.Based on the results from the population stratification section, the Purunã breed seems to be genetically closer to Charolais, Canchim, and Angus, with the highest genomic contribution from the Charolais breed , K = 3. The maintenance of low levels of inbreeding is also desirable in composite breeds, once an advantage of crossbreeding is heterosis. Such heterosis is influenced by the genetic distance between the parental breeds and the level of inbreeding in the population, which can affect the degree of heterosis retention . As showPED could be expected when a complete, deep (many generations recorded) and no (or few) errors in the pedigree files. In the case of the Purunã breed, the information in the pedigree files goes to, on average, 2.35 generations. As the Purunã pedigree is shallow, the use of genomic information to estimate the inbreeding coefficients is a great alternative to access the inbreeding levels of the individuals. These inbreeding metrics, in addition, are not dependent on pedigree information, taking into account the Mendelian sampling variation  related to bovine heat-tolerance (RDH5 (11-cis retinol dehydrogenase 5) associated with feed conversion (AMDHD1 (amidohydrolase domain containing 1) related to reproduction , feed conversion rate (RDH5), and reproduction (AMDHD1). This could indicate a possible pressure of selection in such regions. For the heterozygosity, the number of HER was elevated, but this was expected since Purunã is a composite breed. Among HER regions, an interesting pathway related to growth was identified with higher variability, containing genes previously associated with carcass weight (SDCBP), meat and carcass quality (MT2A), and marbling deposition (CISH).As observed in the admixture analyses, the Purunã breed received a more significant genetic contribution to its formation from Charolais, Canchim, and Angus. The inbreeding levels for Purunã were small based on multiple inbreeding metrics. Higher LD values were observed for markers with small distances and a faster decrease associated with an increase in the distance between the markers , indicating that a denser panel of markers is necessary to achieve higher levels of accuracy in a genomic selection of Purunã. A high Ne (>100) was observed in all metrics evaluated and the consistency of gametic phase analyses resulted in a small correlation among all breeds, which determines that a multi-breed genetic evaluation for Purunã might not be advantageous. An interesting homozygous region was found in the BTA5 with significant pathways related to behavior and genes related to traits such as heat-tolerance (


Triticum aestivum L.), we performed a genome-wide association study (GWAS). GWAS mapping panel used in this study consists of advanced breeding lines from the IARI stress breeding programme produced by pairwise and complex crosses. Phenotyping was done at multi locations namely New Delhi, Karnal, Indore, Jharkhand and Pune with augmented-RCBD design under different moisture and heat stress regimes, namely timely sown irrigated (IR), timely sown restricted irrigated (RI) and late sown (LS) conditions. Yield and its component traits, viz., Days to Heading (DH), Days to Maturity (DM), Normalized Difference Vegetation Index (NDVI), Chlorophyll Content (SPAD), Canopy temperature (CT), Plant Height (PH), Thousand grain weight (TGW), Grain weight per spike (GWPS), Plot Yield (PLTY) and Biomass (BMS) were phenotyped. Analysis of variance and descriptive statistics revealed significant differences among the studied traits. Genotyping was done using the 35k SNP Wheat Breeder's Genotyping Array. Population structure and diversity analysis using filtered 10,546 markers revealed two subpopulations with sufficient diversity. A large whole genome LD block size of 7.15 MB was obtained at half LD decay value. Genome-wide association search identified 57 unique markers associated with various traits across the locations. Twenty-three markers were identified to be stable, among them nine pleiotropic markers were also identified. In silico search of the identified markers against the IWGSC ref genome revealed the presence of a majority of the SNPs at or near the gene coding region. These SNPs can be used for marker-assisted transfer of genes/QTLs after validation to develop climate-resilient cultivars.Identification of marker trait association is a prerequisite for marker-assisted breeding. To find markers linked with traits under heat and drought stress in bread wheat ( Triticum aestivum L.) is the staple food crop of one-third of the world population , GWAS is one of the effective methods. For the prediction of candidate genes, GWAS has been widely used in several crops using genome-wide dense markers  having 35,143 SNPs and well suited for high-throughput genotyping in hexaploid wheat  trials were conducted in 2019 in Delhi and in 2020 in Delhi, Indore, Pune and Jharkhand. LS trial was conducted only in IARI New Delhi in the years 2019 and 2020 , ARI, Pune—PUNE , IIWBR, Karnal—IIWBR , IARI, Jharkhand—JR  and IARI RS, Indore—IND . Irrigated trials (IR) were conducted in and 2020 . A totaland 2020  with 295The standard procedure for data collection was followed as given in the manual ‘Wheat Physiological Breeding II: A Field Guide to Wheat Phenotyping' . SNP marker data were obtained in the Hap Map format. The chip had 35,143 SNPs; however, after filtration for monomorphic alleles, minor allele frequency (MAF) was >0.05, missing data frequency was >0.2 and heterozygote frequency >0.25; a total of 10,546 SNPs were retained for GWAS analysis.DNA isolation was carried out using the leaves of 7-days-old seedlings grown under controlled conditions. DNA was extracted using the CTAB method  were analyzed using the r package ‘augmentedRCBD'  by Husson et al. . Graphicviz., burn-in cycles and MCMC (Monte Carlo Markov Chains) were set to 100,000. Three iterations for each k value ranging from 1 to 7 were conducted to determine the population structure. The ideal number of delta K (subpopulations) was found out by the Evanno method . Furthermore, the filtered 10,546 SNPs were analyzed using TASSEL 5.0   and the distance at the half LD Decay point was noted.The filtered SNP markers were utilized for determining marker-trait associations using TASSEL v 5.0. The https://zzlab.net/GAPIT/gapit_help_document.pdf) using the “BLINK” (Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway) model. The BLINK model is presumed to be superior in identifying QTNs and avoiding false positives to decipher true associations  value. Stringent selection of MTAs was done using the Bonferroni correction  to avoid false positives and a Manhattan plot was drawn to represent MTAs. Stable MTAs across the location were found out with a significant p-value cut-off at 0.001. Pleiotropic SNPs having association with more than one trait were also found out. Stable and pleiotropic SNPs were compared with the IWGSC Reference genome using BLAST search in the ensemble plants platform (http://plants.ensembl.org/Triticum_aestivum/Tools/Blast). To identify the candidate genes associated with significant SNPs, gene coding regions located within the 10 KB flanking region of the MTAs were considered.The filtered 10,546 SNPs and the location-wise “adjusted mean” for each trait were used for the genome-wide association analysis using GAPIT v3 in R with PCA 3 and default parameters  .K-value obtained was 2 indicating 2 subpopulations in the GWAS panel  value was calculated among all the possible pairs of SNPs in each chromosome using TASSEL 5.0  value of >3. The highest number of SNPs were obtained for the trait NDVI (242 MTAs) and the lowest for the SPAD (19 MTAs). Associated SNPs identified for each trait under different conditions are listed with their respective p-values (log10(p) >5.32) to increase the stringency of selection, and 57 SNPs were obtained that were located on 18 different chromosomes  by the stable MTAs ranged from 3.87% in PH to 19.22% in DM  and Ae. tauschii (D), might have evolved independently and under different selection pressures throughout the domestication and modern breeding objectives  are presumed to be the true association, which is supported by the presence of the candidate gene , AX-94988124 (5B) and AX-94415907 (5B) were linked stably to the traits, plot yield, GWPS and PH, respectively. Three SNPs, AX-94513007, AX-94598030 and AX-94759710, were identified for TGW on chromosomes 1A, 3D and 6D. In previous studies, markers linked with TGW were observed in 6D  and DH (11). Easy phenotyping and having high accuracy might lead to a greater number of associations between DH and NDVI. DH-linked SNPs were detected on chromosomes 3A, 2D, 1D, 5D, 3A, 5B, and 6D are on par with earlier reports like the presence of VRN genes responsible for flowering on chromosomes 5A, 5B and 5D  and the Bill & Melinda Gate Foundation (BMGF) under the project ICAR-BMGF (Grant number: OPP1194767).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


This is expected to be used as basic data for the application of gene editing using CRISPR/Cas9 technology and for establishing a breeding strategy for high eating quality rice using molecular genetic technology.Ninety-five percent of the general nutrients in rice are concentrated in the rice bran and germ, and many nutrients such as vitamins, minerals, dietary fiber, and essential fatty acids, as well as antioxidants such as tocopherol, are lost during milling. In this study, we investigated the thickness of seed coat and aleurone layers using a 294 rice core collection, and found candidate genes related to thickness of seed coat and aleurone layers, by performing a genome wide association study (GWAS) analysis using whole genome resequencing data. Two primer pairs that can be used as high-resolution melting (HRM) markers were developed. As a result of genotyping BC Rice is one of the major grains in the world, especially in Asian food culture, and is used as a staple food . In geneGenome-wide association study (GWAS) is a method of searching the entire genome for phenotypes showing continuous variation and mutations representing associations in natural individuals, or for lines using whole genome sequences ,8. In orA core collection should be a group that can be used to analyze multiple traits, including as much genetic diversity as possible, with the minimum number of genetic resources in one crop, and should have high usability in research. The reduced size of a core collection is the key to manageability and represents the diversity of the entire collection, allowing analysis to be handled more efficiently and effectively. Zhao et al.  performeIn this study, we investigated the thickness of seed coat and aleurone layers using a 294 rice core collection, and candidate genes related to thickness of seed coat and aleurone layers were found by performing GWAS using SNPs in whole genome resequencing. In this way, SNP markers were developed to provide efficient molecular tools for improving the eating quality and processability of brown rice in breeding programs. This study will contribute to the understanding of the mechanisms of seed coat and aleurone layer formation in rice and will provide a basis for future research for breeding brown rice with an excellent taste and processability.Regarding the rice genetic resources used in this study, 166 core groups were first selected using introduced species and domestic breeding lines, weedy rice, and IRRI resources among 24,000 genetic resources collected from around the world. A total of 294 rice genome resequencing groups were finally constructed by secondarily selecting additional varieties that can be headed in the domestic climate and have a high utility value in Korea. Of the total 294 rice genome resequencing groups, the 225 domestic breeding lines consisted of 38 Indica, 178 Japonica, and 9 Tropical Japonica .Seeds of 294 brown rice genetic resources were cut into two halves using a razor blade, and then seeds were stained with Oil Red O dye used for lipid staining . Oil Redhttps://www.ncbi.nlm.nih.gov/assembly/GCF_001433935.1/ accessed on 30 July 2019), which is a standard genome sequence of rice [Genomic DNA of 294 rice accessions was extracted using DNeasy Plant Mini Kits , according to the kit manual instructions from 0.5 g of young leaf tissues. Quality of DNA was checked by diluting a sample extracted using a Quant-iT BR assay kit  and then measuring the concentration and quality of DNA. Tecan F200  was used to measure the optical density value, and the extracted DNA was confirmed by electrophoresis at 1% agarose gel. Genome re-sequencing was performed on an Illumina HiSeq 2500 platform , according to its standard sequencing protocol. Data analysis was compared with IRSGP1.0 downloaded from NCBI . Haplotypes were used for comparative phenotypic analysis.We used Haploview (v.4.2) for analysis of the LD block and haplotype analysis . We defihttps://shigen.nig.ac.jp/rice/oryzabase/ accessed on 30 July 2019) and the NCBI database (https://www.ncbi.nlm.nih.gov/ accessed on 30 July 2019) [Adjacent significant SNPs associated with target traits within a physical distance of 200 kb were screened for candidate genes. Genomic DNA sequence and cDNA region for the mRNA transcripts of each candidate genes were searched and extracted from the Rice Genome Annotation Project (ly 2019) .https://primer3plus.com accessed on 23 September 2020) [Molecular markers associated with the seed coat and aleurone layers were developed for high resolution melting (HRM) analysis. HRM specific primer sets based on target SNPs were designed using the web-based software Primer3plus (er 2020) . HRM anaFor the accuracy of seed coat and aleurone layers thickness, the cross-section of each seed was divided into five parts, namely the dorsal, upper lateral, lateral, lower lateral, and ventral part. The distribution of the seed coat and aleurone layer thickness of 294 rice core collection is as shown in p ≤ 0.001) were confirmed. PCA and kinship analyses were performed for population structure analysis using high-quality 1,842,515 SNPs, with a compressed mixed linear model (CMLM). A 294 rice core collection panel was determined by graphical representation of the K value on a screen plot, which indicated the presence of three sub-populations. This result was confirmed by the variation in the first 10 major components (PCs) showing inflection points in PC2, suggesting that the first two PCs dominated the population structure on the association analysis  SNPs with a read depth of 4× or more and 200× or less, MAF > 0.05, genotyping quality ≥ 20, and Hardy–Weinberg equilibrium  values of significant SNPs from the correlation analysis of the seed coat and aleurone layers thickness. It was confirmed that significant SNPs were detected based on −log10(p) > 5 in the Manhattan plot, according to the thickness of seed coat and aleurone layers. It was found that SNPs located on rice chromosomes 1, 3, 4, and 10 were more significant than other SNPs. In addition, the expected p-value of x-axis and observed p-value from significant SNPs showed a diagonal linear shape in the QQ plot, which means that the discovered SNPs expressed their characteristics well, with normality and significance. As a result of searching for SNPs that were correlated with aleurone layers thickness in this study, the top 10 most significant SNPs by region of aleurone layers are shown in 10(p) value of 5.94. The SNP related to the middle aleurone layers part was ‘S3_20312675’, which was highly associated with a –log10(p) value of 6.53. In addition, a significant SNP related to the thinnest aleurone layers part was ‘S1_6459856’, which showed a −log10 value of 6.00. The available haplotype analysis formats are ped and map files; the ped file requires individual information and individual genotype data for each marker, and the map file requires a marker name and marker location. LD is displayed as pairwise D’ values. Each color represents the magnitude and significance of pairs of LD, and the gradation from red to white is from a high LD value to a low value. Red diamonds without numbers correspond to a D’ value of 1.0. GWAS analysis using GAPIT usually uses about 1 million SNPs to simultaneously test for associations with traits . GWAS wahttps://rapdb.dna.affrc.go.jp/index.html accessed on 30 July 2019) and NCBI database (https://www.ncbi.nlm.nih.gov/gene/ accessed on 30 July 2019), based on the most significant SNP positions on each chromosome , THICK ALEURONE 2, which displayed functions as DNA demethylase and is related to endosperm development . Os01g022F2 , such as Samgwang, and eight individuals with a homozygous genotype (T/T), such as Seolgaeng, and 30 individuals with a heterozygous genotype (T/C) were identified. In addition, as a result of HRM analysis using the “TA2S1-148” marker, 26 individuals with a heterozygous genotype (C/A), out of 50 BC2F2 populations of Samgwang X Seolgaeng, were found to match their genotype and phenotype  located in rice chromosome 1 was similarly located to ROS1a (Os01g0218032), which is related to seed coat and aleurone layer thickness. Base changes in the OsROS1 gene cause mutations in the aleurone cell layer through inhibition of two putative transcription factor genes, RISBZ1 and RPBF, which increase aleurone layers due to lower levels of DNA hyper-methylation and expression . In null9856 locaAs a result of identifying the allelic mutations of the Os01g0218032 gene in the 294 rice core collection, two missense mutations were found in the first exon of the Os01g0218032 gene. S1_6444737 (C > T) is mutated from serine to phenylalanine at amino acid position 163. In addition, in the case of S1_6445148 (A > C), a SNP mutation was made, resulting in a lysine to threonine mutation at amino acid position 301. Three varieties with a thin seed coat and aleurone layers, and three varieties with thick ones, were selected to test genotypes, and their melting curve patterns were compared using the designed HRM markers. It was confirmed that the melting curve patterns were differentiated by genotypes, homo (P1): hetero: homo (P2) (1:2:1), so that they could be used as markers for selecting the thickness of the seed coat and aleurone layers of brown rice. In this study, two primer pairs that can be used as HRM markers were finally developed by GWAS analysis of the 294 rice core collection. As a result of genotyping with the 50 BC2F2 individuals derived from the cross between “Samgwang” and “Seolgaeng”, it was confirmed that different melting curve patterns are shown, depending on the thickness of seed coat and aleurone layers using a corresponding HRM marker. In conclusion, it was possible to develop the HRM markers for selecting seed coat and aleurone layer thickness. SNP-based HRM markers have been studied in various fields, such as disease resistance and susceptibility discrimination, with sequencing through cross breeding. Furthermore, markers for quantitative traits were developed. The markers developed in this study enable efficient testing of cross populations and provide materials that can be applied, not only to aleurone layers, but also in constructing breeding populations using various traits. In addition, gene mapping and QTL analysis are possible using the developed HRM molecular markers.This is expected to be used as basic data for the application of gene editing technology using CRISPR/Cas9 and for the establishment of a breeding strategy for high eating quality rice using molecular genetic technology.


Wheat is grown and consumed worldwide, making it an important staple food crop for both its calorific and nutritional content. In places where wheat is used as a staple food, suboptimal micronutrient content levels, especially of grain iron (Fe) and zinc (Zn), can lead to malnutrition. Grain nutrient content is influenced by abiotic stresses, such as drought and heat stress. The best method for addressing micronutrient deficiencies is the biofortification of food crops. The prerequisites for marker-assisted varietal development are the identification of the genomic region responsible for high grain iron and zinc contents and an understanding of their genetics.A total of 193 diverse wheat genotypes were evaluated under drought and heat stress conditions across the years at the Indian Agricultural Research Institute (IARI), New Delhi, under timely sown irrigated (IR), restricted irrigated (RI) and late sown (LS) conditions. Grain iron content (GFeC) and grain zinc content (GZnC) were estimated from both the control and treatment groups. Genotyping of all the lines under study was carried out with the single nucleotide polymorphisms (SNPs) from Breeder’s 35K Axiom Array.In silico analysis demonstrated the presence of 28 potential candidate genes in the flanking region of 16 linked SNPs, such as synaptotagmin-like mitochondrial-lipid-binding domain, HAUS augmin-like complex, di-copper center-containing domain, protein kinase, chaperonin Cpn60, zinc finger, NUDIX hydrolase, etc. Expression levels of these genes in vegetative tissues and grain were also found. Utilization of identified markers in marker-assisted breeding may lead to the rapid development of biofortified wheat genotypes to combat malnutrition.Three subgroups were observed in the association panel based on both principal component analysis (PCA) and dendrogram analysis. A large whole-genome linkage disequilibrium (LD) block size of 3.49 Mb was observed. A genome-wide association study identified 16 unique stringent marker trait associations for GFeC, GZnC, and 1000-grain weight (TGW).  Triticum aestivum L.) is one of the most extensively cultivated crops in the world and contributes a major portion of the calories in the global diet. Wheat provides up to 60% of daily energy needs in developing and underdeveloped nations  and zinc (Zn), is not at its optimal level, and milling further decreases this concentration . EnhanciAmong the most common human micronutrient deficiencies worldwide, iron and zinc deficiencies are important ones . HemogloThe majority of wheat-growing land is affected by sporadic rains, rising temperatures, and heat waves on a regular basis. Heat and drought stress have an impact on grain-filling time, starch accumulation, and seed size. In addition, during heat and drought stress, the grain sink capacity reduces to a greater extent . Food anThe direct uptake of minerals from the soil and/or the remobilization of minerals held in vegetative tissues during grain filling are the two main sources of grain mineral supply . AccordiRecent improvements in high-throughput genotyping and phenotyping have boosted the feasibility of discovering the genetic basis of complex traits , such asThe precise modulation of a complex quantitative trait for adaptation to a specific environmental condition, such as drought or heat stress, necessitates the identification of relevant genetic regions, such as QTLs . GWAS isThere have been attempts to identify the genomic regions targeting grain nutrient content such as iron and zin using GWASs . HoweverThe association panel under investigation included 193 genetically diverse bread wheat genotypes , with 19In both years, the collection of grain for grain iron content (GFeC) analysis, grain zinc content (GZnC) analysis, and 1000-grain weight (TGW) estimation was carried out by hand picking 20 random spikes from each line into polythene bags. Hand threshing was done to avoid contamination in machines. GFeC and GZnC were estimated using around 20 g of the grain sample from each genotype. A high-throughput energy-dispersive X-ray fluorescence (ED-XRF) spectrometric, bench-top, non-destructive machine  , located® Best Practices genotyping work flow . SNPs were filtered, and monomorphic markers and markers with a minor allele frequency (MAF) of 5%, missing data of more than 10%, and heterozygote frequency greater than 50% were eliminated from the study. The remaining 13,947 SNPs were analyzed further.Genomic DNA from leaf samples of all 193 genotypes was isolated with the cetyltrimethylammonium bromide (CTAB) extraction method  followed2, heritability), and adjusted means of phenotypic data were calculated by year and by treatment  using the R package ‘augmentedRCBD’ , respectively. iEnv is the effect of ith environment and i × Genj, Envi × IdCheckj, Envi × Genj,Env and i × CheckjEnv are the interaction effects. µ is the mean and e is the error component . Pearson correlation coefficient values among the traits in each environment were also calculated.Graphical representations of the phenotypic data with frequency distribution and box plot were generated using “ggplot2” package from R software. SNP distribution by chromosome with an SNP density plot was generated using the web tool SR-Plots (https://itol.embl.de/) to draw the dendrogram.Diversity in the GWAS panel was identified by marker-based principal component analysis (PCA) and neighbor-joining (NJ) dendrogram analysis using Genome Association and Prediction Integrated Tool (GAPIT) v3  and trair2-value, between markers were calculated from TASSEL 5.0, and the LD decay curve was drawn for each A, B, and D genome along with the whole genome. Pairwise r2-values were plotted against the distance in base pairs (BP) to estimate LD block size and the distance at the half-LD decay point was noted  was calculated from the pairwise as noted .-log10(p) values. Marker trait associations (MTAs) in all six environments for GFeC, GZnC, and TGW were found to have a significant p-value of< 0.0001. For stringent selection, a Bonferroni correction was applied .A total of 13,947 Filtered SNPs and adjusted means for each environment, BLUPs calculated from the two-season data for IR, RI, and LS , and A total of 13,947 Filtered SNPs and best linear unbiased predictions (BLUPs) across the six environments were used to decipher the associated markers with traits using ‘BLINK’ (Bayesian-information and linkage-disequilibrium iteratively nested keyway)  under GAhttps://plants.ensembl.org/Triticum_aestivum/Tools/Blast) against the bread wheat reference genome IWGSC (RefSeq v1.0). Putative candidate transcripts within a 100-kb flanking region of SNPs were identified by region comparison followed by protein produced (or coded) by them using InterPro Classification of protein families (https://www.ebi.ac.uk/interpro/). A further gene ontology study of identified genes was carried out to know their biological process, cellular components, and molecular functions from Expression Atlas (https://www.ebi.ac.uk/gxa/home). Expression analysis of putative candidate genes linked to the identified SNPs was carried out in the Wheat Expression Browser by expVIP (http://www.wheat-expression.com/). Expression of genes in high-level tissue, that is, root, shoot/leaves, spike, and grains, was noted down in the transcript per million (TPM). A heat map based on expression data was generated using the ‘ggplot2’ package in R. Genes with a higher expression level were identified.Associated markers identified using GWAS were subjected to a basic local alignment search tool (BLAST) search using the sequence information of the markers. The BLAST search was carried out using the data web service Ensembl Plants  , except under RI in 2020, which showed a non-significant correlation. The correlation of TGW with GFeC and GZnC was non-significant in 2020 under all three treatments. However, in 2021, TGW showed a positive correlation with GFeC under the IR and RI conditions, and a negative correlation with GZnC under IR and LS . A large LD block size of 3.49 Mb was observed for the whole genome indicating that SNPs at this block act as inheritance blocks, whereas subgenomes A, B, and D had block sizes of 2.48 Mb, 4.29 Mb, and 3.82 Mb, respectively (Linkage disequilibrium (LD) between the marker pairs was calculated as p-value of<0.0001. Among them, five, six, and 10 unique, treatment-specific SNPs were linked to GFeC, GZnC, and TGW, respectively. Similarly, based on combined BLUPs, five SNPs were linked to GFeC, and 10 were linked to TGW (10(p) >5.45) and 16 MTAs  were retained and they were depicted with Manhattan and quantile–quantile (Q–Q) plots  for all three traits put together at the cut-off Q) plots 8. SNP Ap) value of 6.92. This MTA was present in the transcribing region codes for restriction endonuclease type II-like protein. In the flanking region of this SNP another two transcribing regions  were identified as coding for cyclophilin-type peptidyl-prolyl cis–trans isomerase domain and tRNA/rRNA methyltransferase, with them having a potential role in protein peptidyl-prolyl isomerization and RNA processing, respectively. A stably identified SNP AX-94926681, linked to TGW, was present in the region coding for ribonuclease H, exonuclease, and RNase T/DNA polymerase III, all of which have crucial roles in nucleic acid binding. Similarly, the location of the transcripts within or near the region of identified MTAs were searched, and are presented in A total of 16 MTAs were retained after stringent selection and were further searched for their candidate genes in a 100-kb flanking region using sequence information from the SNPs identified with the BLAST search in Ensembl Plants. The SNP AX-94393306, which was present on chromosome 7B, was linked to GFeC and was BLASTed to gene codes for HAUS augmin–like complex subunit 2. A gene ontology study found that the gene was an integral part of the microtubule organizing center, as it plays a role in spindle assembly during cell division. In a 100-kb flanking region of this SNP, there is another transcript coding for the P-loop-containing nucleoside triphosphate hydrolase. Similarly, SNP AX-94850629 was within the region coding for a serine–threonine/tyrosine-protein kinase, which is a catalytic domain with a major role in protein phosphorylation. Two other transcripts coding for the glycoside hydrolase superfamily and the bifunctional inhibitor/plant lipid transfer protein/seed storage helical domain were present in the flanking region of the SNP. Only one MTA AX-95095792 was found to be linked to GZnC at a –log10 in both years showed significant variation among the traits. The trait variations are a prerequisite for genetic studies and a breeding program of the nutrient status of the grain . Trait vThe coefficient of variation (CV) and broad-sense heritability for GZnC and TKW were higher compared with GFeC. TGW was having high heritability throughout the six environments followed by GZnC, which was similar to previous reports by We observed that the Pearson correlation coefficient was positive between GFeC and GZnC under all the treatments (except RI in 2020), which was similar to the results reported by The use of structured populations influences the identification of MTAs through GWASs . To nullThe linkage disequilibrium (LD) decay over genetic distance in a population determines the density of marker coverage needed to perform GWASs. A faster LD decay indicates the requirement of a higher marker density to capture the markers close enough to the causal loci . In the p-value of<0.0001. However, to enhance the stringency of selection to avoid false positives, a Bonferroni correction was applied. A total of 16 stringent markers were found: five for GFeC, one for GZnC, and 10 for TGW. Bonferroni correction is a method to counteract the multiple comparisons problem to reduce type 1 error, i.e., false positives  with GFeC were found on chromosomes 3A, 3B, 4B, 6B, and 7D; there have been similar reports on chromosome 3B , whereassistance , iron desistance , and homsistance . As wheasistance  and on tsistance . Iron hasistance . There isistance . The cansistance , was idesistance . The secca napus  and 2S acommunis . Furthercommunis  was mainStringent selection for MTAs left only one marker AX-95095792 on chromosome 4B linked with grain zinc content. The candidate region is mainly responsible for gene regulation and translation-related activities. Cyclophilin-type peptidyl-prolyl cis–trans isomerase domain, tRNA/rRNA methyltransferase, and SpoU-type proteins were reported in the candidate region. They have functions in the isomerization of the prolyl–peptide bond  and tRNAArabidopsis thaliana   , and polmANK114) . Most ofThe development of biofortified wheat varieties through the transfer of genomic regions associated with higher concentrations of micronutrients in grain will aid in the health of millions of malnourished people suffering from hidden hunger. Genetic biofortification is easy, economical, and sustainable in the long term. Identification of genomic regions using linked markers is a prerequisite to transfer them into the popular cultivars to develop a variety with a higher grain nutrient content. The association mapping panel used in this study with 193 wheat genotypes revealed that GFeC, GZnC, and TGW are complex traits that are quantitatively inherited and strongly influenced by abiotic stress, such as drought and heat stress. The significant positive correlation between the GFeC and GZnC, as well as their high heritability, suggests that simultaneous improvement of both traits can be possible. Five of the 16 stringent MTAs identified were linked to GFeC, one to GZnC, and 10 to TGW, and were located near novel candidate genes that have a direct or indirect effect on traits. Several putative candidate genes identified were found to encode proteins that have important molecular functions in plant ionic balance, protein and enzyme metabolism, and abiotic stress responsiveness, etc. Furthermore, identified MTAs could be subjected to validation and utilized in marker-assisted breeding programs to create biofortified varieties.https://doi.org/10.5061/dryad.0cfxpnw6c.All the raw data used in this study is available at PS, NJ, GS, and HK conceptualized the investigation and edited the manuscript. ND conducted the investigation and prepared the draft of the manuscript. ND, CM, and KM generated the phenotypic data. ND, HK, PS, SS, and DC contributed to the generation of genotyping data. ND and HK did the statistical and GWAS analysis. All authors contributed to the article and approved the submitted version.


Oryza sativa L.) appearance quality, which is mainly defined by grain shape and chalkiness, is an important target in rice breeding. In this study, we first re-sequenced 137 indica accessions and then conducted a genome-wide association study (GWAS) for six agronomic traits with the 2,998,034 derived single nucleotide polymorphisms (SNPs) by using the best linear unbiased prediction (BLUP) values for each trait. The results revealed that 195 SNPs had significant associations with the six agronomic traits. Based on the genome-wide linkage disequilibrium (LD) blocks, candidate genes for the target traits were detected within 100 kb upstream and downstream of the relevant SNP loci. Results indicate that six quantitative trait loci (QTLs) significantly associated with six traits  were identified for haplotype analysis. Among these QTLs, two (qTGW4.2 and qGW6.1), were overlapped with FLO19 and OsbZIP47, respectively, and the remaining four were novel QTLs. These candidate genes were further validated by haplotype block construction.Rice ( Oryza sativa L.) is a major staple crop in Asian countries, feeding more than half of the world’s population. Higher yield and quality are two main objectives in rice breeding. Although different countries may have different preferences for rice quality, high-quality rice varieties are always favored by both consumers and producers. Rice consumers in India, southern China, Pakistan, Bangladesh, Sri Lanka, some southeast Asian countries and the USA prefer long and thin rice grains with a fluffy to firm texture and medium to high straight-chain starch contents. However, people in Japan, Korea and northern China prefer short, round, soft and sticky rice grains with low straight-chain starch content. The improvement of the quality of major crops through breeding of superior varieties with higher yield, nutrition and resistance is essential for adequate, reliable and sustainable food supply in the world [Rice  and lowest (6.66 mm) in HN, and its average value in the 137 accessions was 8.43, 8.23 and 8.69 mm in HN, GX and JX, respectively. The GW value ranged from 2.00 to 3.51 mm, and was averagely 2.75, 2.32 and 2.52 mm in HN, GX and JX, respectively. The mean of GLWR in the 137 accessions was 3.45, 3.31 and 3.51 in HN, GX and JX, respectively. The GLWR value was the lowest in GX (2.33) and the highest in JX (4.91). PGWC was averagely 0.27, 0.32 and 0.32 in HN, GX and JX, respectively, and ranged from 0.03 to 0.81.B2) averaged across the three environments was 0.41, 0.52, 0.43, 0.98, 0.78 and 0.67 for TGW, GL, GW, GLWR, PGWC and DEC, respectively (p < 0.001) except for the effect of environment on TGW (p = 0.0012) , and the maximum value was observed in GX and JX (0.25). The broad-sense heritability (Hectively . ANOVA a 0.0012) . The pai 0.0012) g–i.The phylogenetic tree a shows tThe maximum LD was 0.30 in the whole population. LD reached half of its initial value at around 92 kb .p value was taken as the lead SNP to reduce redundant association signals of different traits. Stringent criterion of −log10 (p) > 5.4 in the three environments was used to determine the association significance of grain weight, grain shape and chalkiness (qTGW4.1 and qTGW4.2 on chromosome 4) were determined to be significantly correlated with TGW, which accounted for 17.7% and 20.4% of the phenotypic variance, respectively  was used to conduct GWAS on grain weight, grain shape and chalkiness . Considering the decay distance of LD in rice, adjacent SNPs with spans less than 200 kb were defined as single QTLs, and the SNP with the lowest alkiness . The resalkiness . A totalalkiness . In geneectively a. The thectively b. Seven ariance. c. Five Qvariance d. Six QTectively e. Four Qectively f.qTGW4.2 (1.12–1.32 Mb on chromosome 4), the 2795 SNPs of ten genes were used for high-density association analysis. The annotated gene with the most significant hit was LOC_Os04g02900  [By using GWAS and gene-based association analysis, combined with haplotype analysis of candidate genes, we screened six candidate genes for six important QTLs controlling the measured traits. Of the three major components  of rice yield, grain weight measured as TGW is the most stable and heritable trait. In this study, one promising gene located in the QTL for grain weight was also identified. For  (FLO19)  encodes qGL12.2, in which LOC_Os12g03040 is annotated as a NAC (NAM/ATAF/CUC) transcription factor playing important roles in regulating plant growth and development as well as biotic and abiotic stress responses. The second one was qGW6.1, in which LOC_Os06g15480 is annotated as a basic region-leucine zipper (bZIP) transcription factor. This gene has been identified in a previous study and designated as OsbZIP47 [OsbZIP47 and recruit the transcriptional co-repressor aberrant spikelet and panicle1 (ASP1) to repress its transcriptional activity. OsbZIP47 overexpression lines showed narrower seeds, which is similar to the wg1 mutant. The wg1-1 osbzip47-c2 double mutant had wider and heavier seeds than the wg1-1 single mutant, and OsbZIP47 was found to negatively regulate grain width by limiting cell proliferation. The third one was qGLWR4.2, in which LOC_Os04g49130 is annotated as a small ubiquitin-like modifier (SUMO)-conjugating enzyme E2. SUMOylation modification is an important eukaryotic post-translational modification regulating many cellular processes in plants, from seed development to stress response. The transcript levels of SUMOylation target genes, including Abscisic Acid (ABA) and gibberellins (GA) associated with cystatin-related epididymal spermatogenic (CREs), are responsive to treatment with these hormones.Rice grains grow inside the spikelet hull with a limited caryopsis space. Therefore, rice grain shape and size are strictly determined by the maternal genotype that controls the cell number and size of glumes. The genes cloned so far provide insights into the regulatory pathway of grain shape and size, including the ubiquitin-proteasome, G-protein signaling, and mitogen-activated protein kinase (MAPK) signaling pathways, as well as plant hormones and transcriptional regulators ,47. In tqPGWC5.1, from which LOC_Os05g01430 was screened as a candidate gene and annotated as polygalacturonase inhibiting protein 3. LOC_Os05g01430 is a plant cell wall glycoprotein inhibiting fungal endopolygalacturonases and modulating their activity to promote the accumulation of elicitor-active oligogalacturonides. The second one was qDEC6.1, in which LOC_Os06g45300 was identified as the candidate gene and annotated as a rice dual-specificity protein kinase. Dual-specificity protein kinases are a group of protein kinases able to phosphorylate both tyrosine and serine/threonine residues. These results improve our understanding of the genetic basis for grain weight, grain shape and chalkiness in rice and provide valuable information for elucidating the molecular mechanisms underlying these traits.Chalkiness is caused by the deposition of starch and storage proteins in the endosperm, which is tightly related to grain filling. Grain filling is a dynamic process related to the source–sink balance and controlled by a complex genetic mechanism and sensitive to environmental conditions ,49,50. IOverall, we obtained a set of QTLs significantly associated with grain weight, grain shape and chalkiness in rice through the GWAS analysis of 137 rice accessions. Candidate genes significantly associated with agronomic traits and functional annotation of each gene were further screened by haplotype block structure analysis. As expected, although a large LD contains many SNPs in a candidate region, our results suggest that the number of candidate genes can be significantly reduced by combining haplotype block structure analysis and gene function annotation. In conclusion, our findings will contribute to future gene functional analysis and provide valuable information for rice gene cloning.http://www.wseen.com/, accessed on 20 April 2022). Subsequently, the weight of these seeds was measured using a high precision electronic balance  and the TGW in grams was subsequently calculated. The scanner was calibrated with a calibration target before each measurement.A total of 137 accessions  from theExcel 2018 was employed for data compilation, and the mean, standard deviation and coefficient of variation of each trait were calculated. The correlation analysis and frequency analysis of six grain quality-related traits were carried out by R . The R package ‘lme4’  was usedhttps://www.illlighta.com/, accessed on 26 April 2019), paired-end sequencing libraries were constructed using 5 µg of genomic DNA, with inserted fragments of approximately 350 bp. The Illumina HiSeq X10 platform was used to obtain the pair-ends of 150 bp reads, and the original sequence was further processed to remove adaptor-containing and low-quality reads. Library construction, sequencing and sequence cleaning were all performed by BGI Shenzhen Company. The reference genome was Shuhui 498 (R498). GATK was used to call SNPs [For each of the 137 accessions to be sequenced, two leaves were collected from a single plant at the tillering stage (one month after seedling transplantation), and genomic DNA was extracted using a standard cetyltrimethylammonium bromide protocol . Accordiall SNPs . The mapall SNPs . SNPs wiall SNPs  was usedThe TASSEL  was usedSNP data of candidate genes were extracted based on genotype of SNP with MAF > 0.05. This dataset only contained double-allele SNPs. Further haplotype analysis excluded heterozygotes and missing alleles. Haplogroups consisting of fewer than 10 accessions were deleted. For the genes found in QTLs, haplotype analysis only used the non-synonymous SNPs in the coding region of these genes for haplotype analysis of R.The software “PopLDdecay” was used to calculate the linkage disequilibrium (LD) between pairs of markers . “R2” wap value of dominance was 3.9 × 10−6 to control the genetic false positive error rate of the population. The SNPs in the same LD region were regarded as one QTL. Here, by referring to the previous report, we used the LD decay distance of 100 kb [p-value was regarded as the lead SNP. The Manhattan plot was drawn using the R package “CMplot”.In this study, we obtained 2,898,034 SNPs (MAF > 0.05) and six sets of phenotypic data. The SNPs and phenotypic data were used to conduct GWAS in the TASSEL (version 5.2.40)  softwaref 100 kb , and thehttp://rice.plantbiology.msu.edu, accessed on 26 April 2022) was used to search for candidate genes in the 200 kb genomic region of the selected SNPs. Among all candidate genes, four types of genes, including expression proteins, hypothetical proteins, retrotransposons and transposons, were excluded. The SNP data of candidate genes were extracted based on genotype of SNP with MAF > 0.05. This dataset only contained double-allele SNPs. Further haplotype analysis excluded heterozygotes and missing alleles. Haplogroups consisting of fewer than 10 accessions were deleted. For the genes found in QTLs, only non-synonymous SNPs in the coding region of these genes were used for the haplotype analysis of R, and Student’s t-test was performed to determine whether this locus could cause changes in rice rain weight, grain shape and chalkiness. R was used to visualize the results.In order to identify candidate genes related to TGW, GL, GW, GLWR, PGWC and DEC, the rice genome annotation project (There are considerable genetic variations for six grain quality traits in the panel consisting of 137 rice accessions. Twenty-seven QTLs were identified using GWAS. Six candidate genes were also screened by high-density association and gene-based haplotype analysis. The findings improve our understanding of the genetic basis for grain weight, grain shape and chalkiness in rice and provide valuable information for elucidating the molecular mechanisms underlying these traits. This study also provides a reference for future marker-assisted breeding with QTL or gene pyramiding to stabilize and improve rice quality.


Due to inconsistent findings in the evidence, a review to critically examine the plethora of evidence from randomized controlled trials (RCTs) in n-3 PUFA research was undertaken. The aim of this review is to study the highest level of evidence and to identify gaps in n-3 PUFA research. RCTs were originally designed for pharmaceutical research and later adopted for nutrition and food-related research. RCTs with active diseases assume that n-3 PUFA will have “drug” like effects, and this high expectation may have led to the inconsistent evidence in the literature. The inconsistency in the literature may be related to varying doses of n-3 PUFA, sources of n-3 PUFA , type of n-3 PUFA (mixture vs. purified), trial duration, population characteristics, sample size, and genetic variation. For future research, there is a need to distinguish between primary and secondary prevention, and to focus RCTs on primary prevention of chronic diseases by n-3 PUFA which is lacking in the literature.Over the years, there has been heightened interest in the health benefits of  N-3 long chain polyunsaturated fatty acids (n-3 PUFA) have an important role in human health and reduction in chronic diseases [N-3 PUFA are presumed to have anti-inflammatory and anti-thrombotic properties, lower plasma triglycerides and low-density lipoprotein (LDL) cholesterol, improve vasomotor and endothelial functions, and inhibit cell growth factors [n-3 PUFA, and they are alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). EPA and DHA most specifically are vital for improved cardiovascular functions, neurodevelopment, and in improving metabolic and immune processes [diseases . N-3 PUF factors ,3. Thererocesses . Howeverrocesses . Since Erocesses . While trocesses .n-3 PUFA, and increased consumption of highly processed foods rich in saturated fats and n-6 PUFA [n-3 PUFA and high in saturated fats and n-6 PUFA is associated with poorer metabolic and cardiovascular health in both men and women [n-3 PUFA, there has been an increased intake of fish oil supplements among the general public, and a plethora of research on the potential effects of n-3 PUFA in reducing incidence of chronic and age-related diseases such as cardiovascular disease (CVD), cancer, type 2 diabetes, and acute macular degeneration (AMD) [n-3 PUFA supplementation on chronic disease prevention have been inconclusive, generally showing a beneficial or neutral, but not negative outcome. Additionally, the conflicting emerging evidence from newly published systematic reviews and meta-analyses (SRMA) of RCTs on the effects of n-3 PUFA on total CVD and its related outcomes, cancer incidence and cancer mortality has raised concerns about the role of n-3 PUFA in reducing and preventing chronic diseases due to challenges inherent to human studies in nutrition [n-3 PUFA as supplements, there is a need to review the highest level of evidence from RCTs and identify study gaps in order to advance the field of n-3 PUFA research.Over the years, due to industrialization, there has been a reduction in the intake of on (AMD) . Howeverutrition ,10,11,12n-3 PUFA RCTs across a range of chronic diseases including CVD, cancer, diabetes, and AMD. These aforementioned diseases were the main focus of this review due to a lack of high quality RCTs in other diseases. We then examined in totality, across all these studies potential factors contributing to apparent differences in outcomes.We initially reviewed the major findings of n-3 PUFA has a protective effect on CVD risk and its related outcomes such as myocardial infarction (MI), stroke, CHD and CVD mortality. Several trials studying the cause-and-effect relationship between n-3 PUFA intake and its effects on CVD and its outcomes have subsequently reported “discordant trial results” [n-3 PUFA [n-3 PUFA for a longer duration was associated with a 13% reduction in major cardiovascular events [Previous observational studies reported that consumption of fish once or twice per week was associated with lower risks of fatal coronary heart disease (CHD), but cohort studies are subject to confounding factors and biases and are unable to establish causality ,14,15. Hresults” . Recent n-3 PUFA ,11,16,17r events .n-3 PUFA  ranging from 0.85 to 4 g daily and with a follow-up ranging from 3.5 to 4.9 years reported significant reductions in CVD, CHD, MI, stroke, and their related fatalities [Large-scale trials, with doses of talities ,19,20,21talities . A recenn-3 PUFA ranging from 0.84 to 4 g daily and with a follow-up of 1 to 7.4 years reported no significant association with CVD and its sub types [n-3 PUFA on cardiovascular outcomes in patients with impaired fasting glucose or with diabetes, but without evidence of atherosclerotic CVD. Both had more than 12,000 participants enrolled, taking 0.84 g of DHA + EPA for more than 6 years, and still failed to show any significant reduction in CVD and its related outcomes [In contrast, eleven large-scale trials with doses of ub types ,30,31,32ub types  and a Stub types  studied outcomes ,29. Moren-3 PUFA may reduce cancer risk by regulating metabolic pathways and inflammatory responses, oxidative stress, and changes in the composition of membrane affecting cell signaling pathways [n-3 PUFA may inhibit breast cancer growth [n-3 PUFA had a significantly lower rate of breast cancer compared to Europe and United States [n-3 PUFA, with a 20% and 26% reduction in breast cancer risk in both Japanese and Singaporean Chinese populations, respectively [n-3 PUFA [In 2020 alone, cancer was the leading cause of morbidity and death globally with approximately 19.3 million new cancer cases and 10 million cancer-related deaths . Since cpathways ,35. It wr growth . Asian cd States . A greatd States . Earlierectively ,40. A mon-3 PUFA .n-3 PUFA on cancer development and cancer mortality risk remains contradictory when considering all cancer types. Epidemiological studies demonstrating significant reductions in cancer risk may have overestimated the effects of n-3 PUFA supplementation, leading us to believe that estimates of cancer risk need to be assessed by cancer type and it is not possible to generalize across all cancers. An earlier SRMA of observational studies showed significant associations between n-3 PUFA and cancer incidence, especially colorectal, lung, prostate, and breast cancer [n-3 PUFA reduced cancer risk by 61% after a 4 year follow up [n-3 PUFA on cancer demonstrated little or no effect on cancer diagnosis and cancer mortality [n-3 PUFA on cancer incidence and mortality for a mean follow-up of 1.5 years found no association with cancer outcomes [However, the evidence of t cancer . More thollow up . In contortality . Consequortality . Most reortality ,44,45,46outcomes ,48,49 , glycosylated hemoglobin (HbA1c), and HOMA of insulin resistance (HOMA-IR) levels [n-3 PUFA supplementation on type 2 diabetes treatment or prevention showed a significant reduction in fasting blood glucose and insulin resistance, but no significant effect on HbA1c was observed [n-3 PUFA on type 2 diabetes among those with impaired glucose tolerance or with metabolic syndrome also found no significant changes in HbA1c, fasting glucose, and insulin sensitivity with the doses of n-3 PUFA ranging from 0.6 to 6 g per day for a duration ranging from 8 weeks to 1 year [n-3 PUFA ranging from 0.41 to 3 g/day for a follow-up of 8 weeks to 1 year as well [n-3 PUFA group after 8 weeks of supplementation with 0.3 g of n-3 PUFA daily and 50,000 IU of vitamin D weekly [A SRMA of 12 RCTs studying the effects of ) levels . Anotherobserved . Trials o 1 year ,56,57,58 as well ,61,62,63D weekly .n-3 PUFA has been postulated as a potentially effective strategy to protect against AMD [n-3 PUFA and reduced risk of AMD [n-3 PUFA for up to 5 years did not reduce the incidence or risk of progression to advanced AMD [Age-related macular degeneration (AMD) accounts for 8.7% of blindness globally in people 60 years and older, while the number of people with AMD is expected to increase to 288 million by 2040 . Consumpinst AMD . Evidenck of AMD ,74,75. Ak of AMD . Conversnced AMD .n-3 PUFA on AMD incidence or progression among 25,871 participants with a mean age of 67.1 years after a median follow-up of 5.3 years [The Age-Related Eye Disease Study 2 (AREDS2), a large-scale study of 4203 participants with a mean age of 73.1 years and at risk for progression to advanced AMD, reported that 1 g of EPA + DHA combined with lutein and zeaxanthin did not reduce the progression to advanced AMD after a median follow-up of 5 years  Table 4Table 4. .3 years  , fasting blood glucose (FBG), and HbA1c. Inconsistent findings in the diabetes literature may be attributed to a lack of observed changes in inflammatory responses, shorter follow-up time, smaller sample size, inadequate dosage of n-3 PUFA, open-label design, higher attrition rate, combined intervention, baseline plasma glucose levels, health status of the study participants, HOMA-R values, and lack of sensitive methods such as the use of euglycemic clamp to detect any changes in insulin sensitivity [n-3 PUFA on type 2 diabetes and its parameters also used combined interventions such as n-3 PUFA with plant sterol or vitamin D. Both plant sterol and vitamin D are known to prevent impaired glucose regulation (IGR) progression to type 2 diabetes and enhance the insulin-regulated glucose transporter type-4 (GLUT-4), respectively, while improving blood glucose levels and insulin resistance [n-3 PUFA due to the beneficial effects of plant sterols and vitamin D.In addition to influencing CVD and cancer outcomes, sitivity ,56,57,63sistance ,62. TherConversely, null effects on FPG and HbA1c were observed after supplementing diets with 1.8 g of EPA daily for 6 months in the Japanese population. This null effect was due to the mean homeostasis model assessment ratio (HOMA-R) of 1.6, which was closer to the upper limit of normal HOMA-R values in the Japanese population, and as a result prevented any further reduction in FPG and HbA1c . Sawada n-3 PUFA research, the long-term consumption of n-3 PUFA has not shown a significant reduction on the incidence and progression of AMD. After a median follow-up of approximately 5 years, no significant reduction in AMD incidence and progression to advanced AMD were observed in the intervention group receiving n-3 PUFA in large-scale trials such as AREDS and VITAL [n-3 PUFAs were attributed to the duration of the trial, the form of EPA and DHA used, as well as the EPA and DHA ratio since the ratio used was mostly designed for CVD related studies [n-3 PUFA [In line with the inconsistent findings related to nd VITAL ,79. Thes studies ,79. In t studies . All AREn-3 PUFA . The VITn-3 PUFA RCTs across chronic diseases such as CVD, cancer, type 2 diabetes, and AMD were reviewed and critically analyzed. However, a potential limitation should also be considered. The totality of evidence was not considered since our focus was on high quality and large RCTs, especially for CVD, cancer, and AMD. Nevertheless, these RCTs provide important perspectives for future studies of any size.This review has a few strengths and limitations that should be addressed. First, this was a comprehensive review where the highest level evidence from n-3 PUFA on CVD, cancer, type 2 diabetes and its related parameters, and AMD was dependent on the types of trials, whether they were primary or secondary prevention, trial design, varying doses and forms of n-3 PUFA, duration of the trials, baseline characteristics of the study participants, ethnicity, geographical locations, health status of study population, the sample size, daily intake of n-3 PUFA, attrition rate, and participants’ adherence to the treatment regimen. To understand the controversies and inconsistencies surrounding n-3 PUFA, we must therefore temper our expectations around RCTs and recognize that in general, n-3 PUFA is not a pharmaceutical drug. We also must recognize that n-3 PUFA nutrition trials are not poorly done but have limitations and that the evidence from these RCTs may be stronger than they really are. The findings from this review of RCTs and n-3 PUFA studies also suggest that there is a need for future research to distinguish between primary and secondary prevention, and to focus more on primary prevention of the aforementioned chronic diseases and determine whether a significant and beneficial effect of n-3 PUFA may be obtained. Primary prevention studies will not be conflicted by co-morbidities, the use of other prescription drugs or other significant confounders. Although the research surrounding n-3 PUFA and age-related chronic diseases are mostly focused on treatments with EPA and DHA combined or EPA alone, future trials of DHA monotherapy are needed to confirm the mechanistic function of DHA since EPA and DHA differ in their biological effects. Overall, the expectations from large n-3 PUFA RCTs have not yielded conclusive evidence, but merely reaffirmed the complexity of human based nutrition research and the need to consider the totality of all study designs and the uniqueness of many different population groups.In conclusion, the evidence from RCTs examining effects of


Sporisorium reilianum is a fungal pathogen that causes head smut in sorghum. In addition to pathotypes (P) 1-4, P5 and P6 were identified recently. In this study, seedlings of Senegalese sorghum, comprising 163 accessions, were evaluated for response to Sporisorium reilianum. Teliospores of pathotype P5 of the pathogen in dilute agar were pipetted onto seedling shoots while still in soil, and inoculated seedlings were submerged under water at 4 days post-inoculation. Signs of infection (noticeable spots) on the first leaf were checked daily up to 6 days post submergence. A genome-wide association study (GWAS) was conducted using 193,727 single-nucleotide polymorphisms (SNPs) throughout the genome based on two types of phenotypic data: whether noticeable spots were shown or not and the average time for an observation of the spots across 163 accessions. When mapped back to the reference sorghum genome, most of the top candidate SNP loci were associated with plant defense or plant stress response-related genes. The identified SNP loci were associated with spot appearance in sorghum seedlings under flooding following inoculation with P5 of Sporisorium reilianum. Sorghum bicolor L. Moench) ranks fifth among the most grown cereals in the world [Sporisorium reilianum (Kühn) Langdon & Fullerton causes head smut in sorghum. This basidiomycete pathogen has two formae speciales, ‘reilianum’ and ‘zeae’, that are, respectively, specialized on sorghum and maize. S. reilianum f. sp. reilianum infection on sorghum can lead to complete panicle loss under the successful penetration and colonization of the plant [S. reilianum f. sp. reilianum is highly virulent on susceptible sorghum cultivars after the successful mating of two haploid yeast-like sporidia resulting from the germination of haploid soilborne teliospores. The sporidia carrying different mating types at both a and b loci recognize each other through a pheromone/pheromone receptor system and undergo anastomosis to form a dikaryotic filament that penetrates the plant surface; they then colonize the host. No noticeable symptoms of infection are detected until the invasion of the undifferentiated floral tissue, which will, at panicle emergence, result in the replacement of the inflorescence by sori containing black masses of teliospores [S. reilianum f. sp. reilianum [S. reilianum f. sp. reilianum in South Texas that appeared on cultivars that were being grown to avoid pathotype 4, which is common in that area. In parallel, the knowledge of plant inhibitory compounds involved in defense mechanisms could allow a new approach for disease management that relies on the plant’s innate immune mechanisms [Sorghum  and therefore are probably governed by minor genes [In that sense, a previous genome-wide association study (GWAS) performed on the sorghum mini core lines for anthracnose, head smut and downy mildew revealed top candidate genes associated with single-nucleotide polymorphisms (SNPs), suggesting that leucine-rich repeat (LRR), tyrosine-kinases and zinc finger proteins are involved in sorghum defense against or genes .S. reilianum, showing that the defense-related genes, pathogen-induced chitinase and Pathogenesis-Related protein 10 (PR-10) were, to some degree, upregulated 24 h post-inoculation [In 1992, Craig and Frederiksen developed a seedling inoculation method for sorghum. In brief, sorghum seedlings were inoculated by infesting the vermiculite surrounding seedling epicotyls with teliospore cultures, and at 4 days post-inoculation, the inoculated seedlings were placed in test tubes filled with water to completely submerge the first leaf ,12. Baseculation .S. reilianum using the method described by Ahn et al. [S. reilianum f. sp. reilianum and sucrose–agar, then submerged in water four days post-inoculation before being evaluated for spot appearance up to six days after submergence. Based on the resulting phenotypes, GWAS was performed using the phenotypic data collected. In a previous study, the same accessions were analyzed for the population structure and linkage disequilibrium; germplasm diversity analysis showed low genetic diversity and slow linkage disequilibrium (LD) decay among the Senegalese accessions [S. reilianum infection.In the present study, seedlings of 163 lines were evaluated for response to the pathotype 5 (P5) of n et al.  in theirn et al. . The seecessions . We hypop < 0.0001. Most of the Senegalese accessions showed spots .No correlation was found between infection rate and time of appearance of symptoms  below 5%.A Manhattan plot was prepared showing the locations of SNP-detected QTLs associated with response to P5 of When mapped back to the published reference sorghum genome, top candidate SNP loci were tagged or located nearest to genes that have previously reported roles in biotic or abiotic resistance/stress responses in most cases.p-value = 0.14) [p-value = 0.66, the two traits might be weakly correlated.As in a previous study, a possible correlation between infection rate and time of appearance of symptoms lacked statistical significance (Pearson’s correlation = −0.12 with  = 0.14) . As in tp-values. As head smut in sorghum is typically labeled as susceptible when even one sample shows symptoms, GWAS based on binary phenotypes (resistant or susceptible) was conducted, and the top candidate genes identified were associated with plant defense or plant stress response-related genes.As average time of spot appearance varied among the tested accessions, timing was expected to identify top candidate SNP loci associated with the trait through GWAS. However, the top candidate SNP loci did not pass additional t-tests to ensure that the SNP loci associated with timing of spot appearance were truly significant . F-box and leucine-rich repeat protein were listed as top candidate defense related genes in Senegalese accessions  and SAP eilianum . The loceilianum . XTHs haeilianum . Indeed,eilianum . To the eilianum . Nagarajeilianum  reportedThe SNP locus S03_73084958 tagged to a protein similar to the signal recognition particle 54 kDa protein 1 (Sobic.003G427400). The chloroplast signal recognition particle (cpSRP) 54 targets the light harvesting complex proteins to thylakoids and plays a critical role . MoreoveThe SNP S01_65477983 was associated with a helix-loop-helix DNA-binding domain containing protein (Sobic.001G430000). The basic helix-loop-helix (bHLH) is a superfamily of transcription factors largely occurring in plants and animals, in addition to being the second largest transcription factor family in eukaryotes after MYB. Transcription factor members of the bHLH target genes are involved in physiological processes such as plant development and stress responses. In a previous study, Fan et al.  reportedThe SNP locus S02_55267519 seemed to be associated with ankyrin repeats (Sobic.002G174700), which are the largest family within the repeat proteins and are abundantly found in bacterial, archaeal, eukaryotic and viral genomes. These repeats are involved in protein-protein interactions to activate or suppress biological processes . In theiAnother SNP locus on the same chromosome, S02_55189900, was weakly similar to the transcription factor WRKY74 (Sobic.002G174300). WRKYs are among the largest transcription factor families in higher plants and regulate many biological processes, including abiotic and biotic stress responses. WRKY transcription factors control gene expression through a combination of positive or negative regulation . Baillo Apart from F-box and leucine-rich repeats, most of the annotated functions of the genes nearest to the SNP loci identified in the present study, have been reported for their role in abiotic stresses responses and/or plant development. This might indicate a possible involvement in pathogen infection, in which case they would be useful in marker-assisted selection against sorghum head smut at the seedling stage, but it seems more likely the genotypic data are related to the environmental switch to flooding under which the seedlings were exposed.Accessions used as positive checks and their mock inoculation worked well . AdditioThe 163 Senegalese sorghum accessions were provided by the USDA-ARS Plant Genetic Resources Conservation Unit. They were chosen since whole genome sequences were available and are quite diverse as shown in other analyses . Along w3)  filled with Metro Mix 200 . The seedlings were grown at 23℃ and 65% relative humidity under LED light for around 12 hours a day in the insectary room within the Plant Pathology and Microbiology (PLPM) building at Texas A&M University .Seedlings were grown as described by Ahn et al. . In brieS. reilianum were provided by Louis K. Prom . The inoculum was prepared following a modified version of that described by Craig and Frederiksen [g for 10 s. The supernatant was then poured off before the teliospores were resuspended in 15 mL of sterile distilled water. The process was repeated three times. After the third wash–resuspension step, 0.5 mL of the suspension (approximately 16 × 106 teliospores) were used to inoculate an Erlenmeyer flask containing 50 mL of sucrose–agar medium . The flask was then incubated on a rotary shaker at 100 rpm at room temperature for 7 to 10 days. Following the incubation period, at least 10 seedlings of each accession were individually inoculated with 1 mL of the resulting culture. BTx635, PI609251 and TAM428 are often used as resistant checks and BTx623, BTx643, and SC748-5 are used as susceptible checks. The inoculated seedlings were surrounded with vermiculite, then the plants were grown in the same conditions as previously described. Four days post-inoculation, the below ground part of each seedling was separated from its upper part by cutting at approximately 0.5 cm below the base of the stem. The seedling was then surface washed with tap water before being submerged in distilled water contained in a 45 mL plastic test tube. Each seedling was checked daily for spot appearance and percentage area coverage, up to six days, and results were recorded. The inoculations for each cultivar were repeated at least 3 times  between the time of spot appearance and the percentage of leaf covered, using JMP Pro 15 . Analysis of variance (ANOVA) for the 163 accessions based on the two parameters were conducted.S. reilianum inoculation were tracked to identify their specific location using the partially annotated sorghum genome sequence version 3.1.1, accessed through the JGI Phytozome 13 website [p < 0.05) based on Student’s T-test using JMP Pro 15  [p-value lower than the threshold (95%) are reported here.The SNP data used for the GWAS were from an integrated sorghum SNPs dataset obtained using Genotyping by Sequencing (GBS) based on the sorghum reference genome version 3.1.1 ,26,27,28 website . The spoNC, USA) . The top


Dielectric sensing based on capacitive measurement technology is a favourable measurement approach in many industries and fields of application. From an electrical point of view, a coupling capacitance must be measured in the presence of stray capacitances. Different receiver circuit structures have been proposed for the underlying displacement current measurement. Ideally, the sensor assembly is directly connected to the sensor circuitry to minimize the influence with respect to these parasitic capacitances. However, under harsh operating conditions, e.g., at high temperatures, the sensor and the receiver circuit must be separated in order to protect the electronics. Consequently, the receiver circuit and the sensor have to be connected by cables, e.g., coaxial cables. The measurement setup differs significantly from the ideal design with a direct connection. In this paper, we investigate the behaviour of three common measurement circuits for capacitive measurements in instrumentations with cables. We study the interaction between the sensor and the electronics and analyse the operating behaviour of the circuit, as well as the operating states of the amplifiers used. We also address cross-sensitivities in the sensor design due to stray capacitances. The analyses are carried out for different cable lengths and measuring frequencies, and conditions for the usability of the circuit are deduced. In addition to the operational behaviour, we also evaluate the circuits by means of a noise analyses. Based on this analysis, we show a direct comparison of the circuits. The analysis is based on simulation studies, as well as collaborative measurements on test circuits where all circuit parameters are provided. The test circuits are realized with dedicated state-of-the-art circuit elements and, together with the analysis approach and the results, thus provide a basis for future developments. The characterisation and monitoring of materials and substance properties based on dielectric sensing is a well-established approach for many scientific and industrial applications . ExampleYet the sensing capabilities of dielectric sensing have also been applied in various other applications; e.g., in  dielectrIn all sensor schemes shown in The subsequent separation of front-end and electronics requires suitable wiring of the elements, e.g., by coaxial cables ,27,28. FWith regard to the properties for capacitive measurement, the influence of the cables on the measurement system must be investigated.  some pF ,30. To mA low-input impedance circuit based on a current to voltage converter . This coIn , a resonAn impedance-matched front-end design is proposed in , in whicFor the measurement of the displacement current In this article, we examine these three different circuit setups in terms of their measurement behaviour and noise performance. This also includes the sensitivity to stray capacitances. Thus, the analysis also extends the work presented in  with regHolistic system analysis by considering the interaction between the sensor and electronics.Investigation of a directly attached sensor and electronics assembly as well as a spatial dislocated arrangement by means of the use of transmission lines.Simulation-based assessments of the achievable signal-to-noise ratio (SNR) and quantitative comparison of the topologies, considering the environmental impacts.Suggestion of established receiver structures and investigation of their applicability for spectroscopic applications.Furthermore, for the test circuits, all circuit elements are stated, providing researchers a quantified basis for further research.This paper is structured as follows. In In this section, we present the different receiver concepts, which are investigated and address their fundamental behaviour. We then outline the further research approach addressed in the  and Cs1 .The corresponding frequency response is shown in the upper plot of The circuitry in The impedance of a resonant inductor–capacitor resistor (LCR) parallel circuit shows a maximum when operated at its resonant frequency . Intrinsmpedance . Thus, tmpedance ,36, yet T, as in , which lT, as in . Figure By utilizing the resonant circuitry to measure the displacement current in mined by (5)f0=12Q of the LCR circuit [To minimize the undesired impact of parasitic capacitances, the implementation of uggested ,30. The uggested . The use circuit .As discussed, the impedance of a resonant LCR parallel circuit shows a maximum when operated at its resonant frequency, thus having an impact on the receiver circuit’s linearity . Figure ications ,30.By comparing the analytical results in Equation , the dirGiven the initial discussion of the three different receiver structures, In this section, we address the behaviour of a low-Z receiver in combination with transmission lines. The impedances To study the influence of the transmission lines, network analyser  measuremdance by (6)Z_RX,Here, In the frequency response plot of the impedance for a 2.5 m long transmission line, two resonances can be observed at about 19 MHz and 38 MHz. The maximum impedance at 19 MHz is caused by a mpedance  by(7)Z_As a result, the properties of the receiver are lost at the sensor. For the 2.5 m long transmission line, the second resonance at 38 MHz is due to a second Hence, for a line length of While the All resonance frequencies lengths are in good agreement with the theoretical values, which can be determined byc is the speed of light in vacuum, and Here, he RG174  cable ushe RG174 10)Z_RXZ_RXc is In this section, we address the output behaviour of the low-Z receiver. For the investigation, we performed measurements of the transmission coefficient For the direct attached assembly the stray capacitance Equation . Hence, To show the behaviour of the operational amplifier, we studied the signals The input impedance the line . ΓRX andIn this section, we present a noise analysis for the low-Z input stage in combination with transmission lines. Therefore, a SPICE simulation is performed, including the amplifier model provided by the manufacturer. Due to the inverting amplifier structure, the current noise at the positive input of the opamp causes no contribution to the noise voltage at the output. The current at the negative input flows over the feedback resistance For In this section, we address the matched receiver. ion line .Transfer coefficient The SPICE-based noise simulation results of the matched input stage including the capacitive Equation , where tThe LCR input stage has been suggested for a direct attached assembly of the sensor and the electronics . In contQ (Q factor) is given byIn this section, we address the design of an LCR receiver input stage, which has an input impedance of uency by ,56(19)fFor the application of the LCR input stage with a transmission line, we design the input stage to have an input impedance of ollowing , we desiQ factor is With a resistance of 1 Ω for The resulting frequency responses of the transfer coefficient for a direct attached assembly ( Devices  has been Devices ,58. The For the grey and red trends depicted in By adjusting In this section, we present measurements of ken from , where tQ factor of this receiver input network is For the measurements depicted in In this section, measurement results for the transfer coefficient A second peak with equal signal strength can be observed at the Q of the LCR receiver input stage. The diagram is the result of a SPICE simulation. For the line length of 2.5 m, the transfer coefficient Q, as this line length is close to the Q leads to higher input impedance of the receiver stage and hence to a reduced shunting of Q shows no distinct improvement, which we understand to be caused again by the detuning due to the line.As for the other receiver structures, we performed SPICE-based noise simulations for the LCR receiver. In the Simple realization feasibility of the circuits possible with moderate effort.Even small transmission line lengths show a transformation of Selection of frequency and transmission line length: the frequency and the transmission line length have to be matched to obtain a A Noise: Same noise floor as with direct connection for The low-Z receiver provides the best performance for a directly attached assembly. With transmission lines, only a Simple realization feasibility of the circuits possible with moderate effort. Variations of Selection of frequency and transmission line length. Due to the matched setup, the frequency can be selected independently from the transmission line length. The receiver provides a constant input impedance.Noise: the receiver structure shows an almost constant noise floor over the whole frequency range.The input impedance of this receiver leads to a minor reduction in the signal, which was addressed in Realization requires tuning of the input stage. The tuning is sensitive towards parasitic effects of the assembly and the opamp. Furthermore, a relevant sensitivity with respect to temperature changes was observed.Selection of frequency and transmission line length: the excitation frequency is a design parameter. If the input stage is tuned to Noise: the noise floor is also shaped by the LCR input stage. In a direct connection setup or a In contrast to the low-Z receiver, the LCR receiver provides a degree of freedom with respect to the length of the transmission line when it is matched. However, we found the tuning of the resonance circuitry to be quite sensitive. An automated tuning, e.g., by means of varactor diodes, as was suggested in ,30, for In the following, the relevant points are briefly discussed, and the coherence between the measurements and the simulation results is highlighted.The impedance Equation . Figure The constant input impedance depicted in In this section, we address the properties of the different receiver circuits by means of the SNR. This comparison naturally has to consider the different characteristics of the circuits; e.g., the low-Z receiver should only be used in a Due to this aspect, a comparison based on the definition of the SNRn obtainSNRdB=10ln obtainSNRdB=10lThe results for a cable length of 2.5 m are depicted by the dashed lines. As expected, the low-Z receiver reaches the same relative SNR for the For the matched LCR receiver, the relative SNR reaches the same level at the resonance frequency as for the directly attached assembly. This behaviour is expected, as the configuration is close to a The results of the relative SNR analysis provide a suitable assessment of the different receivers and are in agreement with the previous results. The low-Z and the Matched receiver show a well-defined behaviour, and the selection of a circuit falls back to the points addressed in In this paper, we have presented the analysis of different receiver topologies for capacitive measurement applications in combination with transmission lines. The analysis treats electrical and system aspects of the behaviour of the different circuit structures. The results definitely support the application of low-Z receiver structures or matched receiver structures. Low-Z receiver structures provide optimal behaviour in a