SQOR and TBCB for BW, MYH10 for YW, RLF for BYADG, and ARHGAP31 for 18MW). Moreover, we found 40 SNPs that had not previously been identified as being associated with growth traits in cattle. These findings will further advance our understanding of the genetic basis for growth traits and will be useful for the molecular breeding of BW, YW, BYADG, and 18MW in the context of genomic selection in beef cattle.Improving the genetic process of growth traits is one of the major goals in the beef cattle industry, as it can increase meat production and reduce the cost of raising animals. Although several quantitative trait loci affecting growth traits in beef cattle have been identified, the genetic architecture of these economically important traits remains elusive. This study aims to map single nucleotide polymorphisms (SNPs) and genes associated with birth weight (BW), yearling weight (YW), average daily gain from birth to yearling (BYADG), and body weight at the age of 18 months (18MW) in a Chinese Simmental beef cattle population using a weighted, single-step, genome-wide association study (wssGWAS). Phenotypic and pedigree data from 6022 animals and genotypes from 744 animals  were used for an association analysis. The results showed that 66 genomic windows explained 1.01–20.15% of the genetic variance for the four examined traits, together with the genes near the top SNP within each window. Furthermore, the identified genomic windows (>1%) explained 50.56%, 57.71%, 61.78%, and 37.82% of the genetic variances for BW, YW, BYADG, and 18MW, respectively. Genes with potential functions in muscle development and regulation of cell growth were highlighted as candidates for growth traits in Simmental cattle ( Bos taurus (Angus) and Bos indicus (Brahman) genotyped for 417 microsatellite markers and detected a total of 35 QTLs for growth traits . Buzanskas et al. [TRAF6, CDH11, KLF7, MIR181A-1 and PRCP) that were associated with growth traits in a population of 855 Charolais beef cattle genotyped for 76,883 SNPs. Although some progress has been made, the genetic architecture of these economically important traits remains poorly understood. Furthermore, the majority of GWASs for growth traits in beef cattle have only used a small sample of genotyped animals and low-density SNP arrays, which has limited the statistical power of the association analysis [Beef cattle provide a large proportion of the meat consumed by humans throughout the world . Improvis et al.  found 18The wssGWAS estimates the SNP effects using genomic estimated breeding values (GEBVs) by solving a blend of pedigrees and SNPs derived matrix H, which was used in weighted single-step genomic best linear unbiased prediction (wssGBLUP). This approach can make full use of genealogical information and phenotypes of genotyped and nongenotyped animals . The weiAll animals used in the current study were treated following the guidelines for the care and use of experimental animals established by the Ministry of Agriculture and Rural Affairs of China. The ethics committee of the Science Research Department of the Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS)  approved this study. The approval ID/permit numbers are SYXK (Beijing) 2008-007 and SYXK (Beijing) 2008-008.The animals used in this study originated from 12 Chinese Simmental beef cattle core farms. These cattle were raised in different regions of China that participated in the national joint beef cattle breeding and genetic improvement program. In brief, a total of 6022 Simmental beef cattle  born from 2001 to 2019 were used in this study. Among them, 6022 animals were used in wssGWAS for BW; 3996 individuals were used in wssGWAS for YW and BYADG; 3137 animals were used in wssGWAS for 18MW. Genealogical information was available for all Chinese Simmental beef cattle . The animals born from 2018 to 2019 were only used in the BW association analysis because many of these cattle lacked phenotypic records for YW and 18MW. Yearling weight and 18MW of Simmental beef cattle was recorded at about 360 ± 30 days and 540 ± 30 days of age, respectively. Average daily gain from birth to yearling was calculated by subtracting the birth weight from the yearling weight and dividing by the number of days during this period. For the four traits under study, outliers beyond three standard deviations were removed before the association analysis.p < 10−6) were removed. In addition, SNPs were also excluded if they were located on the sex chromosomes or had no positional information. After the QC, a final set of 596,297 SNPs for 744 Simmental beef cattle were retained for subsequent analyses.A total of 744 Chinese Simmental beef cattle was genotyped using Illumina BovineHD BeadChips, which contained 777,962 SNPs. Quality control (QC) procedures were conducted using the PLINK v1.07 software  . Individy represented a vector of phenotypic observations; b was the vector of fixed effects. In this study, sex, year of birth, use types , and farms were treated as fixed effects for all traits. In addition, age (in days) was included in models for YW, 18MW and BYADG as fixed effects. a was the vector of additive genetic effects and e denoted the residuals; and W and Z were the incidence matrices of b and a, respectively. It was assumed thatH was a blend of pedigrees and the SNP derived matrix and I denoted the identity matrix. The inverse of matrix H was calculated as follows:A denoted the numerator relationship matrix based on the pedigree for all individuals; A22 was the numerator relationship matrix for the genotyped animals; and the G matrix was a genomic relationship matrix that was constructed as described by Vanraden [Z was an incidence matrix adjusted for allele frequencies, and D denoted a diagonal matrix of weights for SNP variances. M was the number of markers, and ith SNP. The SNP effects and weights for wssGWAS were calculated iteratively as follows [t = 1,In the first iteration, set Estimate GEBV for all animals using ssGBLUP approach;Compute SNP effects asi was the ith SNP;Calculate SNP weights for the next iteration usingNormalize SNP weights to keep the total genetic variance constant asGt+1)G=ZDZ follows :In the ith SNP window was calculated as:ith window consisting of 20 adjacent SNPs; jth SNP within the ith window. Because the windows size is 20, the proportion of variance assigned to SNP 1 is calculated from SNP 1 to 20, for SNP 2 it goes from 2 to 21, and so forth. Therefore, the SNP that contributed approximately equally to the 20-adjacent-SNP window was defined as the most important marker (top SNP).In this study, the procedure was run for three iterations as used in Wang et al.  and the p < 0.05) and genes involved in biological processes were highlighted [Genomic windows that explained more than 1.0% of the genetic variance were selected as possible QTL regions associated with growth traits in Chinese Simmental beef cattle. Genes were searched using the Ensembl database  based onhlighted .Descriptive statistics of the observed phenotypes are shown in We performed a wssGWAS in Simmental beef cattle populations to map genetic markers and genes associated with BW, YW, BYADG and 18MW. The wssGWAS results were represented by the proportion of genetic variance explained by windows of 20 successive SNPs . GenomicSQOR). SQOR is a protein coding gene that may interact with the inner mitochondrial membrane in a monotopic fashion and catalyze the mammalian metabolism of H2S (hydrogen sulfide) in human [2S may function in skeletal muscle wasting/fibrosis as a result of metabolic complications (such as from obesity), which implied that the features of H2S reversed muscle damage and moderated metabolic myopathy. The second most important window  was located inside the tubulin folding cofactor B (TBCB) gene, which plays a role in modulating cytoskeletal activity [TBCB gene as a potential candidate for BW in cattle is reasonable [Analysis was undertaken of the association with BW identified 18 genomic windows that were located on BTA1, 2, 3, 7, 10, 13, 16, 17, 18, 20, 21, 22, and 27. The identified genomic windows explained 1.07–7.89% of the additive genetic variances for BW. Genes nearest to the peak SNPs within each window were treated as potential associated candidates with BW . Among tin human . Veerankasonable . BW is aasonable . ResultsMYH10) gene. The MYH10 gene is a member of the myosin superfamily which shares the common features of ATP hydrolysis (ATPase enzyme activity), actin binding, and potential for kinetic energy transduction [MYH10 is isolated from muscle cells and with functions in contractile, it is also related to myosin in nonmuscle cells [MYH10 takes part in a pathway related to growth and development in chickens and significantly upregulated the expression pattern at the transcript level. Furthermore, we evaluated the LD pattern of the SNPs in the region around 28.47–28.90 Mb on BTA19. The LD analysis revealed that the region located on MYH10 showed a high LD level between the top SNP and nearby SNPs, implying a potential selection signature with regard to YW in Simmental beef cattle  were identified  were associated with YW . The prosduction . Myosin sduction . MYH10 ile cells . Moreovele cells  found thf cattle a. Therefentified . ResultsFAM183A) and rearranged L-Myc fusion (RLF). FAM183A has been reported to play a role in autosomal recessive intellectual disability and is expressed in the human brain [FAM183A plays a role in influencing growth traits in domesticated animals and even in the mouse, therefore, further functional studies are required. RLF encodes a Zn-15 related zinc finger protein and has a general role in transcriptional regulation of fetal and adult tissues in humans [RLF has been reported to play a role in increasing DNA methylation at a number of elements related to transcriptional regulation and is involved in maintaining epigenetic marks at CpG island shores and enhancers [Notably, three windows located on BTA3  were found to be associated with both YW and BYADG, implying a pleiotropic effect for growth traits in beef cattle. Two genes which were adjacent to the top SNP within each window were mined, namely, family with sequence similarity 183 member A . ARHGAP31 encodes a GTPase-activating protein (GAP) and plays a role in regulating the cellular processes of cycling between an inactive GDP-bound and active GTP-bound conformation [ARHGAP31) and its surrounding SNPs in gene transmembrane protein 39A (TMEM39A) (TMEM39A in growth needs further investigation.ormation . GAP hasormation . MoreoveTMEM39A) b. The poCLIC5), aldehyde dehydrogenase 1 family member A3 (ALDH1A3), and MYH10 genes; calcium-dependent cell-cell adhesion via plasma membrane cell adhesion molecules (GO: 0016339) consisting of cadherin 13 (CDH13) and neuroligin 1 (NLGN1) genes; motor neuron axon guidance (GO:0008045) including activated leukocyte cell adhesion molecule (ALCAM) and forkhead box P1 (FOXP1) genes. Given the potential roles of the three GO terms in biological processes, their involvement in the growth traits were further analyzed. We searched genes function based on literature reports. Then, the MYH10, CDH13, and FOXP1 genes were highlighted as the main candidates for the growth traits of the three terms, respectively. Notably, MYH10 gene has been highlighted as a strong candidate in the association analysis for YW and additional laboratory functional experiments would be needed. CDH13 gene encodes a member of the cadherin superfamily and acts as a negative regulator of axon growth during neural differentiation [FOXP1 gene plays an important role in the regulation of tissue and cell type-specific gene transcription during both development and adulthood and controls adipocyte differentiation [In an attempt to better understand the biological processes and pathways shared by the trait-associated genes, we searched 51 genes near the SNPs within each window of the four growth traits. We then performed KEGG and GO enrichment analysis. Three GO terms and no KEGG pathways were enriched for the growth traits analyzed. The enriched GO terms are involved in neuromuscular processes controlling balance (GO: 0050885) consisting of chloride intracellular channel 5  using the GWAS strategy ,41. HoweSQOR and TBCB for BW, MYH10 for YW, RLF for BYADG, and ARHGAP31 for 18MW. Specifically, the identified genomic regions will be useful for the genetic improvement of growth traits by allowing the associated SNPs to be assigned with higher weights in genomic selection.In conclusion, we identified 66 nonredundant windows which explained 1.01–20.15% of the additive genetic variance for growth traits in Chinese Simmental beef cattle using the wssGWAS approach. Genes with potential functions in muscle development and regulation of cell growth were highlighted as candidates for growth traits in cattle, such as


Brassica napus L.) is an important oil crop world-widely cultivated, and seed oil content (SOC) is one of the most important traits for rapeseed. To increase SOC, many efforts for promoting the function of genes on lipid biosynthesis pathway have been previously made. However, seed oil formation is a dynamic balance between lipid synthesis and breakdown. It is, therefore, also reasonable to weaken or eliminate the function of genes involved in lipid degradation for a higher final SOC.Rapeseed  gene, that was associated with SOC. In particular, six single-nucleotide-polymorphisms (SNPs) in the promoter region of BnaC07g30920D were associated with the significant reduction of SOC, leading to a 4.7–6.2% reduction of SOCs. We performed in silico analysis to show a total of 40 PTLs, which were divided into four clades, evenly distributed on the A and C subgenomes of Brassica napus. RNA-seq analysis unveiled that BnPTLs were preferentially expressed in reproductive tissues especially maturing seeds.We applied a genome-wide association study (GWAS) on SOC in a collection of 290 core germplasm accessions. A total of 2,705,480 high-quality SNPs were used in the GWAS, and we identified BnaC07g30920D, a BnPTL gene, that was associated with SOC using GWAS and performed in silico analysis of 40 PTLs in Brassica napus. The results enrich our knowledge about the SOC formation in rapeseed and facilitate the future study in functional characterization of BnPTL genes.We identified The online version contains supplementary material available at 10.1186/s12870-020-02774-w. Brassica napus L., AACC, 2n = 38) is the third-largest source of vegetable oil in the world after soybean and palm [Rapeseed , LEC2, ABA INSENSITIVE3, FUSCA3 and WRINKLED1 are key regulators of seed FA biosynthesis and are expressed in seeds. Their expressions overlap various developmental stages [Seed oil accumulation is a dynamic balance between lipid biosynthesis and breakdown. Lipid biosynthesis in plant seeds is activated after seed embryo formation and it involves a series of pathways, such as carbohydrate metabolism, fatty acids (FAs) elongation, triacylglycerol (TAG) synthesis and oil-body formation . In briel stages , 9.The accumulation of lipids in rapeseed is not a unilateral synthesis process but a dynamic balance between anabolism and catabolism. Seed lipids reduce at the late stage of seed maturation at an approximate rate of 10% on average . The freBnSFAR1 to BnSFAR5 belonging to the GDSL family are involved in the decomposition of lipids in developing seeds of rapeseed, and thus reduced SOC [It is estimated that the reduction of SOC at the late maturation stage of rapeseed results in a loss of about 20 million tons of oil per year . A previuced SOC . HoweverPatatin is a single lipolytic acyl-hydrolase with broad substrate specificity, firstly found in potato tubers in 1970 . InsteadIn the past decades, quantitative trait locus (QTL) mapping unearthed plenty of information on the molecular mechanism controlling the formation of total FAs , 25 and BnaC07g30920D, a BnPTL gene, that was significantly associated with SOC. We analyzed the expression of Bna.C07.PTL in various tissues, and performed in silico analysis to show an overall distribution of the PTL family in the rapeseed genome.To better understand the genetic control of SOC in a polyploid genome of rapeseed, we performed GWAS on SOC in a collection of 290 core germplasm accessions representing a total of 991 genetic accessions originated from 39 countries of the world. We identified P < 0.01) (Table We measured the SOC of the 290 core accessions Table S at two lp < 10− 5) were mapped on chromosome C07 (Chr.C07) and random chromosome An (Chr.Ann) for the experiment at CX -like protein. The SNP, ChrC07_35249208, on Chr.C07 was linked (R2 = 0.68) with BnaC07g30920D  region, and 2 SNPs within introns , we performed GWAS on SOC in a population including 290 accessions at CX and JH. The Manhattan Plots show that most significantly associated SNPs  associated with SOC  and AGRIS (https://agris-knowledgebase.org/), and identified the SNPs such as T/G  and A/T  locating in the CAAT-box, which associated with SOC changes.To know the effect of these SNPs on SOC, we performed single SNP association tests for the 55 SNPs among the 290 accessions with different SOCs. The test showed that six SNPs, namely T/G of ChrC07_35,181,691, C/T of ChrC07_35,181,026, C/T of ChrC07_35,179,748, A/T of ChrC07_35,179,634, A/G of ChrC07_35,179,572 and A/G of ChrC07_35,177,247 on the 5′-end regulatory region of BnaC07g30920D was analyzed based on two transcriptome datasets  in the seeds at 16 DAP, 40 DAP and in germinating seeds 20 HAS . An InterPro search identified a total of 40 genes containing IPR002641, which were annotated as PTLs. Information such as locus ID, genomic sequence length, coding sequence (CDS) and protein size of putative PTLs as well as the number of their exons and introns were provided in Table SBnPTLs ranged from 1411 to 8211 bp, 873 to 4278 bp, and 290 to 1425 aa, respectively  distributed on random chromosomes, the remaining 36 BnPTLs were mapped on chromosomes A01-A10 and C01-C09. Notably, there were no BnPTLs located on chromosomes A02, A06 and C05  located on Chromosome A03, whereas, there was merely one BnPTL homolog which located on chromosomes A04, A05, A08, A10, C02, C06 and C09  and 11 (27.5%) BnPTLs had three and seven exons interrupted by two and six introns, respectively (Table SBnaA05g06080D), 6 exons (3 genes), 8 exons , 10 exons , 13 exons  or 18 exons (2 genes), respectively.Gene structure analysis showed that the number of exons of y Table S, Fig. 5.BnPTLs, we carried out a phylogenetic analysis of BnPTLs. In brief, BnPTLs could be divided into four clades (I-IV)  than those in Clade I and Clade III (3.1 on average) (Table STo elucidate the evolutionary relationship of IV) Fig. . The Clay Table S, Fig. 5.) Table S, Fig. 5.BnPTLs, we analyzed the conserved domain of BnPTLs. The catalytic regions consisting of DGGGXX, elements for phosphate- or anion-binding, and GXSXG which is the esterase box, were built up with 20–30 amino acids and length of the sequences varied among different clades, suggesting various biological functions of the BnPTL homologs  and wilting pistils (362 on average), while the expression of BnPTLs in stems (73 on average), roots (112 on average) and leaves (52 on average) were relatively low, indicating that BnPTLs were expressed preferentially in reproductive tissues. Among 40 BnPTLs, extremely significant expression of some genes such as BnSDP1 (BnaCnng10420D) in wilting pistils (1991) and BnSDP1-Like  in stamens (984), could be observed, suggesting their potential roles in these tissues  in 40-DAP seeds  > 1). On the other hand, the expression of six genes was downregulated in the 40-DAP seeds relative to the 16-DAP seeds (defined by log2 (expression folds) < 1) , namely SFAR1 to SFAR5 belonging to the GDSL family were involved in the decomposition of lipids in developing seeds of Arabidopsis thaliana, and thus reduced SOC and changed fatty acid composition [BnSFAR1 and BnSFAR4 were successfully silenced by random chemical inducement , and BnSFAR4 and BnSFAR5 were edited by CRISPR-Cas9 mediated technology. Mutant lines with up to 12.1% higher SOC by TILLING and 14.5% higher SOC by CRISPR-Cas9 were created [PTL (BnaC07g30920D) as a candidate gene, which might involve in degrading lipids. We excluded the other 33 genes either from their annotations  is a TAG lipase which is present in many plants such as  tissues , 42–44. DENSE AND ERECT PANICLE3 (DEP3), a PTL in rice, was found to play a significant role in the formation of vascular bundles in rice [pPLAIIIδ had a marked impact on auxin-responsive cell morphology and organ size in Arabidopsis and B. napus [VdPLP, a PTL in Verticillium dahliae, was involved in cell wall integrity and required for pathogenicity [BnPTLs in rapeseed into four clades in light of their gene structure and nucleotide sequence similarity. However, the detailed functions of most members of the BnPTL family remain to be explored.In general, lipases are acyl-hydrolases hydrolyzing the ester bond of mono-, di- and triglycerides (TAGs) into fatty acids and glycerol . Lipases in rice ; pPLAIIIB. napus ; VdPLP, genicity . We clasBrassica napus) germplasm collection. A total of 5.56 million SNPs and 1.86 million indels were identified by mapping the reads to a reference genome (‘Darmor-bzh’) [BnPTL genes.In a recent study, we reported the resequencing of 991 accessions of a world-wide rapeseed (or-bzh’) . In thisor-bzh’) , 53–55, BnPTL gene was identified to be associated with SOC using a GWAS on SOC in a collection of 290 core germplasm accessions. A total of 40 BnPTL genes were characterized and divided into four clades, evenly distributed on the A and C subgenomes of B. napus. Moreover, RNA-seq analysis unveiled that BnPTLs were preferentially expressed in reproductive tissues especially maturing seeds. These results enrich our knowledge about the SOC formation in rapeseed and provide more information for future study in functional characterization of BnPTL genes in B. napus.Patatin-like lipases (PTLs) are widely present in plants and the functions of most PTLs are little understood. In the present study, a https://gbis.ipk-gatersleben.de/gbis2i/faces/index.jsf) in Gatersleben, Germany, and the rest were from the Provincial Key Laboratory of Crop Gene Resources of Zhejiang University in China. The information about their ID, country origin and SOC of each accession was listed in Table SThe 290 core accessions of rapeseed germplasm for GWAS were selected from a worldwide germplasm collection of 991 accessions . A part http://www.ibm.com/cn-zh/analytics/spss-statistics-software; V21.0.0.0). Variations in SOC were analyzed by analysis of variance (ANOVA).Mature seeds of the 290 core accessions were harvested for SOC measurement. SOCs were determined using near-infrared spectroscopy . Three biological replicates of each accession were measured. Phenotypic analysis including the mean, standard deviation, correlation coefficient, and minimum and maximum values of SOC from 290 accessions were calculated and analyzed using SPSS  [http://www.maizegenetics.net/tassel) [p-value of each SNP was calculated with -log10p > 5 as the suggestive threshold. Seventy-five kilobase sequence regions adjacent to significantly associated SNPs were searched for associated genes. The R package “LDheatmap”  was used to describe the degree of correlation between significantly associated SNP and candidate genes.A total of 2,705,480 high-quality SNPs  among the 290 core accessions were extracted and used for GWAS. The K-value, which represents the genetic relations between samples, was calculated by Plink software  . TASSEL /tassel)  with an B. napus  were retrieved from Brassica napus Genome Browser in GENOSCOPE (http://www.genoscope.cns.fr/brassicanapus/) [http://www.ebi.ac.uk/interpro/search/sequence/; V79.0) [BnPTLs across the rapeseed genome was visualized using MapGene2Chromosome V2.0 (http://mg2c.iask.in/mg2c_v2.0/).The full genome sequence, protein sequences and their locations of anapus/) . Protein; V79.0)  with thehttp://linux1.softberry.com/berry.phtml?topic=plantprom&group=data&subgroup=plantprom) [https://agris-knowledgebase.org/) [We BLAST the nucleotide sequence 5 Kb upstream to the start codon in the databases PlantProm (antprom)  and AGRIse.org/) .BnPTL protein sequences using ClustalW of the Molecular Evolutionary Genetics Analysis (MEGAX) program [BnPTL protein sequences in MEGAX. The gene structure and phylogenetic tree were combined using Gene Structure Display Server V2.0  [BnPTLs were searched based on the previous study [Multiple alignments were performed for  program . The unredu.cn/) . The conus study .B. napus cultivar ‘ZS 11’ which was released in the previous study [http://www.ncbi.nlm.nih.gov/) (ID: PRJNA394926). DESeq2 R package  [BnPTLs was constructed with Morpheus (http://software.broadinstitute.org/morpheus).Transcriptome data from 12 tissues of the us study  were ret; V3.11)  was applAdditional file 1: Figure S1. The frequency distribution for SOC of 290 rapeseed accessions in CX (A) and JH (B).Additional file 2: Figure S2. Manhattan plots of GWAS for CX (A) and Jinhua (B). The blue line represents a significant threshold (−log10p = 5). The black triangle indicates the SNP, ChrC07_35249208, associated with BnaC07g30920D.Additional file 3: Figure S3. Quantile-quantile (Q-Q) plots of GWAS for CX (A) and JH (B). The Y-axis is the observed negative base 10 logarithms of the P-values and the X-axis is the expected observed negative base 10 logarithms of the P-values under the assumption that the P-values follow a uniform  distribution.Additional file 4: Figure S4. The association of allelic changes in the CDS region and intron of BnaC07g30920D with SOC. ChrC07_35175173 (A) and ChrC07_35175214 (B) were located in introns, while ChrC07_35175691 (C) and ChrC07_35176144 (D) were located in the CDS region.Additional file 5: Table S1. The accession codes, names, origins of the 290 genetic materials and their SOC at two experimental sites.Additional file 6: Table S2. The SNPs associated with SOC on Chr.C07 and associated gene IDs on Chr.C07.Additional file 7: Table S3. The SNPs across BnaC07g30920D.Additional file 8: Table S4. The genome-wide analysis of 40 BnPTLs in rapeseed.Additional file 9: Table S5. The expression values  of BnPTLs in 12 different tissues of rapeseed.Additional file 10: Table S6. The expression values  of BnPTLs at different seed developmental stages of rapeseed.


Historically, dietary interventions that elevate βHB production by the liver, such as high-fat diets and partial starvation, have been used to treat chronic disease with varying degrees of success, owing to the potential downsides of such diets. The recent development of an ingestible βHB monoester provides a new tool to quickly and accurately raise blood ketone concentration, opening a myriad of potential health applications. The βHB monoester is a salt-free βHB precursor that yields only the biologically active d-isoform of the metabolite, the pharmacokinetics of which have been studied, as has safety for human consumption in athletes and healthy volunteers. This review describes fundamental concepts of endogenous and exogenous ketone body metabolism, the differences between the βHB monoester and other exogenous ketones and summarises the disease-specific biochemical and physiological rationales behind its clinical use in diabetes, neurodegenerative diseases, heart failure, sepsis related muscle atrophy, migraine, and epilepsy. We also address the limitations of using the βHB monoester as an adjunctive nutritional therapy and areas of uncertainty that could guide future research.Much of the world's prominent and burdensome chronic diseases, such as diabetes, Alzheimer's, and heart disease, are caused by impaired metabolism. By acting as both an efficient fuel and a powerful signalling molecule, the natural ketone body,  As 1.75 grams of muscle protein must be broken down to produce 1 gram of glucose, lean tissue mass would quickly atrophy in order to feed the glucose-deprived brain in early starvation [Having a higher H:C ratio than pyruvate 2 vs. 1.3), ketone bodies are more reduced, so can yield more free energy per mol of oxygen to fuel ATP production , ketone . More th2 and acetone (both fat-soluble molecules that will diffuse out of hepatocytes and exit the body through ventilation), or be reduced by βHB dehydrogenase into βHB, which exits through the MCT [In fasting, the combination of low insulin with high cortisol and glucagon stimulates adipocytes to release non-esterified fatty acids into the bloodstream. Fatty acids are taken up by hepatocytes, which is where ketogenesis takes place . Acetyl- the MCT .At target tissues, βHB is oxidised into AcAc in the reverse reaction catalysed by βHB dehydrogenase. Next, in the rate-limiting step of ketolysis, succinyl-CoA-3-oxaloacid CoA transferase (SCOT) converts AcAc and succinyl-CoA into AcAc-CoA and succinate. AcAc-CoA is then cleaved by ACAT to yield two molecules of Acetyl-CoA that can enter the Krebs cycle. As an economic survival adaptation, hepatocytes do not express the rate-limiting enzyme, SCOT, and are thus incapable of metabolising the ketone bodies they produce . Also, iBoth AcAc and βHB are excreted by the kidneys and, notably, their reabsorption is increased in extended starvation. Importantly, although ketones are indeed acidic, acid-base disturbances in diabetic ketoacidosis are not caused by ketone body accumulation , but by volume contraction and changes in bicarbonate and electrolyte concentrations ,15.Ketogenesis occurs in response to metabolic demand for oxidative fuel when carbohydrate stores are low, a state communicated to adipocytes as a low insulin, high cortisol, high glucagon state. βHB also auto-regulates its own production by inhibiting lipolysis via PUMA-G nicotinic receptors on adipocytes  and decr+/NADH-coupled near-equilibrium reaction and that the ratio of AcAc/βHB is proportional to the mitochondrial NAD+/NADH ratio. In other words, the activity of βHB dehydrogenase modulates the mitochondrial redox potential and ensures ketone bodies are terminally oxidised in proportion to the cellular ATP demand [+-coupled transporters, the NAD+/NADH ratio in the target tissues influences cellular uptake and oxidation proportionally to their energetic needs [It is worth noticing that the conversion of AcAc into βHB, or vice versa, occurs in an NADP demand . Furtheric needs .d-form of βHB and 50% the l-form of βHB [d-form of βHB and is salt-free.As previously described, the production of ketone bodies is a dynamic process with multiple inputs and regulatory points. Therefore, is not possible to accurately set blood βHB concentrations with the interventions that rely on endogenous ketosis. The advent of exogenous sources of ketone bodies, such as the salts and esters, has enabled the accurate manipulation of ketone body blood concentration and introduced an entirely new metabolic state: exogenous ketosis . Howeverm of βHB , which im of βHB . MoreoveA notable ‘middle point' between endogenous and exogenous ketogenic interventions is the consumption of medium-chain triglycerides (MCTS) . After iAfter ingestion, the βHB monoester bond is cleaved by esterases in the gut wall, yielding βHB and butanediol in equal amounts. Both are absorbed into the portal circulation and the latter is taken up by the liver, where it is converted into βHB by alcohol dehydrogenase (ADH). βHB leaves the hepatocytes via the MCT. Pharmacokinetic studies have shown that, in the fasted and resting states, the βHB monoester can induce ketoses for 3–4 h, with a peak at ∼1 h that is dose-dependent . The metTo date, most of the studies involving the βHB monoester in humans have focused on exercise in athletes, but the rationale behind this approach is outside the scope of this review . AlthougArguably, the most exciting applications of the βHB monoester are therapeutic because many diseases are caused or exacerbated by energy deficits or abnormal metabolic regulation, thus inducing ketosis may be beneficial . In the Ketogenic diets have been used as a treatment for epilepsy for centuries . AlthougThere is still some debate on whether the mechanism for the ketogenic diet effect is due to the elevated ketone bodies themselves. Mouse studies have suggested that changes in fat metabolism and/or the composition of the microbiome may underly the benefits . On the Neurodegenerative diseases are multifactorial with complex underlying pathophysiological mechanisms. Not surprisingly, therefore, the standard monotherapeutic drug approach to conditions such as Alzheimer's and Parkinson's diseases has consistently failed to produce therapeutic or preventative treatments.By addressing several of the key pathological mechanisms underlying neurodegenerative diseases simultaneously, ketogenic nutrition, including using a βHB monoester drink, could slow or halt the progression of conditions such as Alzheimer's and Parkinson's diseases. A more comprehensive and detailed review can be found elsewhere ; howeverFirst, neurodegenerative diseases are marked by defects in mitochondrial metabolism, such as decreased ATP production, increase ROS production, complex IV dysfunction in Alzheimer's disease and complex I dysfunction in Parkinson's disease . KetonesSecond, neurodegenerative diseases almost universally feature neuroinflammation. By inhibiting pathologic microglial activation  and key Third, ketones can protect against the toxicity of neurotoxins, such as Aβ oligomers and MPP+ in models of Alzheimer's and Parkinson's, respectively .It is unlikely that a single intervention  can alone salvage or repair a severely diseased brain. However, by combining interventions, along with other lifestyle changes , it may be possible to develop a multifaceted approach to effectively treat these debilitating diseases. A study involving a monthly intervention of the βHB monoester in people living with Parkinson's disease is currently in progress .Today, cardiovascular disease is the most frequent cause of death . Ketone In recent years, ketogenic diets have yielded positive results, to the point of reversing type 2 diabetes in 54% of patients in one 2-year study . HoweverArguably, ketone metabolism evolved to prevent muscle atrophy during prolonged catabolism  and the The βHB monoester is a promising treatment for of numerous diseases. However, more research is needed to describe its safety for specific patient populations, to assess the magnitude of its clinical benefits, and to increase our understanding about the underlying mechanisms behind its therapeutic effects. Additionally, allowing the quick and accurate manipulation of βHB concentration in the blood, it is a useful tool for ketone metabolism research.Since most pathologies are induced or aggravated by energy deficits or abnormal metabolic regulation, the induction of ketosis may be broadly beneficial. However, safety and accurately increasing blood βHB concentration is difficult with most ketogenic interventions, the exception being the βHB monoester.Importance: Yielding only the d-isoform of βHB and being salt-free, the βHB monoester is a promising ketotherapeutic. Additionally, its pharmacological profile has been thoroughly studied.Current thinking: The safety and tolerability of consuming the βHB monoester, three times per day for one month, has been demonstrated in healthy volunteers [lunteers ; howeverlunteers  and Parklunteers  patientsFuture directions: As discussed, among the most exciting clinical applications of the βHB monoester are chronic diseases. However, due to its half-life, these applications would require patients to consume drinks frequently in order to sustain a fasting-like blood βHB concentration for a prolonged time. Additionally, the current price of chronically sustaining exogenous ketosis with the βHB monoester would impose an enormous financial burden for most patients and health systems. More research is needed to assess disease-specific cost-benefit ratios. Finally, despite the good tolerability reported in some studies, the bitter taste may impair adherence, particularly in children, the elderly and the critically ill.


Sub1A-1 allele from variety FR13A. Therefore, we analysed the allelic sequence variation in three Sub1 genes in a panel of 179 rice genotypes and its association with submergence tolerance. Population structure and diversity analysis based on a 36-plex genome wide genic-SNP assay grouped these genotypes into two major categories representing Indica and Japonica cultivar groups with further sub-groupings into Indica, Aus, Deepwater and Aromatic-Japonica cultivars. Targetted re-sequencing of the Sub1A, Sub1B and Sub1C genes identfied 7, 7 and 38 SNPs making 8, 9 and 67 SNP haplotypes, respectively. Haplotype networks and phylogenic analysis revealed evolution of Sub1B and Sub1A genes by tandem duplication and divergence of the ancestral Sub1C gene in that order. The alleles of Sub1 genes in tolerant reference variety FR13A seem to have evolved most recently. However, no consistent association could be found between the Sub1 allelic variation and submergence tolerance probably due to low minor allele frequencies and presence of exceptions to the known Sub1A-1 association in the genotype panel. We identified 18 cultivars with non-Sub1A-1 source of submergence tolerance which after further mapping and validation in bi-parental populations will be useful for development of superior flood tolerant rice cultivars.Erratic rainfall leading to flash flooding causes huge yield losses in lowland rice. The traditional varieties and landraces of rice possess variable levels of tolerance to submergence stress, but gene discovery and utilization of these resources has been limited to the  Indian sub-continent and South-East Asia are the most important centers of diversity for rice (Oryza sativa L.), where rice is widely cultivated on the flood plains of the major river basins and deltas. Rice cultivation in these areas is not only crucial for the world food security but is also closely associated with the tradition, culture and customs of the people3. It is a semi-aquatic plant that can be grown in a wide range of environments varying from dry rain-fed upland to deep-water lowland rice ecosystems. Submergence at vegetative stage is quite often followed by reproductive stage drought in the rain-fed lowland river plains of India and South-East Asia. The majority of rice growing areas in this region suffers from abiotic stresses such as drought, flooding and salinity4. Worldwide, 15 million hectares of rice suffers from flooding stress of one kind or other causing huge economic losses5. Different kinds of flooding include: (i) flooding during seed germination, (ii) complete submergence for 1–2 weeks due to flash flooding at seedling or tillering stage, (iii) stagnant water flooding of 25–50 cm in the areas with poor drainage, near the banks of overflowing rivers, (iv) saline water flooding in the coastal and tidal areas, and (v) deep water flooding lasting for a prolonged period of more than a month with water levels ranging from a 50 cm to several meters6. Traditional varieties and landraces of rice are available for cultivation in each of these situations. These are low yielding but can be useful source of genes for breeding high yielding stress tolerant rice cultivars. Several QTLs and genes for different kinds of flooding stress have been identified from these germplasm16. During 1970s hundreds of landraces of rice collected from the flood-prone areas of the world were screened for flash flood submergence tolerance at IRRI. Consequently, FR13A, FR43B, Kurkarruppan, Goda Heenati, Thuvalu, etc. were recognized as flash flooding tolerant.Rice is the most important staple food crop for more than half of the global population and accounts for approximately 20% of the daily food calorie intakeSUB1) on the chromosome 917. This was followed by map-based cloning of three ethylene response factor-like genes, Sub1A, Sub1B, and Sub1C, and demonstration of the role of Sub1A in submergence tolerance by genetic transformation of a sensitive variety Liaogeng19. Analysis of 21 rice accessions showed that all of them possessed Sub1B and Sub1C genes, but only a sub-set of 12 cultivars possessed the Sub1A gene, responsible for the submergence tolerance in FR13A19. The Sub1A positive varieties possessed one of the two alleles, Sub1A-1 or Sub1A-2, which were associated with the tolerant or intolerant phenotype, respectively19. At the protein sequence level, the only difference is that Sub1A-1 allele codes for amino acid Ser186, whereas Sub1A-2 codes for a Pro186 at this position, the remaining protein is identical. Phylogenetic analysis of the Sub1 genes from wild rice accessions has revealed that Sub1A arose from the duplication of Sub1B gene20. However, there are limited studies on sequence variation of the Sub1B and Sub1C genes and Sub1A-independent mechanisms of submergence tolerance in flood-tolerant rice germplasm. Earlier, a set of 109 rice genotypes including A and C genome wild rice species were analyzed for stem elongation index and percent survival after 14 days of submergence followed by seven days of recovery and identified Sub1A-dependent and Sub1A-independent mechanisms of survival21. Further, a set of 160 rice varieties representing deep water, flood and tidal prone areas were analyzed for haplotype variation in the Sub1A and Sub1C genes using CAPS markers to identify three and four haplotypes, respectively22. Recently, Zhao et al.23 developed a pan-genome of rice based on deep coverage re-sequencing of 66 diverse rice genotypes, representing different rice cultivar groups and 14 Oryza rufipogon accessions, but they analyzed the presence of Sub1A gene only and not Sub1B and Sub1C genes in the set.Aus cultivar FR13A was found to be one of the best sources for submergence tolerance. Genetic studies using a population derived from FR13A identified a major QTL Submergence 1 , International Rice Research Institute (IRRI), Philippines and National Bureau of Plant Genetic Resources (NBPGR), New Delhi, India, along with additional genotypes obtained directly from rice breeders and farmers for population structure and sequence variation in the Sub1 genes. A set of 179 diverse rice accessions was assembled and screened for vegetative stage submergence tolerance at multiple locations to identify new donors. Further, the three Sub1 genes were amplified and sequenced to obtain SNP haplotype information and its possible association with the performance of the varieties under submergence stress.Submergence tolerant rice cultivars have been developed in the background of several popular mega varieties of rice and deployed for commercial cultivation across South and South-East Asia using marker-assisted backcross breeding to transfer 0.8 to 7.8 Mbp of the donor chromosome 9 segment around the A set of 179 diverse rice genotypes was used  from each of the 12 rice chromosomes27. The 36-plex premixed assays were manufactured and validated by Sequenom Corporation (San Diego). The 30-mer pre-amplification primers and variable length genotyping primers were designed by AssayDesign 3.1 software. Sequenom MassARRAY Typer v3.4 Software was employed for SNP visualization and allele calling. The STRUCTURE v2.3.1 software was used to infer ancestral lineages and clusters of similar genotypes28. The clustering of genotypes were run for a range of K values from 1 to 9 with the admixture model and correlated allele frequency, replicated three times for each K value. A burn-in time span of 25,000 steps followed by 100,000 Monte Carlo Markov Chain replicates was implemented for each run. The ΔK values were plotted with Ln(PD) derived for each K value to generate Evanno plot29. Structure harvester software was used to calculate the optimum population structure (http://taylor0.biology.ucla.edu). The proportion of genome of an individual that belongs to each inferred population (admixture) was also estimated. A dendrogram of the 179 rice cultivars was constructed based on their 36 SNP scores. Concatenated SNP markers were aligned using ClustalW programme inbuilt in the BioEdit tool30. The tree was constructed using neighbor-joining method and phylogeny of the tree was tested using interior-branch test approach with 10,000 bootstrap replications. The genetic distance between sequences were analyzed using proportional (p) distances algorithm inbuilt in the MEGAX software31. The developed tree was visualized in figtree v1.4.3 programme (http://tree.bio.ed.ac.uk/software/figtree/)32.DNA extraction was performed using QIAGEN DNeasy Plant Mini Kit  from de-husked rice seeds as per manufacturer’s protocol. Sequenom MassARRAY system was used for the SNP genotyping . The Sequenom MassARRAY multiplex assay, based on iPLEX gold chemistry, was designed for 36 SNPs, representing conserved single-copy rice genesSub1 genes was downloaded from the NCBI GenBank  and PCR primers were designed using the Primer3 software . Gene-specific PCR marker AEX1 was used to crosscheck the presence of Sub1A-1 allele25. The haplotype information based on high-quality SNPs in the three Sub1 genes was compiled manually for each rice genotype. Haplotype networks were constructed for analysis of genealogical relationship using Network software33 and haplotype diversity was calculated using DnaSP v5.1034.Data from the Ion Torrent PGM sequencing runs were processed using the Ion Torrent platform-specific software to generate sequence reads, trim adapter sequences and SNP calling by aligning with the reference sequence of the O. rufipogon wild rice accessions, were downloaded from the RicePanGenome database (http://db.ncgr.ac.cn/RicePanGenome/) and a local database was created. The reference sequence of Sub1A, Sub1B and Sub1C genes was used for BLASTN search35 against a locally created BLAST database of 67 genomes (including Nipponbare reference genome) using the parameters: -word size = 5, -perc identity = 80, -qcovs = 80. The results were tabulated and the hits were filtered at a bit score of ≥1000, nucleotide sequences of all the filtered hits were extracted and fasta files were created. Multiple sequence alignment of the fasta files was performed using Clustalw36, using default parameters and the SNP/InDel information was tabulated manually.Draft genome assemblies of 66 rice genotypes, including 52 cultivars and 14 Sub1A, Sub1B and Sub1C gene homologs in the non-redundant “Green plant” database downloaded from NCBI (https://www.ncbi.nlm.nih.gov/). For similarity search a standalone NCBI-BLASTX programme was used with default search parameters. Further, we also searched for the copy numbers of rice Sub1C gene homologs in the reference genomes of 20 different monocotyledonous, dicotyledonous, moss and algal species with BLASTN programme using search parameters optimized earlier for such analysis37. Full-length sequence of the Sub1A, Sub1B and Sub1C genes present in each of the 13 rice varieties and Sub1C gene homolog from Sorghum bicolor, used as an outgroup, were aligned using Clustalw inbuilt in the BioEdit software30. The maximum likelihood based phylogenetic tree of the three genes was developed using RAxML v8.2.12 software38 and statistical reliability for each node support was estimated from 1000 replicates of non-parametric bootstrap with HKY85 model. The Sub1C gene homolog in Sorghum bicolor was used as outgroup for rooting the tree. The tree was visualized in figtree v. 1.4.332. The divergence times of Sub1A, Sub1B and Sub1C genes were estimated using the formula, T2 = (KABT1/KAX + KBX), where T2 = divergence time of Sub1A and Sub1B, T1 = divergence time of sorghum and rice, taken as 50–70 Mya41, KAB, KAX, and KBX are the synonymous substitution values between Sub1A and Sub1B of rice, Sub1A of rice and Sub1C of sorghum, and Sub1B of rice and Sub1C of sorghum, respectively42. The synonymous substitution values for each pair of genes were estimated using DnaSP programme43.We searched for the presence of rice 25. Seeds were sown in plastic trays filled with soil, each tray accommodating 10 entries with 30 plants each in Alpha Plus design to enable randomization of all entries in replication. After 14 days from sowing, the number of seedlings was recorded and the trays containing seedlings were transferred to a concrete submergence tank filled up to 1.5 m height with tap water. Once the susceptible IR42 plants showed severe stress symptoms (about 15 days of submergence) the plants were de-submerged and left for regeneration. Plant survival was recorded 21 days after de-submergence. At NDUAT, in Kharif season of 2012, experiment was laid out in augmented block design with two replications in concrete submergence pond of 20 × 10 × 1.2 m dimensions. Each block had 14 test entries plus two tolerant (Samba Mahsuri-Sub1 and FR13A) and one susceptible (Swarna) check. One-month old seedlings were transplanted in the ponds with row lengths of 2 m, and after establishment for two weeks the plants were submerged for 18 days. In one set as the plants grew under submerged conditions the canopies of highly elongating plants were cut to prevent breathing from the air. Fifteen days after de-submergence the number of surviving plants were recorded for both with and without canopy. The submergence screening was repeated at NDUAT in 2013 but in a lowland field with natural flooding. Experimental design and checks were similar to 2012 but this time with direct seeded rice with spacing of 20 × 10 cm. One month after sowing the fields were naturally flooded under 45–50 cm depths of water for 15 days. Then flood receded and plant survival was recorded 15 days after de-submergence. At BHU, in Kharif season of 2012, rice seeds were planted directly in a muddy pond and after 21 days of growth submergence was imposed for 14 days and then water was pumped out and plant survival was recorded one week after de-submergence. At RPCAU, in Kharif season of 2012, one-month old direct seeded rice plants in augmented design with FR13A and Samba Mahsuri-Sub1 as tolerant checks and IR42 as sensitive check replicated in every block were flooded with 1 m depth of water in a submergence tank. Percent survival of seedlings was recorded after 12 days of submergence followed by 15 days of recovery on de-submergence.Screening for survival under submergence was carried out at International Rice Research Institute (IRRI), Las Banos, Philippines; Narendra Deo University of Agriculture and Technology (NDUAT), Ayodhya, India; Banaras Hindu University (BHU), Varanasi, India; and Dr. Rajendra Prasad Central Agricultural University (RPCAU), Samastipur, India. At IRRI in 2012, the vegetative stage submergence screening was performed using an established protocolSub1 genes along with its position in the sequence was fed into TASSEL v3 (2011) software44 to find association between Sub1 SNPs and submergence tolerance. A General Linear Model (GLM) analysis was employed, as MLM model did not produce any significant association, and Manhattan plots were prepared. In addition, the relationship of SNP haplotypes of Sub1A, Sub1B and Sub1C genes, Structure sub-populations and percent survival after submergence of the rice cultivars was also visualized in 3D scatter plots drawn using NCSS v19.0.3 software  on IRRI 2012 phenotypic data. All the SNP haplotypes of Sub1A and Sub1B genes were used in the 3D scatter plot, but for Sub1C gene only those haplotypes with cultivar frequency of ≥3 were taken for clear visualization.The phenotypic performance as percent survival after submergence treatment, and SNP data for each of the three Genetic variability of the 179 rice genotypes was assessed using a genome wide 36-plex SNP assay. SNP marker 12-1794 showed the highest major allele frequency of 0.9665, while 07-4304 showed the lowest major allele frequency of 0.4804 Table . The optA NJ diversity tree of 179 rice cultivars developed based on the 36 genome-wide SNP markers formed a separate cluster of 22 Aromatic-Japonica cultivars corresponding to the Structure sub-population 4 , −21 (InDel), −14, −9, −6 and −1. In the intronic region there were 5 SNPs in Sub1A and 4 SNPs in Sub1B, whereas Sub1C has no intron 45. Recently, a high-depth of coverage (average 115×) sequence data on 66 rice genomes, including 52 cultivars and 14 wild rice accessions, has been analyzed for the presence of Sub1A gene23, but not for allelic sequence variation in any of the three Sub1 genes. We analyzed these 66 genomes along with the reference Nipponbare genome and FR13A Sub1 genes for allelic sequence variation and compared it with our targeted re-sequencing results. Detailed results for the three Sub1 genes are presented in Supplementary Figs. Sub1A gene is shown in Table Sub1A gene were common to the 66 genomes but one SNP at nucleotide position 878 was unique to the 179 cultivars set. Two SNPs were unique to the 66 genomes set but both of these were contributed by wild rice genotypes e.g. W0141. For the Sub1B gene only three of the seven SNPs identified by targeted re-sequencing were common to the 66 genomes, the remaining four SNPs were unique to the 179 cultivars set. Twenty-nine SNPs, mostly contributed by wild rice genotypes, were unique to the 66 genomes set  phylogenetic tree of the three genes was developed. Full-length sequence of the Sub1A, Sub1B and Sub1C genes present in each of the 13 rice varieties were taken along with the Sub1C homolog in Sorghum bicolor, used as an outgroup  at this position codes for amino acid proline186 resulting in submergence susceptible phenotype19. Since there was no new SNP in the exonic region in any of the varieties sequenced, no novel protein haplotype of the Sub1A gene was found. The eight SNP haplotypes resulted in two protein haplotypes, corresponding to the tolerant allele Sub1A-1 and intolerant allele Sub1A-2. SNP haplotypes H3-H8 coded for protein haplotype 1, whereas SNP haplotypes H1 and H2 produced protein haplotype 2 , while H4  codes for protein haplotype 2 , seven of which including two InDels were in the 5′ upstream region and 31 were in the exon  and networks were constructed using Hapstar software version 0.647. But, due to presence of additional InDels and heterozygous calls, much larger number of haplotypes was obtained . Hence, for simplicity only manually curated high-quality variants, i.e. eight haplotypes for Sub1A gene in 96 cultivars, nine haplotypes for Sub1B gene in 110 cultivars and 67 haplotypes for Sub1C gene in 174 cultivars were considered for the network analysis. Although 38 high-quality SNPs/InDels in Sub1C gene of 174 cultivars formed total 67 haplotypes , SNP haplotypes and percent survival of the cultivars under submergence stress based on survival data of IRRI 2012  of FR13A, where nine cultivars with haplotype H8 showed survival rates below 50%  showed more than 70% survival. Since Sub1A-1 (Haplotype H8) is the established tolerant allele, we analyzed all the genotypes using functional allele-specific PCR marker AEX125 to reconfirm the presence of Sub1A1 allele  or synonymous haplotypes (H3-H7) for the Sub1A gene  of the Sub1A gene  Japonica and Aromatic cultivar groups, and (ii) Indica and Aus cultivar groups. Four cultivars in sub-population 1 and 14 cultivars in sub-population 2 showed inferred ancestry of less than 80% of the group they belonged, indicating admixture types. It was interesting that Structure analysis at K value of four separated all the 14 admixture types from Indica sub-population and three out of four admixture types from Aromatic-Japonica sub-population to make a separate group of 17 Aus cultivars each with a high Fst value of greater than 0.80. This indicates that Aus cultivars share common ancestry with both Indica and Japonica groups and may provide a link between the two. Aus cultivars are important source of genes for tolerance to abiotic stress including flood and drought. They have short duration, adaptation to multiple stresses and are grown all over Indian sub-continent and adjoining regions of South-East Asia. Aromatic cultivars are known to be closer to Japonica group and Aus cultivars are closer to Indica group54. Isozyme-based diversity analysis by Glasszmann54, classified rice into six cultivar groups, with only one Japonica group  but two additional groups of Rayada and Ashina, representing deep water and floating rice of Bangladesh and India, respectively. Studies with large number of genomic SSR/SNP markers have reported five groups of rice cultivars, namely Indica, Tropical Japonica, Temperate Japonica, Aromatic and Aus52. Our results are in closer agreement with the classical work of Glaszmann54 employing 15 isozyme marker loci, likely because we used 36 SNP markers from conserved single-copy genes distributed on the 12 rice chromosomes.Genetic diversity and population structure of uncharacterized flood tolerant rice germplasm is an essential pre-requisite for association studies for identification of new QTLs and genes. Germplasm characterization using high-throughput genotyping methods and development of breeding strategy based on this has already started in riceSub1A gene and its functional validation by genetic transformation has proven its role in submergence tolerance of rice19. But after the original work, which also included sequencing of Sub1A, Sub1B and Sub1C genes from 21 rice cultivars, no further studies have been undertaken on allelic sequence variation in the Sub1 genes, although marker based allele surveys have been done for Sub1A and Sub1C genes in larger sets of cultivars50. Here, we generated high-quality sequence information by targeted re-sequencing of pooled PCR products from 96 cultivars for Sub1A, 110 cultivars for Sub1B and 174 cultivars for Sbu1C gene, resulting in comprehensive allelic sequence information. We also analysed the publicly available whole genome sequence data on 66 rice genomes for this purpose. These together identified 9 SNPs in Sub1A, 37 SNPs in Sub1B and 56 SNPs plus four InDels in Sub1C gene. Of these, one, four and 10 SNPs were unique to the 179 cultivars set, whereas two, 29 and 22 SNPs were unique to the 66 genomes set, respectively. Most of the unique SNPs in the 66 genomes set were due to 14 wild rice accessions  showed poor survival after submergence. On the other hand several cultivars with the sensitive Sub1A-2 allele (haplotype H2) showed high level of tolerance across locations . However, these exceptions need further analysis in bi-parental segregating populations. Importance of Sub1-independent mechanism of submergence tolerance has been highlighted decades ago56 and major non-Sub1 QTLs have been identified using Madabaru/IR72 and Chehrang-Sub1/IR10F365 populations, suggesting Sub1-independent mechanism of vegetative stage submergence tolerance21.No consistent association was found between SNPs in the ots Fig. , where nSub1 genes by targeted re-sequencing of 179 rice cultivars. Further, we identified 32 cultivars showing consistently high level of submergence tolerance in five trials at three different locations. Fourteen of these cultivars possessed the known Sub1A-1 dependent submergence tolerance, but 18 cultivars have Sub1A-1 independent submergence tolerance as they are either null for the Sub1A or possess submergence sensitive allele Sub1A-2 (haplotypes H1 and H2), although cultivar Goda Heenati is shown in other studies to possess the tolerant allele19. There is a need to identify the submergence tolerance QTLs and underlying genes in these cultivars using bi-parental mapping populations for effective utilization in rice breeding programme. We also report for the first time presence of Sub1A gene in Japonica and Aromatic groups of rice cultivars including Kola Joha and Karnal Local, which will be useful in breeding flood-tolerant aromatic rice cultivars.To conclude, we identified several new alleles of the three Supplementary Figures and Tables.Supplementary Dataset 1.Supplementary Dataset 2.


Identifying the loci and dissecting the genetic architecture underlying wheat yield- and quality-related traits are essential for wheat breeding. A genome-wide association study was conducted using a high-density 90 K SNP array to analyze the yield- and quality-related traits of 543 bread wheat varieties.TraesCS7A01G482000, TraesCS4B01G343700, and TraesCS6B01G295400) related to spikelet number per spike, diameter of the first internode, and grain volume. The three candidate genes were further analyzed using stage- and tissue- specific gene expression data derived from an RNA-seq analysis. These genes are promising candidates for enhancing yield- and quality-related traits in wheat.A total of 11,140 polymorphic SNPs were distributed on 21 chromosomes, including 270 significant SNPs associated with 25 yield- and quality-related traits. Additionally, 638 putative candidate genes were detected near the significant SNPs based on BLUP data, including three (The results of this study provide a new insight to understand the genetic basis of wheat yield and quality. Furthermore, the markers detected in this study may be applicable for marker-assisted selection in wheat breeding programs.The online version contains supplementary material available at 10.1186/s12870-021-02925-7. Triticum aestivum L.), which is a widely cultivated cereal crop that is highly adaptable, provides approximately 21% of the total calories and 23% of protein in the human diet (www.fao.org/faostat/en). As a staple food for about 35–40% of the global population, wheat is a good source of nutrients and has unique gluten properties, making it useful for producing diverse food products [Bread wheat  blocks may lead to the identification of false positive associations . RegardiThe aim of this study was to identify the stable loci and candidate genes significantly associated with wheat yield and quality. The results described herein may be useful for revealing the genetic basis of yield and quality. The corresponding SNP markers that were identified may ultimately facilitate the breeding of new high-quality and high-yielding wheat varieties.2) for most traits was approximately 0.80, with the highest and lowest heritabilities detected for PH (0.92) and PET (0.70). Accordingly, most traits were stable and largely determined by genetic factors. The correlation coefficient was highest (0.970) between wet gluten content (WGC) and grain protein content (GPC), but was also relatively high between TGW and GPR (0.919), wet gluten content (WGC) and flour content (FC) (0.842).The phenotypic data for the 543 wheat lines characterized regarding growth- and development-related traits, yield-related traits, and quality-related traits in six environments are listed in Table The 90 K wheat iSelect SNP array with 81,587 SNPs was used for genotyping. After a quality control step, 11,140 SNP markers remained for the association mapping . A totalA total of 28 significant SNP loci for the flag leaf length (FLL) were detected on chromosomes 1B, 1D, 2A, 2B, 2D, 3A, 3B, 4B, 5A, 5B, 5D, 6B, 6D, 7A, and 7B, accounting for 2.44–4.12% of the phenotypic variation. Regarding the flag leaf width (FLW), 33 significant SNP loci were detected on 13 chromosomes  and explained about 2.52–6.92% of the phenotypic variation. For the flag leaf area (FLA), the 40 significant SNP loci identified across six environments were detected on 16 chromosomes  and explained about 2.58–6.37% of the phenotypic variation. For the flag leaf angle (FA), 13 significant SNP loci were detected on nine chromosomes , accounting for about 2.06–3.35% of the phenotypic variation. Of the 68 SNPs identified for the flag leaf-associated traits, 11 pleiotropic loci were associated with three traits.For the maximum tiller number (MTN), 37 significant SNP loci were detected on 14 chromosomes  and explained about 1.80–5.32% of the phenotypic variation. Five significant SNP loci for the heading date (HD) were distributed on chromosomes 2A and 5A, accounting for 2.73–5.94% of the phenotypic variation. Seven significant SNP loci for the mature period (MP) were detected on chromosomes 2B, 3B, 6A, and 7B and accounted for 2.70–3.75% of the phenotypic variation. The seven significant SNP loci for the grain-filling period (GFP) were detected on chromosomes 2A, 2B, 2D, and 7B and accounted for 1.37–2.99% of the phenotypic variation. Nine significant SNP loci for the grain-filling rate (GFR) were detected on chromosomes 1A, 1B, 2A, 2B, 4A, 4B, and 5B, accounting for 2.39–3.59% of the phenotypic variation. Eight significant SNP loci for TGW were detected on chromosomes 1B, 2B, 3A, 3B, 4B, and 6D, explaining 2.29–3.59% of the phenotypic variation.For PH, 20 significant SNP loci were detected on 10 chromosomes  and explained about 1.96–4.42% of the phenotypic variation. For the diameter of the first internode (FD), 16 significant SNP loci were detected on nine chromosomes  and explained about 1.53–4.85% of the phenotypic variation. Regarding the length of the first internode (FIL), 17 significant SNP loci were detected on eight chromosomes  and explained about 2.13–5.76% of the phenotypic variation. For the diameter of the second internode (SD), the 18 significant SNP loci identified across six environments were detected on eight chromosomes , explaining about 2.15–5.02% of the phenotypic variation. For the length of the second internode (SIL), 15 significant SNP loci were detected on seven chromosomes  and explained about 2.57–5.74% of the phenotypic variation. For TH, 45 significant SNP loci were detected on 15 chromosomes , accounting for approximately 2.5–7.13% of the phenotypic variation. Among them, it is noteworthy that three loci on chromosome 4B are associated with FD and SD.For spike length (SL), nine significant SNP loci were distributed on seven chromosomes  and explained about 2.42–4.33% of the phenotypic variation. Regarding the spikelet number per spike (SNS), 14 significant SNP loci were detected on six chromosomes , explaining about 1.27–4.02% of the phenotypic variation. The nine significant SNP loci for KNPS were detected on chromosomes 1A, 3A, 4A, 4B, and 5D and accounted for 2.58–4.32% of the phenotypic variation. Regarding the percentage of spike-bearing tillers (PET), 11 significant SNP loci were detected on 10 chromosomes  and explained about 2.74–3.55% of the phenotypic variation. For the spike number per mu (EPM), 17 significant SNP loci were detected on eight chromosomes  and explained about 2.33–8.47% of the phenotypic variation.For the grain volume (GV), 17 significant SNP loci were detected on five chromosomes  and explained about 2.52–5.01% of the phenotypic variation. The two significant SNP loci for GPC detected on chromosomes 1A and 3D accounted for 2.35–2.53% of the phenotypic variation. Two significant SNP loci for WGC were detected on chromosomes 3D and 5D and accounted for 2.43–2.90% of the phenotypic variation. Ten significant SNP loci for the flour content (FC) were detected on chromosomes 1B, 4B, 5A, 5B, 6A, and 7B, accounting for 1.80–3.27% of the phenotypic variation.In our study, the 200-, 380-, and 600-kb sequences flanking the related SNPs in subgenomes A, B, and D, respectively, were identified as potential candidate gene regions. A total of 638 putative candidate genes detected of the significant SNPs flanking-regions based on BLUP data were identified by screening the annotated genes in the recently released genome sequence (IWGSC RefSeq v1.0) Table . We perfTraesCS7A01G482000. This locus was detected in four environments and in the BLUP model. SNP (C/A) at this location in the relevant genomic regions cause amino acids to change from leucine to isoleucine . The SNP (G/A) leads to changes in amino acids from arginine to histidine  on chromosome 6B  was identified as a candidate gene for SNS through map-based cloning [TaAPO-A1. Dobrovolskaya et al. [WFZP (WHEAT FRIZZY PANICLE) gene related to SNS on chromosome 2D. This gene is within one LD of a stable SNS-related locus (GENE-0787_85) revealed in the current study. For the quality-related traits, our GPC locus (GENE-0411_807) overlaps the GPC QTL mapped to chromosome 1A by Kumar et al. [We completed a broad-scale comparison of the results of our study and those of previous investigations. For PH, a locus (RAC875_rep_c105718_585) on chromosome 4D identified in four environments based on BLUP data is about one LD from a QTL (QPH.caas-4DS) described by Li et al. , This pr cloning –29. Two a et al.  cloned ar et al. . Li et ar et al.  identifiTraesCS7A01G482000, which is related to SNS, was predicted to encode a haloacid dehalogenase (HAD)-like hydrolase domain-containing protein. In rice, the overexpression of OsHAD1 reportedly leads to enhanced phosphatase activity and increased total and soluble P contents in Pi-deficient transgenic seedlings during the early panicle development stage. Increasing the P uptake rate can promote spikelet formation [In addition, we have also identified new stable SNPs that affect specific genes and have been detected in multiple environments. For example, ormation , 34. TheTraesCS4B01G343700. In Arabidopsis, AtVPS25 (vacuolar protein sorting-associated protein) regulates auxin biosynthesis via its effects on the expression of specific auxin-related genes. An increase in the auxin content of wheat plants may lead to increased stalk diameters and enhanced lodging resistance [TraesCS4B01G343700 in different tissues and developmental stages proved that this gene is highly expressed during the stem and internode development stage. The expression level peaked in the milk grain stage, implying the changes occurring in this stage have important implications for the lodging resistance of wheat plants.Lodging resistance is another important yield-related trait. Four significant SNPs for internode diameter were identified on chromosome 4B. These SNPs were detected within sistance –37. A trTraesCS6B01G295400, which encodes a LisH domain-like protein, as a candidate gene. In rice, OsLIS-L1  is important for male gametophyte formation, with mutations to this gene resulting in abnormal development. Additionally, the protein encoded by this gene influences grain characteristics and is closely related to the floral development and grain-filling stages [Regarding the quality-related trait GV, we identified g stages . An analIn summary, we conducted a GWAS based on the wheat 90 K SNP array to investigate the yield- and quality-related traits in 543 major wheat accessions. The resulting data were analyzed to identify relevant SNP loci and candidate genes. We are currently developing Kompetitive Allele-Specific PCR markers for the significant loci. These markers will enable researchers and breeders to efficiently transfer alleles into elite wheat genotypes . AdditioA bread wheat panel of 543 genotypes including cultivars, regional test lines, and introduced parental lines was used, the details have been published in our previous paper . During Twenty-five phenotypic traits were measured, including growth and development-related traits , yield-related traits , and quality-related traits . The data recorded for each trait are summarized in Table The descriptive statistical analysis and correlation analysis for the phenotypic data were completed using the SPSS 25.0 software. Pearson’s correlation coefficients were calculated to evaluate the correlations among the traits.The wheat 90 K Illumina Infinium SNP array was used to genotype the association panel containing 543 accessions. The SNP data were clustered and automatically called using the Illumina BeadStudio genotyping software . The data were filtered to remove alleles with a detection rate less than 0.1 and a minor allele frequency less than 0.05 . AdditioP value threshold was calculated based on the number of markers  as described by Li et al. [P value of 1/11,140 (−log10P = 4.05) was used as the criterion for identifying significant SNPs.The population structure, relative kinship, and LD were analyzed in a previous study . In the i et al. . Regardiwww.wheatgenome.org/) was used for predicting candidate genes for the significant sites revealed by the genome-wide association analysis. Specifically, candidate genes around the significant sites were identified according to the differences in the LD decay distance among chromosomal groups. The expression profiles of putative candidate genes were analyzed using a wheat gene expression database available online (http://www.wheat-expression.com/). This database, which includes 850 wheat RNA-sequencing samples and an annotated genome, reveals the similarities and differences between homoeolog expression levels in diverse tissues, developmental stages, and cultivars [The ‘Chinese Spring’ Genome database  excluding SNS, FD and GV traits. The horizontal line represents the significance threshold (−log10P = 4.05).Additional file 2: Table S1 Categories and descriptive statistics for the 25 yield- and quality-related traits evaluated in six different environments. Table S2 Correlations among 25 yield- and quality-related traits. Table S3 Basic details regarding the SNP markers used for a genome-wide association study of 543 wheat genotypes. Table S4 Information regarding the candidate genes identified based on BLUP data.