Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa

Genome-Wide Association Study for Seed Dormancy Using Re-Sequenced Germplasm under Multiple Conditions in Rice

Seed dormancy is a key factor used to determine seed germination in rice production. So far, only a few genes controlling seed dormancy have been reported, and the genetic mechanism of rice seed dormancy is still elusive. In this study, a population of 195 diverse re-sequenced accessions from 40 countries was evaluated for the seed germination rate (GR) without dormancy breaking (WDB) as a control and under dry heating (DH) and gibberellic acid (GA) treatments, as dormancy breaking agents to identify QTLs for seed dormancy. Phenotypic assessment revealed that these accessions had abundant variations in seed dormancy. GWAS using 1,120,223 high-quality single nucleotide polymorphisms (SNPs) and a mixed linear model (MLM) incorporating both principal components (PCs) and kinship (K) identified 30 QTLs on 10 chromosomes, accounting for 7.3-20.4% of the phenotypic variance in GR. Ten of the QTLs were located in the regions of previously reported QTLs, while the rest were novel ones. Thirteen high-confidence candidate genes were predicted for the four QTLs detected in two or three conditions (qGR4-4, qGR4-5, qGR8 and qGR11-4) and one QTL with a large effect (qGR3). These genes were highly expressed during seed development and were significantly regulated by various hormone treatments. This study provides new insights into the genetic and molecular basis of rice seed dormancy/germination. The accessions with moderate and strong dormancy and markers for the QTLs and candidate genes are useful for attaining a proper level of seed dormancy.

Progress in rice sheath blight resistance research

Rice sheath blight (ShB) disease poses a major threat to rice yield throughout the world. However, the defense mechanisms against ShB in rice remain largely unknown. ShB resistance is a typical quantitative trait controlled by multiple genes. With the rapid development of molecular methods, many quantitative trait loci (QTLs) related to agronomic traits, biotic and abiotic stresses, and yield have been identified by genome-wide association studies. The interactions between plants and pathogens are controlled by various plant hormone signaling pathways, and the pathways synergistically or antagonistically interact with each other, regulating plant growth and development as well as the defense response. This review summarizes the regulatory effects of hormones including auxin, ethylene, salicylic acid, jasmonic acid, brassinosteroids, gibberellin, abscisic acid, strigolactone, and cytokinin on ShB and the crosstalk between the various hormones. Furthermore, the effects of sugar and nitrogen on rice ShB resistance, as well as information on genes related to ShB resistance in rice and their effects on ShB are also discussed. In summary, this review is a comprehensive description of the QTLs, hormones, nutrition, and other defense-related genes related to ShB in rice. The prospects of targeting the resistance mechanism as a strategy for controlling ShB in rice are also discussed.

Identification of Candidate Genes for Salt Tolerance at the Seedling Stage Using Integrated Genome-Wide Association Study and Transcriptome Analysis in Rice

Salt stress is a major constraint in rice production worldwide. Salt stress is estimated to cause annual losses of 30-50% in rice production. Discovering and deploying salt-resistance genes are the most effective ways to control salt stress. We performed a genome-wide association study (GWAS) to detect QTLs related to salt tolerance at the seedling stage using the japonica-multiparent advanced generation intercross (MAGIC) population. Four QTLs (qDTS1-1, qDTS1-2, qDTS2, and qDTS9) associated with salt tolerance were identified on chromosomes 1, 2, and 9. Among these QTLs, a novel QTL, qDTS1-2, was located between flanking SNPs (1354576 and id1028360) on chromosome 1, with the largest -log10(P) value of 5.81 and a total phenotypic variance of 15.2%. RNA-seq analysis revealed that among the seven differentially expressed genes (DEGs) commonly identified in both P6 and JM298 showing salt tolerance, two upregulated genes, Os01g0963600 (ASR transcription factor) and Os01g0975300 (OsMYB48), related to salt and drought tolerance, were also involved in the target region of qDTS1-2. The results of this study can provide insights into further understanding of salt tolerance mechanisms and developing DNA markers for marker-assisted selection (MAS) breeding to improve the salt tolerance of cultivars in rice breeding programs.

Genetic and molecular basis of carotenoid metabolism in cereals

Carotenoids are vital pigments for higher plants and play a crucial function in photosynthesis and photoprotection. Carotenoids are precursors of vitamin A synthesis and contribute to human nutrition and health. However, cereal grain endosperm contains a minor carotenoid measure and a scarce supply of provitamin A content. Therefore, improving the carotenoids in cereal grain is of major importance. Carotenoid content is governed by multiple candidate genes with their additive effects. Studies on genes related to carotenoid metabolism in cereals would increase the knowledge of potential metabolic steps of carotenoids and enhance the quality of crop plants. Recognizing the metabolism and carotenoid accumulation in various staple cereal crops over the last few decades has broadened our perspective on the interdisciplinary regulation of carotenogenesis. Meanwhile, the amelioration in metabolic engineering approaches has been exploited to step up the level of carotenoid and valuable industrial metabolites in many crops, but wheat is still considerable in this matter. In this study, we present a comprehensive overview of the consequences of biosynthetic and catabolic genes on carotenoid biosynthesis, current improvements in regulatory disciplines of carotenogenesis, and metabolic engineering of carotenoids. A panoptic and deeper understanding of the regulatory mechanisms of carotenoid metabolism and genetic manipulation (genome selection and gene editing) will be useful in improving the carotenoid content of cereals.

MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

A superior gene allele involved in abscisic acid signaling enhances drought tolerance and yield in chickpea

Identifying potential molecular tags for drought tolerance is essential for achieving higher crop productivity under drought stress. We employed an integrated genomics-assisted breeding and functional genomics strategy involving association mapping, fine mapping, map-based cloning, molecular haplotyping and transcript profiling in the introgression lines (ILs)- and near isogenic lines (NILs)-based association panel and mapping population of chickpea (Cicer arietinum). This combinatorial approach delineated a bHLH (basic helix-loop-helix) transcription factor, CabHLH10 (Cicer arietinum bHLH10) underlying a major QTL, along with its derived natural alleles/haplotypes governing yield traits under drought stress in chickpea. CabHLH10 binds to a cis-regulatory G-box promoter element to modulate the expression of RD22 (responsive to desiccation 22), a drought/abscisic acid (ABA)-responsive gene (via a trans-expression QTL), and two strong yield-enhancement photosynthetic efficiency (PE) genes. This, in turn, upregulates other downstream drought-responsive and ABA signaling genes, as well as yield-enhancing PE genes, thus increasing plant adaptation to drought with reduced yield penalty. We showed that a superior allele of CabHLH10 introgressed into the NILs improved root and shoot biomass and PE, thereby enhancing yield and productivity during drought without compromising agronomic performance. Furthermore, overexpression of CabHLH10 in chickpea and Arabidopsis (Arabidopsis thaliana) conferred enhanced drought tolerance by improving root and shoot agro-morphological traits. These findings facilitate translational genomics for crop improvement and the development of genetically tailored, climate-resilient, high-yielding chickpea cultivars.

Sample size determination for training set optimization in genomic prediction

A practical approach is developed to determine a cost-effective optimal training set for selective phenotyping in a genomic prediction study. An R function is provided to facilitate the application of the approach. Genomic prediction (GP) is a statistical method used to select quantitative traits in animal or plant breeding. For this purpose, a statistical prediction model is first built that uses phenotypic and genotypic data in a training set. The trained model is then used to predict genomic estimated breeding values (GEBVs) for individuals within a breeding population. Setting the sample size of the training set usually takes into account time and space constraints that are inevitable in an agricultural experiment. However, the determination of the sample size remains an unresolved issue for a GP study. By applying the logistic growth curve to identify prediction accuracy for the GEBVs and the training set size, a practical approach was developed to determine a cost-effective optimal training set for a given genome dataset with known genotypic data. Three real genome datasets were used to illustrate the proposed approach. An R function is provided to facilitate widespread application of this approach to sample size determination, which can help breeders to identify a set of genotypes with an economical sample size for selective phenotyping.

Identification of a key locus, qNL3.1, associated with seed germination under salt stress via a genome-wide association study in rice

Two causal OsTTL and OsSAPK1 genes of the key locus qNL3.1 significantly associated with seed germination under salt stress were identified via a genome-wide association study, which could improve rice seed germination under salt stress. Rice is a salt-sensitive crop, and its seed germination determines subsequent seedling establishment and yields. In this study, 168 accessions were investigated for the genetic control of seed germination under salt stress based on the germination rate (GR), germination index (GI), time at which 50% germination was achieved (T₅₀) and mean level (ML). Extensive natural variation in seed germination was observed among accessions under salt stress. Correlation analysis showed significantly positive correlations among GR, GI and ML and a negative correlation with T₅₀ during seed germination under salt stress. Forty-nine loci significantly associated with seed germination under salt stress were identified, and seven of these were identified in both years. By comparison, 16 loci were colocated with the previous QTLs, and the remaining 33 loci might be novel. qNL3.1, colocated with qLTG-3, was simultaneously identified with the four indices in two years and might be a key locus for seed germination under salt stress. Analysis of candidate genes showed that two genes, the similar to transthyretin-like protein OsTTL and the serine/threonine protein kinase OsSAPK1, were the causal genes of qNL3.1. Germination tests indicated that both Osttl and Ossapk1 mutants significantly reduced seed germination under salt stress compared to the wild type. Haplotype analysis showed that Hap.1 of OsTTL and Hap.1 of OsSAPK1 genes were excellent alleles, and their combination resulted in high seed germination under salt stress. Eight accessions with elite performance of seed germination under salt stress were identified, which could improve rice seed germination under salt stress.

A comparison of methods for training population optimization in genomic selection

Maximizing CDmean and Avg_GRM_self were the best criteria for training set optimization. A training set size of 50-55% (targeted) or 65-85% (untargeted) is needed to obtain 95% of the accuracy.  With the advent of genomic selection (GS) as a widespread breeding tool, mechanisms to efficiently design an optimal training set for GS models became more relevant, since they allow maximizing the accuracy while minimizing the phenotyping costs. The literature described many training set optimization methods, but there is a lack of a comprehensive comparison among them. This work aimed to provide an extensive benchmark among optimization methods and optimal training set size by testing a wide range of them in seven datasets, six different species, different genetic architectures, population structure, heritabilities, and with several GS models to provide some guidelines about their application in breeding programs. Our results showed that targeted optimization (uses information from the test set) performed better than untargeted (does not use test set data), especially when heritability was low. The mean coefficient of determination was the best targeted method, although it was computationally intensive. Minimizing the average relationship within the training set was the best strategy for untargeted optimization. Regarding the optimal training set size, maximum accuracy was obtained when the training set was the entire candidate set. Nevertheless, a 50-55% of the candidate set was enough to reach 95-100% of the maximum accuracy in the targeted scenario, while we needed a 65-85% for untargeted optimization. Our results also suggested that a diverse training set makes GS robust against population structure, while including clustering information was less effective. The choice of the GS model did not have a significant influence on the prediction accuracies.

CRISPR-Cas: A robust technology for enhancing consumer-preferred commercial traits in crops

The acceptance of new crop varieties by consumers is contingent on the presence of consumer-preferred traits, which include sensory attributes, nutritional value, industrial products and bioactive compounds production. Recent developments in genome editing technologies provide novel insight to identify gene functions and improve the various qualitative and quantitative traits of commercial importance in plants. Various conventional as well as advanced gene-mutagenesis techniques such as physical and chemical mutagenesis, CRISPR-Cas9, Cas12 and base editors are used for the trait improvement in crops. To meet consumer demand, breakthrough biotechnologies, especially CRISPR-Cas have received a fair share of scientific and industrial interest, particularly in plant genome editing. CRISPR-Cas is a versatile tool that can be used to knock out, replace and knock-in the desired gene fragments at targeted locations in the genome, resulting in heritable mutations of interest. This review highlights the existing literature and recent developments in CRISPR-Cas technologies (base editing, prime editing, multiplex gene editing, epigenome editing, gene delivery methods) for reliable and precise gene editing in plants. This review also discusses the potential of gene editing exhibited in crops for the improvement of consumer-demanded traits such as higher nutritional value, colour, texture, aroma/flavour, and production of industrial products such as biofuel, fibre, rubber and pharmaceuticals. In addition, the bottlenecks and challenges associated with gene editing system, such as off targeting, ploidy level and the ability to edit organelle genome have also been discussed.

The Pyramiding of Elite Allelic Genes Related to Grain Number Increases Grain Number per Panicle Using the Recombinant Lines Derived from Indica-japonica Cross in Rice

Indica(xian)-japonica(geng) hybrid rice has many heterosis traits that can improve rice yield. However, the traditional hybrid technology will struggle to meet future needs for the development of higher-yield rice. Available genomics resources can be used to efficiently understand the gene-trait association trait for rice breeding. Based on the previously constructed high-density genetic map of 272 high-generation recombinant inbred lines (RILs) originating from the cross of Luohui 9 (indica, as female) and RPY geng (japonica, as male) and high-quality genomes of parents, here, we further explore the genetic basis for an important complex trait: possible causes of grain number per panicle (GNPP). A total of 20 genes related to grains number per panicle (GNPP) with the differences of protein amino acid between LH9 and RPY were used to analyze genotype combinations, and PCA results showed a combination of PLY1, LAX1, DTH8 and OSH1 from the RPY geng with PYL4, SP1, DST and GNP1 from Luohui 9 increases GNPP. In addition, we also found that the combination of LAX1-T2 and GNP1-T3 had the most significant increase in GNPP. Notably, Molecular Breeding Knowledgebase (MBK) showed a few aggregated rice cultivars, LAX1-T2 and GNP1-T3, which may be a result of the natural geographic isolation between the two gene haplotypes. Therefore, we speculate that the pyramiding of japonica-type LAX-T2 with indica-type GNP1-T3 via hybridization can significantly improve rice yield by increasing GNPP.

K. S, Vadivel R, T. K. D, Raj R, Pooniya V, Babu S, Rathore SS, L. M, Tiwari G. Nitrogen use efficiency-a key to enhance crop productivity under a changing climate

Nitrogen (N) is an essential element required for the growth and development of all plants. On a global scale, N is agriculture's most widely used fertilizer nutrient. Studies have shown that crops use only 50% of the applied N effectively, while the rest is lost through various pathways to the surrounding environment. Furthermore, lost N negatively impacts the farmer's return on investment and pollutes the water, soil, and air. Therefore, enhancing nitrogen use efficiency (NUE) is critical in crop improvement programs and agronomic management systems. The major processes responsible for low N use are the volatilization, surface runoff, leaching, and denitrification of N. Improving NUE through agronomic management practices and high-throughput technologies would reduce the need for intensive N application and minimize the negative impact of N on the environment. The harmonization of agronomic, genetic, and biotechnological tools will improve the efficiency of N assimilation in crops and align agricultural systems with global needs to protect environmental functions and resources. Therefore, this review summarizes the literature on nitrogen loss, factors affecting NUE, and agronomic and genetic approaches for improving NUE in various crops and proposes a pathway to bring together agronomic and environmental needs.

Prediction of heading date, culm length, and biomass from canopy-height-related parameters derived from time-series UAV observations of rice

Unmanned aerial vehicles (UAVs) are powerful tools for monitoring crops for high-throughput phenotyping. Time-series aerial photography of fields can record the whole process of crop growth. Canopy height (CH), which is vertical plant growth, has been used as an indicator for the evaluation of lodging tolerance and the prediction of biomass and yield. However, there have been few attempts to use UAV-derived time-series CH data for field testing of crop lines. Here we provide a novel framework for trait prediction using CH data in rice. We generated UAV-based digital surface models of crops to extract CH data of 30 Japanese rice cultivars in 2019, 2020, and 2021. CH-related parameters were calculated in a non-linear time-series model as an S-shaped plant growth curve. The maximum saturation CH value was the most important predictor for culm length. The time point at the maximum CH contributed to the prediction of days to heading, and was able to predict stem and leaf weight and aboveground weight, possibly reflecting the association of biomass with duration of vegetative growth. These results indicate that the CH-related parameters acquired by UAV can be useful as predictors of traits typically measured by hand.

Genome-Wide Association Study Revealed SNP Alleles Associated with Seed Size Traits in African Yam Bean (Sphenostylis stenocarpa (Hochst ex. A. Rich.) Harms)

Seed size is an important yield and quality-determining trait in higher plants and is also crucial to their evolutionary fitness. In African yam bean (AYB), seed size varies widely among different accessions. However, the genetic basis of such variation has not been adequately documented. A genome-wide marker-trait association study was conducted to identify genomic regions associated with four seed size traits (seed length, seed width, seed thickness, and 100-seed weight) in a panel of 195 AYB accessions. A total of 5416 SNP markers were generated from the diversity array technology sequence (DArTseq) genotype-by-sequencing (GBS)- approach, in which 2491 SNPs were retained after SNP quality control and used for marker-trait association analysis. Significant phenotypic variation was observed for the traits. Broad-sense heritability ranged from 50.0% (seed width) to 66.4% (seed length). The relationships among the traits were positive and significant. Genome-wide association study (GWAS) using the general linear model (GLM) and the mixed linear model (MLM) approaches identified 12 SNP markers significantly associated with seed size traits across the six test environments. The 12 makers explained 6.5-10.8% of the phenotypic variation. Two markers (29420334|F|0-52:C>G-52:C>G and 29420736|F|0-57:G>T-57:G>T) with pleiotropic effects associated with seed width and seed thickness were found. A candidate gene search identified five significant markers (100026424|F|0-37:C>T-37:C>T, 100041049|F|0-42:G>C-42:G>C, 100034480|F|0-31:C>A-31:C>A, 29420365|F|0-55:C>G-55:C>G, and 29420736|F|0-57:G>T-57:G>T) located close to 43 putative genes whose encoding protein products are known to regulate seed size traits. This study revealed significant makers not previously reported for seed size in AYB and could provide useful information for genomic-assisted breeding in AYB.

GWAS analysis to elucidate genetic composition underlying a photoperiod-insensitive rice population, North Korea

Heading date (Hd) is one of the main factors determining rice production and regional adaptation. To identify the genetic factors involved in the wide regional adaptability of rice, we conducted a genome-wide association study (GWAS) with 190 North Korean rice accessions selected for non-precocious flowering in the Philippines, a low-latitude region. Using both linear mixed models (LMM) and fixed and random model circulating probability unification (FarmCPU), we identified five significant loci for Hd in trials in 2018 and 2019. Among the five lead single nucleotide polymorphisms (SNPs), three were located adjacent to the known Hd genes, Heading date 3a (Hd3a), Heading date 5 (Hd5), and GF14-c. In contrast, three SNPs were located in novel loci with minor effects on heading. Further GWAS analysis for photoperiod insensitivity (PS) revealed no significant genes associated with PS, supporting that this North Korean (NK) population is largely photoperiod-insensitive. Haplotyping analysis showed that more than 80% of the NK varieties harbored nonfunctional alleles of major Hd genes investigated, of which a nonfunctional allele of Heading date 1 (Hd1) was observed in 66% of the varieties. Geographical distribution analysis of Hd allele combination types showed that nonfunctional alleles of floral repressor Hd genes enabled rice cultivation in high-latitude regions. In contrast, Hd1 alleles largely contributed to the wide regional adaptation of rice varieties. In conclusion, an allelic combination of Hd genes is critical for rice cultivation across wide areas.

Genome-wide association study reveals novel genomic regions governing agronomic and grain quality traits and superior allelic combinations for Basmati rice improvement

BACKGROUND

Basmati is a speciality segment in the rice genepool characterised by explicit grain quality. For the want of suitable populations, genome-wide association study (GWAS) in Basmati rice has not been attempted.

MATERIALS

To address this gap, we have performed a GWAS on a panel of 172 elite Basmati multiparent population comprising of potential restorers and maintainers. Phenotypic data was generated for various agronomic and grain quality traits across seven different environments during two consecutive crop seasons. Based on the observed phenotypic variation, three agronomic traits namely, days to fifty per cent flowering, plant height and panicle length, and three grain quality traits namely, kernel length before cooking, length breadth ratio and kernel length after cooking were subjected to GWAS. Genotyped with 80K SNP array, the population was subjected to principal component analysis to stratify the underlying substructure and subjected to the association analysis using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model.

RESULTS

We identified 32 unique MTAs including 11 robust MTAs for the agronomic traits and 25 unique MTAs including two robust MTAs for the grain quality traits. Six out of 13 robust MTAs were novel. By genome annotation, six candidate genes associated with the robust MTAs were identified. Further analysis of the allelic combinations of the robust MTAs enabled the identification of superior allelic combinations in the population. This information was utilized in selecting 77 elite Basmati rice genotypes from the panel.

CONCLUSION

This is the first ever GWAS study in Basmati rice which could generate valuable information usable for further breeding through marker assisted selection, including enhancing of heterosis.

Genome-wide association study of agronomic traits related to nitrogen use efficiency in wheat

GWAS identified 347 QTLs associated with eight traits related to nitrogen use efficiency in a 389-count wheat panel. Four novel candidate transcription factor genes were verified using qRT-PCR. Nitrogen is an essential nutrient for plants that determines crop yield. Improving nitrogen use efficiency (NUE) should considerably increase wheat yield and reduce the use of nitrogen fertilisers. However, knowledge on the genetic basis of NUE during wheat maturity is limited. In this study, a diversity panel incorporating 389 wheat accessions was phenotyped for eight NUE-related agronomic traits across five different environments. A total of 347 quantitative trait loci (QTLs) for low nitrogen tolerance indices (ratio of agronomic characters under low and high nitrogen conditions) were identified through a genome-wide association study utilising 397,384 single nucleotide polymorphisms (SNPs) within the MLM (Q + K) model, including 11 stable QTLs. Furthermore, 69 candidate genes were predicted for low nitrogen tolerance indices of best linear unbiased predictions values of the eight studied agronomic traits, and four novel candidate transcription factors (TraesCS5A02G237500 for qFsnR5A.2, TraesCS5B02G384500 and TraesCS5B02G384600 for qSLR5B.1, and TraesCS3B02G068800 for qTKWR3B.1) showed differing expression patterns in contrasting low-nitrogen-tolerant wheat genotypes. Moreover, the number of favourable marker alleles calculated using NUE that were significantly related to SNP in accessions decreased over the decades, indicating a decline in the NUE of the 389 wheat varieties. These findings denote promising NUE markers that could be useful in breeding high-NUE wheat varieties, and the candidate genes could further detail the NUE-related regulation network in wheat.

GWAS in tetraploid potato: identification and validation of SNP markers associated with glycoalkaloid content

Genome-wide association studies (GWAS) are a useful tool to unravel the genetic architecture of complex traits, but the results can be difficult to interpret. Population structure, genetic heterogeneity, and rare alleles easily result in false positive or false negative associations. This paper describes the analysis of a GWAS panel combined with three bi-parental mapping populations to validate GWAS results, using phenotypic data for steroidal glycoalkaloid (SGA) accumulation and the ratio (SGR) between the two major glycoalkaloids α-solanine and α-chaconine in potato tubers. SGAs are secondary metabolites in the Solanaceae family, functional as a defence against various pests and pathogens and in high quantities toxic for humans. With GWAS, we identified five quantitative trait loci (QTL) of which Sga1.1, Sgr8.1, and Sga11.1 were validated, but not Sga3.1 and Sgr7.1. In the bi-parental populations, Sga5.1 and Sga7.1 were mapped, but these were not identified with GWAS. The QTLs Sga1.1, Sga7.1, Sgr7.1, and Sgr8.1 co-localize with genes GAME9, GAME 6/GAME 11, SGT1, and SGT2, respectively. For other genes involved in SGA synthesis, no QTLs were identified. The results of this study illustrate a number of pitfalls in GWAS of which population structure seems the most important. We also show that introgression breeding for disease resistance has introduced new haplotypes to the gene pool involved in higher SGA levels in certain pedigrees. Finally, we show that high SGA levels remain unpredictable in potato but that α-solanine/α-chaconine ratio has a predictable outcome with specific SGT1 and SGT2 haplotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01344-2.

Pharmacological potential of seaweed-associated heterotrophic bacterium Bacillus atrophaeus

Extremities in marine environmental conditions led the marine macroalga-associated bacteria to adapt and biosynthesize potential bioactive agents. The myriad of marine macroalgae and the bacterial flora they are associated with constitute a potential source of bioactive components with significant biotechnological and pharmacological applications. Heterotrophic bacteria associated with the intertidal macroalgae were isolated and assessed for their pharmacological properties. Subsequently, Firmicutes dominated more than half of the 152 cultivable isolates from macroalgae-associated bacteria collected from the Gulf of Mannar (9°17'0'' N, 79°7'0'' E), on Peninsular India's southern coast. A total of 43 of those demonstrated steady antibacterial activities against a wide range of nosocomial pathogens. Among the bacteria isolated from marine macroalgae, Bacillus atrophaeus SHB2097 (MW821482) exhibited significant antimicrobial activities against clinically important pathogens. Organic extract of B. atrophaeus SHB2097 showed potential antimicrobial activities against test pathogens (minimum inhibitory concentration 6.25 µg/mL). Organic extract of B. atrophaeus SHB2097 revealed promising inhibition potential against cyclooxygenase-2 (IC₉₀ 53.26 µg/mL) and 5-lipoxygenase (IC₉₀ 9.74 µg/mL). The carbolytic enzyme α-glucosidase inhibition potential of the organic extract of the studied heterotrophic bacterium was significantly greater than (IC₉₀ 118 µg/mL) than that displayed by acarbose (IC₉₀ 645 µg/mL, p < 0.05). The significance of nuclear magnetic resonance-centered analyses of distinguishing signals in the organic extract and correlating those with bioactive potential was accentuated. The utilities of nuclear magnetic resonance-based fingerprinting emphasized the assessment of the distinctive signals in the solvent extracts and their correlation with the pharmacological properties. Thus, the heterotrophic B. atrophaeus SHB2097 could be used to develop potential therapeutic and biomedical agents.

RNA-Seq and Genome-Wide Association Studies Reveal Potential Genes for Rice Seed Shattering

The loss of the shattering ability is one of the key events in rice domestication. The strength of the seed shattering ability is closely related to the harvest yield and the adaptability of modern mechanical harvesting methods. In this study, using a population of 587 natural rice cultivars, quantitative trait loci associated with seed shattering were detected by genome-wide association studies (GWASs). We consider the quantitative trait loci (QTLs) qBTS1 and qBTS3 to be the key loci for seed shattering in rice. Additionally, the abscission zone (AZ) and nonabscission zone (NAZ) of materials with a loss of shattering (DZ129) and easy shattering (W517) were subjected to RNA-Seq, and high-quality differential expression profiles were obtained. The AZ-specific differentially expressed genes (DEGs) of W517 were significantly enriched in plant hormone signal transduction, while the AZ-specific DEGs of DZ129 were enriched in phenylpropanoid biosynthesis. We identified candidate genes for the lignin-associated laccase precursor protein (LOC_Os01g63180) and the glycoside hydrolase family (LOC_Os03g14210) in the QTLs qBTS1 (chromosome 1) and qBTS3 (chromosome 3), respectively. In summary, our findings lay the foundation for the further cloning of qBTS1 and qBTS3, which would provide new insights into seed shattering in rice.

Genome-Wide Association Study Identifies a Rice Panicle Blast Resistance Gene Pb3 Encoding NLR Protein

Rice blast is a worldwide fungal disease that seriously affects the yield and quality of rice. Identification of resistance genes against rice blast disease is one of the effective ways to control this disease. However, panicle blast resistance genes, which are useful in the fields, have rarely been studied due to the difficulty in phenotypic identification and the environmental influences. Here, panicle blast resistance-3 (Pb3) was identified by a genome-wide association study (GWAS) based on the panicle blast resistance phenotypes of 230 Rice Diversity Panel I (RDP-I) accessions with 700,000 single-nucleotide polymorphism (SNP) markers. A total of 16 panicle blast resistance loci (PBRLs) within three years including one repeated locus PBRL3 located in chromosome 11 were identified. In addition, 7 genes in PBRL3 were identified as candidate genes by haplotype analysis, which showed significant differences between resistant and susceptible varieties. Among them, one nucleotide-binding domain and Leucine-rich Repeat (NLR) gene Pb3 was highly conserved in multiple resistant rice cultivars, and its expression was significantly induced after rice blast inoculation. Evolutionary analysis showed that Pb3 was a typical disease resistance gene containing coiled-coil, NB-ARC, and LRR domains. T-DNA insertion mutants and CRISPR lines of Pb3 showed significantly reduced panicle blast resistance. These results indicate that Pb3 is a panicle blast resistance gene and GWAS is a rapid method for identifying panicle blast resistance in rice.

Genetic background- and environment-independent QTL and candidate gene identification of appearance quality in three MAGIC populations of rice

Many QTL have been identified for grain appearance quality by linkage analysis (LA) in bi-parental mapping populations and by genome-wide association study (GWAS) in natural populations in rice. However, few of the well characterized genes/QTL have been successfully applied in molecular rice breeding due to genetic background (GB) and environment effects on QTL expression and deficiency of favorable alleles. In this study, GWAS and LA were performed to identify QTL for five grain appearance quality-related traits using three multi-parent advanced generation inter-cross (MAGIC) populations. A total of 22 QTL on chromosomes 1-3, 5-8 were identified by GWAS for five traits in DC1, DC2 and 8way, and four combined populations DC12 (DC1+DC2), DC18 (DC1+8way), DC28 (DC2+8way) and DC128 (DC1+DC2+8way). And a total of 42 QTL were identified on all 12 chromosomes except 10 by LA in the three single populations. Among 20 QTL identified by GWAS in DC1, DC2 and 8way, 10, four and three QTL were commonly detected in DC18, DC28, and DC128, respectively. Similarly, among 42 QTL detected by LA in the three populations, four, one and two QTL were commonly detected in DC18, DC28, and DC128, respectively. There was no QTL mapped together in DC12 by both two mapping methods, indicating that GB could greatly affect the mapping results, and it was easier to map the common QTL among populations with similar GB. The 8way population was more powerful for QTL mapping than the DC1, DC2 and various combined populations. Compared with GWAS, LA can not only identify large-effect QTL, but also identify minor-effect ones. Among 11 QTL simultaneously detected by the two methods in different GBs and environments, eight QTL corresponded to known genes, including AqGL3b and AqGLWR3a for GL and GLWR, AqGW5a, AqGLWR5, AqDEC5 and AqPGWC5 for GW, GLWR, DEC and PGWC, and AqDEC6b and AqPGWC6b for DEC and PGWC, respectively. AqGL7, AqGL3c/AqGLWR3b, AqDEC6a/AqPGWC6a, and AqPGWC7 were newly identified and their candidate genes were analyzed and inferred. It was discussed to further improve grain appearance quality through designed QTL pyramiding strategy based on the stable QTL identified in the MAGIC populations.

Molecular Breeding and Drought Tolerance in Chickpea

Cicer arietinum L. is the third greatest widely planted imperative pulse crop worldwide, and it belongs to the Leguminosae family. Drought is the utmost common abiotic factor on plants, distressing their water status and limiting their growth and development. Chickpea genotypes have the natural ability to fight drought stress using certain strategies viz., escape, avoidance and tolerance. Assorted breeding methods, including hybridization, mutation, and marker-aided breeding, genome sequencing along with omics approaches, could be used to improve the chickpea germplasm lines(s) against drought stress. Root features, for instance depth and root biomass, have been recognized as the greatest beneficial morphological factors for managing terminal drought tolerance in the chickpea. Marker-aided selection, for example, is a genomics-assisted breeding (GAB) strategy that can considerably increase crop breeding accuracy and competence. These breeding technologies, notably marker-assisted breeding, omics, and plant physiology knowledge, underlined the importance of chickpea breeding and can be used in future crop improvement programmes to generate drought-tolerant cultivars(s).

Targeted Identification of Rice Grain-Associated Gene Allelic Variation Through Mutation Induction, Targeted Sequencing, and Whole Genome Sequencing Combined with a Mixed-Samples Strategy

BACKGROUND

The mining of new allelic variation and the induction of new genetic variability are the basis for improving breeding efficiency.

RESULTS

In this study, in total, 3872 heavy ion-irradiated M₂ generation rice seeds and individual leaves were collected. The grain length was between 8 and 10.22 mm. The grain width was between 1.54 and 2.87 mm. The results showed that there was extensive variation in granulotype. The allelic variation in GS3 and GW5 was detected in 484 mixed samples (8:1) using targeted sequencing technology, and 12 mixed samples containing potential mutations and 15 SNPs were obtained; combined with Sanger sequencing and phenotype data, 13 key mutants and their corresponding SNPs were obtained; protein structural and functional analysis of key mutants screened out 6 allelic variants leading to altered grain shape, as well as the corresponding mutants, including long-grain mutants GS3-2 and GS3-7, short-grain mutants GS3-3 and GS3-5, wide-grain mutant GW5-1 and narrow-grain mutant GW5-4; whole genome sequencing identified new grain length gene allelic variants GS3-G1, GS3-G2 and GS3-G3.

CONCLUSION

Based on the above studies, we found 6 granulotype mutants and 9 granulotype-related allelic variants, which provided new functional gene loci and a material basis for molecular breeding and genotype mutation and phenotype analysis. We propose a method for targeted identification of allelic variation in rice grain type genes by combining targeted sequencing of mixed samples and whole genome sequencing. The method has the characteristics of low detection cost, short detection period, and flexible detection of traits and genes.

Host genetic determinants drive compartment-specific assembly of tea plant microbiomes

Diverse host factors drive microbial variation in plant-associated environments, whereas their genetic mechanisms remain largely unexplored. To address this, we coupled the analyses of plant genetics and microbiomes in this study. Using 100 tea plant (Camellia sinensis) cultivars, the microbiomes of rhizosphere, root endosphere and phyllosphere showed clear compartment-specific assembly, whereas the subpopulation differentiation of tea cultivars exhibited small effects on microbial variation in each compartment. Through microbiome genome-wide association studies, we examined the interactions between tea genetic loci and microbial variation. Notably, genes related to the cell wall and carbon catabolism were heavily linked to root endosphere microbial composition, whereas genes related to the metabolism of metal ions and small organic molecules were overrepresented in association with rhizosphere microbial composition. Moreover, a set of tea genetic variants, including the cytoskeleton-related formin homology interacting protein 1 gene, were strongly associated with the β-diversity of phyllosphere microbiomes, implying their interactions with the overall structure of microbial communities. Our results create a catalogue of tea genetic determinants interacting with microbiomes and reveal the compartment-specific microbiome assembly driven by host genetics.

Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice

Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log₁₀ p-value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.

Identification of QTNs, QTN-by-environment interactions and genes for yield-related traits in rice using 3VmrMLM

Rice, which supports more than half the population worldwide, is one of the most important food crops. Thus, potential yield-related quantitative trait nucleotides (QTNs) and QTN-by-environment interactions (QEIs) have been used to develop efficient rice breeding strategies. In this study, a compressed variance component mixed model, 3VmrMLM, in genome-wide association studies was used to detect QTNs for eight yield-related traits of 413 rice accessions with 44,000 single nucleotide polymorphisms. These traits include florets per panicle, panicle fertility, panicle length, panicle number per plant, plant height, primary panicle branch number, seed number per panicle, and flowering time. Meanwhile, QTNs and QEIs were identified for flowering times in three different environments and five subpopulations. In the detections, a total of 7~23 QTNs were detected for each trait, including the three single-environment flowering time traits. In the detection of QEIs for flowering time in the three environments, 21 QTNs and 13 QEIs were identified. In the five subpopulation analyses, 3~9 QTNs and 2~4 QEIs were detected for each subpopulation. Based on previous studies, we identified 87 known genes around the significant/suggested QTNs and QEIs, such as LOC_Os06g06750 (OsMADS5) and LOC_Os07g47330 (FZP). Further differential expression analysis and functional enrichment analysis identified 30 candidate genes. Of these candidate genes, 27 genes had high expression in specific tissues, and 19 of these 27 genes were homologous to known genes in Arabidopsis. Haplotype difference analysis revealed that LOC_Os04g53210 and LOC_Os07g42440 are possibly associated with yield, and LOC_Os04g53210 may be useful around a QEI for flowering time. These results provide insights for future breeding for high quality and yield in rice.

Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress

Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.

Development of SNP Markers from GWAS for Selecting Seed Coat and Aleurone Layers in Brown Rice (Oryza sativa L.)

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 BC₂F₂ individuals derived from a cross between “Samgwang” and “Seolgaeng”, and using corresponding HRM markers, it was possible to finally develop HRM markers for selecting seed coat and aleurone layer thickness. 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.

Genetic Dissection of Epistatic Interactions Contributing Yield-Related Agronomic Traits in Rice Using the Compressed Mixed Model

Rice (Oryza sativa) is one of the most important cereal crops in the world, and yield-related agronomic traits, including plant height (PH), panicle length (PL), and protein content (PC), are prerequisites for attaining the desired yield and quality in breeding programs. Meanwhile, the main effects and epistatic effects of quantitative trait nucleotides (QTNs) are all important genetic components for yield-related quantitative traits. In this study, we conducted genome-wide association studies (GWAS) for 413 rice germplasm resources, with 36,901 single nucleotide polymorphisms (SNPs), to identify QTNs, QTN-by-QTN interaction (QQI), and their candidate genes, using a multi-locus compressed variance component mixed model, 3VmrMLM. As a result, two significant QTNs and 56 paired QQIs were detected, amongst 5219 genes of these QTNs, and 26 genes were identified as the yield-related confirmed genes, such as LCRN1, OsSPL3, and OsVOZ1 for PH, and LOG and QsBZR1 for PL. To reveal the substantial contributions related to the variation of yield-related agronomic traits in rice, we further implemented an enrichment analysis and expression analysis. As the results showed, 114 genes, nearly all significant QQIs, were involved in 37 GO terms; for example, the macromolecule metabolic process (GO:0043170), intracellular part (GO:0044424), and binding (GO:0005488). It was revealed that most of the QQIs and the candidate genes were significantly involved in the biological process, molecular function, and cellular component of the target traits. The demonstrated genetic interactions play a critical role in yield-related agronomic traits of rice, and such epistatic interactions contributed to large portions of the missing heritability in GWAS. These results help us to understand the genetic basis underlying the inheritance of the three yield-related agronomic traits and provide implications for rice improvement.

Comprehensive QTL analyses of nitrogen use efficiency in indica rice

Nitrogen-use efficiency (NUE) in rice is a complex quantitative trait involved in multiple biological processes and agronomic traits; however, the genetic basis and regulatory network of NUE remain largely unknown. We constructed a high-resolution microarray-based genetic map for 261 recombinant inbred lines derived from two indica parents. Using 2,345 bin markers, comprehensive analyses of quantitative trait loci (QTLs) of seven key agronomic traits under two different N levels were performed. A total of 11 non-redundant QTLs for effective panicle number (EPN), 7 for grain number per panicle, 13 for thousand-grain weight, 2 for seed-setting percentage, 15 for plant height, 12 for panicle length, and 6 for grain yield per plant were identified. The QTL regions were as small as 512 kb on average, and more than half spanned an interval smaller than 100 kb. Using this advantage, we identified possible candidate genes of two major EPN-related QTLs. One QTL detected under both N levels possibly encodes a DELLA protein SLR1, which is known to regulate NUE, although the natural variations of this protein have not been reported. The other QTL detected only under a high N level could encode the transcription factor OsbZIP59. We also predicted the possible candidate genes for another three of the NUE-related QTLs. Our results provide a reference for improving NUE-related QTL cloning and promote our understanding of NUE regulation in indica rice.

Genome-Wide Association Study of Salt Tolerance-Related Traits during Germination and Seedling Development in an Intermedium-Spike Barley Collection

Increased salinity is one of the major consequences of climatic change affecting global crop production. The early stages in the barley (Hordeum vulgare L.) life cycle are considered the most critical phases due to their contributions to final crop yield. Particularly, the germination and seedling development are sensitive to numerous environmental stresses, especially soil salinity. In this study, we aimed to identify SNP markers linked with germination and seedling development at 150 mM NaCl as a salinity treatment. We performed a genome-wide association study (GWAS) using a panel of 208 intermedium-spike barley (H. vulgare convar. intermedium (Körn.) Mansf.) accessions and their genotype data (i.e., 10,323 SNPs) using the genome reference sequence of “Morex”. The phenotypic results showed that the 150 mM NaCl salinity treatment significantly reduced all recorded germination and seedling-related traits compared to the control treatment. Furthermore, six accessions (HOR 11747, HOR 11718, HOR 11640, HOR 11256, HOR 11275 and HOR 11291) were identified as the most salinity tolerant from the intermedium-spike barley collection. GWAS analysis indicated that a total of 38 highly significantly associated SNP markers under control and/or salinity traits were identified. Of these, two SNP markers on chromosome (chr) 1H, two on chr 3H, and one on chr 4H were significantly linked to seedling fresh and dry weight under salinity stress treatment. In addition, two SNP markers on chr 7H were also significantly associated with seedling fresh and dry weight but under control condition. Under salinity stress, one SNP marker on chr 1H, 5H and 7H were detected for more than one phenotypic trait. We found that in most of the accessions exhibiting the highest salinity tolerance, most of the salinity-related QTLs were presented. These results form the basis for detailed studies, leading to improved salt tolerance breeding programs in barley.

Advancement in the Breeding, Biotechnological and Genomic Tools towards Development of Durable Genetic Resistance against the Rice Blast Disease

Rice production needs to be sustained in the coming decades, as the changeable climatic conditions are becoming more conducive to disease outbreaks. The majority of rice diseases cause enormous economic damage and yield instability. Among them, rice blast caused by Magnaportheoryzae is a serious fungal disease and is considered one of the major threats to world rice production. This pathogen can infect the above-ground tissues of rice plants at any growth stage and causes complete crop failure under favorable conditions. Therefore, management of blast disease is essentially required to sustain global food production. When looking at the drawback of chemical management strategy, the development of durable, resistant varieties is one of the most sustainable, economic, and environment-friendly approaches to counter the outbreaks of rice blasts. Interestingly, several blast-resistant rice cultivars have been developed with the help of breeding and biotechnological methods. In addition, 146 R genes have been identified, and 37 among them have been molecularly characterized to date. Further, more than 500 loci have been identified for blast resistance which enhances the resources for developing blast resistance through marker-assisted selection (MAS), marker-assisted backcross breeding (MABB), and genome editing tools. Apart from these, a better understanding of rice blast pathogens, the infection process of the pathogen, and the genetics of the immune response of the host plant are very important for the effective management of the blast disease. Further, high throughput phenotyping and disease screening protocols have played significant roles in easy comprehension of the mechanism of disease spread. The present review critically emphasizes the pathogenesis, pathogenomics, screening techniques, traditional and molecular breeding approaches, and transgenic and genome editing tools to develop a broad spectrum and durable resistance against blast disease in rice. The updated and comprehensive information presented in this review would be definitely helpful for the researchers, breeders, and students in the planning and execution of a resistance breeding program in rice against this pathogen.

Heritability estimation for a linear combination of phenotypes via ridge regression

MOTIVATION

The joint analysis of multiple phenotypes is important in many biological studies, such as plant and animal breeding. The heritability estimation for a linear combination of phenotypes is designed to account for correlation information. Existing methods for estimating heritability mainly focus on single phenotypes under random-effect models. These methods also require some stringent conditions, which calls for a more flexible and interpretable method for estimating heritability. Fixed-effect models emerge as a useful alternative.

RESULTS

In this paper, we propose a novel heritability estimator based on multivariate ridge regression for linear combinations of phenotypes, yielding accurate estimates in both sparse and dense cases. Under mild conditions in the high-dimensional setting, the proposed estimator appears to be consistent and asymptotically normally distributed. Simulation studies show that the proposed estimator is promising under different scenarios. Compared with independently combined heritability estimates in the case of multiple phenotypes, the proposed method significantly improves the performance by considering correlations among those phenotypes. We further demonstrate its application in heritability estimation and correlation analysis for the Oryza sativa rice dataset.

AVAILABILITY AND IMPLEMENTATION

An R package implementing the proposed method is available at https://github.com/xg-SUFE1/MultiRidgeVar, where covariance estimates are also given together with heritability estimates.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development and validation of an optimized marker set for genomic selection in southern U.S. rice breeding programs

The potential of genomic selection (GS) to increase the efficiency of breeding programs has been clearly demonstrated; however, the implementation of GS in rice (Oryza sativa L.) breeding programs has been limited. In recent years, efforts have begun to work toward implementing GS into the Louisiana State University (LSU) Agricultural Center rice breeding program. One of the first steps for successful GS implementation is to establish a suitable marker set for the target germplasm and a reliable, cost-effective genotyping platform capable of providing informative marker data with an adequate turnaround time. The objective of this study was to develop a marker set for routine GS and demonstrate its effectiveness in southern U.S. rice germplasm. The utility of the resulting marker set, the LSU500, for GS applications was demonstrated using four years of breeding data across 7,607 experimental lines and four elite biparental populations. The predictive ability of GS ranged from 0.13 to 0.78 for key traits across different market classes and yield trials. Comparisons between phenotypic selection and GS within biparental populations demonstrates similar performance of GS compared with phenotypic selection in predicting future performance. The prediction accuracies obtained with the LSU500 marker set demonstrates the utility of this marker set for cost-effective GS applications in southern U.S. rice breeding programs. The LSU500 marker set has been established through the genotyping service provider Agriplex Genomics, and in the future, it will undergo improvements to reduce the cost and increase the accuracy of GS.

A Bayesian random regression method using mixture priors for genome-enabled analysis of time-series high-throughput phenotyping data

The recent advancement in image-based phenotyping platforms enables the acquisition of large-scale nondestructive crop phenotypes measured at frequent intervals. To further understand the underlying genetic basis over a physiological process and improve plant breeding programs, the question of how to efficiently utilize these time-series measurements in genome-enabled analysis including genomic prediction and genome-wide association studies (GWASs) should be considered. In this paper, a Bayesian random regression model with mixture priors is developed to introduce more meaningful biological assumptions to the analysis of longitudinal traits. The mixture prior for marker effects in Bayes Cπ is implemented in our developed model (RR-BayesC) for demonstration purpose. The estimation of single-nucleotide polymorphism-specific effects that are related to the dynamic performance of crops and the accuracy of genomic prediction by RR-BayesC were studied through both simulated and real rice (Oryza sativa L.) data. For genomic prediction, three predictive scenarios were studied. In the simulated study, RR-BayesC showed a significantly higher prediction accuracy than that obtained by single-trait analysis, especially for days when heritability were low. In real data analysis, RR-BayesC showed relatively high prediction accuracy when forecast is required for phenotypes at later period (e.g., from 0.94 to 0.98 for lines with observations at an earlier period and from 0.65 to 0.67 for lines without any observations). For GWASs, inference of single markers and inference of genomic windows were conducted. In the simulated study, RR-BayesC showed its promising ability to distinguish quantitative trait loci (QTL) that are invariant to temporal covariates and QTL that interact with time. An association study of real data was also presented to demonstrate the application of RR-BayesC in real data analysis. In this paper, we develop a Bayesian random regression model that is able to incorporate mixture priors to marker effects and show improved performance of genomic prediction and GWASs for longitudinal data analysis based on both simulated and real data. The software tool JWAS offers routines to perform our proposed random regression analysis.

Candidate genes for anthocyanin pigmentation in rice stem revealed by GWAS and whole-genome resequencing

Anthocyanin pigment as a phenolic secondary metabolite is accumulated in areal organs of some rice cultivars. Despite several research attempts, the majority of genomic regions and candidate genes for purple-colored stem (Ps) resulting from anthocyanin pigmentation of rice leaf sheath have not been identified. A genome-wide association study (GWAS) and whole-genome resequencing (WGR) analysis was applied for genetic dissection of anthocyanin pigmentation of rice stem. Using GWAS, the genomic regions (on chromosomes 2, 4, and 6) tagged to eight single-nucleotide polymorphisms (SNPs) were identified to be significantly associated with purple stem, and in the vicinity of GWAS signals, 19 genes were highlighted as putative candidate genes. To narrow down the genomic regions more highly associated to the trait, a WGR study on recombinant inbred lines (RIL) with opposite phenotypes was conducted. After defining the DNA variation between reference genome, maternal parent and the two sister lines, a narrow genomic region on the short arm of chromosome 6 (4.7-6.2 Mbp interval) was identified to be highly associated with anthocyanin pigmentation of rice stem. In the interval, a few candidate genes with probable role in anthocyanin biosynthesis and accumulation were identified, which included five structural genes involved in the known pathways [one chalcone isomerase (CHI), two glycosyl transferases, and two UDP-flavonoid-3-O-glucosyl (UFGT) transferases] and two transcription factors [one basic helix-loop-helix (bHLH)- and one myeloblastosis (MYB)-coding genes]. The identified candidate genes can be used in breeding programs of rice or other Gramineae species for anthocyanin accumulation in areal organs.

Genetic basis of sorghum leaf width and its potential as a surrogate for transpiration efficiency

Leaf width was correlated with plant-level transpiration efficiency and associated with 19 QTL in sorghum, suggesting it could be a surrogate for transpiration efficiency in large breeding program. Enhancing plant transpiration efficiency (TE) by reducing transpiration without compromising photosynthesis and yield is a desirable selection target in crop improvement programs. While narrow individual leaf width has been correlated with greater intrinsic water use efficiency in C₄ species, the extent to which this translates to greater plant TE has not been investigated. The aims of this study were to evaluate the correlation of leaf width with TE at the whole-plant scale and investigate the genetic control of leaf width in sorghum. Two lysimetry experiments using 16 genotypes varying for stomatal conductance and three field trials using a large sorghum diversity panel (n = 701 lines) were conducted. Negative associations of leaf width with plant TE were found in the lysimetry experiments, suggesting narrow leaves may result in reduced plant transpiration without trade-offs in biomass accumulation. A wide range in width of the largest leaf was found in the sorghum diversity panel with consistent ranking among sorghum races, suggesting that environmental adaptation may have a role in modifying leaf width. Nineteen QTL were identified by genome-wide association studies on leaf width adjusted for flowering time. The QTL identified showed high levels of correspondence with those in maize and rice, suggesting similarities in the genetic control of leaf width across cereals. Three a priori candidate genes for leaf width, previously found to regulate dorsoventrality, were identified based on a 1-cM threshold. This study provides useful physiological and genetic insights for potential manipulation of leaf width to improve plant adaptation to diverse environments.

A consensus map for quality traits in durum wheat based on genome-wide association studies and detection of ortho-meta QTL across cereal species

The present work focused on the identification of durum wheat QTL hotspots from a collection of genome-wide association studies, for quality traits, such as grain protein content and composition, yellow color, fiber, grain microelement content (iron, magnesium, potassium, selenium, sulfur, calcium, cadmium), kernel vitreousness, semolina, and dough quality test. For the first time a total of 10 GWAS studies, comprising 395 marker-trait associations (MTA) on 57 quality traits, with more than 1,500 genotypes from 9 association panels, were used to investigate consensus QTL hotspots representative of a wide durum wheat genetic variation. MTA were found distributed on all the A and B genomes chromosomes with minimum number of MTA observed on chromosome 5B (15) and a maximum of 45 on chromosome 7A, with an average of 28 MTA per chromosome. The MTA were equally distributed on A (48%) and B (52%) genomes and allowed the identification of 94 QTL hotspots. Synteny maps for QTL were also performed in Zea mays, Brachypodium, and Oryza sativa, and candidate gene identification allowed the association of genes involved in biological processes playing a major role in the control of quality traits.

Genome-Wide Associative Study of Phenotypic Parameters of the 3D Body Model of Aberdeen Angus Cattle with Multiple Depth Cameras

Simple Summary

This article aims to develop a new approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals. Genome-wide associations analysis revealed the following potential loci of quantitative traits on cattle chromosomes for chest width, chest girth, and meat output on bones.

Abstract

In beef cattle breeding, genome-wide association studies (GWAS) using single nucleotide polymorphisms (SNPs) arrays can reveal many loci of various production traits, such as growth, productivity, and meat quality. With the development of genome sequencing technologies, new opportunities are opening up for more accurate identification of areas associated with these traits. This article aims to develop a novel approach to the lifetime evaluation of cattle by 3-D visualization of economic-biological and genetic features. The purpose of this study was to identify significant variants underlying differences in the qualitative characteristics of meat, using imputed data on the sequence of the entire genome. Samples of biomaterial of young Aberdeen-Angus breed cattle (n = 96) were the material for carrying out genome-wide SNP genotyping. Genotyping was performed using a high-density DNA chip Bovine GPU HD BeadChip (Illumina Inc., San Diego, CA, USA), containing ~150 thousand SNPs. The following indicators were selected as phenotypic features: chest width and chest girth retrieved by 3-D model and meat output on the bones. Correlation analysis showed a reliable positive relationship between chest width and meat output on the bones, which can potentially be used for lifetime evaluation of meat productivity of animals.

Selective and comparative genome architecture of Asian cultivated rice (Oryza sativa L.) attributed to domestication and modern breeding

INTRODUCTION

Rice, Oryza sativa L. (Os), is one of the oldest domesticated cereals that has also gone through extensive improvement in modern breeding.

OBJECTIVES

How rice was domesticated and impacted by modern breeding.

METHODS

We performed comprehensive analyses of genomic sequences of 504 accessions of Os and 456 accessions of O. rufipogon/O. nivara (Or).

RESULTS

The natural selection on Or before domestication and the natural and artificial selection during domestication together shaped the well-differentiated genomes of two subspecies, geng(j) (japonica) and xian(i) (indica), while breeding has made apparent genomic imprints between landrace and modern varieties of each subspecies, and also between primary modern and advanced modern varieties of xian(i). Selection during domestication and breeding left genome-wide selective signals covering ∼ 22.8 % and ∼ 8.6 % of the Os genome, significantly reduced within-population genomic diversity by ∼ 22 % in xian(i) and ∼ 53 % in geng(j) plus more pronounced subspecific differentiation. Only ∼ 10 % reduction in the total genomic diversity was observed between the Os and Or populations, indicating domestication did not suffer severe genetic bottleneck.

CONCLUSION

Our results revealed clear differentiation of the Or accessions into three large populations, two of which correspond to the well-differentiated Os subspecies, geng(j) and xian(i). Improved productivity and common changes in the same suit of adaptive traits in xian(i) and geng(j) during domestication and breeding resulted apparently from compensatory and convergent selections for different genes/alleles acting in the common KEGG terms and/or same gene families, and thus maintaining or even increasing the within population diversity and subspecific differentiation of Os, while more genes/alleles of novel function were selected during domestication than modern breeding. Our results supported the multiple independent domestication of Os in Asia and suggest the more efficient utilization of the rich diversity within Os by exploiting inter-subspecific and among population diversity in future rice improvement.

The divergence of brassinosteroid sensitivity between rice subspecies involves natural variation conferring altered internal auto-binding of OsBSK2

Japonica/geng and indica/xian are two major rice (Oryza sativa) subspecies with multiple divergent traits, but how these traits are related and interact within each subspecies remains elusive. Brassinosteroids (BRs) are a class of steroid phytohormones that modulate many important agronomic traits in rice. Here, using different physiological assays, we revealed that japonica rice exhibits an overall lower BR sensitivity than indica. Extensive screening of BR signaling genes led to the identification of a set of genes distributed throughout the primary BR signaling pathway with divergent polymorphisms. Among these, we demonstrate that the C38/T variant in BR Signaling Kinase2 (OsBSK2), causing the amino acid change P13L, plays a central role in mediating differential BR signaling in japonica and indica rice. OsBSK2^L13 in indica plays a greater role in BR signaling than OsBSK2^P13 in japonica by affecting the auto-binding and protein accumulation of OsBSK2. Finally, we determined that OsBSK2 is involved in a number of divergent traits in japonica relative to indica rice, including grain shape, tiller number, cold adaptation, and nitrogen-use efficiency. Our study suggests that the natural variation in OsBSK2 plays a key role in the divergence of BR signaling, which underlies multiple divergent traits between japonica and indica.

Sorghum Association Panel whole-genome sequencing establishes cornerstone resource for dissecting genomic diversity

Association mapping panels represent foundational resources for understanding the genetic basis of phenotypic diversity and serve to advance plant breeding by exploring genetic variation across diverse accessions. We report the whole-genome sequencing (WGS) of 400 sorghum (Sorghum bicolor (L.) Moench) accessions from the Sorghum Association Panel (SAP) at an average coverage of 38× (25-72×), enabling the development of a high-density genomic marker set of 43 983 694 variants including single-nucleotide polymorphisms (approximately 38 million), insertions/deletions (indels) (approximately 5 million), and copy number variants (CNVs) (approximately 170 000). We observe slightly more deletions among indels and a much higher prevalence of deletions among CNVs compared to insertions. This new marker set enabled the identification of several novel putative genomic associations for plant height and tannin content, which were not identified when using previous lower-density marker sets. WGS identified and scored variants in 5-kb bins where available genotyping-by-sequencing (GBS) data captured no variants, with half of all bins in the genome falling into this category. The predictive ability of genomic best unbiased linear predictor (GBLUP) models was increased by an average of 30% by using WGS markers rather than GBS markers. We identified 18 selection peaks across subpopulations that formed due to evolutionary divergence during domestication, and we found six F_st peaks resulting from comparisons between converted lines and breeding lines within the SAP that were distinct from the peaks associated with historic selection. This population has served and continues to serve as a significant public resource for sorghum research and demonstrates the value of improving upon existing genomic resources.

Selection of parental lines for plant breeding via genomic prediction

A set of superior parental lines is imperative for the development of high-performing inbred lines in any biparental crossing program for crops. The main objectives of this study are to (a) develop a genomic prediction approach to identify superior parental lines for multi-trait selection, and (b) generate a software package for users to execute the proposed approach before conducting field experiments. According to different breeding goals of the target traits, a novel selection index integrating information from genomic-estimated breeding values (GEBVs) of candidate accessions was proposed to evaluate the composite performance of simulated progeny populations. Two rice (Oryza sativa L.) genome datasets were analyzed to illustrate the potential applications of the proposed approach. One dataset applied to the parental selection for producing inbred lines with satisfactory performance in primary and secondary traits simultaneously. The other one applied to demonstrate the application of producing inbred lines with high adaptability to different environments. Overall, the results showed that incorporating GEBV and genomic diversity into a selection strategy based on the proposed selection index could assist in selecting superior parents to meet the desired breeding goals and increasing long-term genetic gain. An R package, called IPLGP, was generated to facilitate the widespread application of the approach.

Maintenance of divergent lineages of the Rice Blast Fungus Pyricularia oryzae through niche separation, loss of sex and post-mating genetic incompatibilities

Many species of fungal plant pathogens coexist as multiple lineages on the same host, but the factors underlying the origin and maintenance of population structure remain largely unknown. The rice blast fungus Pyricularia oryzae is a widespread model plant pathogen displaying population subdivision. However, most studies of natural variation in P. oryzae have been limited in genomic or geographic resolution, and host adaptation is the only factor that has been investigated extensively as a contributor to population subdivision. In an effort to complement previous studies, we analyzed genetic and phenotypic diversity in isolates of the rice blast fungus covering a broad geographical range. Using single-nucleotide polymorphism genotyping data for 886 isolates sampled from 152 sites in 51 countries, we showed that population subdivision of P. oryzae in one recombining and three clonal lineages with broad distributions persisted with deeper sampling. We also extended previous findings by showing further population subdivision of the recombining lineage into one international and three Asian clusters, and by providing evidence that the three clonal lineages of P. oryzae were found in areas with different prevailing environmental conditions, indicating niche separation. Pathogenicity tests and bioinformatic analyses using an extended set of isolates and rice varieties indicated that partial specialization to rice subgroups contributed to niche separation between lineages, and differences in repertoires of putative virulence effectors were consistent with differences in host range. Experimental crosses revealed that female sterility and early post-mating genetic incompatibilities acted as strong additional barriers to gene flow between clonal lineages. Our results demonstrate that the spread of a fungal pathogen across heterogeneous habitats and divergent populations of a crop species can lead to niche separation and reproductive isolation between distinct, widely distributed, lineages.

Genome-Wide Association Analysis of Resistance to Pseudoperonospora cubensis in Citron Watermelon

Cultivated sweet watermelon (Citrullus lanatus) is an important vegetable crop for millions of people around the world. There are limited sources of resistance to economically important diseases within C. lanatus, whereas C. amarus has a reservoir of traits that can be exploited to improve C. lanatus for resistance to biotic and abiotic stresses. Cucurbit downy mildew (CDM), caused by Pseudoperonospora cubensis, is an emerging threat to watermelon production. We screened 122 C. amarus accessions for resistance to CDM over two tests (environments). The accessions were genotyped by whole-genome resequencing to generate 2,126,759 single nucleotide polymorphic (SNP) markers. A genome-wide association study was deployed to uncover marker-trait associations and identify candidate genes underlying resistance to CDM. Our results indicate the presence of wide phenotypic variability (1.1 to 57.8%) for leaf area infection, representing a 50.7-fold variation for CDM resistance across the C. amarus germplasm collection. Broad-sense heritability estimate was 0.55, implying the presence of moderate genetic effects for resistance to CDM. The peak SNP markers associated with resistance to P. cubensis were located on chromosomes Ca03, Ca05, Ca07, and Ca11. The significant SNP markers accounted for up to 30% of the phenotypic variation and were associated with promising candidate genes encoding leucine-rich repeat receptor-like protein kinase and the WRKY transcription factor. This information will be useful in understanding the genetic architecture of the P. cubensis-Citrullus spp. patho-system as well as development of resources for genomics-assisted breeding for resistance to CDM in watermelon.

A GWAS identified a major QTL for resistance to Fusarium wilt (Fusarium oxysporum f. sp. vasinfectum) race 4 in a MAGIC population of Upland cotton and a meta-analysis of QTLs for Fusarium wilt resistance

A major QTL conferring resistance to Fusarium wilt race 4 in a narrow region of chromosome D02 was identified in a MAGIC population of 550 RILs of Upland cotton. Numerous studies have been conducted to investigate the genetic basis of Fusarium wilt (FW, caused by Fusarium oxysporum f. sp. vasinfectum, FOV) resistance using bi-parental and association mapping populations in cotton. In this study, a multi-parent advanced generation inter-cross (MAGIC) population of 550 recombinant inbred lines (RILs), together with their 11 Upland cotton (Gossypium hirsutum) parents, was used to identify QTLs for FOV race 4 (FOV4) resistance. Among the parents, Acala Ultima, M-240 RNR, and Stoneville 474 were the most resistant, while Deltapine Acala 90, Coker 315, and Stoneville 825 were the most susceptible. Twenty-two MAGIC lines were consistently resistant to FOV4. Through a genome-wide association study (GWAS) based on 473,516 polymorphic SNPs, a major FOV4 resistance QTL within a narrow region on chromosomes D02 was detected, allowing identification of 14 candidate genes. Additionally, a meta-analysis of 133 published FW resistance QTLs showed a D subgenome and individual chromosome bias and no correlation between homeologous chromosome pairs. This study represents the first GWAS study using a largest genetic population and the most comprehensive meta-analysis for FW resistance in cotton. The results illustrated that 550 lines were not enough for high resolution mapping to pinpoint a candidate gene, and experimental errors in phenotyping cotton for FW resistance further compromised the accuracy and precision in QTL localization and identification of candidate genes. This study identified FOV4-resistant parents and MAGIC lines, and the first major QTL for FOV4 resistance in Upland cotton, providing useful information for developing FOV4-resistant cultivars and further genomic studies towards identification of causal genes for FOV4 resistance in cotton.

Genetic control of grain appearance quality in rice

Grain appearance, one of the key determinants of rice quality, reflects the ability to attract consumers, and is characterized by four major properties: grain shape, chalkiness, transparency, and color. Mining of valuable genes, genetic mechanisms, and breeding cultivars with improved grain appearance are essential research areas in rice biology. However, grain appearance is a complex and comprehensive trait, making it challenging to understand the molecular details, and therefore, achieve precise improvement. This review highlights the current findings of grain appearance control, including a detailed description of the key genes involved in the formation of grain appearance, and the major environmental factors affecting chalkiness. We also discuss the integration of current knowledge on valuable genes to enable accurate breeding strategies for generation of rice grains with superior appearance quality.

Genome-Wide Association Mapping Reveals Novel Putative Gene Candidates Governing Reproductive Stage Heat Stress Tolerance in Rice

Temperature rise predicted for the future will severely affect rice productivity because the crop is highly sensitive to heat stress at the reproductive stage. Breeding tolerant varieties is an economically viable option to combat heat stress, for which the knowledge of target genomic regions associated with the reproductive stage heat stress tolerance (RSHT) is essential. A set of 192 rice genotypes of diverse origins were evaluated under natural field conditions through staggered sowings for RSHT using two surrogate traits, spikelet fertility and grain yield, which showed significant reduction under heat stress. These genotypes were genotyped using a 50 k SNP array, and the association analysis identified 10 quantitative trait nucleotides (QTNs) for grain yield, of which one QTN (qHTGY8.1) was consistent across the different models used. Only two out of 10 MTAs coincided with the previously reported QTLs, making the remaing eight novel. A total of 22 QTNs were observed for spikelet fertility, among which qHTSF5.1 was consistently found across three models. Of the QTNs identified, seven coincided with previous reports, while the remaining QTNs were new. The genes near the QTNs were found associated with the protein–protein interaction, protein ubiquitination, stress signal transduction, and so forth, qualifying them to be putative for RSHT. An in silico expression analysis revealed the predominant expression of genes identified for spikelet fertility in reproductive organs. Further validation of the biological relevance of QTNs in conferring heat stress tolerance will enable their utilization in improving the reproductive stage heat stress tolerance in rice.