Genome-Wide Association Studies for Pasmo Resistance in Flax (Linum usitatissimum L.)

Genome-wide association study and genomic selection of flax powdery mildew in Xinjiang Province

Flax powdery mildew (PM), caused by Oidium lini, is a globally distributed fungal disease of flax, and seriously impairs its yield and quality. To data, only three resistance genes and a few putative quantitative trait loci (QTL) have been reported for flax PM resistance. To dissect the resistance mechanism against PM and identify resistant genetic regions, based on four years of phenotypic datasets (2017, 2019 to 2021), a genome-wide association study (GWAS) was performed on 200 flax core accessions using 674,074 SNPs and 7 models. A total of 434 unique quantitative trait nucleotides (QTNs) associated with 331 QTL were detected. Sixty-four loci shared in at least two datasets were found to be significant in haplotype analyses, and 20 of these sites were shared by multiple models. Simultaneously, a large-effect locus (qDI 11.2) was detected repeatedly, which was present in the mapping study of flax pasmo resistance loci. Oil flax had more QTL with positive-effect or favorable alleles (PQTL) and showed higher PM resistance than fiber flax, indicating that effects of these QTL were mainly additive. Furthermore, an excellent resistant variety C120 was identified and can be used to promote planting. Based on 331 QTLs identified through GWAS and the statistical model GBLUP, a genomic selection (GS) model related to flax PM resistance was constructed, and the prediction accuracy rate was 0.96. Our results provide valuable insights into the genetic basis of resistance and contribute to the advancement of breeding programs.

New Insights into the Genetic Basis of Lysine Accumulation in Rice Revealed by Multi-Model GWAS

Lysine is an essential amino acid that cannot be synthesized in humans. Rice is a global staple food for humans but has a rather low lysine content. Identification of the quantitative trait nucleotides (QTNs) and genes underlying lysine content is crucial to increase lysine accumulation. In this study, five grain and three leaf lysine content datasets and 4,630,367 single nucleotide polymorphisms (SNPs) of 387 rice accessions were used to perform a genome-wide association study (GWAS) by ten statistical models. A total of 248 and 71 common QTNs associated with grain/leaf lysine content were identified. The accuracy of genomic selection/prediction RR-BLUP models was up to 0.85, and the significant correlation between the number of favorable alleles per accession and lysine content was up to 0.71, which validated the reliability and additive effects of these QTNs. Several key genes were uncovered for fine-tuning lysine accumulation. Additionally, 20 and 30 QTN-by-environment interactions (QEIs) were detected in grains/leaves. The QEI-sf0111954416 candidate gene LOC_Os01g21380 putatively accounted for gene-by-environment interaction was identified in grains. These findings suggested the application of multi-model GWAS facilitates a better understanding of lysine accumulation in rice. The identified QTNs and genes hold the potential for lysine-rich rice with a normal phenotype.

Multi-omics analysis reveals novel loci and a candidate regulatory gene of unsaturated fatty acids in soybean (Glycine max (L.) Merr)

BACKGROUND

Soybean is a major oil crop; the nutritional components of soybean oil are mainly controlled by unsaturated fatty acids (FA). Unsaturated FAs mainly include oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). The genetic architecture of unsaturated FAs in soybean seeds has not been fully elucidated, although many independent studies have been conducted. A 3 V multi-locus random single nucleotide polymorphism (SNP)-effect mixed linear model (3VmrMLM) was established to identify quantitative trait loci (QTLs) and QTL-by-environment interactions (QEIs) for complex traits.

RESULTS

In this study, 194 soybean accessions with 36,981 SNPs were calculated using the 3VmrMLM model. As a result, 94 quantitative trait nucleotides (QTNs) and 19 QEIs were detected using single-environment (QTN) and multi-environment (QEI) methods. Three significant QEIs, namely rs4633292, rs39216169, and rs14264702, overlapped with a significant single-environment QTN.

CONCLUSIONS

For QTNs and QEIs, further haplotype analysis of candidate genes revealed that the Glyma.03G040400 and Glyma.17G236700 genes were beneficial haplotypes that may be associated with unsaturated FAs. This result provides ideas for the identification of soybean lipid-related genes and provides insights for breeding high oil soybean varieties in the future.

Medicinal Plants for the Treatment of Neuropathic Pain: A Review of Randomized Controlled Trials

Neuropathic pain is a disabling condition caused by various diseases and can profoundly impact the quality of life. Unfortunately, current treatments often do not produce complete amelioration and can be associated with potential side effects. Recently, herbal drugs have garnered more attention as an alternative or a complementary treatment. In this article, we summarized the results of randomized clinical trials to evaluate the effects of various phytomedicines on neuropathic pain. In addition, we discussed their main bioactive components and potential mechanisms of action to provide a better view of the application of herbal drugs for treating neuropathic pain.

The Genetic Dissection of Nitrogen Use-Related Traits in Flax (Linum usitatissimum L.) at the Seedling Stage through the Integration of Multi-Locus GWAS, RNA-seq and Genomic Selection

Nitrogen (N), the most important macro-nutrient for plant growth and development, is a key factor that determines crop yield. Yet its excessive applications pollute the environment and are expensive. Hence, studying nitrogen use efficiency (NUE) in crops is fundamental for sustainable agriculture. Here, an association panel consisting of 123 flax accessions was evaluated for 21 NUE-related traits at the seedling stage under optimum N (N+) and N deficiency (N-) treatments to dissect the genetic architecture of NUE-related traits using a multi-omics approach integrating genome-wide association studies (GWAS), transcriptome analysis and genomic selection (GS). Root traits exhibited significant and positive correlations with NUE under N- conditions (r = 0.33 to 0.43, p < 0.05). A total of 359 QTLs were identified, accounting for 0.11% to 23.1% of the phenotypic variation in NUE-related traits. Transcriptomic analysis identified 1034 differentially expressed genes (DEGs) under contrasting N conditions. DEGs involved in N metabolism, root development, amino acid transport and catabolism and others, were found near the QTLs. GS models to predict NUE stress tolerance index (NUE_STI) trait were tested using a random genome-wide SNP dataset and a GWAS-derived QTLs dataset. The latter produced superior prediction accuracy (r = 0.62 to 0.79) compared to the genome-wide SNP marker dataset (r = 0.11) for NUE_STI. Our results provide insights into the QTL architecture of NUE-related traits, identify candidate genes for further studies, and propose genomic breeding tools to achieve superior NUE in flax under low N input.

Genome-wide association studies using multi-models and multi-SNP datasets provide new insights into pasmo resistance in flax

Introduction

Flax (Linum usitatissimum L.) is an economically important crop due to its oil and fiber. However, it is prone to various diseases, including pasmo caused by the fungus Septoria linicola.

Methods

In this study, we conducted field evaluations of 445 flax accessions over a five-year period (2012-2016) to assess their resistance to pasmo A total of 246,035 single nucleotide polymorphisms (SNPs) were used for genetic analysis. Four statistical models, including the single-locus model GEMMA and the multi-locus models FarmCPU, mrMLM, and 3VmrMLM, were assessed to identify quantitative trait nucleotides (QTNs) associated with pasmo resistance.

Results

We identified 372 significant QTNs or 132 tag QTNs associated with pasmo resistance from five pasmo resistance datasets (PAS2012-PAS2016 and the 5-year average, namely PASmean) and three genotypic datasets (the all SNPs/ALL, the gene-based SNPs/GB and the RGA-based SNPs/RGAB). The tag QTNs had R2 values of 0.66-16.98% from the ALL SNP dataset, 0.68-20.54%from the GB SNP dataset, and 0.52-22.42% from the RGAB SNP dataset. Of these tag QTNs, 93 were novel. Additionally, 37 resistance gene analogs (RGAs)co-localizing with 39 tag QTNs were considered as potential candidates for controlling pasmo resistance in flax and 50 QTN-by-environment interactions(QEIs) were identified to account for genes by environmental interactions. Nine RGAs were predicted as candidate genes for ten QEIs.

Discussion

Our results suggest that pasmo resistance in flax is polygenic and potentially influenced by environmental factors. The identified QTNs provide potential targets for improving pasmo resistance in flax breeding programs. This study sheds light on the genetic basis of pasmo resistance and highlights the importance of considering both genetic and environmental factors in breeding programs for flax.

Overview and Management of the Most Common Eukaryotic Diseases of Flax (Linum usitatissimum)

Flax is an important crop cultivated for its seeds and fibers. It is widely grown in temperate regions, with an increase in cultivation areas for seed production (linseed) in the past 50 years and for fiber production (fiber flax) in the last decade. Among fiber-producing crops, fiber flax is the most valuable species. Linseed is the highest omega-3 oleaginous crop, and its consumption provides several benefits for animal and human health. However, flax production is impacted by various abiotic and biotic factors that affect yield and quality. Among biotic factors, eukaryotic diseases pose a significant threat to both seed production and fiber quality, which highlights the economic importance of controlling these diseases. This review focuses on the major eukaryotic diseases that affect flax in the field, describing the pathogens, their transmission modes and the associated plant symptoms. Moreover, this article aims to identify the challenges in disease management and provide future perspectives to overcome these biotic stresses in flax cultivation. By emphasizing the key diseases and their management, this review can aid in promoting sustainable and profitable flax production.

Molecular Advances to Combat Different Biotic and Abiotic Stresses in Linseed (Linum usitatissimum L.): A Comprehensive Review

Flax, or linseed, is considered a "superfood", which means that it is a food with diverse health benefits and potentially useful bioactive ingredients. It is a multi-purpose crop that is prized for its seed oil, fibre, nutraceutical, and probiotic qualities. It is suited to various habitats and agro-ecological conditions. Numerous abiotic and biotic stressors that can either have a direct or indirect impact on plant health are experienced by flax plants as a result of changing environmental circumstances. Research on the impact of various stresses and their possible ameliorators is prompted by such expectations. By inducing the loss of specific alleles and using a limited number of selected varieties, modern breeding techniques have decreased the overall genetic variability required for climate-smart agriculture. However, gene banks have well-managed collectionns of landraces, wild linseed accessions, and auxiliary Linum species that serve as an important source of novel alleles. In the past, flax-breeding techniques were prioritised, preserving high yield with other essential traits. Applications of molecular markers in modern breeding have made it easy to identify quantitative trait loci (QTLs) for various agronomic characteristics. The genetic diversity of linseed species and the evaluation of their tolerance to abiotic stresses, including drought, salinity, heavy metal tolerance, and temperature, as well as resistance to biotic stress factors, viz., rust, wilt, powdery mildew, and alternaria blight, despite addressing various morphotypes and the value of linseed as a supplement, are the primary topics of this review.

Identification of new potential molecular actors related to fiber quality in flax through Omics

One of the biggest challenges for a more widespread utilization of plant fibers is to better understand the different molecular factors underlying the variability in fineness and mechanical properties of both elementary and scutched fibers. Accordingly, we analyzed genome-wide transcription profiling from bast fiber bearing tissues of seven different flax varieties (4 spring, 2 winter fiber varieties and 1 winter linseed) and identified 1041 differentially expressed genes between varieties, of which 97 were related to cell wall metabolism. KEGG analysis highlighted a number of different enriched pathways. Subsequent statistical analysis using Partial Least-Squares Discriminant Analysis showed that 73% of the total variance was explained by the first 3 X-variates corresponding to 56 differentially expressed genes. Calculation of Pearson correlations identified 5 genes showing a strong correlation between expression and morphometric data. Two-dimensional gel proteomic analysis on the two varieties showing the most discriminant and significant differences in morphometrics revealed 1490 protein spots of which 108 showed significant differential abundance. Mass spectrometry analysis successfully identified 46 proteins representing 32 non-redundant proteins. Statistical clusterization based on the expression level of genes corresponding to the 32 proteins showed clear discrimination into three separate clusters, reflecting the variety type (spring-/winter-fiber/oil). Four of the 32 proteins were also highly correlated with morphometric features. Examination of predicted functions for the 9 (5 + 4) identified genes highlighted lipid metabolism and senescence process. Calculation of Pearson correlation coefficients between expression data and retted fiber mechanical measurements (strength and maximum force) identified 3 significantly correlated genes. The genes were predicted to be connected to cell wall dynamics, either directly (Expansin-like protein), or indirectly (NAD(P)-binding Rossmann-fold superfamily protein). Taken together, our results have allowed the identification of molecular actors potentially associated with the determination of both in-planta fiber morphometrics, as well as ex-planta fiber mechanical properties, both of which are key parameters for elementary fiber and scutched fiber quality in flax.

Multi-locus genome-wide association study of fusarium head blight in relation to days to anthesis and plant height in a spring wheat association panel

Fusarium head blight (FHB) is a highly destructive fungal disease of wheat to which host resistance is quantitatively inherited and largely influenced by the environment. Resistance to FHB has been associated with taller height and later maturity; however, a further understanding of these relationships is needed. An association mapping panel (AMP) of 192 predominantly Canadian spring wheat was genotyped with the wheat 90K single-nucleotide polymorphism (SNP) array. The AMP was assessed for FHB incidence (INC), severity (SEV) and index (IND), days to anthesis (DTA), and plant height (PLHT) between 2015 and 2017 at three Canadian FHB-inoculated nurseries. Seven multi-environment trial (MET) datasets were deployed in a genome-wide association study (GWAS) using a single-locus mixed linear model (MLM) and a multi-locus random SNP-effect mixed linear model (mrMLM). MLM detected four quantitative trait nucleotides (QTNs) for INC on chromosomes 2D and 3D and for SEV and IND on chromosome 3B. Further, mrMLM identified 291 QTNs: 50 (INC), 72 (SEV), 90 (IND), 41 (DTA), and 38 (PLHT). At two or more environments, 17 QTNs for FHB, DTA, and PLHT were detected. Of these 17, 12 QTNs were pleiotropic for FHB traits, DTA, and PLHT on chromosomes 1A, 1D, 2D, 3B, 5A, 6B, 7A, and 7B; two QTNs for DTA were detected on chromosomes 1B and 7A; and three PLHT QTNs were located on chromosomes 4B and 6B. The 1B DTA QTN and the three pleiotropic QTNs on chromosomes 1A, 3B, and 6B are potentially identical to corresponding quantitative trait loci (QTLs) in durum wheat. Further, the 3B pleiotropic QTN for FHB INC, SEV, and IND co-locates with TraesCS3B02G024900 within the Fhb1 region on chromosome 3B and is ~3 Mb from a cloned Fhb1 candidate gene TaHRC. While the PLHT QTN on chromosome 6B is putatively novel, the 1B DTA QTN co-locates with a disease resistance protein located ~10 Mb from a Flowering Locus T1-like gene TaFT3-B1, and the 7A DTA QTN is ~5 Mb away from a maturity QTL QMat.dms-7A.3 of another study. GWAS and QTN candidate genes enabled the characterization of FHB resistance in relation to DTA and PLHT. This approach should eventually generate additional and reliable trait-specific markers for breeding selection, in addition to providing useful information for FHB trait discovery.

Genetic dissection of thousand-seed weight in linseed (Linum usitatissimum L.) using multi-locus genome-wide association study

Flaxseed/linseed is an important oilseed crop having applications in the food, nutraceutical, and paint industry. Seed weight is one of the most crucial determinants of seed yield in linseed. Here, quantitative trait nucleotides (QTNs) associated with thousand-seed weight (TSW) have been identified using multi-locus genome-wide association study (ML-GWAS). Field evaluation was carried out in five environments in multi-year-location trials. SNP genotyping information of the AM panel of 131 accessions comprising 68,925 SNPs was employed for ML-GWAS. From the six ML-GWAS methods employed, five methods helped identify a total of 84 unique significant QTNs for TSW. QTNs identified in ≥ 2 methods/environments were designated as stable QTNs. Accordingly, 30 stable QTNs have been identified for TSW accounting up to 38.65% trait variation. Alleles with positive effect on trait were analyzed for 12 strong QTNs with r ² ≥ 10.00%, which showed significant association of specific alleles with higher trait value in three or more environments. A total of 23 candidate genes have been identified for TSW, which included B3 domain-containing transcription factor, SUMO-activating enzyme, protein SCARECROW, shaggy-related protein kinase/BIN2, ANTIAUXIN-RESISTANT 3, RING-type E3 ubiquitin transferase E4, auxin response factors, WRKY transcription factor, and CBS domain-containing protein. In silico expression analysis of candidate genes was performed to validate their possible role in different stages of seed development process. The results from this study provide significant insight and elevate our understanding on genetic architecture of TSW trait in linseed.

Diversity of Linum genetic resources in global genebanks: from agro-morphological characterisation to novel genomic technologies - a review

Linseed or flaxseed is a well-recognized nutritional food with nutraceutical properties owing to high omega-3 fatty acid (α-Linolenic acid), dietary fiber, quality protein, and lignan content. Currently, linseed enjoys the status of a 'superfood' and its integration in the food chain as a functional food is evolving continuously as seed constituents are associated with lowering the risk of chronic ailments, such as heart diseases, cancer, diabetes, and rheumatoid arthritis. This crop also receives much attention in the handloom and textile sectors as the world's coolest fabric linen is made up of its stem fibers which are endowed with unique qualities such as luster, tensile strength, density, bio-degradability, and non-hazardous nature. Worldwide, major linseed growing areas are facing erratic rainfall and temperature patterns affecting flax yield, quality, and response to biotic stresses. Amid such changing climatic regimes and associated future threats, diverse linseed genetic resources would be crucial for developing cultivars with a broad genetic base for sustainable production. Furthermore, linseed is grown across the world in varied agro-climatic conditions; therefore it is vital to develop niche-specific cultivars to cater to diverse needs and keep pace with rising demands globally. Linseed genetic diversity conserved in global genebanks in the form of germplasm collection from natural diversity rich areas is expected to harbor genetic variants and thus form crucial resources for breeding tailored crops to specific culinary and industrial uses. Global genebank collections thus potentially play an important role in supporting sustainable agriculture and food security. Currently, approximately 61,000 germplasm accessions of linseed including 1,127 wild accessions are conserved in genebanks/institutes worldwide. This review analyzes the current status of Linum genetic resources in global genebanks, evaluation for agro-morphological traits, stress tolerance, and nutritional profiling to promote their effective use for sustainable production and nutrition enhancement in our modern diets.

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.

Resequencing worldwide spinach germplasm for identification of field resistance QTLs to downy mildew and assessment of genomic selection methods

Downy mildew, commercially the most important disease of spinach, is caused by the obligate oomycete Peronospora effusa. In the past two decades, new pathogen races have repeatedly overcome the resistance used in newly released cultivars, urging the need for more durable resistance. Commercial spinach cultivars are bred with major R genes to impart resistance to downy mildew pathogens and are effective against some pathogen races/isolates. This work aimed to evaluate the worldwide USDA spinach germplasm collections and commercial cultivars for resistance to downy mildew pathogen in the field condition under natural inoculum pressure and conduct genome wide association analysis (GWAS) to identify resistance-associated genomic regions (alleles). Another objective was to evaluate the prediction accuracy (PA) using several genomic prediction (GP) methods to assess the potential implementation of genomic selection (GS) to improve spinach breeding for resistance to downy mildew pathogen. More than four hundred diverse spinach genotypes comprising USDA germplasm accessions and commercial cultivars were evaluated for resistance to downy mildew pathogen between 2017-2019 in Salinas Valley, California and Yuma, Arizona. GWAS was performed using single nucleotide polymorphism (SNP) markers identified via whole genome resequencing (WGR) in GAPIT and TASSEL programs; detected 14, 12, 5, and 10 significantly associated SNP markers with the resistance from four tested environments, respectively; and the QTL alleles were detected at the previously reported region of chromosome 3 in three of the four experiments. In parallel, PA was assessed using six GP models and seven unique marker datasets for field resistance to downy mildew pathogen across four tested environments. The results suggest the suitability of GS to improve field resistance to downy mildew pathogen. The QTL, SNP markers, and PA estimates provide new information in spinach breeding to select resistant plants and breeding lines through marker-assisted selection (MAS) and GS, eventually helping to accumulate beneficial alleles for durable disease resistance.

Integrated omics approaches for flax improvement under abiotic and biotic stress: Current status and future prospects

Flax (Linum usitatissimum L.) or linseed is one of the important industrial crops grown all over the world for seed oil and fiber. Besides oil and fiber, flax offers a wide range of nutritional and therapeutic applications as a feed and food source owing to high amount of α-linolenic acid (omega-3 fatty acid), lignans, protein, minerals, and vitamins. Periodic losses caused by unpredictable environmental stresses such as drought, heat, salinity-alkalinity, and diseases pose a threat to meet the rising market demand. Furthermore, these abiotic and biotic stressors have a negative impact on biological diversity and quality of oil/fiber. Therefore, understanding the interaction of genetic and environmental factors in stress tolerance mechanism and identification of underlying genes for economically important traits is critical for flax improvement and sustainability. In recent technological era, numerous omics techniques such as genomics, transcriptomics, metabolomics, proteomics, phenomics, and ionomics have evolved. The advancements in sequencing technologies accelerated development of genomic resources which facilitated finer genetic mapping, quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection in major cereal and oilseed crops including flax. Extensive studies in the area of genomics and transcriptomics have been conducted post flax genome sequencing. Interestingly, research has been focused more for abiotic stresses tolerance compared to disease resistance in flax through transcriptomics, while the other areas of omics such as metabolomics, proteomics, ionomics, and phenomics are in the initial stages in flax and several key questions remain unanswered. Little has been explored in the integration of omic-scale data to explain complex genetic, physiological and biochemical basis of stress tolerance in flax. In this review, the current status of various omics approaches for elucidation of molecular pathways underlying abiotic and biotic stress tolerance in flax have been presented and the importance of integrated omics technologies in future research and breeding have been emphasized to ensure sustainable yield in challenging environments.

Identification of major genomic regions for soybean seed weight by genome-wide association study

The hundred-seed weight (HSW) is an important yield component and one of the principal breeding traits in soybean. More than 250 quantitative trait loci (QTL) for soybean HSW have been identified. However, most of them have a large genomic region or are environmentally sensitive, which provide limited information for improving the phenotype in marker-assisted selection (MAS) and identifying the candidate genes. Here, we utilized 281 soybean accessions with 58,112 single nucleotide polymorphisms (SNPs) to dissect the genetic basis of HSW in across years in the northern Shaanxi province of China through one single-locus (SL) and three multi-locus (ML) genome-wide association study (GWAS) models. As a result, one hundred and fifty-four SNPs were detected to be significantly associated with HSW in at least one environment via SL-GWAS model, and 27 of these 154 SNPs were detected in all (three) environments and located within 7 linkage disequilibrium (LD) block regions with the distance of each block ranged from 40 to 610 Kb. A total of 15 quantitative trait nucleotides (QTNs) were identified by three ML-GWAS models. Combined with the results of different GWAS models, the 7 LD block regions associated with HSW detected by SL-GWAS model could be verified directly or indirectly by the results of ML-GWAS models. Eleven candidate genes underlying the stable loci that may regulate seed weight in soybean were predicted. The significantly associated SNPs and the stable loci as well as predicted candidate genes may be of great importance for marker-assisted breeding, polymerization breeding, and gene discovery for HSW in soybean.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01310-y.

Insights into the Genetic Architecture and Genomic Prediction of Powdery Mildew Resistance in Flax (Linum usitatissimum L.)

Powdery mildew (PM), caused by the fungus Oidium lini in flax, can cause defoliation and reduce seed yield and quality. To date, one major dominant gene (Pm1) and three quantitative trait loci (QTL) on chromosomes 1, 7 and 9 have been reported for PM resistance. To fully dissect the genetic architecture of PM resistance and identify QTL, a diverse flax core collection of 372 accessions augmented with an additional 75 breeding lines were sequenced, and PM resistance was evaluated in the field for eight years (2010–2017) in Morden, Manitoba, Canada. Genome-wide association studies (GWAS) were performed using two single-locus and seven multi-locus statistical models with 247,160 single nucleotide polymorphisms (SNPs) and the phenotypes of the 447 individuals for each year separately as well as the means over years. A total of 349 quantitative trait nucleotides (QTNs) were identified, of which 44 large-effect QTNs (R² = 10–30%) were highly stable over years. The total number of favourable alleles per accession was significantly correlated with PM resistance (r = 0.74), and genomic selection (GS) models using all identified QTNs generated significantly higher predictive ability (r = 0.93) than those constructed using the 247,160 genome-wide random SNP (r = 0.69), validating the overall reliability of the QTNs and showing the additivity of PM resistance in flax. The QTNs were clustered on the distal ends of all 15 chromosomes, especially on chromosome 5 (0.4–5.6 Mb and 9.4–16.9 Mb) and 13 (4.7–5.2 Mb). To identify candidate genes, a dataset of 3230 SNPs located in resistance gene analogues (RGAs) was used as input for GWAS, from which an additional 39 RGA-specific QTNs were identified. Overall, 269 QTN loci harboured 445 RGAs within the 200 Kb regions spanning the QTNs, including 45 QTNs located within the RGAs. These RGAs supported by significant QTN/SNP allele effects were mostly nucleotide binding site and leucine-rich repeat receptors (NLRs) belonging to either coiled-coil (CC) NLR (CNL) or toll interleukin-1 (TIR) NLR (TNL), receptor-like kinase (RLK), receptor-like protein kinase (RLP), transmembrane-coiled-coil (TM-CC), WRKY, and mildew locus O (MLO) genes. These results constitute an important genomic tool for resistance breeding and gene cloning for PM in flax.

Genome-Wide Association Study of Salt Tolerance at the Seed Germination Stage in Flax (Linum usitatissimum L.)

Soil salinization seriously affects the growth and distribution of flax. However, there is little information about the salt tolerance of flax. In this study, the salt tolerance of 200 diverse flax accessions during the germination stage was evaluated, and then the Genome-wide Association Study (GWAS) was carried out based on the relative germination rate (RGR), relative shoot length (RSL) and relative root length (RRL), whereby quantitative trait loci (QTLs) related to salt tolerance were identified. The results showed that oil flax had a better salt tolerance than fiber flax. A total of 902 single nucleotide polymorphisms (SNPs) were identified on 15 chromosomes. These SNPs were integrated into 64 QTLs, explaining 14.48 to 29.38% (R²) of the phenotypic variation. In addition, 268 candidate genes were screened by combining previous transcriptome data and homologous gene annotation. Among them, Lus10033213 is a single-point SNP repeat mapping gene, which encodes a Glutathione S-transferase (GST). This study is the first to use GWAS to excavate genes related to salt tolerance during the germination stage of flax. The results of this study provide important information for studying the genetic mechanism of salt tolerance of flax, and also provide the possibility to improve the salt tolerance of flax.

A study using single-locus and multi-locus genome-wide association study to identify genes associated with teat number in Hu sheep

The multiple teats trait is common in many species of mammals and is considered related to lactation ability in swine. However, in Hu sheep, related gene research is still relatively limited. In this study, a genome‐wide association study was used to identify genetic markers and genes related to the number of teats in the Hu sheep population, a native Chinese sheep breed. A single marker method and several multi‐locus methods were utilized. A total of 61 SNPs were found to be related to the number of teats. Among these, 11 SNPs and one SNP were consistently detected by two and three multi‐locus models respectively. Four SNPs were concordantly identified between the single marker and multi‐locus methods. We also performed quantitative real‐time PCR testing of these identified candidate genes, identifying three genes with significantly different expression. Our study suggested that the LHFP, DPYSL2, and TDP‐43 genes may be related to the number of teats in sheep. The combination of single and multi‐locus GWAS detected additional SNPs not found with only one model. Our results provide new and important insights into the genetic mechanisms of the mammalian multiparous teat phenotype. These findings may be useful for future breeding and understanding the genetics of sheep and other livestock.

Genes Associated with the Flax Plant Type (Oil or Fiber) Identified Based on Genome and Transcriptome Sequencing Data

As a result of the breeding process, there are two main types of flax (Linum usitatissimum L.) plants. Linseed is used for obtaining seeds, while fiber flax is used for fiber production. We aimed to identify the genes associated with the flax plant type, which could be important for the formation of agronomically valuable traits. A search for polymorphisms was performed in genes involved in the biosynthesis of cell wall components, lignans, fatty acids, and ion transport based on genome sequencing data for 191 flax varieties. For 143 of the 424 studied genes (4CL, C3'H, C4H, CAD, CCR, CCoAOMT, COMT, F5H, HCT, PAL, CTL, BGAL, ABC, HMA, DIR, PLR, UGT, TUB, CESA, RGL, FAD, SAD, and ACT families), one or more polymorphisms had a strong correlation with the flax type. Based on the transcriptome sequencing data, we evaluated the expression levels for each flax type-associated gene in a wide range of tissues and suggested genes that are important for the formation of linseed or fiber flax traits. Such genes were probably subjected to the selection press and can determine not only the traits of seeds and stems but also the characteristics of the root system or resistance to stresses at a particular stage of development, which indirectly affects the ability of flax plants to produce seeds or fiber.

Advances and Challenges for QTL Analysis and GWAS in the Plant-Breeding of High-Yielding: A Focus on Rapeseed

Yield is one of the most important agronomic traits for the breeding of rapeseed (Brassica napus L), but its genetic dissection for the formation of high yield remains enigmatic, given the rapid population growth. In the present review, we review the discovery of major loci underlying important agronomic traits and the recent advancement in the selection of complex traits. Further, we discuss the benchmark summary of high-throughput techniques for the high-resolution genetic breeding of rapeseed. Biparental linkage analysis and association mapping have become powerful strategies to comprehend the genetic architecture of complex agronomic traits in crops. The generation of improved crop varieties, especially rapeseed, is greatly urged to enhance yield productivity. In this sense, the whole-genome sequencing of rapeseed has become achievable to clone and identify quantitative trait loci (QTLs). Moreover, the generation of high-throughput sequencing and genotyping techniques has significantly enhanced the precision of QTL mapping and genome-wide association study (GWAS) methodologies. Furthermore, this study demonstrates the first attempt to identify novel QTLs of yield-related traits, specifically focusing on ovule number per pod (ON). We also highlight the recent breakthrough concerning single-locus-GWAS (SL-GWAS) and multi-locus GWAS (ML-GWAS), which aim to enhance the potential and robust control of GWAS for improved complex traits.

Genome-Wide Association Study Identifies Genomic Regions for Important Morpho-Agronomic Traits in Mesoamerican Common Bean

The population growth trend in recent decades has resulted in continuing efforts to guarantee food security in which leguminous plants, such as the common bean (Phaseolus vulgaris L.), play a particularly important role as they are relatively cheap and have high nutritional value. To meet this demand for food, the main target for genetic improvement programs is to increase productivity, which is a complex quantitative trait influenced by many component traits. This research aims to identify Quantitative Trait Nucleotides (QTNs) associated with productivity and its components using multi-locus genome-wide association studies. Ten morpho-agronomic traits [plant height (PH), first pod insertion height (FPIH), number of nodules (NN), pod length (PL), total number of pods per plant (NPP), number of locules per pod (LP), number of seeds per pod (SP), total seed weight per plant (TSW), 100-seed weight (W100), and grain yield (YLD)] were evaluated in four environments for 178 Mesoamerican common bean domesticated accessions belonging to the Brazilian Diversity Panel. In order to identify stable QTNs, only those identified by multiple methods (mrMLM, FASTmrMLM, pLARmEB, and ISIS EM-BLASSO) or in multiple environments were selected. Among the identified QTNs, 64 were detected at least thrice by different methods or in different environments, and 39 showed significant phenotypic differences between their corresponding alleles. The alleles that positively increased the corresponding traits, except PH (for which lower values are desired), were considered favorable alleles. The most influenced trait by the accumulation of favorable alleles was PH, showing a 51.7% reduction, while NN, TSW, YLD, FPIH, and NPP increased between 18 and 34%. Identifying QTNs in several environments (four environments and overall adjusted mean) and by multiple methods reinforces the reliability of the associations obtained and the importance of conducting these studies in multiple environments. Using these QTNs through molecular techniques for genetic improvement, such as marker-assisted selection or genomic selection, can be a strategy to increase common bean production.

Genome-Wide Association Study to Map Genomic Regions Related to the Initiation Time of Four Growth Stage Traits in Soybean

The time to flowering (DF), pod beginning (DPB), seed formation (DSF), and maturity initiation (DMI) in soybean (Glycine max [L.] Merr) are important characteristics of growth stage traits (GSTs) in Chinese summer-sowing soybean, and are influenced by genetic as well as environmental factors. To better understand the molecular mechanism underlying the initiation times of GSTs, we investigated four GSTs of 309 diverse soybean accessions in six different environments and Best Linear Unbiased Prediction values. Furthermore, the genome-wide association study was conducted by a Fixed and random model Circulating Probability Unification method using over 60,000 single nucleotide polymorphism (SNP) markers to identify the significant quantitative trait nucleotide (QTN) regions with phenotypic data. As a result, 212 SNPs within 102 QTN regions were associated with four GSTs. Of which, eight stable regions were repeatedly detected in least three datasets for one GST. Interestingly, half of the QTN regions overlapped with previously reported quantitative trait loci or well-known soybean growth period genes. The hotspots associated with all GSTs were concentrated on chromosome 10. E2 (Glyma10g36600), a gene with a known function in regulating flowering and maturity in soybean, is also found on this chromosome. Thus, this genomic region may account for the strong correlation among the four GSTs. All the significant SNPs in the remaining 7 QTN regions could cause the significant phenotypic variation with both the major and minor alleles. Two hundred and seventy-five genes in soybean and their homologs in Arabidopsis were screened within ± 500 kb of 7 peak SNPs in the corresponding QTN regions. Most of the genes are involved in flowering, response to auxin stimulus, or regulation of seed germination, among others. The findings reported here provide an insight for genetic improvement which will aid in breeding of soybean cultivars that can be adapted to the various summer sowing areas in China and beyond.

Genome-wide association study for grain mineral content in a Brazilian common bean diversity panel

QTNs significantly associated to nine mineral content in grains of common bean were identified. The accumulation of favorable alleles was associated with a gradually increasing nutrient content in the grain. Biofortification is one of the strategies developed to address malnutrition in developing countries, the aim of which is to improve the nutritional content of crops. The common bean (Phaseolus vulgaris L.), a staple food in several African and Latin American countries, has excellent nutritional attributes and is considered a strong candidate for biofortification. The objective of this study was to identify genomic regions associated with nutritional content in common bean grains using 178 Mesoamerican accessions belonging to a Brazilian Diversity Panel (BDP) and 25,011 good-quality single nucleotide polymorphisms. The BDP was phenotyped in three environments for nine nutrients (phosphorus, potassium, calcium, magnesium, copper, manganese, sulfur, zinc, and iron) using four genome-wide association multi-locus methods. To obtain more accurate results, only quantitative trait nucleotides (QTNs) that showed repeatability (i.e., those detected at least twice using different methods or environments) were considered. Forty-eight QTNs detected for the nine minerals showed repeatability and were considered reliable. Pleiotropic QTNs and overlapping genomic regions surrounding the QTNs were identified, demonstrating the possible association between the deposition mechanisms of different nutrients in grains. The accumulation of favorable alleles in the same accession was associated with a gradually increasing nutrient content in the grain. The BDP proved to be a valuable source for association studies. The investigation of different methods and environments showed the reliability of markers associated with minerals. The loci identified in this study will potentially contribute to the improvement of Mesoamerican common beans, particularly carioca and black beans, the main groups consumed in Brazil.

Multi-locus genome-wide association studies for five yield-related traits in rice

BACKGROUND

Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs.

RESULTS

A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately.

CONCLUSIONS

These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.

Genome-wide identification and expression analysis of the WRKY transcription factor family in flax (Linum usitatissimum L.)

BACKGROUND

Members of the WRKY protein family, one of the largest transcription factor families in plants, are involved in plant growth and development, signal transduction, senescence, and stress resistance. However, little information is available about WRKY transcription factors in flax (Linum usitatissimum L.).

RESULTS

In this study, comprehensive genome-wide characterization of the flax WRKY gene family was conducted that led to prediction of 102 LuWRKY genes. Based on bioinformatics-based predictions of structural and phylogenetic features of encoded LuWRKY proteins, 95 LuWRKYs were classified into three main groups (Group I, II, and III); Group II LuWRKYs were further assigned to five subgroups (IIa-e), while seven unique LuWRKYs (LuWRKYs 96-102) could not be assigned to any group. Most LuWRKY proteins within a given subgroup shared similar motif compositions, while a high degree of motif composition variability was apparent between subgroups. Using RNA-seq data, expression patterns of the 102 predicted LuWRKY genes were also investigated. Expression profiling data demonstrated that most genes associated with cellulose, hemicellulose, or lignin content were predominantly expressed in stems, roots, and less in leaves. However, most genes associated with stress responses were predominantly expressed in leaves and exhibited distinctly higher expression levels in developmental stages 1 and 8 than during other stages.

CONCLUSIONS

Ultimately, the present study provides a comprehensive analysis of predicted flax WRKY family genes to guide future investigations to reveal functions of LuWRKY proteins during plant growth, development, and stress responses.

Genetic dissection of flowering time in flax (Linum usitatissimum L.) through single- and multi-locus genome-wide association studies

In a rapidly changing climate, flowering time (FL) adaptation is important to maximize seed yield in flax (Linum usitatissimum L.). However, our understanding of the genetic mechanism underlying FL in this multipurpose crop remains limited. With the aim of dissecting the genetic architecture of FL in flax, a genome-wide association study (GWAS) was performed on 200 accessions of the flax core collection evaluated in four environments. Two single-locus and six multi-locus models were applied using 70,935 curated single nucleotide polymorphism (SNP) markers. A total of 40 quantitative trait nucleotides (QTNs) associated with 27 quantitative trait loci (QTL) were identified in at least two environments. The number of QTL with positive-effect alleles in accessions was significantly correlated with FL (r = 0.77 to 0.82), indicating principally additive gene actions. Nine QTL were significant in at least three of the four environments accounting for 3.06-14.71% of FL variation. These stable QTL spanned regions that harbored 27 Arabidopsis thaliana and Oryza sativa FL-related orthologous genes including FLOWERING LOCUS T (Lus10013532), FLOWERING LOCUS D (Lus10028817), transcriptional regulator SUPERMAN (Lus10021215), and gibberellin 2-beta-dioxygenase 2 (Lus10037816). In silico gene expression analysis of the 27 FL candidate gene orthologous suggested that they might play roles in the transition from vegetative to reproductive phase, flower development and fertilization. Our results provide new insights into the QTL architecture of flowering time in flax, identify potential candidate genes for further studies, and demonstrate the effectiveness of combining different GWAS models for the genetic dissection of complex traits.

Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

Wheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

Loci harboring genes with important role in drought and related abiotic stress responses in flax revealed by multiple GWAS models

QTNs associated with drought tolerance traits and indices were identified in a flax mini-core collection through multiple GWAS models and phenotyping at multiple locations under irrigated and non-irrigated field conditions. Drought is a critical phenomenon challenging today's agricultural sector. Crop varieties adapted to moisture deficit are becoming vital. Flax can be greatly affected by limiting moisture conditions, especially during the early development and reproductive stages. Here, a mini-core collection comprising genotypes from more than 20 major growing countries was evaluated for 11 drought-related traits in irrigated and non-irrigated fields for 3 years. Heritability of the traits ranged from 44.7 to 86%. Six of the 11 traits showed significant phenotypic difference between irrigated and non-irrigated conditions. A genome-wide association study (GWAS) was performed for these six traits and their corresponding stress indices based on 106 genotypes and 12,316 single nucleotide polymorphisms (SNPs) using six multi-locus and one single-locus models. The SNPs were then assigned to 8050 linkage disequilibrium (LD) blocks to which a restricted two-stage multi-locus multi-allele GWAS was applied. A total of 144 quantitative trait nucleotides (QTNs) and 13 LD blocks were associated with at least one trait or stress index. Of these, 16 explained more than 15% of the genetic variance. Most large-effect QTN loci harbored gene(s) previously predicted to play role(s) in the associated traits. Genes mediating responses to abiotic stresses resided at loci associated with stress indices. Flax genes Lus10009480 and Lus10030150 that are predicted to encode WAX INDUCER1 and STRESS-ASSOCIATED PROTEIN (SAP), respectively, are among the important candidates detected. Accessions with multiple favorable alleles outperformed others for grain yield, thousand seed weight and fiber/biomass in non-irrigated conditions, suggesting their potential usefulness in breeding and genomic selection.

An evolutionary population structure model reveals pleiotropic effects of GmPDAT for traits related to seed size and oil content in soybean

Seed oil traits in soybean that are of benefit to human nutrition and health have been selected for during crop domestication. However, these domesticated traits have significant differences across various evolutionary types. In this study, we found that the integration of evolutionary population structure (evolutionary types) with genome-wide association studies increased the power of gene detection, and it identified one locus for traits related to seed size and oil content on chromosome 13. This domestication locus, together with another one in a 200-kb region, was confirmed by the GEMMA and EMMAX software. The candidate gene, GmPDAT, had higher expressional levels in high-oil and large-seed accessions than in low-oil and small-seed accessions. Overexpression lines had increased seed size and oil content, whereas RNAi lines had decreased seed size and oil content. The molecular mechanism of GmPDAT was deduced based on results from linkage analysis for triacylglycerols and on histocytological comparisons of transgenic soybean seeds. Our results illustrate a new approach for identifying domestication genes with pleiotropic effects.

Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize

BACKGROUND

Kernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection.

RESULTS

In this study, one single-locus method (MLM) and six multilocus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models.

CONCLUSIONS

These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

Identification of quantitative trait nucleotides and candidate genes for soybean seed weight by multiple models of genome-wide association study

BACKGROUND

Seed weight is a complex yield-related trait with a lot of quantitative trait loci (QTL) reported through linkage mapping studies. Integration of QTL from linkage mapping into breeding program is challenging due to numerous limitations, therefore, Genome-wide association study (GWAS) provides more precise location of QTL due to higher resolution and diverse genetic diversity in un-related individuals.

RESULTS

The present study utilized 573 breeding lines population with 61,166 single nucleotide polymorphisms (SNPs) to identify quantitative trait nucleotides (QTNs) and candidate genes for seed weight in Chinese summer-sowing soybean. GWAS was conducted with two single-locus models (SLMs) and six multi-locus models (MLMs). Thirty-nine SNPs were detected by the two SLMs while 209 SNPs were detected by the six MLMs. In all, two hundred and thirty-one QTNs were found to be associated with seed weight in YHSBLP with various effects. Out of these, seventy SNPs were concurrently detected by both SLMs and MLMs on 8 chromosomes. Ninety-four QTNs co-localized with previously reported QTL/QTN by linkage/association mapping studies. A total of 36 candidate genes were predicted. Out of these candidate genes, four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 and Glyma19g28070) were identified by the integration of co-expression network. Among them, three were relatively expressed higher in the high HSW genotypes at R5 stage compared with low HSW genotypes except Glyma12g33280. Our results show that using more models especially MLMs are effective to find important QTNs, and the identified HSW QTNs/genes could be utilized in molecular breeding work for soybean seed weight and yield.

CONCLUSION

Application of two single-locus plus six multi-locus models of GWAS identified 231 QTNs. Four hub genes (Glyma06g44510, Glyma08g06420, Glyma12g33280 & Glyma19g28070) detected via integration of co-expression network among the predicted candidate genes.

Evaluation of variant calling tools for large plant genome re-sequencing

BACKGROUND

Discovering single nucleotide polymorphisms (SNPs) from agriculture crop genome sequences has been a widely used strategy for developing genetic markers for several applications including marker-assisted breeding, population diversity studies for eco-geographical adaption, genotyping crop germplasm collections, and others. Accurately detecting SNPs from large polyploid crop genomes such as wheat is crucial and challenging. A few variant calling methods have been previously developed but they show a low concordance between their variant calls. A gold standard of variant sets generated from one human individual sample was established for variant calling tool evaluations, however hitherto no gold standard of crop variant set is available for wheat use. The intent of this study was to evaluate seven SNP variant calling tools (FreeBayes, GATK, Platypus, Samtools/mpileup, SNVer, VarScan, VarDict) with the two most popular mapping tools (BWA-mem and Bowtie2) on wheat whole exome capture (WEC) re-sequencing data from allohexaploid wheat.

RESULTS

We found the BWA-mem mapping tool had both a higher mapping rate and a higher accuracy rate than Bowtie2. With the same mapping quality (MQ) cutoff, BWA-mem detected more variant bases in mapping reads than Bowtie2. The reads preprocessed with quality trimming or duplicate removal did not significantly affect the final mapping performance in terms of mapped reads. Based on the concordance and receiver operating characteristic (ROC), the Samtools/mpileup variant calling tool with BWA-mem mapping of raw sequence reads outperformed other tests followed by FreeBayes and GATK in terms of specificity and sensitivity. VarDict and VarScan were the poorest performing variant calling tools with the wheat WEC sequence data.

CONCLUSION

The BWA-mem and Samtools/mpileup pipeline, with no need to preprocess the raw read data before mapping onto the reference genome, was ascertained the optimum for SNP calling for the complex wheat genome re-sequencing. These results also provide useful guidelines for reliable variant identification from deep sequencing of other large polyploid crop genomes.

Three-dimensional genetic networks among seed oil-related traits, metabolites and genes reveal the genetic foundations of oil synthesis in soybean

Although the biochemical and genetic basis of lipid metabolism is clear in Arabidopsis, there is limited information concerning the relevant genes in Glycine max (soybean). To address this issue, we constructed three-dimensional genetic networks using six seed oil-related traits, 52 lipid metabolism-related metabolites and 54 294 SNPs in 286 soybean accessions in total. As a result, 284 and 279 candidate genes were found to be significantly associated with seed oil-related traits and metabolites by phenotypic and metabolic genome-wide association studies and multi-omics analyses, respectively. Using minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) analyses, six seed oil-related traits were found to be significantly related to 31 metabolites. Among the above candidate genes, 36 genes were found to be associated with oil synthesis (27 genes), amino acid synthesis (four genes) and the tricarboxylic acid (TCA) cycle (five genes), and four genes (GmFATB1a, GmPDAT, GmPLDα1 and GmDAGAT1) are already known to be related to oil synthesis. Using this information, 133 three-dimensional genetic networks were constructed, 24 of which are known, e.g. pyruvate-GmPDAT-GmFATA2-oil content. Using these networks, GmPDAT, GmAGT and GmACP4 reveal the genetic relationships between pyruvate and the three major nutrients, and GmPDAT, GmZF351 and GmPgs1 reveal the genetic relationships between amino acids and seed oil content. In addition, GmCds1, along with average temperature in July and the rainfall from June to September, influence seed oil content across years. This study provides a new approach for the construction of three-dimensional genetic networks and reveals new information for soybean seed oil improvement and the identification of gene function.

Identification of QTNs Controlling 100-Seed Weight in Soybean Using Multilocus Genome-Wide Association Studies

Hundred-seed weight (HSW) is an important measure of yield and a useful indicator to monitor the inheritance of quantitative traits affected by genotype and environmental conditions. To identify quantitative trait nucleotides (QTNs) and mine genes useful for breeding high-yielding and high-quality soybean (Glycine max) cultivars, we conducted a multilocus genome-wide association study (GWAS) on HSW of soybean based on phenotypic data from 20 different environments and genotypic data for 109,676 single-nucleotide polymorphisms (SNPs) in 144 four-way recombinant inbred lines. Using five multilocus GWAS methods, we identified 118 QTNs controlling HSW. Among these, 31 common QTNs were detected by various methods or across multiple environments. Pathway analysis identified three potential candidate genes associated with HSW in soybean. We used allele information to study the common QTNs in 20 large-seed and 20 small-seed lines and identified a higher percentage of superior alleles in the large-seed lines than in small-seed lines. These observations will contribute to construct the gene networks controlling HSW in soybean, which can improve the genetic understanding of HSW, and provide assistance for molecular breeding of soybean large-seed varieties.

High-throughput sequencing techniques to flax genetics and breeding

Flax (Linum usitatissimum L.) is an important oil and fiber crop. Using modern methods for flax breeding allows accelerating the introduction of some desired genes into the genotypes of future varieties. Today, an important condition for their creation is the development of research, that is based on next-generation sequencing (NGS). This review summarizes the results obtained using NGS in flax research. To date, a linkage map with a high marker density has been obtained for L. usitatissimum, which is already being used for a more efficient search for quantitative traits loci. Comparative studies of transcriptomes and miRNomes of flax under stress and in control conditions elucidated molecular-genetic mechanisms of abiotic and biotic stress responses. The very accurate model for genomic selection of flax resistant to pasmo was constructed. Based on NGS-sequencing also some details of the genus Linum evolution were clarified. The knowledge systematized in the review can be useful for researchers working in flax breeding and whereas fundamental interest for understanding the phylogenetic relationships within the genus Linum, the ontogenesis, and the mechanisms of the response of flax plants to various stress factors.

Mapping Quantitative Trait Loci onto Chromosome-Scale Pseudomolecules in Flax

Quantitative trait loci (QTL) are genomic regions associated with phenotype variation of quantitative traits. To date, a total of 313 QTL for 31 quantitative traits have been reported in 14 studies on flax. Of these, 200 QTL from 12 studies were identified based on genetic maps, the scaffold sequences, or the pre-released chromosome-scale pseudomolecules. Molecular markers for QTL identification differed across studies but the most used ones were simple sequence repeats (SSRs) or single nucleotide polymorphisms (SNPs). To uniquely map the SSR and SNP markers from different references onto the recently released chromosome-scale pseudomolecules, methods with several scripts and database files were developed to locate PCR- and SNP-based markers onto the same reference, co-locate QTL, and scan genome-wide candidate genes. Using these methods, 195 out of 200 QTL were successfully sorted onto the 15 flax chromosomes and grouped into 133 co-located QTL clusters; the candidate genes that co-located with these QTL clusters were also predicted. The methods and tools presented in this article facilitate marker re-mapping to a new reference, genome-wide QTL analysis, candidate gene scanning, and breeding applications in flax and other crops.

Genomic Prediction Accuracy of Seven Breeding Selection Traits Improved by QTL Identification in Flax

Molecular markers are one of the major factors affecting genomic prediction accuracy and the cost of genomic selection (GS). Previous studies have indicated that the use of quantitative trait loci (QTL) as markers in GS significantly increases prediction accuracy compared with genome-wide random single nucleotide polymorphism (SNP) markers. To optimize the selection of QTL markers in GS, a set of 260 lines from bi-parental populations with 17,277 genome-wide SNPs were used to evaluate the prediction accuracy for seed yield (YLD), days to maturity (DTM), iodine value (IOD), protein (PRO), oil (OIL), linoleic acid (LIO), and linolenic acid (LIN) contents. These seven traits were phenotyped over four years at two locations. Identification of quantitative trait nucleotides (QTNs) for the seven traits was performed using three types of statistical models for genome-wide association study: two SNP-based single-locus (SS), seven SNP-based multi-locus (SM), and one haplotype-block-based multi-locus (BM) models. The identified QTNs were then grouped into QTL based on haplotype blocks. For all seven traits, 133, 355, and 1208 unique QTL were identified by SS, SM, and BM, respectively. A total of 1420 unique QTL were obtained by SS+SM+BM, ranging from 254 (OIL, LIO) to 361 (YLD) for individual traits, whereas a total of 427 unique QTL were achieved by SS+SM, ranging from 56 (YLD) to 128 (LIO). SS models alone did not identify sufficient QTL for GS. The highest prediction accuracies were obtained using single-trait QTL identified by SS+SM+BM for OIL (0.929 ± 0.016), PRO (0.893 ± 0.023), YLD (0.892 ± 0.030), and DTM (0.730 ± 0.062), and by SS+SM for LIN (0.837 ± 0.053), LIO (0.835 ± 0.049), and IOD (0.835 ± 0.041). In terms of the number of QTL markers and prediction accuracy, SS+SM outperformed other models or combinations thereof. The use of all SNPs or QTL of all seven traits significantly reduced the prediction accuracy of traits. The results further validated that QTL outperformed high-density genome-wide random markers, and demonstrated that the combined use of single and multi-locus models can effectively identify a comprehensive set of QTL that improve prediction accuracy, but further studies on detection and removal of redundant or false-positive QTL to maximize prediction accuracy and minimize the number of QTL markers in GS are warranted.

Evaluation of Genomic Prediction for Pasmo Resistance in Flax

Pasmo (Septoria linicola) is a fungal disease causing major losses in seed yield and quality and stem fibre quality in flax. Pasmo resistance (PR) is quantitative and has low heritability. To improve PR breeding efficiency, the accuracy of genomic prediction (GP) was evaluated using a diverse worldwide core collection of 370 accessions. Four marker sets, including three defined by 500, 134 and 67 previously identified quantitative trait loci (QTL) and one of 52,347 PR-correlated genome-wide single nucleotide polymorphisms, were used to build ridge regression best linear unbiased prediction (RR-BLUP) models using pasmo severity (PS) data collected from field experiments performed during five consecutive years. With five-fold random cross-validation, GP accuracy as high as 0.92 was obtained from the models using the 500 QTL when the average PS was used as the training dataset. GP accuracy increased with training population size, reaching values >0.9 with training population size greater than 185. Linear regression of the observed PS with the number of positive-effect QTL in accessions provided an alternative GP approach with an accuracy of 0.86. The results demonstrate the GP models based on marker information from all identified QTL and the 5-year PS average is highly effective for PR prediction.