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

Drought susceptibility of modern rice varieties: an effect of linkage of drought tolerance with undesirable traits

Green Revolution (GR) rice varieties are high yielding but typically drought sensitive. This is partly due to the tight linkage between the loci governing plant height and drought tolerance. This linkage is illustrated here through characterization of qDTY1.1, a QTL for grain yield under drought that co-segregates with the GR gene sd1 for semi-dwarf plant height. We report that the loss of the qDTY1.1 allele during the GR was due to its tight linkage in repulsion with the sd1 allele. Other drought-yield QTLs (qDTY) also showed tight linkage with traits rejected in GR varieties. Genetic diversity analysis for 11 different qDTY regions grouped GR varieties separately from traditional drought-tolerant varieties, and showed lower frequency of drought tolerance alleles. The increased understanding and breaking of the linkage between drought tolerance and undesirable traits has led to the development of high-yielding drought-tolerant dwarf lines with positive qDTY alleles and provides new hope for extending the benefits of the GR to drought-prone rice-growing regions.

Variable-Selection Emerges on Top in Empirical Comparison of Whole-Genome Complex-Trait Prediction Methods

Accurate prediction of complex traits based on whole-genome data is a computational problem of paramount importance, particularly to plant and animal breeders. However, the number of genetic markers is typically orders of magnitude larger than the number of samples (p >> n), amongst other challenges. We assessed the effectiveness of a diverse set of state-of-the-art methods on publicly accessible real data. The most surprising finding was that approaches with feature selection performed better than others on average, in contrast to the expectation in the community that variable selection is mostly ineffective, i.e. that it does not improve accuracy of prediction, in spite of p >> n. We observed superior performance despite a somewhat simplistic approach to variable selection, possibly suggesting an inherent robustness. This bodes well in general since the variable selection methods usually improve interpretability without loss of prediction power. Apart from identifying a set of benchmark data sets (including one simulated data), we also discuss the performance analysis for each data set in terms of the input characteristics.

Analysis of non-coding transcriptome in rice and maize uncovers roles of conserved lncRNAs associated with agriculture traits

Long non-coding RNAs (lncRNAs) have recently been found to widely exist in eukaryotes and play important roles in key biological processes. To extend our knowledge of lncRNAs in crop plants we performed both non-directional and strand-specific RNA-sequencing experiments to profile non-coding transcriptomes of various rice and maize organs at different developmental stages. Analysis of more than 3 billion reads identified 22 334 long intergenic non-coding RNAs (lincRNAs) and 6673 pairs of sense and natural antisense transcript (NAT). Many lincRNA genes were associated with epigenetic marks. Expression of rice lincRNA genes was significantly correlated with that of nearby protein-coding genes. A set of NAT genes also showed expression correlation with their sense genes. More than 200 rice lincRNA genes had homologous non-coding sequences in the maize genome. Much more lincRNA and NAT genes were derived from conserved genomic regions between the two cereals presenting positional conservation. Protein-coding genes flanking or having a sense-antisense relationship to these conserved lncRNA genes were mainly involved in development and stress responses, suggesting that the associated lncRNAs might have similar functions. Integrating previous genome-wide association studies (GWAS), we found that hundreds of lincRNAs contain trait-associated SNPs (single nucleotide polymorphisms [SNPs]) suggesting their putative contributions to developmental and agriculture traits.

Loci, genes, and mechanisms associated with tolerance to ferrous iron toxicity in rice (Oryza sativa L.)

A genome-wide association study in rice yielded loci and candidate genes associated with tolerance to iron toxicity, and revealed biochemical mechanisms associated with tolerance in contrasting haplotypes. Iron toxicity is a major nutrient disorder affecting rice. Therefore, understanding the genetic and physiological mechanisms associated with iron toxicity tolerance is crucial in adaptive breeding and biofortification. We conducted a genome-wide association study (GWAS) by exposing a population of 329 accessions representing all subgroups of rice to ferrous iron stress (1000 ppm, 5 days). Expression patterns and sequence polymorphisms of candidate genes were investigated, and physiological hypotheses related to candidate loci were tested using a subset of contrasting haplotypes. Both iron including and excluding tolerant genotypes were observed, and shoot iron concentrations explained around 15.5 % of the variation in foliar symptom formation. GWAS for seven traits yielded 20 SNP markers exceeding a significance threshold of -log10 P > 4.0, which represented 18 distinct loci. One locus mapped for foliar symptom formation on chromosome 1 contained two putative glutathione-S-transferases, which were strongly expressed under iron stress and showed sequence polymorphisms in complete linkage disequilibrium with the most significant SNP. Contrasting haplotypes for this locus showed significant differences in dehydroascorbate reductase activity, which affected the plants' redox status under iron stress. We conclude that maintaining foliar redox homeostasis under iron stress represented an important tolerance mechanism associated with a locus identified through GWAS.

"Missing" G x E Variation Controls Flowering Time in Arabidopsis thaliana

Understanding how genetic variation interacts with the environment is essential for understanding adaptation. In particular, the life cycle of plants is tightly coordinated with local environmental signals through complex interactions with the genetic variation (G x E). The mechanistic basis for G x E is almost completely unknown. We collected flowering time data for 173 natural inbred lines of Arabidopsis thaliana from Sweden under two growth temperatures (10°C and 16°C), and observed massive G x E variation. To identify the genetic polymorphisms underlying this variation, we conducted genome-wide scans using both SNPs and local variance components. The SNP-based scan identified several variants that had common effects in both environments, but found no trace of G x E effects, whereas the scan using local variance components found both. Furthermore, the G x E effects appears to be concentrated in a small fraction of the genome (0.5%). Our conclusion is that G x E effects in this study are mostly due to large numbers of allele or haplotypes at a small number of loci, many of which correspond to previously identified flowering time genes.

Exploring a Tomato Landraces Collection for Fruit-Related Traits by the Aid of a High-Throughput Genomic Platform

During its evolution and domestication Solanum lycopersicum has undergone various genetic ‘bottlenecks’ and extreme inbreeding of limited genotypes. In Europe the tomato found a secondary centre for diversification, which resulted in a wide array of fruit shape variation given rise to a range of landraces that have been cultivated for centuries. Landraces represent a reservoir of genetic diversity especially for traits such as abiotic stress resistance and high fruit quality. Information about the variation present among tomato landrace populations is still limited. A collection of 123 genotypes from different geographical areas was established with the aim of capturing a wide diversity. Eighteen morphological traits were evaluated, mainly related to the fruit. About 45% of morphological variation was attributed to fruit shape, as estimated by the principal component analysis, and the dendrogram of relatedness divided the population in subgroups mainly on the basis of fruit weight and locule number. Genotyping was carried out using the tomato array platform SolCAP able to interrogate 7,720 SNPs. In the whole collection 87.1% markers were polymorphic but they decreased to 44–54% when considering groups of genotypes with different origin. The neighbour-joining tree analysis clustered the 123 genotypes into two main branches. The STRUCTURE analysis with K = 3 also divided the population on the basis of fruit size. A genomic-wide association strategy revealed 36 novel markers associated to the variation of 15 traits. The markers were mapped on the tomato chromosomes together with 98 candidate genes for the traits analyzed. Six regions were evidenced in which candidate genes co-localized with 19 associated SNPs. In addition, 17 associated SNPs were localized in genomic regions lacking candidate genes. The identification of these markers demonstrated that novel variability was captured in our germoplasm collection. They might also provide a viable indirect selection tool in future practical breeding programs.

Exploring a Tomato Landraces Collection for Fruit-Related Traits by the Aid of a High-Throughput Genomic Platform

During its evolution and domestication Solanum lycopersicum has undergone various genetic 'bottlenecks' and extreme inbreeding of limited genotypes. In Europe the tomato found a secondary centre for diversification, which resulted in a wide array of fruit shape variation given rise to a range of landraces that have been cultivated for centuries. Landraces represent a reservoir of genetic diversity especially for traits such as abiotic stress resistance and high fruit quality. Information about the variation present among tomato landrace populations is still limited. A collection of 123 genotypes from different geographical areas was established with the aim of capturing a wide diversity. Eighteen morphological traits were evaluated, mainly related to the fruit. About 45% of morphological variation was attributed to fruit shape, as estimated by the principal component analysis, and the dendrogram of relatedness divided the population in subgroups mainly on the basis of fruit weight and locule number. Genotyping was carried out using the tomato array platform SolCAP able to interrogate 7,720 SNPs. In the whole collection 87.1% markers were polymorphic but they decreased to 44-54% when considering groups of genotypes with different origin. The neighbour-joining tree analysis clustered the 123 genotypes into two main branches. The STRUCTURE analysis with K = 3 also divided the population on the basis of fruit size. A genomic-wide association strategy revealed 36 novel markers associated to the variation of 15 traits. The markers were mapped on the tomato chromosomes together with 98 candidate genes for the traits analyzed. Six regions were evidenced in which candidate genes co-localized with 19 associated SNPs. In addition, 17 associated SNPs were localized in genomic regions lacking candidate genes. The identification of these markers demonstrated that novel variability was captured in our germoplasm collection. They might also provide a viable indirect selection tool in future practical breeding programs.

Genome-wide Association Mapping of Quantitative Trait Loci (QTLs) for Contents of Eight Elements in Brown Rice (Oryza sativa L.)

An association mapping of quantitative trait loci (QTLs) regulating the concentrations of eight elements in brown rice (Oryza sativa L.) was performed using USDA mini-core subset cultivated in two different environments. In addition, correlation between the grain elemental concentrations was also studied. A total of 60 marker loci associated with 8 grain elemental concentrations were identified, and these loci were clustered into 37 genomic regions. Twenty new QTLs were found to be associated with important elements such as Zn, Fe, and P, along with others. Fe concentration was associated with the greatest number of markers in two environments. In addition, several important elemental/metal transporter genes were identified in a few mapped regions. Positive correlation was observed within all grain elemental concentrations. In summary, the results provide insight into the genetic basis of rice grain element accumulation and may help in the identification of genes associated with the accumulation of Zn, Fe, and other essential elements in rice.

LinkImpute: Fast and Accurate Genotype Imputation for Nonmodel Organisms

Obtaining genome-wide genotype data from a set of individuals is the first step in many genomic studies, including genome-wide association and genomic selection. All genotyping methods suffer from some level of missing data, and genotype imputation can be used to fill in the missing data and improve the power of downstream analyses. Model organisms like human and cattle benefit from high-quality reference genomes and panels of reference genotypes that aid in imputation accuracy. In nonmodel organisms, however, genetic and physical maps often are either of poor quality or are completely absent, and there are no panels of reference genotypes available. There is therefore a need for imputation methods designed specifically for nonmodel organisms in which genomic resources are poorly developed and marker order is unreliable or unknown. Here we introduce LinkImpute, a software package based on a k-nearest neighbor genotype imputation method, LD-kNNi, which is designed for unordered markers. No physical or genetic maps are required, and it is designed to work on unphased genotype data from heterozygous species. It exploits the fact that markers useful for imputation often are not physically close to the missing genotype but rather distributed throughout the genome. Using genotyping-by-sequencing data from diverse and heterozygous accessions of apples, grapes, and maize, we compare LD-kNNi with several genotype imputation methods and show that LD-kNNi is fast, comparable in accuracy to the best-existing methods, and exhibits the least bias in allele frequency estimates.

Genetic properties of the MAGIC maize population: a new platform for high definition QTL mapping in Zea mays

BACKGROUND

Maize (Zea mays) is a globally produced crop with broad genetic and phenotypic variation. New tools that improve our understanding of the genetic basis of quantitative traits are needed to guide predictive crop breeding. We have produced the first balanced multi-parental population in maize, a tool that provides high diversity and dense recombination events to allow routine quantitative trait loci (QTL) mapping in maize.

RESULTS

We produced 1,636 MAGIC maize recombinant inbred lines derived from eight genetically diverse founder lines. The characterization of 529 MAGIC maize lines shows that the population is a balanced, evenly differentiated mosaic of the eight founders, with mapping power and resolution strengthened by high minor allele frequencies and a fast decay of linkage disequilibrium. We show how MAGIC maize may find strong candidate genes by incorporating genome sequencing and transcriptomics data. We discuss three QTL for grain yield and three for flowering time, reporting candidate genes. Power simulations show that subsets of MAGIC maize might achieve high-power and high-definition QTL mapping.

CONCLUSIONS

We demonstrate MAGIC maize's value in identifying the genetic bases of complex traits of agronomic relevance. The design of MAGIC maize allows the accumulation of sequencing and transcriptomics layers to guide the identification of candidate genes for a number of maize traits at different developmental stages. The characterization of the full MAGIC maize population will lead to higher power and definition in QTL mapping, and lay the basis for improved understanding of maize phenotypes, heterosis included. MAGIC maize is available to researchers.

Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

BACKGROUND

Mechanized dry seeded rice can save both labour and water resources. Rice seedling establishment is sensitive to sowing depth while mesocotyl elongation facilitates the emergence of deeply sown seeds.

RESULTS

A set of 270 rice accessions, including 170 from the mini-core collection of Chinese rice germplasm (C Collection) and 100 varieties used in a breeding program for drought resistance (D Collection), was screened for mesocotyl lengths of seedlings grown in water (MLw) in darkness and in 5 cm sand culture (MLs). Twenty six accessions (10.53 %) have MLw longer than 1.0 cm. Eleven accessions had the highest mesocotyl lengths, i.e. 1.4 - 5.05 cm of MLw and 3.0 - 6.4 cm in 10 cm sand culture, including 7 upland landraces or varieties. The genotypic data of 1,019,883 SNPs were developed by re-sequencing of those accessions. A whole-genome SNP array (Rice SNP50) was used to genotype 24 accessions as a validation panel, giving 98.41 % of consistent SNPs with the re-sequencing data in average. GWAS based on compressed mixed linear model was conducted using GAPIT. Based on a threshold of -log(P) ≥8.0, 13 loci were associated to MLw on rice chromosome 1, 3, 4, 5, 6 and 9, respectively. Three associated loci, on chromosome 3, 6, and 10, were detected for MLs. A set of 99 associated SNPs for MLw, based on a compromised threshold (-log(P) ≥7.0), located in intergenic regions or different positions of 36 annotated genes, including one cullin and one growth regulating factor gene.

CONCLUSIONS

Higher proportion and extension of elongated mesocotyls were observed in the mini-core collection of rice germplasm and upland rice landraces or varieties, possibly causing the correlation between mesocotyl elongation and drought resistance. GWAS found 13 loci for mesocotyl length measured in dark germination that confirmed the previously reported co-location of two QTLs across populations and experiments. Associated SNPs hit 36 annotated genes including function-matching candidates like cullin and GRF. The germplasm with elongated mesocotyl, especially upland landraces or varieties, and the associated SNPs could be useful in further studies and breeding of mechanized dry seeded rice.

Biallelic and Genome Wide Association Mapping of Germanium Tolerant Loci in Rice (Oryza sativa L.)

Rice plants accumulate high concentrations of silicon. Silicon has been shown to be involved in plant growth, high yield, and mitigating biotic and abiotic stresses. However, it has been demonstrated that inorganic arsenic is taken up by rice through silicon transporters under anaerobic conditions, thus the ability to efficiently take up silicon may be considered either a positive or a negative trait in rice. Germanium is an analogue of silicon that produces brown lesions in shoots and leaves, and germanium toxicity has been used to identify mutants in silicon and arsenic transport. In this study, two different genetic mapping methods were performed to determine the loci involved in germanium sensitivity in rice. Genetic mapping in the biparental cross of Bala × Azucena (an F6 population) and a genome wide association (GWA) study with 350 accessions from the Rice Diversity Panel 1 were conducted using 15 μM of germanic acid. This identified a number of germanium sensitive loci: some co-localised with previously identified quantitative trait loci (QTL) for tissue silicon or arsenic concentration, none co-localised with Lsi1 or Lsi6, while one single nucleotide polymorphism (SNP) was detected within 200 kb of Lsi2 (these are genes known to transport silicon, whose identity was discovered using germanium toxicity). However, examining candidate genes that are within the genomic region of the loci detected above reveals genes homologous to both Lsi1 and Lsi2, as well as a number of other candidate genes, which are discussed.

Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer

Leaves are the plant's solar panel and food factory, and leaf traits are always key issues to investigate in plant research. Traditional methods for leaf trait measurement are time-consuming. In this work, an engineering prototype has been established for high-throughput leaf scoring (HLS) of a large number of Oryza sativa accessions. The mean absolute per cent of errors in traditional measurements versus HLS were below 5% for leaf number, area, shape, and colour. Moreover, HLS can measure up to 30 leaves per minute. To demonstrate the usefulness of HLS in dissecting the genetic bases of leaf traits, a genome-wide association study (GWAS) was performed for 29 leaf traits related to leaf size, shape, and colour at three growth stages using HLS on a panel of 533 rice accessions. Nine associated loci contained known leaf-related genes, such as Nal1 for controlling the leaf width. In addition, a total of 73, 123, and 177 new loci were detected for traits associated with leaf size, colour, and shape, respectively. In summary, after evaluating the performance with a large number of rice accessions, the combination of GWAS and high-throughput leaf phenotyping (HLS) has proven a valuable strategy to identify the genetic loci controlling rice leaf traits.

Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping

Early vigour of rice, defined as seedling capacity to accumulate shoot dry weight (SDW) rapidly, is a complex trait. It depends on a genotype propensity to assimilate, store, and/or use non-structural carbohydrates (NSC) for producing large and/or numerous leaves, involving physiological trade-offs in the expression of component traits and, possibly, physiological and genetic linkages. This study explores a plant-model-assisted phenotyping approach to dissect the genetic architecture of rice early vigour, applying the Genome Wide Association Study (GWAS) to morphological and NSC measurements, as well as fitted parameters for the functional-structural plant model, Ecomeristem. Leaf size, number, SDW, and source-leaf NSC concentration were measured on a panel of 123 japonica accessions. The data were used to estimate Ecomeristem genotypic parameters driving organ appearance rate, size, and carbon dynamics. GWAS was performed based on 12 221 single-nucleotide polymorphisms (SNP). Twenty-three associations were detected at P <1×10(-4) and 64 at P <5×10(-4). Associations for NSC and model parameters revealed new regions related to early vigour that had greater significance than morphological traits, providing additional information on the genetic control of early vigour. Plant model parameters were used to characterize physiological and genetic trade-offs among component traits. Twelve associations were related to loci for cloned genes, with nine related to organogenesis, plant height, cell size or cell number. The potential use of these associations as markers for breeding is discussed.

The RING-Finger Ubiquitin Ligase HAF1 Mediates Heading date 1 Degradation during Photoperiodic Flowering in Rice

The photoperiodic response is one of the most important factors determining heading date in rice (Oryza sativa). Although rhythmic expression patterns of flowering time genes have been reported to fine-tune the photoperiodic response, posttranslational regulation of key flowering regulators has seldom been elucidated in rice. Heading date 1 (Hd1) encodes a zinc finger transcription factor that plays a crucial role in the photoperiodic response, which determines rice regional adaptability. However, little is known about the molecular mechanisms of Hd1 accumulation during the photoperiod response. Here, we identify a C3HC4 RING domain-containing E3 ubiquitin ligase, Heading date Associated Factor 1 (HAF1), which physically interacts with Hd1. HAF1 mediates ubiquitination and targets Hd1 for degradation via the 26S proteasome-dependent pathway. The haf1 mutant exhibits a later flowering heading date under both short-day and long-day conditions. In addition, the haf1 hd1 double mutant headed as late as hd1 plants under short-day conditions but exhibited a heading date similar to haf1 under long-day conditions, thus indicating that HAF1 may determine heading date mainly through Hd1 under short-day conditions. Moreover, high levels of Hd1 accumulate in haf1. Our results suggest that HAF1 is essential to precise modulation of the timing of Hd1 accumulation during the photoperiod response in rice.

Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding

Genomic selection (GS) is a promising strategy for enhancing genetic gain. We investigated the accuracy of genomic estimated breeding values (GEBV) in four inter-related synthetic populations that underwent several cycles of recurrent selection in an upland rice-breeding program. A total of 343 S_2:4 lines extracted from those populations were phenotyped for flowering time, plant height, grain yield and panicle weight, and genotyped with an average density of one marker per 44.8 kb. The relative effect of the linkage disequilibrium (LD) and minor allele frequency (MAF) thresholds for selecting markers, the relative size of the training population (TP) and of the validation population (VP), the selected trait and the genomic prediction models (frequentist and Bayesian) on the accuracy of GEBVs was investigated in 540 cross validation experiments with 100 replicates. The effect of kinship between the training and validation populations was tested in an additional set of 840 cross validation experiments with a single genomic prediction model. LD was high (average r² = 0.59 at 25 kb) and decreased slowly, distribution of allele frequencies at individual loci was markedly skewed toward unbalanced frequencies (MAF average value 15.2% and median 9.6%), and differentiation between the four synthetic populations was low (F_ST ≤0.06). The accuracy of GEBV across all cross validation experiments ranged from 0.12 to 0.54 with an average of 0.30. Significant differences in accuracy were observed among the different levels of each factor investigated. Phenotypic traits had the biggest effect, and the size of the incidence matrix had the smallest. Significant first degree interaction was observed for GEBV accuracy between traits and all the other factors studied, and between prediction models and LD, MAF and composition of the TP. The potential of GS to accelerate genetic gain and breeding options to increase the accuracy of predictions are discussed.

Recent trends and perspectives of molecular markers against fungal diseases in wheat

Wheat accounts for 19% of the total production of major cereal crops in the world. In view of ever increasing population and demand for global food production, there is an imperative need of 40-60% increase in wheat production to meet the requirement of developing world in coming 40 years. However, both biotic and abiotic stresses are major hurdles for attaining the goal. Among the most important diseases in wheat, fungal diseases pose serious threat for widening the gap between actual and attainable yield. Fungal disease management, mainly, depends on the pathogen detection, genetic and pathological variability in population, development of resistant cultivars and deployment of effective resistant genes in different epidemiological regions. Wheat protection and breeding of resistant cultivars using conventional methods are time-consuming, intricate and slow processes. Molecular markers offer an excellent alternative in development of improved disease resistant cultivars that would lead to increase in crop yield. They are employed for tagging the important disease resistance genes and provide valuable assistance in increasing selection efficiency for valuable traits via marker assisted selection (MAS). Plant breeding strategies with known molecular markers for resistance and functional genomics enable a breeder for developing resistant cultivars of wheat against different fungal diseases.

Natural Variation in the Flag Leaf Morphology of Rice Due to a Mutation of the NARROW LEAF 1 Gene in Oryza sativa L

We investigated the natural variations in the flag leaf morphology of rice. We conducted a principal component analysis based on nine flag leaf morphology traits using 103 accessions from the National Institute of Agrobiological Sciences Core Collection. The first component explained 39% of total variance, and the variable with highest loading was the width of the flag leaf (WFL). A genome-wide association analysis of 102 diverse Japanese accessions revealed that marker RM6992 on chromosome 4 was highly associated with WFL. In analyses of progenies derived from a cross between Takanari and Akenohoshi, the most significant quantitative trait locus (QTL) for WFL was in a 10.3-kb region containing the NARROW LEAF 1 (NAL1) gene, located 0.4 Mb downstream of RM6992. Analyses of chromosomal segment substitution lines indicated that a mutation (G1509A single-nucleotide mutation, causing an R233H amino acid substitution in NAL1) was present at the QTL. This explained 13 and 20% of total variability in WFL and the distance between small vascular bundles, respectively. The mutation apparently occurred during rice domestication and spread into japonica, tropical japonica, and indica subgroups. Notably, one accession, Phulba, had a NAL1 allele encoding only the N-terminal, or one-fourth, of the wild-type peptide. Given that the Phulba allele and the histidine-type allele showed essentially the same phenotype, the histidine-type allele was regarded as malfunctional. The phenotypes of transgenic plants varied depending on the ratio of histidine-type alleles to arginine-type alleles, raising the possibility that H(233)-type products function differently from and compete with R(233)-type products.

Genetic characteristics of soybean resistance to HG type 0 and HG type 1.2.3.5.7 of the cyst nematode analyzed by genome-wide association mapping

BACKGROUND

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most fatal pests of soybean (Glycine max (L.) Merr.) worldwide and causes huge loss of soybean yield each year. Multiple sources of resistance are urgently needed for effective management of SCN via the development of resistant cultivars. The aim of the present study was to investigate the genetic architecture of resistance to SCN HG Type 0 (race 3) and HG Type 1.2.3.5.7 (race 4) in landraces and released elite soybean cultivars mostly from China.

RESULTS

A total of 440 diverse soybean landraces and elite cultivars were screened for resistance to SCN HG Type 0 and HG Type 1.2.3.5.7. Exactly 131 new sources of SCN resistance were identified. Lines were genotyped by SNP markers detected by the Specific Locus Amplified Fragment Sequencing (SLAF-seq) approach. A total of 36,976 SNPs were identified with minor allele frequencies (MAF) > 4% that were present in 97% of all the genotypes. Genome-wide association mapping showed that a total of 19 association signals were significantly related to the resistance for the two HG Types. Of the 19 association signals, eight signals overlapped with reported QTL including Rhg1 and Rhg4 genes. Another eight were located in the linked regions encompassing known QTL. Three QTL were found that were not previously reported. The average value of female index (FI) of soybean accessions with resistant alleles was significantly lower than those with susceptible alleles for each peak SNP. Disease resistance proteins with leucine rich regions, cytochrome P450s, protein kinases, zinc finger domain proteins, RING domain proteins, MYB and WRKY transcription activation families were identified. Such proteins may participate in the resistant reaction to SCN and were frequently found in the tightly linked genomic regions of the peak SNPs.

CONCLUSIONS

GWAS extended understanding of the genetic architecture of SCN resistance in multiple genetic backgrounds. Nineteen association signals were obtained for the resistance to the two Hg Types of SCN. The multiple beneficial alleles from resistant germplasm sources will be useful for the breeding of cultivars with improved resistance to SCN. Analysis of genes near association signals may facilitate the recognition of the causal gene(s) underlying SCN resistances.

Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci

Background

Interaction and genetic control for traits influencing the adaptation of the rice crop to varying environments was studied in a mapping population derived from parents (Moroberekan and Swarna) contrasting for drought tolerance, yield potential, lodging resistance, and adaptation to dry direct seeding. A BC₂F₃-derived mapping population for traits related to these four trait groups was phenotyped to understand the interactions among traits and to map and align QTLs using composite interval mapping (CIM). The study also aimed to identify QTLs for the four trait groups as composite traits using multivariate least square interval mapping (MLSIM) to further understand the genetic control of these traits.

Results

Significant correlations between drought- and yield-related traits at seedling and reproductive stages respectively with traits for adaptation to dry direct-seeded conditions were observed. CIM and MLSIM methods were applied to identify QTLs for univariate and composite traits. QTL clusters showing alignment of QTLs for several traits within and across trait groups were detected at chromosomes 3, 4, and 7 through CIM. The largest number of QTLs related to traits belonging to all four trait groups were identified on chromosome 3 close to the qDTY_3.2 locus. These included QTLs for traits such as bleeding rate, shoot biomass, stem strength, and spikelet fertility. Multivariate QTLs were identified at loci supported by univariate QTLs such as on chromosomes 3 and 4 as well as at distinctly different loci on chromosome 8 which were undetected through CIM.

Conclusion

Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change. Understanding the genetics and trade-offs related to each of these environments and cultivation practices thus becomes highly important to develop varieties with stability of yield across them. This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments. With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0249-1) contains supplementary material, which is available to authorized users.

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.

Integrating Image-Based Phenomics and Association Analysis to Dissect the Genetic Architecture of Temporal Salinity Responses in Rice

Salinity affects a significant portion of arable land and is particularly detrimental for irrigated agriculture, which provides one-third of the global food supply. Rice (Oryza sativa), the most important food crop, is salt sensitive. The genetic resources for salt tolerance in rice germplasm exist but are underutilized due to the difficulty in capturing the dynamic nature of physiological responses to salt stress. The genetic basis of these physiological responses is predicted to be polygenic. In an effort to address this challenge, we generated temporal imaging data from 378 diverse rice genotypes across 14 d of 90 mm NaCl stress and developed a statistical model to assess the genetic architecture of dynamic salinity-induced growth responses in rice germplasm. A genomic region on chromosome 3 was strongly associated with the early growth response and was captured using visible range imaging. Fluorescence imaging identified four genomic regions linked to salinity-induced fluorescence responses. A region on chromosome 1 regulates both the fluorescence shift indicative of the longer term ionic stress and the early growth rate decline during salinity stress. We present, to our knowledge, a new approach to capture the dynamic plant responses to its environment and elucidate the genetic basis of these responses using a longitudinal genome-wide association model.

Collinearity analysis of Brassica A and C genomes based on an updated inferred unigene order

This data article includes SNP scoring across lines of the Brassica napus TNDH population based on Illumina sequencing of mRNA, expanded to 75 lines. The 21, 323 mapped markers defined 887 recombination bins, representing an updated genetic linkage map for the species. Based on this new map, 5 genome sequence scaffolds were split and the order and orientation of scaffolds updated to establish a new pseudomolecule specification. The order of unigenes and SNP array probes within these pseudomolecules was determined. Unigenes were assessed for sequence similarity to the A and C genomes. The 57, 246 that mapped to both enabled the collinearity of the A and C genomes to be illustrated graphically. Although the great majority was in collinear positions, some were not. Analyses of 60 such instances are presented, suggesting that the breakdown in collinearity was largely due to either the absence of the homoeologue on one genome (resulting in sequence match to a paralogue) or multiple similar sequences being present. The mRNAseq datasets for the TNDH lines are available from the SRA repository (ERA283648); the remaining datasets are supplied with this article.

Genome-wide high-throughput SNP discovery and genotyping for understanding natural (functional) allelic diversity and domestication patterns in wild chickpea

We identified 82489 high-quality genome-wide SNPs from 93 wild and cultivated Cicer accessions through integrated reference genome- and de novo-based GBS assays. High intra- and inter-specific polymorphic potential (66-85%) and broader natural allelic diversity (6-64%) detected by genome-wide SNPs among accessions signify their efficacy for monitoring introgression and transferring target trait-regulating genomic (gene) regions/allelic variants from wild to cultivated Cicer gene pools for genetic improvement. The population-specific assignment of wild Cicer accessions pertaining to the primary gene pool are more influenced by geographical origin/phenotypic characteristics than species/gene-pools of origination. The functional significance of allelic variants (non-synonymous and regulatory SNPs) scanned from transcription factors and stress-responsive genes in differentiating wild accessions (with potential known sources of yield-contributing and stress tolerance traits) from cultivated desi and kabuli accessions, fine-mapping/map-based cloning of QTLs and determination of LD patterns across wild and cultivated gene-pools are suitably elucidated. The correlation between phenotypic (agromorphological traits) and molecular diversity-based admixed domestication patterns within six structured populations of wild and cultivated accessions via genome-wide SNPs was apparent. This suggests utility of whole genome SNPs as a potential resource for identifying naturally selected trait-regulating genomic targets/functional allelic variants adaptive to diverse agroclimatic regions for genetic enhancement of cultivated gene-pools.

Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica

BACKGROUND

The use of high throughput genome-sequencing technologies has uncovered a large extent of structural variation in eukaryotic genomes that makes important contributions to genomic diversity and phenotypic variation. When the genomes of different strains of a given organism are compared, whole genome resequencing data are typically aligned to an established reference sequence. However, when the reference differs in significant structural ways from the individuals under study, the analysis is often incomplete or inaccurate.

RESULTS

Here, we use rice as a model to demonstrate how improvements in sequencing and assembly technology allow rapid and inexpensive de novo assembly of next generation sequence data into high-quality assemblies that can be directly compared using whole genome alignment to provide an unbiased assessment. Using this approach, we are able to accurately assess the "pan-genome" of three divergent rice varieties and document several megabases of each genome absent in the other two.

CONCLUSIONS

Many of the genome-specific loci are annotated to contain genes, reflecting the potential for new biological properties that would be missed by standard reference-mapping approaches. We further provide a detailed analysis of several loci associated with agriculturally important traits, including the S5 hybrid sterility locus, the Sub1 submergence tolerance locus, the LRK gene cluster associated with improved yield, and the Pup1 cluster associated with phosphorus deficiency, illustrating the utility of our approach for biological discovery. All of the data and software are openly available to support further breeding and functional studies of rice and other species.

CARMO: a comprehensive annotation platform for functional exploration of rice multi-omics data

High-throughput technology is gradually becoming a powerful tool for routine research in rice. Interpretation of biological significance from the huge amount of data is a critical but non-trivial task, especially for rice, for which gene annotations rely heavily on sequence similarity rather than direct experimental evidence. Here we describe the annotation platform for comprehensive annotation of rice multi-omics data (CARMO), which provides multiple web-based analysis tools for in-depth data mining and visualization. The central idea involves systematic integration of 1819 samples from omics studies and diverse sources of functional evidence (15 401 terms), which are further organized into gene sets and higher-level gene modules. In this way, the high-throughput data may easily be compared across studies and platforms, and integration of multiple types of evidence allows biological interpretation from the level of gene functional modules with high confidence. In addition, the functions and pathways for thousands of genes lacking description or validation may be deduced based on concerted expression of genes within the constructed co-expression networks or gene modules. Overall, CARMO provides comprehensive annotations for transcriptomic datasets, epi-genomic modification sites, single nucleotide polymorphisms identified from genome re-sequencing, and the large gene lists derived from these omics studies. Well-organized results, as well as multiple tools for interactive visualization, are available through a user-friendly web interface. Finally, we illustrate how CARMO enables biological insights using four examples, demonstrating that CARMO is a highly useful resource for intensive data mining and hypothesis generation based on rice multi-omics data. CARMO is freely available online (http://bioinfo.sibs.ac.cn/carmo).

Genome-Wide Association Study of Agronomic Traits in Common Bean

A genome-wide association study (GWAS) using a global Andean diversity panel (ADP) of 237 genotypes of common bean (Phaseolus vulgaris L.) was conducted to gain insight into the genetic architecture of phenology, biomass, yield components, and seed yield traits. The panel was evaluated for 2 yr in field trials in Michigan and genotyped with 5398 single nucleotide polymorphism (SNP) markers. After correcting for population structure and cryptic relatedness, significant SNP markers associated with several agronomic traits were identified. Positional candidate genes, including Phvul.001G221100 on P. vulgaris (Pv) chromosome 01, associated with days to flowering and maturity were identified. Significant SNPs for seed yield were identified on Pv03 and Pv09 and colocalized with quantitative trait loci (QTL) for yield from previous studies conducted in several environments and contrasting genetic backgrounds. The majority of germplasm carrying the alleles with positive effects on seed yield was of African origin and largely underutilized in US breeding programs. The study provided insights into the genetic architecture of agronomic traits in Andean beans.

The Impact of Climate on the Spread of Rice to North-Eastern China: A New Look at the Data from Shandong Province

Moving crops outside of their original centers of domestication was sometimes a challenging process. Because of its substantial heat requirements, moving rice agriculture outside of its homelands of domestication was not an easy process for farmers in the past. Using crop niche models, we examine the constraints faced by ancient farmers and foragers as they moved rice to its most northerly extent in Ancient China: Shandong province. Contrary to previous arguments, we find that during the climatic optimum rice could have been grown in the region. Climatic cooling following this date had a clear impact on the distribution of rice, one that may have placed adaptive pressure on rice to develop a temperate phenotype. Following the development of this temperate phenotype, rice agriculture could once again become implanted in select areas of north-eastern China.

Assessment of Genetic Heterogeneity in Structured Plant Populations Using Multivariate Whole-Genome Regression Models

Plant breeding populations exhibit varying levels of structure and admixture; these features are likely to induce heterogeneity of marker effects across subpopulations. Traditionally, structure has been dealt with as a potential confounder, and various methods exist to "correct" for population stratification. However, these methods induce a mean correction that does not account for heterogeneity of marker effects. The animal breeding literature offers a few recent studies that consider modeling genetic heterogeneity in multibreed data, using multivariate models. However, these methods have received little attention in plant breeding where population structure can have different forms. In this article we address the problem of analyzing data from heterogeneous plant breeding populations, using three approaches: (a) a model that ignores population structure [A-genome-based best linear unbiased prediction (A-GBLUP)], (b) a stratified (i.e., within-group) analysis (W-GBLUP), and (c) a multivariate approach that uses multigroup data and accounts for heterogeneity (MG-GBLUP). The performance of the three models was assessed on three different data sets: a diversity panel of rice (Oryza sativa), a maize (Zea mays L.) half-sib panel, and a wheat (Triticum aestivum L.) data set that originated from plant breeding programs. The estimated genomic correlations between subpopulations varied from null to moderate, depending on the genetic distance between subpopulations and traits. Our assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power. In general, the multivariate approach appeared slightly more robust than either the A- or the W-GBLUP.

Single-copy gene based 50 K SNP chip for genetic studies and molecular breeding in rice

Single nucleotide polymorphism (SNP) is the most abundant DNA sequence variation present in plant genomes. Here, we report the design and validation of a unique genic-SNP genotyping chip for genetic and evolutionary studies as well as molecular breeding applications in rice. The chip incorporates 50,051 SNPs from 18,980 different genes spanning 12 rice chromosomes, including 3,710 single-copy (SC) genes conserved between wheat and rice, 14,959 SC genes unique to rice, 194 agronomically important cloned rice genes and 117 multi-copy rice genes. Assays with this chip showed high success rate and reproducibility because of the SC gene based array with no sequence redundancy and cross-hybridisation problems. The usefulness of the chip in genetic diversity and phylogenetic studies of cultivated and wild rice germplasm was demonstrated. Furthermore, its efficacy was validated for analysing background recovery in improved mega rice varieties with submergence tolerance developed through marker-assisted backcross breeding.

A Ranking Approach to Genomic Selection

Background

Genomic selection (GS) is a recent selective breeding method which uses predictive models based on whole-genome molecular markers. Until now, existing studies formulated GS as the problem of modeling an individual’s breeding value for a particular trait of interest, i.e., as a regression problem. To assess predictive accuracy of the model, the Pearson correlation between observed and predicted trait values was used.

Contributions

In this paper, we propose to formulate GS as the problem of ranking individuals according to their breeding value. Our proposed framework allows us to employ machine learning methods for ranking which had previously not been considered in the GS literature. To assess ranking accuracy of a model, we introduce a new measure originating from the information retrieval literature called normalized discounted cumulative gain (NDCG). NDCG rewards more strongly models which assign a high rank to individuals with high breeding value. Therefore, NDCG reflects a prerequisite objective in selective breeding: accurate selection of individuals with high breeding value.

Results

We conducted a comparison of 10 existing regression methods and 3 new ranking methods on 6 datasets, consisting of 4 plant species and 25 traits. Our experimental results suggest that tree-based ensemble methods including McRank, Random Forests and Gradient Boosting Regression Trees achieve excellent ranking accuracy. RKHS regression and RankSVM also achieve good accuracy when used with an RBF kernel. Traditional regression methods such as Bayesian lasso, wBSR and BayesC were found less suitable for ranking. Pearson correlation was found to correlate poorly with NDCG. Our study suggests two important messages. First, ranking methods are a promising research direction in GS. Second, NDCG can be a useful evaluation measure for GS.

Population genetic structure of Oryza sativa in East and Southeast Asia and the discovery of elite alleles for grain traits

We investigated the nuclear simple sequence repeat (SSR) genotypes of 532 rice (Oryza sativa L.) accessions collected from East and Southeast Asia and detected abundant genetic diversity within the population. We identified 6 subpopulations and found a tendency towards directional evolution in O. sativa from low to high latitudes, with levels of linkage disequilibrium (LD) in the 6 subpopulations ranging from 10 to 30 cM. We then investigated the phenotypic data for grain length, grain width, grain thickness and 1,000-grain weight over 4 years. Using a genome-wide association analysis, we identified 17 marker-trait associations involving 14 SSR markers on 12 chromosome arms, and 8 of the 17 associations were novel. The elite alleles were mined based on the phenotypic effects of the detected quantitative trait loci (QTLs). These elite alleles could be used to improve target traits through optimal cross designs, with the expected results obtained by pyramiding or substituting the elite alleles per QTL (independent of possible epistatic effects). Together, these results provide an in-depth understanding of the genetic diversity pattern among rice-grain traits across a broad geographic scale, which has potential use in future research work, including studies related to germplasm conservation and molecular breeding by design.

A genome-wide SNP scan accelerates trait-regulatory genomic loci identification in chickpea

We identified 44844 high-quality SNPs by sequencing 92 diverse chickpea accessions belonging to a seed and pod trait-specific association panel using reference genome- and de novo-based GBS (genotyping-by-sequencing) assays. A GWAS (genome-wide association study) in an association panel of 211, including the 92 sequenced accessions, identified 22 major genomic loci showing significant association (explaining 23-47% phenotypic variation) with pod and seed number/plant and 100-seed weight. Eighteen trait-regulatory major genomic loci underlying 13 robust QTLs were validated and mapped on an intra-specific genetic linkage map by QTL mapping. A combinatorial approach of GWAS, QTL mapping and gene haplotype-specific LD mapping and transcript profiling uncovered one superior haplotype and favourable natural allelic variants in the upstream regulatory region of a CesA-type cellulose synthase (Ca_Kabuli_CesA3) gene regulating high pod and seed number/plant (explaining 47% phenotypic variation) in chickpea. The up-regulation of this superior gene haplotype correlated with increased transcript expression of Ca_Kabuli_CesA3 gene in the pollen and pod of high pod/seed number accession, resulting in higher cellulose accumulation for normal pollen and pollen tube growth. A rapid combinatorial genome-wide SNP genotyping-based approach has potential to dissect complex quantitative agronomic traits and delineate trait-regulatory genomic loci (candidate genes) for genetic enhancement in crop plants, including chickpea.

Genetic Architecture of Natural Variation in Rice Chlorophyll Content Revealed by a Genome-Wide Association Study

Chlorophyll content is one of the most important physiological traits as it is closely related to leaf photosynthesis and crop yield potential. So far, few genes have been reported to be involved in natural variation of chlorophyll content in rice (Oryza sativa) and the extent of variations explored is very limited. We conducted a genome-wide association study (GWAS) using a diverse worldwide collection of 529 O. sativa accessions. A total of 46 significant association loci were identified. Three F2 mapping populations with parents selected from the association panel were tested for validation of GWAS signals. We clearly demonstrated that Grain number, plant height, and heading date7 (Ghd7) was a major locus for natural variation of chlorophyll content at the heading stage by combining evidence from near-isogenic lines and transgenic plants. The enhanced expression of Ghd7 decreased the chlorophyll content, mainly through down-regulating the expression of genes involved in the biosynthesis of chlorophyll and chloroplast. In addition, Narrow leaf1 (NAL1) corresponded to one significant association region repeatedly detected over two years. We revealed a high degree of polymorphism in the 5' UTR and four non-synonymous SNPs in the coding region of NAL1, and observed diverse effects of the major haplotypes. The loci or candidate genes identified would help to fine-tune and optimize the antenna size of canopies in rice breeding.

A bi-filtering method for processing single nucleotide polymorphism array data improves the quality of genetic map and accuracy of quantitative trait locus mapping in doubled haploid populations of polyploid Brassica napus

Background

Single nucleotide polymorphism (SNP) markers have a wide range of applications in crop genetics and genomics. Due to their polyploidy nature, many important crops, such as wheat, cotton and rapeseed contain a large amount of repeat and homoeologous sequences in their genomes, which imposes a huge challenge in high-throughput genotyping with sequencing and/or array technologies. Allotetraploid Brassica napus (AACC, 2n = 4x = 38) comprises of two highly homoeologous sub-genomes derived from its progenitor species B. rapa (AA, 2n = 2x = 20) and B. oleracea (CC, 2n = 2x = 18), and is an ideal species to exploit methods for reducing the interference of extensive inter-homoeologue polymorphisms (mHemi-SNPs and Pseudo-simple SNPs) between closely related sub-genomes.

Results

Based on a recent B. napus 6K SNP array, we developed a bi-filtering procedure to identify unauthentic lines in a DH population, and mHemi-SNPs and Pseudo-simple SNPs in an array data matrix. The procedure utilized both monomorphic and polymorphic SNPs in the DH population and could effectively distinguish the mHemi-SNPs and Pseudo-simple SNPs that resulted from superposition of the signals from multiple SNPs. Compared with conventional procedure for array data processing, the bi-filtering method could minimize the pseudo linkage relationship caused by the mHemi-SNPs and Pseudo-simple SNPs, thus improving the quality of SNP genetic map. Furthermore, the improved genetic map could increase the accuracies of mapping of QTLs as demonstrated by the ability to eliminate non-real QTLs in the mapping population.

Conclusions

The bi-filtering analysis of the SNP array data represents a novel approach to effectively assigning the multi-loci SNP genotypes in polyploid B. napus and may find wide applications to SNP analyses in polyploid crops.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1559-4) contains supplementary material, which is available to authorized users.

Optimization of genomic selection training populations with a genetic algorithm

In this article, we imagine a breeding scenario with a population of individuals that have been genotyped but not phenotyped. We derived a computationally efficient statistic that uses this genetic information to measure the reliability of genomic estimated breeding values (GEBV) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals. This subset was phenotyped to create the training set that was used in a genomic selection model to estimate GEBV in the test set. Our results show that, compared to a random sample of the same size, the use of a set of individuals selected by our method improved accuracies. We implemented the proposed training selection methodology on four sets of data on Arabidopsis, wheat, rice and maize. This dynamic model building process that takes genotypes of the individuals in the test sample into account while selecting the training individuals improves the performance of genomic selection models.

Optimization of genomic selection training populations with a genetic algorithm

In this article, we imagine a breeding scenario with a population of individuals that have been genotyped but not phenotyped. We derived a computationally efficient statistic that uses this genetic information to measure the reliability of genomic estimated breeding values (GEBV) for a given set of individuals (test set) based on a training set of individuals. We used this reliability measure with a genetic algorithm scheme to find an optimized training set from a larger set of candidate individuals. This subset was phenotyped to create the training set that was used in a genomic selection model to estimate GEBV in the test set. Our results show that, compared to a random sample of the same size, the use of a set of individuals selected by our method improved accuracies. We implemented the proposed training selection methodology on four sets of data on Arabidopsis, wheat, rice and maize. This dynamic model building process that takes genotypes of the individuals in the test sample into account while selecting the training individuals improves the performance of genomic selection models.

Ultra-high density intra-specific genetic linkage maps accelerate identification of functionally relevant molecular tags governing important agronomic traits in chickpea

We discovered 26785 and 16573 high-quality SNPs differentiating two parental genotypes of a RIL mapping population using reference desi and kabuli genome-based GBS assay. Of these, 3625 and 2177 SNPs have been integrated into eight desi and kabuli chromosomes, respectively in order to construct ultra-high density (0.20-0.37 cM) intra-specific chickpea genetic linkage maps. One of these constructed high-resolution genetic map has potential to identify 33 major genomic regions harbouring 35 robust QTLs (PVE: 17.9-39.7%) associated with three agronomic traits, which were mapped within <1 cM mean marker intervals on desi chromosomes. The extended LD (linkage disequilibrium) decay (~15 cM) in chromosomes of genetic maps have encouraged us to use a rapid integrated approach (comparative QTL mapping, QTL-region specific haplotype/LD-based trait association analysis, expression profiling and gene haplotype-based association mapping) rather than a traditional QTL map-based cloning method to narrow-down one major seed weight (SW) robust QTL region. It delineated favourable natural allelic variants and superior haplotype-containing one seed-specific candidate embryo defective gene regulating SW in chickpea. The ultra-high-resolution genetic maps, QTLs/genes and alleles/haplotypes-related genomic information generated and integrated strategy for rapid QTL/gene identification developed have potential to expedite genomics-assisted breeding applications in crop plants, including chickpea for their genetic enhancement.

Unmasking Novel Loci for Internal Phosphorus Utilization Efficiency in Rice Germplasm through Genome-Wide Association Analysis

Depletion of non-renewable rock phosphate reserves and phosphorus (P) fertilizer price increases has renewed interest in breeding P-efficient varieties. Internal P utilization efficiency (PUE) is of prime interest because there has been no progress to date in breeding for high PUE. We characterized the genotypic variation for PUE present within the rice gene pool by using a hydroponic system that assured equal plant P uptake, followed by mapping of loci controlling PUE via Genome-Wide Association Studies (GWAS). Loci associated with PUE were mapped on chromosomes 1, 4, 11 and 12. The highest PUE was associated with a minor indica-specific haplotype on chromosome 1 and a rare aus-specific haplotype on chromosome 11. Comparative variant and expression analysis for genes contained within the chromosome 1 haplotype identified high priority candidate genes. Differences in coding regions and expression patterns between genotypes of contrasting haplotypes, suggested functional alterations for two predicted nucleic acid-interacting proteins that are likely causative for the observed differences in PUE. The loci reported here are the first identified for PUE in any crop that is not confounded by differential P uptake among genotypes. Importantly, modern rice varieties lacked haplotypes associated with superior PUE, and would thus benefit from targeted introgressions of these loci from traditional donors to improve plant growth in phosphorus-limited cropping systems.

Whole genome sequencing of elite rice cultivars as a comprehensive information resource for marker assisted selection

Current advances in sequencing technologies and bioinformatics revealed the genomic background of rice, a staple food for the poor people, and provided the basis to develop large genomic variation databases for thousands of cultivars. Proper analysis of this massive resource is expected to give novel insights into the structure, function, and evolution of the rice genome, and to aid the development of rice varieties through marker assisted selection or genomic selection. In this work we present sequencing and bioinformatics analyses of 104 rice varieties belonging to the major subspecies of Oryza sativa. We identified repetitive elements and recurrent copy number variation covering about 200 Mbp of the rice genome. Genotyping of over 18 million polymorphic locations within O. sativa allowed us to reconstruct the individual haplotype patterns shaping the genomic background of elite varieties used by farmers throughout the Americas. Based on a reconstruction of the alleles for the gene GBSSI, we could identify novel genetic markers for selection of varieties with high amylose content. We expect that both the analysis methods and the genomic information described here would be of great use for the rice research community and for other groups carrying on similar sequencing efforts in other crops.

Ultrahigh-density linkage map for cultivated cucumber (Cucumis sativus L.) using a single-nucleotide polymorphism genotyping array

Genotyping arrays are tools for high-throughput genotyping, which is beneficial in constructing saturated genetic maps and therefore high-resolution mapping of complex traits. Since the report of the first cucumber genome draft, genetic maps have been constructed mainly based on simple-sequence repeats (SSRs) or on combinations of SSRs and sequence-related amplified polymorphism (SRAP). In this study, we developed the first cucumber genotyping array consisting of 32,864 single-nucleotide polymorphisms (SNPs). These markers cover the cucumber genome with a median interval of ~2 Kb and have expected genotype calls in parents/F₁ hybridizations as a training set. The training set was validated with Fluidigm technology and showed 96% concordance with the genotype calls in the parents/F₁ hybridizations. Application of the genotyping array was illustrated by constructing a 598.7 cM genetic map based on a ‘9930’ × ‘Gy14’ recombinant inbred line (RIL) population comprised of 11,156 SNPs. Marker collinearity between the genetic map and reference genomes of the two parents was estimated at R² = 0.97. We also used the array-derived genetic map to investigate chromosomal rearrangements, regional recombination rate, and specific regions with segregation distortions. Finally, 82% of the linkage-map bins were polymorphic in other cucumber variants, suggesting that the array can be applied for genotyping in other lines. The genotyping array presented here, together with the genotype calls of the parents/F₁ hybridizations as a training set, should be a powerful tool in future studies with high-throughput cucumber genotyping. An ultrahigh-density linkage map constructed by this genotyping array on RIL population may be invaluable for assembly improvement, and for mapping important cucumber QTLs.

Loss of floral repressor function adapts rice to higher latitudes in Europe

The capacity to discriminate variations in day length allows plants to align flowering with the most favourable season of the year. This capacity has been altered by artificial selection when cultivated varieties became adapted to environments different from those of initial domestication. Rice flowering is promoted by short days when HEADING DATE 1 (Hd1) and EARLY HEADING DATE 1 (Ehd1) induce the expression of florigenic proteins encoded by HEADING DATE 3a (Hd3a) and RICE FLOWERING LOCUS T 1 (RFT1). Repressors of flowering antagonize such induction under long days, maintaining vegetative growth and delaying flowering. To what extent artificial selection of long day repressor loci has contributed to expand rice cultivation to Europe is currently unclear. This study demonstrates that European varieties activate both Hd3a and RFT1 expression regardless of day length and their induction is caused by loss-of-function mutations at major long day floral repressors. However, their contribution to flowering time control varies between locations. Pyramiding of mutations is frequently observed in European germplasm, but single mutations are sufficient to adapt rice to flower at higher latitudes. Expression of Ehd1 is increased in varieties showing reduced or null Hd1 expression under natural long days, as well as in single hd1 mutants in isogenic backgrounds. These data indicate that loss of repressor genes has been a key strategy to expand rice cultivation to Europe, and that Ehd1 is a central node integrating floral repressive signals.

Variations in CYP78A13 coding region influence grain size and yield in rice

Grain size is one of the most important determinants of crop yield in cereals. Here, we identified a dominant mutant, big grain2 (bg2-D) from our enhancer-trapping population. Genetic analysis and SiteFinding PCR (polymerase chain reaction) revealed that BG2 encodes a cytochrome P450, OsCYP78A13. Sequence search revealed that CYP78A13 has a paralogue Grain Length 3.2 (GL3.2, LOC_Os03g30420) in rice with distinct expression patterns, analysis of transgenic plants harbouring either CYP78A13 or GL3.2 showed that both can promote grain growth. Sequence polymorphism analysis with 1529 rice varieties showed that the nucleotide diversity at CYP78A13 gene body and the 20 kb flanking region in the indica varieties were markedly higher than those in japonica varieties. Further, comparison of the genomic sequence of CYP78A13 in the japonica cultivar Nipponbare and the indica cultivar 9311 showed that there were three InDels in the promoter region and eight SNPs (single nucleotide polymorphism) in its coding sequence. Detailed examination of the transgenic plants with chimaeric constructs suggested that variation in CYP78A13 coding region is responsible for the variation of grain yield. Taken together, our results suggest that the variations in CYP78A13 in the indica varieties hold potential in rice breeding for application of grain yield improvement.

Loci and candidate gene identification for resistance to Sclerotinia sclerotiorum in soybean (Glycine max L. Merr.) via association and linkage maps

Soybean white mold (SWM), caused by Sclerotinia sclerotiorum ((Lib.) W. Phillips), is currently considered to be the second most important cause of soybean yield loss due to disease. Research is needed to identify SWM-resistant germplasm and gain a better understanding of the genetic and molecular basis of SWM resistance in soybean. Stem pigmentation after treatment with oxaloacetic acid is an effective indicator of resistance to SWM. A total of 128 recombinant inbred lines (RILs) derived from a cross of 'Maple Arrow' (partial resistant to SWM) and 'Hefeng 25' (susceptible) and 330 diverse soybean cultivars were screened for the soluble pigment concentration of their stems, which were treated with oxalic acid. Four quantitative trait loci (QTLs) underlying soluble pigment concentration were detected by linkage mapping of the RILs. Three hundred and thirty soybean cultivars were sequenced using the whole-genome encompassing approach and 25 179 single-nucleotide polymorphisms (SNPs) were detected for the fine mapping of SWM resistance genes by genome-wide association studies. Three out of five SNP markers representing a linkage disequilibrium (LD) block and a single locus on chromosome 13 (Gm13) were significantly associated with the soluble pigment content of stems. Three more SNPs that represented three minor QTLs for the soluble pigment content of stems were identified on another three chromosomes by association mapping. A major locus with the largest effect on Gm13 was found both by linkage and association mapping. Four potential candidate genes involved in disease response or the anthocyanin biosynthesis pathway were identified at the locus near the significant SNPs (<60 kbp). The beneficial allele and candidate genes should be useful in soybean breeding for improving resistance to SWM.

Employing genome-wide SNP discovery and genotyping strategy to extrapolate the natural allelic diversity and domestication patterns in chickpea

The genome-wide discovery and high-throughput genotyping of SNPs in chickpea natural germplasm lines is indispensable to extrapolate their natural allelic diversity, domestication, and linkage disequilibrium (LD) patterns leading to the genetic enhancement of this vital legume crop. We discovered 44,844 high-quality SNPs by sequencing of 93 diverse cultivated desi, kabuli, and wild chickpea accessions using reference genome- and de novo-based GBS (genotyping-by-sequencing) assays that were physically mapped across eight chromosomes of desi and kabuli. Of these, 22,542 SNPs were structurally annotated in different coding and non-coding sequence components of genes. Genes with 3296 non-synonymous and 269 regulatory SNPs could functionally differentiate accessions based on their contrasting agronomic traits. A high experimental validation success rate (92%) and reproducibility (100%) along with strong sensitivity (93-96%) and specificity (99%) of GBS-based SNPs was observed. This infers the robustness of GBS as a high-throughput assay for rapid large-scale mining and genotyping of genome-wide SNPs in chickpea with sub-optimal use of resources. With 23,798 genome-wide SNPs, a relatively high intra-specific polymorphic potential (49.5%) and broader molecular diversity (13-89%)/functional allelic diversity (18-77%) was apparent among 93 chickpea accessions, suggesting their tremendous applicability in rapid selection of desirable diverse accessions/inter-specific hybrids in chickpea crossbred varietal improvement program. The genome-wide SNPs revealed complex admixed domestication pattern, extensive LD estimates (0.54-0.68) and extended LD decay (400-500 kb) in a structured population inclusive of 93 accessions. These findings reflect the utility of our identified SNPs for subsequent genome-wide association study (GWAS) and selective sweep-based domestication trait dissection analysis to identify potential genomic loci (gene-associated targets) specifically regulating important complex quantitative agronomic traits in chickpea. The numerous informative genome-wide SNPs, natural allelic diversity-led domestication pattern, and LD-based information generated in our study have got multidimensional applicability with respect to chickpea genomics-assisted breeding.

Genomic prediction of biological shape: elliptic Fourier analysis and kernel partial least squares (PLS) regression applied to grain shape prediction in rice (Oryza sativa L.)

Shape is an important morphological characteristic both in animals and plants. In the present study, we examined a method for predicting biological contour shapes based on genome-wide marker polymorphisms. The method is expected to contribute to the acceleration of genetic improvement of biological shape via genomic selection. Grain shape variation observed in rice (Oryza sativa L.) germplasms was delineated using elliptic Fourier descriptors (EFDs), and was predicted based on genome-wide single nucleotide polymorphism (SNP) genotypes. We applied four methods including kernel PLS (KPLS) regression for building a prediction model of grain shape, and compared the accuracy of the methods via cross-validation. We analyzed multiple datasets that differed in marker density and sample size. Datasets with larger sample size and higher marker density showed higher accuracy. Among the four methods, KPLS showed the highest accuracy. Although KPLS and ridge regression (RR) had equivalent accuracy in a single dataset, the result suggested the potential of KPLS for the prediction of high-dimensional EFDs. Ordinary PLS, however, was less accurate than RR in all datasets, suggesting that the use of a non-linear kernel was necessary for accurate prediction using the PLS method. Rice grain shape can be predicted accurately based on genome-wide SNP genotypes. The proposed method is expected to be useful for genomic selection in biological shape.

Genome-wide association mapping for yield and other agronomic traits in an elite breeding population of tropical rice (Oryza sativa)

Genome-wide association mapping studies (GWAS) are frequently used to detect QTL in diverse collections of crop germplasm, based on historic recombination events and linkage disequilibrium across the genome. Generally, diversity panels genotyped with high density SNP panels are utilized in order to assay a wide range of alleles and haplotypes and to monitor recombination breakpoints across the genome. By contrast, GWAS have not generally been performed in breeding populations. In this study we performed association mapping for 19 agronomic traits including yield and yield components in a breeding population of elite irrigated tropical rice breeding lines so that the results would be more directly applicable to breeding than those from a diversity panel. The population was genotyped with 71,710 SNPs using genotyping-by-sequencing (GBS), and GWAS performed with the explicit goal of expediting selection in the breeding program. Using this breeding panel we identified 52 QTL for 11 agronomic traits, including large effect QTLs for flowering time and grain length/grain width/grain-length-breadth ratio. We also identified haplotypes that can be used to select plants in our population for short stature (plant height), early flowering time, and high yield, and thus demonstrate the utility of association mapping in breeding populations for informing breeding decisions. We conclude by exploring how the newly identified significant SNPs and insights into the genetic architecture of these quantitative traits can be leveraged to build genomic-assisted selection models.

Association mapping in sunflower (Helianthus annuus L.) reveals independent control of apical vs. basal branching

BACKGROUND

Shoot branching is an important determinant of plant architecture and influences various aspects of growth and development. Selection on branching has also played an important role in the domestication of crop plants, including sunflower (Helianthus annuus L.). Here, we describe an investigation of the genetic basis of variation in branching in sunflower via association mapping in a diverse collection of cultivated sunflower lines.

RESULTS

Detailed phenotypic analyses revealed extensive variation in the extent and type of branching within the focal population. After correcting for population structure and kinship, association analyses were performed using a genome-wide collection of SNPs to identify genomic regions that influence a variety of branching-related traits. This work resulted in the identification of multiple previously unidentified genomic regions that contribute to variation in branching. Genomic regions that were associated with apical and mid-apical branching were generally distinct from those associated with basal and mid-basal branching. Homologs of known branching genes from other study systems (i.e., Arabidopsis, rice, pea, and petunia) were also identified from the draft assembly of the sunflower genome and their map positions were compared to those of associations identified herein. Numerous candidate branching genes were found to map in close proximity to significant branching associations.

CONCLUSIONS

In sunflower, variation in branching is genetically complex and overall branching patterns (i.e., apical vs. basal) were found to be influenced by distinct genomic regions. Moreover, numerous candidate branching genes mapped in close proximity to significant branching associations. Although the sunflower genome exhibits localized islands of elevated linkage disequilibrium (LD), these non-random associations are known to decay rapidly elsewhere. The subset of candidate genes that co-localized with significant associations in regions of low LD represents the most promising target for future functional analyses.