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

A genomic perspective on the important genetic mechanisms of upland adaptation of rice

BACKGROUND

Cultivated rice consists of two important ecotypes, upland and irrigated, that have respectively adapted to either dry land or irrigated cultivation. Upland rice, widely adopted in rainfed upland areas in virtue of its little water requirement, contains abundant untapped genetic resources, such as genes for drought adaptation. With water shortage exacerbated and population expanding, the need for breeding crop varieties with drought adaptation becomes more and more urgent. However, a previous oversight in upland rice research reveals little information regarding its genetic mechanisms for upland adaption, greatly hindering progress in harnessing its genetic resources for breeding and cultivation.

RESULTS

In this study, we selected 84 upland and 82 irrigated accessions from all over the world, phenotyped them under both irrigated and dry land environments, and investigated the phylogenetic relations and population structure of the upland ecotype using whole genome variation data. Further comparative analysis yields a list of differentiated genes that may account for the phenotypic and physiological differences between upland and irrigated rice.

CONCLUSIONS

This study represents the first genomic investigation in a large sample of upland rice, providing valuable gene list for understanding upland rice adaptation, especially drought-related adaptation, and its subsequent utilization in modern agriculture.

Harvesting the promising fruits of genomics: applying genome sequencing technologies to crop breeding

Rajeev Varshney, Ryohei Terauchi, and Susan McCouch summarize the current and future uses of next-generation sequencing technologies, both for developing crops with improved traits and for increasing the efficiency of modern plant breeding, as a step towards meeting the challenge of feeding a growing world population.

Next generation sequencing (NGS) technologies are being used to generate whole genome sequences for a wide range of crop species. When combined with precise phenotyping methods, these technologies provide a powerful and rapid tool for identifying the genetic basis of agriculturally important traits and for predicting the breeding value of individuals in a plant breeding population. Here we summarize current trends and future prospects for utilizing NGS-based technologies to develop crops with improved trait performance and increase the efficiency of modern plant breeding. It is our hope that the application of NGS technologies to plant breeding will help us to meet the challenge of feeding a growing world population.

Genome-wide association analyses provide genetic and biochemical insights into natural variation in rice metabolism

Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

Variation in inflorescence development is an important target of selection for numerous crop species, including many members of the Poaceae (grasses). In Asian rice (Oryza sativa), inflorescence (panicle) architecture is correlated with yield and grain-quality traits. However, many rice breeders continue to use composite phenotypes in selection pipelines, because measuring complex, branched panicles requires a significant investment of resources. We developed an open-source phenotyping platform, PANorama, which measures multiple architectural and branching phenotypes from images simultaneously. PANorama automatically extracts skeletons from images, allows users to subdivide axes into individual internodes, and thresholds away structures, such as awns, that normally interfere with accurate panicle phenotyping. PANorama represents an improvement in both efficiency and accuracy over existing panicle imaging platforms, and flexible implementation makes PANorama capable of measuring a range of organs from other plant species. Using high-resolution phenotypes, a mapping population of recombinant inbred lines, and a dense single-nucleotide polymorphism data set, we identify, to our knowledge, the largest number of quantitative trait loci (QTLs) for panicle traits ever reported in a single study. Several areas of the genome show pleiotropic clusters of panicle QTLs, including a region near the rice Green Revolution gene SEMIDWARF1. We also confirm that multiple panicle phenotypes are distinctly different among a small collection of diverse rice varieties. Taken together, these results suggest that clusters of small-effect QTLs may be responsible for varietal or subpopulation-specific panicle traits, representing a significant opportunity for rice breeders selecting for yield performance across different genetic backgrounds.

Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars

BACKGROUND

High-yielding cultivars of rice (Oryza sativa L.) have been developed in Japan from crosses between overseas indica and domestic japonica cultivars. Recently, next-generation sequencing technology and high-throughput genotyping systems have shown many single-nucleotide polymorphisms (SNPs) that are proving useful for detailed analysis of genome composition. These SNPs can be used in genome-wide association studies to detect candidate genome regions associated with economically important traits. In this study, we used a custom SNP set to identify introgressed chromosomal regions in a set of high-yielding Japanese rice cultivars, and we performed an association study to identify genome regions associated with yield.

RESULTS

An informative set of 1152 SNPs was established by screening 14 high-yielding or primary ancestral cultivars for 5760 validated SNPs. Analysis of the population structure of high-yielding cultivars showed three genome types: japonica-type, indica-type and a mixture of the two. SNP allele frequencies showed several regions derived predominantly from one of the two parental genome types. Distinct regions skewed for the presence of parental alleles were observed on chromosomes 1, 2, 7, 8, 11 and 12 (indica) and on chromosomes 1, 2 and 6 (japonica). A possible relationship between these introgressed regions and six yield traits (blast susceptibility, heading date, length of unhusked seeds, number of panicles, surface area of unhusked seeds and 1000-grain weight) was detected in eight genome regions dominated by alleles of one parental origin. Two of these regions were near Ghd7, a heading date locus, and Pi-ta, a blast resistance locus. The allele types (i.e., japonica or indica) of significant SNPs coincided with those previously reported for candidate genes Ghd7 and Pi-ta.

CONCLUSIONS

Introgression breeding is an established strategy for the accumulation of QTLs and genes controlling high yield. Our custom SNP set is an effective tool for the identification of introgressed genome regions from a particular genetic background. This study demonstrates that changes in genome structure occurred during artificial selection for high yield, and provides information on several genomic regions associated with yield performance.

Genetic dissection of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping approaches

To understand the genetic basis of tolerance to drought and heat stresses in chickpea, a comprehensive association mapping approach has been undertaken. Phenotypic data were generated on the reference set (300 accessions, including 211 mini-core collection accessions) for drought tolerance related root traits, heat tolerance, yield and yield component traits from 1-7 seasons and 1-3 locations in India (Patancheru, Kanpur, Bangalore) and three locations in Africa (Nairobi, Egerton in Kenya and Debre Zeit in Ethiopia). Diversity Array Technology (DArT) markers equally distributed across chickpea genome were used to determine population structure and three sub-populations were identified using admixture model in STRUCTURE. The pairwise linkage disequilibrium (LD) estimated using the squared-allele frequency correlations (r2; when r2<0.20) was found to decay rapidly with the genetic distance of 5 cM. For establishing marker-trait associations (MTAs), both genome-wide and candidate gene-sequencing based association mapping approaches were conducted using 1,872 markers (1,072 DArTs, 651 single nucleotide polymorphisms [SNPs], 113 gene-based SNPs and 36 simple sequence repeats [SSRs]) and phenotyping data mentioned above employing mixed linear model (MLM) analysis with optimum compression with P3D method and kinship matrix. As a result, 312 significant MTAs were identified and a maximum number of MTAs (70) was identified for 100-seed weight. A total of 18 SNPs from 5 genes (ERECTA, 11 SNPs; ASR, 4 SNPs; DREB, 1 SNP; CAP2 promoter, 1 SNP and AMDH, 1SNP) were significantly associated with different traits. This study provides significant MTAs for drought and heat tolerance in chickpea that can be used, after validation, in molecular breeding for developing superior varieties with enhanced drought and heat tolerance.

Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in rice

Background

In genetic analysis of agronomic traits, quantitative trait loci (QTLs) that control the same phenotype are often closely linked. Furthermore, many QTLs are localized in specific genomic regions (QTL clusters) that include naturally occurring allelic variations in different genes. Therefore, linkage among QTLs may complicate the detection of each individual QTL. This problem can be resolved by using populations that include many potential recombination sites. Recently, multi-parent populations have been developed and used for QTL analysis. However, their efficiency for detection of linked QTLs has not received attention. By using information on rice, we simulated the construction of a multi-parent population followed by cycles of recurrent crossing and inbreeding, and we investigated the resulting genome structure and its usefulness for detecting linked QTLs as a function of the number of cycles of recurrent crossing.

Results

The number of non-recombinant genome segments increased linearly with an increasing number of cycles. The mean and median lengths of the non-recombinant genome segments decreased dramatically during the first five to six cycles, then decreased more slowly during subsequent cycles. Without recurrent crossing, we found that there is a risk of missing QTLs that are linked in a repulsion phase, and a risk of identifying linked QTLs in a coupling phase as a single QTL, even when the population was derived from eight parental lines. In our simulation results, using fewer than two cycles of recurrent crossing produced results that differed little from the results with zero cycles, whereas using more than six cycles dramatically improved the power under most of the conditions that we simulated.

Conclusion

Our results indicated that even with a population derived from eight parental lines, fewer than two cycles of crossing does not improve the power to detect linked QTLs. However, using six cycles dramatically improved the power, suggesting that advanced intercrossing can help to resolve the problems that result from linkage among QTLs.

Identification of candidate genes associated with positive and negative heterosis in rice

To identify the genes responsible for yield related traits, and heterosis, massively parallel signature sequencing (MPSS) libraries were constructed from leaves, roots and meristem tissues from the two parents, 'Nipponbare' and '93-11', and their F1 hybrid. From the MPSS libraries, 1-3 million signatures were obtained. Using cluster analysis, commonly and specifically expressed genes in the parents and their F1 hybrid were identified. To understand heterosis in the F1 hybrid, the differentially expressed genes in the F1 hybrid were mapped to yield related quantitative trait loci (QTL) regions using a linkage map constructed from 131 polymorphic simple sequence repeat markers with 266 recombinant inbred lines derived from a cross between Nipponbare and 93-11. QTLs were identified for yield related traits including days to heading, plant height, plant type, number of tillers, main panicle length, number of primary branches per main panicle, number of kernels per main panicle, total kernel weight per main panicle, 1000 grain weight and total grain yield per plant. Seventy one QTLs for these traits were mapped, of which 3 QTLs were novel. Many highly expressed chromatin-related genes in the F1 hybrid encoding histone demethylases, histone deacetylases, argonaute-like proteins and polycomb proteins were located in these yield QTL regions. A total of 336 highly expressed transcription factor (TF) genes belonging to 50 TF families were identified in the yield QTL intervals. These findings provide the starting genomic materials to elucidate the molecular basis of yield related traits and heterosis in rice.

Genetic diversity of amylose content and RVA pasting parameters in 20 rice accessions grown in Hainan, China

Starch physicochemical properties determine the eating and cooking quality of rice. The genetic diversity in the apparent amylose content (AAC) and pasting viscosity parameters of 20 geographically diverse rice accessions were investigated. It was found that AAC and pasting viscosities differed widely among different accessions, but each accession performed relatively stably across two environments. Analysis of variance (ANOVA) indicated that all traits were predominantly controlled by genotypic variance, but the genotype×environment interaction effects were also significant except for AAC and PT. Significant correlations were found for each parameter between 2years (P<0.001). Association mapping identified a total of 22 main-effect quantitative trait loci (QTLs) responsible for all traits except for CPV. This study showed that starch physicochemical properties of rice were highly stable and mainly controlled by genetic factors, and gave insight into the molecular improvement of eating quality using marker assisted breeding with the identified QTLs/genes.

Identification and quantification of phytochelatins in roots of rice to long-term exposure: evidence of individual role on arsenic accumulation and translocation

Rice has the predilection to take up arsenic in the form of methylated arsenic (o-As) and inorganic arsenic species (i-As). Plants defend themselves using i-As efflux systems and the production of phytochelatins (PCs) to complex i-As. Our study focused on the identification and quantification of phytochelatins by HPLC-ICP-MS/ESI-MS, relating them to the several variables linked to As exposure. GSH, 11 PCs, and As-PC complexes from the roots of six rice cultivars (Italica Carolina, Dom Sofid, 9524, Kitrana 508, YRL-1, and Lemont) exposed to low and high levels of i-As were compared with total, i-As, and o-As in roots, shoots, and grains. Only Dom Sofid, Kitrana 508, and 9524 were found to produce higher levels of PCs even when exposed to low levels of As. PCs were only correlated to i-As in the roots (r=0.884, P <0.001). However, significant negative correlations to As transfer factors (TF) roots-grains (r= -0.739, P <0.05) and shoots-grains (r= -0.541, P <0.05), suggested that these peptides help in trapping i-As but not o-As in the roots, reducing grains' i-As. Italica Carolina reduced i-As in grains after high exposure, where some specific PCs had a special role in this reduction. In Lemont, exposure to elevated levels of i-As did not result in higher i-As levels in the grains and there were no significant increases in PCs or thiols. Finally, the high production of PCs in Kitrana 508 and Dom Sofid in response to high As treatment did not relate to a reduction of i-As in grains, suggesting that other mechanisms such as As-PC release and transport seems to be important in determining grain As in these cultivars.

Association mapping of seed quality traits using the Canadian flax (Linum usitatissimum L.) core collection

KEY MESSAGE

The identification of stable QTL for seed quality traits by association mapping of a diverse panel of linseed accessions establishes the foundation for assisted breeding and future fine mapping in linseed. Linseed oil is valued for its food and non-food applications. Modifying its oil content and fatty acid (FA) profiles to meet market needs in a timely manner requires clear understanding of their quantitative trait loci (QTL) architectures, which have received little attention to date. Association mapping is an efficient approach to identify QTL in germplasm collections. In this study, we explored the quantitative nature of seed quality traits including oil content (OIL), palmitic acid, stearic acid, oleic acid, linoleic acid (LIO) linolenic acid (LIN) and iodine value in a flax core collection of 390 accessions assayed with 460 microsatellite markers. The core collection was grown in a modified augmented design at two locations over 3 years and phenotypic data for all seven traits were obtained from all six environments. Significant phenotypic diversity and moderate to high heritability for each trait (0.73-0.99) were observed. Most of the candidate QTL were stable as revealed by multivariate analyses. Nine candidate QTL were identified, varying from one for OIL to three for LIO and LIN. Candidate QTL for LIO and LIN co-localized with QTL previously identified in bi-parental populations and some mapped nearby genes known to be involved in the FA biosynthesis pathway. Fifty-eight percent of the QTL alleles were absent (private) in the Canadian cultivars suggesting that the core collection possesses QTL alleles potentially useful to improve seed quality traits. The candidate QTL identified herein will establish the foundation for future marker-assisted breeding in linseed.

Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies

Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.

The impact of population structure on genomic prediction in stratified populations

Impacts of population structure on the evaluation of genomic heritability and prediction were investigated and quantified using high-density markers in diverse panels in rice and maize. Population structure is an important factor affecting estimation of genomic heritability and assessment of genomic prediction in stratified populations. In this study, our first objective was to assess effects of population structure on estimations of genomic heritability using the diversity panels in rice and maize. Results indicate population structure explained 33 and 7.5% of genomic heritability for rice and maize, respectively, depending on traits, with the remaining heritability explained by within-subpopulation variation. Estimates of within-subpopulation heritability were higher than that derived from quantitative trait loci identified in genome-wide association studies, suggesting 65% improvement in genetic gains. The second objective was to evaluate effects of population structure on genomic prediction using cross-validation experiments. When population structure exists in both training and validation sets, correcting for population structure led to a significant decrease in accuracy with genomic prediction. In contrast, when prediction was limited to a specific subpopulation, population structure showed little effect on accuracy and within-subpopulation genetic variance dominated predictions. Finally, effects of genomic heritability on genomic prediction were investigated. Accuracies with genomic prediction increased with genomic heritability in both training and validation sets, with the former showing a slightly greater impact. In summary, our results suggest that the population structure contribution to genomic prediction varies based on prediction strategies, and is also affected by the genetic architectures of traits and populations. In practical breeding, these conclusions may be helpful to better understand and utilize the different genetic resources in genomic prediction.

A high-density SNP genotyping array for rice biology and molecular breeding

A high-density single nucleotide polymorphism (SNP) array is critically important for geneticists and molecular breeders. With the accumulation of huge amounts of genomic re-sequencing data and available technologies for accurate SNP detection, it is possible to design high-density and high-quality rice SNP arrays. Here we report the development of a high-density rice SNP array and its utility. SNP probes were designed by screening more than 10 000 000 SNP loci extracted from the re-sequencing data of 801 rice varieties and an array named RiceSNP50 was produced on the Illumina Infinium platform. The array contained 51 478 evenly distributed markers, 68% of which were within genic regions. Several hundred rice plants with parent/F1 relationships were used to generate a high-quality cluster file for accurate SNP calling. Application tests showed that this array had high genotyping accuracy, and could be used for different objectives. For example, a core collection of elite rice varieties was clustered with fine resolution. Genome-wide association studies (GWAS) analysis correctly identified a characterized QTL. Further, this array was successfully used for variety verification and trait introgression. As an accurate high-throughput genotyping tool, RiceSNP50 will play an important role in both functional genomics studies and molecular breeding.

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs (within 200 kb, the estimated global linkage disequilibrium previously employed in this rice panel). This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel.

Natural variation of rice strigolactone biosynthesis is associated with the deletion of two MAX1 orthologs

Rice (Oryza sativa) cultivar Azucena--belonging to the Japonica subspecies--exudes high strigolactone (SL) levels and induces high germination of the root parasitic plant Striga hermonthica. Consistent with the fact that SLs also inhibit shoot branching, Azucena is a low-tillering variety. In contrast, Bala, an Indica cultivar, is a low-SL producer, stimulates less Striga germination, and is highly tillered. Using a Bala × Azucena F6 population, a major quantitative trait loci--qSLB1.1--for the exudation of SL, tillering, and induction of Striga germination was detected on chromosome 1. Sequence analysis of the corresponding locus revealed a rearrangement of a 51- to 59-kbp stretch between 28.9 and 29 Mbp in the Bala genome, resulting in the deletion of two cytochrome P450 genes--SLB1 and SLB2--with high homology to the Arabidopsis SL biosynthesis gene, MAX1. Both rice genes rescue the Arabidopsis max1-1 highly branched mutant phenotype and increase the production of the SL, ent-2'-epi-5-deoxystrigol, when overexpressed in Bala. Furthermore, analysis of this region in 367 cultivars of the publicly available Rice Diversity Panel population shows that the rearrangement at this locus is a recurrent natural trait associated with the Indica/Japonica divide in rice.

Genomewide discovery of DNA polymorphisms in rice cultivars with contrasting drought and salinity stress response and their functional relevance

Next-generation sequencing technologies provide opportunities to understand the genetic basis of phenotypic differences, such as abiotic stress response, even in the closely related cultivars via identification of large number of DNA polymorphisms. We performed whole-genome resequencing of three rice cultivars with contrasting responses to drought and salinity stress (sensitive IR64, drought-tolerant Nagina 22 and salinity-tolerant Pokkali). More than 356 million 90-bp paired-end reads were generated, which provided about 85% coverage of the rice genome. Applying stringent parameters, we identified a total of 1 784 583 nonredundant single-nucleotide polymorphisms (SNPs) and 154 275 InDels between reference (Nipponbare) and the three resequenced cultivars. We detected 401 683 and 662 509 SNPs between IR64 and Pokkali, and IR64 and N22 cultivars, respectively. The distribution of DNA polymorphisms was found to be uneven across and within the rice chromosomes. One-fourth of the SNPs and InDels were detected in genic regions, and about 3.5% of the total SNPs resulted in nonsynonymous changes. Large-effect SNPs and InDels, which affect the integrity of the encoded protein, were also identified. Further, we identified DNA polymorphisms present in the differentially expressed genes within the known quantitative trait loci. Among these, a total of 548 SNPs in 232 genes, located in the conserved functional domains, were identified. The data presented in this study provide functional markers and promising target genes for salinity and drought tolerance and present a valuable resource for high-throughput genotyping and molecular breeding for abiotic stress traits in rice.

Association mapping provides insights into the origin and the fine structure of the sorghum aluminum tolerance locus, AltSB

Root damage caused by aluminum (Al) toxicity is a major cause of grain yield reduction on acid soils, which are prevalent in tropical and subtropical regions of the world where food security is most tenuous. In sorghum, Al tolerance is conferred by SbMATE, an Al-activated root citrate efflux transporter that underlies the major Al tolerance locus, AltSB, on sorghum chromosome 3. We used association mapping to gain insights into the origin and evolution of Al tolerance in sorghum and to detect functional variants amenable to allele mining applications. Linkage disequilibrium across the AltSB locus decreased much faster than in previous reports in sorghum, and reached basal levels at approximately 1000 bp. Accordingly, intra-locus recombination events were found to be extensive. SNPs and indels highly associated with Al tolerance showed a narrow frequency range, between 0.06 and 0.1, suggesting a rather recent origin of Al tolerance mutations within AltSB. A haplotype network analysis suggested a single geographic and racial origin of causative mutations in primordial guinea domesticates in West Africa. Al tolerance assessment in accessions harboring recombinant haplotypes suggests that causative polymorphisms are localized to a ∼6 kb region including intronic polymorphisms and a transposon (MITE) insertion, whose size variation has been shown to be positively correlated with Al tolerance. The SNP with the strongest association signal, located in the second SbMATE intron, recovers 9 of the 14 highly Al tolerant accessions and 80% of all the Al tolerant and intermediately tolerant accessions in the association panel. Our results also demonstrate the pivotal importance of knowledge on the origin and evolution of Al tolerance mutations in molecular breeding applications. Allele mining strategies based on associated loci are expected to lead to the efficient identification, in diverse sorghum germplasm, of Al tolerant accessions able maintain grain yields under Al toxicity.

Evolution of crop species: genetics of domestication and diversification

Domestication is a good model for the study of evolutionary processes because of the recent evolution of crop species (<12,000 years ago), the key role of selection in their origins, and good archaeological and historical data on their spread and diversification. Recent studies, such as quantitative trait locus mapping, genome-wide association studies and whole-genome resequencing studies, have identified genes that are associated with the initial domestication and subsequent diversification of crops. Together, these studies reveal the functions of genes that are involved in the evolution of crops that are under domestication, the types of mutations that occur during this process and the parallelism of mutations that occur in the same pathways and proteins, as well as the selective forces that are acting on these mutations and that are associated with geographical adaptation of crop species.

DNA fingerprinting in botany: past, present, future

Almost three decades ago Alec Jeffreys published his seminal Nature papers on the use of minisatellite probes for DNA fingerprinting of humans (Jeffreys and colleagues Nature 1985, 314:67-73 and Nature 1985, 316:76-79). The new technology was soon adopted for many other organisms including plants, and when Hilde Nybom, Kurt Weising and Alec Jeffreys first met at the very First International Conference on DNA Fingerprinting in Berne, Switzerland, in 1990, everybody was enthusiastic about the novel method that allowed us for the first time to discriminate between humans, animals, plants and fungi on the individual level using DNA markers. A newsletter coined "Fingerprint News" was launched, T-shirts were sold, and the proceedings of the Berne conference filled a first book on "DNA fingerprinting: approaches and applications". Four more conferences were about to follow, one on each continent, and Alec Jeffreys of course was invited to all of them. Since these early days, methodologies have undergone a rapid evolution and diversification. A multitude of techniques have been developed, optimized, and eventually abandoned when novel and more efficient and/or more reliable methods appeared. Despite some overlap between the lifetimes of the different technologies, three phases can be defined that coincide with major technological advances. Whereas the first phase of DNA fingerprinting ("the past") was dominated by restriction fragment analysis in conjunction with Southern blot hybridization, the advent of the PCR in the late 1980s gave way to the development of PCR-based single- or multi-locus profiling techniques in the second phase. Given that many routine applications of plant DNA fingerprinting still rely on PCR-based markers, we here refer to these methods as "DNA fingerprinting in the present", and include numerous examples in the present review. The beginning of the third phase actually dates back to 2005, when several novel, highly parallel DNA sequencing strategies were developed that increased the throughput over current Sanger sequencing technology 1000-fold and more. High-speed DNA sequencing was soon also exploited for DNA fingerprinting in plants, either in terms of facilitated marker development, or directly in the sense of "genotyping-by-sequencing". Whereas these novel approaches are applied at an ever increasing rate also in non-model species, they are still far from routine, and we therefore treat them here as "DNA fingerprinting in the future".

Variation in Rubisco activase (RCAβ) gene promoters and expression in soybean [Glycine max (L.) Merr]

Understanding the genetic basis of Rubisco activase (RCA) gene regulation and altering its expression levels to optimize Rubisco activation may provide an approach to enhance plant productivity. However, the genetic mechanisms and the effect of RCA expression on phenotype are still unknown in soybean. This work analysed the expression of RCA genes and demonstrated that two RCA isoforms presented different expression patterns. Compared with GmRCAα, GmRCAβ was expressed at higher mRNA and protein levels. In addition, GmRCAα and GmRCAβ were positively correlated with chlorophyll fluorescence parameters and seed yield, suggesting that changes in expression of RCA has a potential applicability in breeding for enhanced soybean productivity. To identify the genetic factors that cause expression level variation of GmRCAβ, expression quantitative trait loci (eQTL) mapping was combined with allele mining in a natural population including 219 landraces. The eQTL mapping showed that a combination of both cis- and trans-acting eQTLs might control GmRCAβ expression. As promoters can affect both cis- and trans-acting eQTLs by altering cis-acting regulatory elements or transcription factor binding sites, this work subsequently focused on the promoter region of GmRCAβ. Single-nucleotide polymorphisms in the GmRCAβ promoter were identified and shown to correlate with expression level diversity. These SNPs were classified into two groups, A and B. Further transient expression showed that GUS expression driven by the group A promoter was stronger than that by the group B promoter, suggesting that promoter sequence types could influence gene expression levels. These results would improve understanding how variation within promoters affects gene expression and, ultimately, phenotypic diversity in natural populations.

HapRice, an SNP haplotype database and a web tool for rice

Genome-wide single nucleotide polymorphism (SNP) analysis is a promising tool to examine the genetic diversity of rice populations and genetic traits of scientific and economic importance. Next-generation sequencing technology has accelerated the re-sequencing of diverse rice varieties and the discovery of genome-wide SNPs. Notably, validation of these SNPs by a high-throughput genotyping system, such as an SNP array, could provide a manageable and highly accurate SNP set. To enhance the potential utility of genome-wide SNPs for geneticists and breeders, analysis tools need to be developed. Here, we constructed an SNP haplotype database, which allows visualization of the allele frequency of all SNPs in the genome browser. We calculated the allele frequencies of 3,334 SNPs in 76 accessions from the world rice collection and 3,252 SNPs in 177 Japanese rice accessions; all these SNPs have been validated in our previous studies. The SNP haplotypes were defined by the allele frequency in each cultivar group (aus, indica, tropical japonica and temperate japonica) for the world rice accessions, and in non-irrigated and three irrigated groups (three variety registration periods) for Japanese rice accessions. We also developed web tools for finding polymorphic SNPs between any two rice accessions and for the primer design to develop cleaved amplified polymorphic sequence markers at any SNP. The 'HapRice' database and the web tools can be accessed at http://qtaro.abr.affrc.go.jp/index.html. In addition, we established a core SNP set consisting of 768 SNPs uniformly distributed in the rice genome; this set is of a practically appropriate size for use in rice genetic analysis.

A whole-genome SNP array (RICE6K) for genomic breeding in rice

The advances in genotyping technology provide an opportunity to use genomic tools in crop breeding. As compared to field selections performed in conventional breeding programmes, genomics-based genotype screen can potentially reduce number of breeding cycles and more precisely integrate target genes for particular traits into an ideal genetic background. We developed a whole-genome single nucleotide polymorphism (SNP) array, RICE6K, based on Infinium technology, using representative SNPs selected from more than four million SNPs identified from resequencing data of more than 500 rice landraces. RICE6K contains 5102 SNP and insertion-deletion (InDel) markers, about 4500 of which were of high quality in the tested rice lines producing highly repeatable results. Forty-five functional markers that are located inside 28 characterized genes of important traits can be detected using RICE6K. The SNP markers are evenly distributed on the 12 chromosomes of rice with the average density of 12 SNPs per 1 Mb and can provide information for polymorphisms between indica and japonica subspecies as well as varieties within indica and japonica groups. Application tests of RICE6K showed that the array is suitable for rice germplasm fingerprinting, genotyping bulked segregating pools, seed authenticity check and genetic background selection. These results suggest that RICE6K provides an efficient and reliable genotyping tool for rice genomic breeding.

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

The mineral concentrations in cereals are important for human health, especially for individuals who consume a cereal subsistence diet. A number of elements, such as zinc, are required within the diet, while some elements are toxic to humans, for example arsenic. In this study we carry out genome-wide association (GWA) mapping of grain concentrations of arsenic, copper, molybdenum and zinc in brown rice using an established rice diversity panel of ∼ 300 accessions and 36.9 k single nucleotide polymorphisms (SNPs). The study was performed across five environments: one field site in Bangladesh, one in China and two in the US, with one of the US sites repeated over two years. GWA mapping on the whole dataset and on separate subpopulations of rice revealed a large number of loci significantly associated with variation in grain arsenic, copper, molybdenum and zinc. Seventeen of these loci were detected in data obtained from grain cultivated in more than one field location, and six co-localise with previously identified quantitative trait loci. Additionally, a number of candidate genes for the uptake or transport of these elements were located near significantly associated SNPs (within 200 kb, the estimated global linkage disequilibrium previously employed in this rice panel). This analysis highlights a number of genomic regions and candidate genes for further analysis as well as the challenges faced when mapping environmentally-variable traits in a highly genetically structured diversity panel.

Natural variations and genome-wide association studies in crop plants

Natural variants of crops are generated from wild progenitor plants under both natural and human selection. Diverse crops that are able to adapt to various environmental conditions are valuable resources for crop improvements to meet the food demands of the increasing human population. With the completion of reference genome sequences, the advent of high-throughput sequencing technology now enables rapid and accurate resequencing of a large number of crop genomes to detect the genetic basis of phenotypic variations in crops. Comprehensive maps of genome variations facilitate genome-wide association studies of complex traits and functional investigations of evolutionary changes in crops. These advances will greatly accelerate studies on crop designs via genomics-assisted breeding. Here, we first discuss crop genome studies and describe the development of sequencing-based genotyping and genome-wide association studies in crops. We then review sequencing-based crop domestication studies and offer a perspective on genomics-driven crop designs.

Application of resequencing to rice genomics, functional genomics and evolutionary analysis

Rice is a model system used for crop genomics studies. The completion of the rice genome draft sequences in 2002 not only accelerated functional genome studies, but also initiated a new era of resequencing rice genomes. Based on the reference genome in rice, next-generation sequencing (NGS) using the high-throughput sequencing system can efficiently accomplish whole genome resequencing of various genetic populations and diverse germplasm resources. Resequencing technology has been effectively utilized in evolutionary analysis, rice genomics and functional genomics studies. This technique is beneficial for both bridging the knowledge gap between genotype and phenotype and facilitating molecular breeding via gene design in rice. Here, we also discuss the limitation, application and future prospects of rice resequencing.

Candidate gene association mapping of Sclerotinia stalk rot resistance in sunflower (Helianthus annuus L.) uncovers the importance of COI1 homologs

Functional markers for Sclerotinia basal stalk rot resistance in sunflower were obtained using gene-level information from the model species Arabidopsis thaliana. Sclerotinia stalk rot, caused by Sclerotinia sclerotiorum, is one of the most destructive diseases of sunflower (Helianthus annuus L.) worldwide. Markers for genes controlling resistance to S. sclerotiorum will enable efficient marker-assisted selection (MAS). We sequenced eight candidate genes homologous to Arabidopsis thaliana defense genes known to be associated with Sclerotinia disease resistance in a sunflower association mapping population evaluated for Sclerotinia stalk rot resistance. The total candidate gene sequence regions covered a concatenated length of 3,791 bp per individual. A total of 187 polymorphic sites were detected for all candidate gene sequences, 149 of which were single nucleotide polymorphisms (SNPs) and 38 were insertions/deletions. Eight SNPs in the coding regions led to changes in amino acid codons. Linkage disequilibrium decay throughout the candidate gene regions declined on average to an r (2) = 0.2 for genetic intervals of 120 bp, but extended up to 350 bp with r (2) = 0.1. A general linear model with modification to account for population structure was found the best fitting model for this population and was used for association mapping. Both HaCOI1-1 and HaCOI1-2 were found to be strongly associated with Sclerotinia stalk rot resistance and explained 7.4 % of phenotypic variation in this population. These SNP markers associated with Sclerotinia stalk rot resistance can potentially be applied to the selection of favorable genotypes, which will significantly improve the efficiency of MAS during the development of stalk rot resistant cultivars.

Genomic regions underlying agronomic traits in linseed (Linum usitatissimum L.) as revealed by association mapping

The extreme climate of the Canadian Prairies poses a major challenge to improve yield. Although it is possible to breed for yield per se, focusing on yield-related traits could be advantageous because of their simpler genetic architecture. The Canadian flax core collection of 390 accessions was genotyped with 464 simple sequence repeat markers, and phenotypic data for nine agronomic traits including yield, bolls per area, 1,000 seed weight, seeds per boll, start of flowering, end of flowering, plant height, plant branching, and lodging collected from up to eight environments was used for association mapping. Based on a mixed model (principal component analysis (PCA) + kinship matrix (K)), 12 significant marker-trait associations for six agronomic traits were identified. Most of the associations were stable across environments as revealed by multivariate analyses. Statistical simulation for five markers associated with 1000 seed weight indicated that the favorable alleles have additive effects. None of the modern cultivars carried the five favorable alleles and the maximum number of four observed in any accessions was mostly in breeding lines. Our results confirmed the complex genetic architecture of yield-related traits and the inherent difficulties associated with their identification while illustrating the potential for improvement through marker-assisted selection.

Association mapping in crop plants: opportunities and challenges

The research area of association mapping (AM) is currently receiving major attention for genetic studies of quantitative traits in all major crops. However, the level of success and utility of AM achieved for crop improvement is not comparable to that in the area of human health care for diagnosis of complex human diseases. These AM studies in plants, as in humans, became possible due to the availability of DNA-based molecular markers and a variety of sophisticated statistical tools that are evolving on a regular basis. In this chapter, we first briefly review the significance of a variety of populations that are used in AM studies, then briefly describe the molecular markers and high-throughput genotyping strategies, and finally describe the approaches used for AM studies. The major part of the chapter is, however, devoted to analysis of reasons why the results of AM have been underutilized in plant breeding. We also examine the opportunities available and challenges faced while using AM for crop improvement programs. This includes a detailed discussion of the issues that have plagued AM studies, and the solutions that have become available to deal with these issues, so that in future, the results of AM studies may prove increasingly fruitful for crop improvement programs.

Candidate gene association mapping of Sclerotinia stalk rot resistance in sunflower (Helianthus annuus L.) uncovers the importance of COI1 homologs

Functional markers for Sclerotinia basal stalk rot resistance in sunflower were obtained using gene-level information from the model species Arabidopsis thaliana. Sclerotinia stalk rot, caused by Sclerotinia sclerotiorum, is one of the most destructive diseases of sunflower (Helianthus annuus L.) worldwide. Markers for genes controlling resistance to S. sclerotiorum will enable efficient marker-assisted selection (MAS). We sequenced eight candidate genes homologous to Arabidopsis thaliana defense genes known to be associated with Sclerotinia disease resistance in a sunflower association mapping population evaluated for Sclerotinia stalk rot resistance. The total candidate gene sequence regions covered a concatenated length of 3,791 bp per individual. A total of 187 polymorphic sites were detected for all candidate gene sequences, 149 of which were single nucleotide polymorphisms (SNPs) and 38 were insertions/deletions. Eight SNPs in the coding regions led to changes in amino acid codons. Linkage disequilibrium decay throughout the candidate gene regions declined on average to an r (2) = 0.2 for genetic intervals of 120 bp, but extended up to 350 bp with r (2) = 0.1. A general linear model with modification to account for population structure was found the best fitting model for this population and was used for association mapping. Both HaCOI1-1 and HaCOI1-2 were found to be strongly associated with Sclerotinia stalk rot resistance and explained 7.4 % of phenotypic variation in this population. These SNP markers associated with Sclerotinia stalk rot resistance can potentially be applied to the selection of favorable genotypes, which will significantly improve the efficiency of MAS during the development of stalk rot resistant cultivars.

Tomato yield heterosis is triggered by a dosage sensitivity of the florigen pathway that fine-tunes shoot architecture

The superiority of hybrids has long been exploited in agriculture, and although many models explaining "heterosis" have been put forth, direct empirical support is limited. Particularly elusive have been cases of heterozygosity for single gene mutations causing heterosis under a genetic model known as overdominance. In tomato (Solanum lycopersicum), plants carrying mutations in SINGLE FLOWER TRUSS (SFT) encoding the flowering hormone florigen are severely delayed in flowering, become extremely large, and produce few flowers and fruits, but when heterozygous, yields are dramatically increased. Curiously, this overdominance is evident only in the background of "determinate" plants, in which the continuous production of side shoots and inflorescences gradually halts due to a defect in the flowering repressor SELF PRUNING (SP). How sp facilitates sft overdominance is unclear, but is thought to relate to the opposing functions these genes have on flowering time and shoot architecture. We show that sft mutant heterozygosity (sft/+) causes weak semi-dominant delays in flowering of both primary and side shoots. Using transcriptome sequencing of shoot meristems, we demonstrate that this delay begins before seedling meristems become reproductive, followed by delays in subsequent side shoot meristems that, in turn, postpone the arrest of shoot and inflorescence production. Reducing SFT levels in sp plants by artificial microRNAs recapitulates the dose-dependent modification of shoot and inflorescence production of sft/+ heterozygotes, confirming that fine-tuning levels of functional SFT transcripts provides a foundation for higher yields. Finally, we show that although flowering delays by florigen mutant heterozygosity are conserved in Arabidopsis, increased yield is not, likely because cyclical flowering is absent. We suggest sft heterozygosity triggers a yield improvement by optimizing plant architecture via its dosage response in the florigen pathway. Exploiting dosage sensitivity of florigen and its family members therefore provides a path to enhance productivity in other crops, but species-specific tuning will be required.

Comparison of SSR and SNP markers in estimation of genetic diversity and population structure of Indian rice varieties

Simple sequence repeat (SSR) and Single Nucleotide Polymorphic (SNP), the two most robust markers for identifying rice varieties were compared for assessment of genetic diversity and population structure. Total 375 varieties of rice from various regions of India archived at the Indian National GeneBank, NBPGR, New Delhi, were analyzed using thirty six genetic markers, each of hypervariable SSR (HvSSR) and SNP which were distributed across 12 rice chromosomes. A total of 80 alleles were amplified with the SSR markers with an average of 2.22 alleles per locus whereas, 72 alleles were amplified with SNP markers. Polymorphic information content (PIC) values for HvSSR ranged from 0.04 to 0.5 with an average of 0.25. In the case of SNP markers, PIC values ranged from 0.03 to 0.37 with an average of 0.23. Genetic relatedness among the varieties was studied; utilizing an unrooted tree all the genotypes were grouped into three major clusters with both SSR and SNP markers. Analysis of molecular variance (AMOVA) indicated that maximum diversity was partitioned between and within individual level but not between populations. Principal coordinate analysis (PCoA) with SSR markers showed that genotypes were uniformly distributed across the two axes with 13.33% of cumulative variation whereas, in case of SNP markers varieties were grouped into three broad groups across two axes with 45.20% of cumulative variation. Population structure were tested using K values from 1 to 20, but there was no clear population structure, therefore Ln(PD) derived Δk was plotted against the K to determine the number of populations. In case of SSR maximum Δk was at K=5 whereas, in case of SNP maximum Δk was found at K=15, suggesting that resolution of population was higher with SNP markers, but SSR were more efficient for diversity analysis.

NAL1 allele from a rice landrace greatly increases yield in modern indica cultivars

Increasing crop production is essential for securing the future food supply in developing countries in Asia and Africa as economies and populations grow. However, although the Green Revolution led to increased grain production in the 1960s, no major advances have been made in increasing yield potential in rice since then. In this study, we identified a gene, SPIKELET NUMBER (SPIKE), from a tropical japonica rice landrace that enhances the grain productivity of indica cultivars through pleiotropic effects on plant architecture. Map-based cloning revealed that SPIKE was identical to NARROW LEAF1 (NAL1), which has been reported to control vein pattern in leaf. Phenotypic analyses of a near-isogenic line of a popular indica cultivar, IR64, and overexpressor lines revealed increases in spikelet number, leaf size, root system, and the number of vascular bundles, indicating the enhancement of source size and translocation capacity as well as sink size. The near-isogenic line achieved 13-36% yield increase without any negative effect on grain appearance. Expression analysis revealed that the gene was expressed in all cell types: panicles, leaves, roots, and culms supporting the pleiotropic effects on plant architecture. Furthermore, SPIKE increased grain yield by 18% in the recently released indica cultivar IRRI146, and increased spikelet number in the genetic background of other popular indica cultivars. The use of SPIKE in rice breeding could contribute to food security in indica-growing regions such as South and Southeast Asia.

Association genetics of chilling injury susceptibility in peach (Prunus persica (L.) Batsch) across multiple years

Peach and nectarine (Prunus persica L.) are highly perishable; they ripen and deteriorate quickly at ambient temperature. Storage at low temperature (0–5 °C) is a common strategy used to slow the ripening processes and to extend shelf life. However, if susceptible varieties are held too long at a low temperature, they will not ripen properly and will develop chilling injury (CI) symptoms like mealiness (M), flesh browning (FB), and flesh bleeding (FBL). Understanding the genetic control of these traits to produce CI resistant cultivars will greatly benefit producers, shippers and consumers. In this study, we evaluated a population of 51 individuals from Pop-DG across 4 years with CI traits observed in one or two time points to detect molecular marker association with selected 960 single-nucleotide polymorphisms (SNPs) from 1,536 SNPs chip. Genotypic and phenotypic data were analyzed by general linear model and mixed linear model to see comparative results from both analyses. Among 960 SNPs used, 22 SNPs were found associated with CI susceptibility traits like M, FB, and FBL. Many SNP markers were located in or close to previously reported quantitative trait loci mapped by linkage analysis.

Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits

Genome-wide association studies are a powerful method to dissect the genetic basis of traits, although in practice the effects of complex genetic architecture and population structure remain poorly understood. To compare mapping strategies we dissected the genetic control of flavonoid pigmentation traits in the cereal grass sorghum by using high-resolution genotyping-by-sequencing single-nucleotide polymorphism markers. Studying the grain tannin trait, we find that general linear models (GLMs) are not able to precisely map tan1-a, a known loss-of-function allele of the Tannin1 gene, with either a small panel (n = 142) or large association panel (n = 336), and that indirect associations limit the mapping of the Tannin1 locus to Mb-resolution. A GLM that accounts for population structure (Q) or standard mixed linear model that accounts for kinship (K) can identify tan1-a, whereas a compressed mixed linear model performs worse than the naive GLM. Interestingly, a simple loss-of-function genome scan, for genotype-phenotype covariation only in the putative loss-of-function allele, is able to precisely identify the Tannin1 gene without considering relatedness. We also find that the tan1-a allele can be mapped with gene resolution in a biparental recombinant inbred line family (n = 263) using genotyping-by-sequencing markers but lower precision in the mapping of vegetative pigmentation traits suggest that consistent gene-level resolution will likely require larger families or multiple recombinant inbred lines. These findings highlight that complex association signals can emerge from even the simplest traits given epistasis and structured alleles, but that gene-resolution mapping of these traits is possible with high marker density and appropriate models.

Genome-wide association mapping of root traits in a japonica rice panel

Rice is a crop prone to drought stress in upland and rainfed lowland ecosystems. A deep root system is recognized as the best drought avoidance mechanism. Genome-wide association mapping offers higher resolution for locating quantitative trait loci (QTLs) than QTL mapping in biparental populations. We performed an association mapping study for root traits using a panel of 167 japonica accessions, mostly of tropical origin. The panel was genotyped at an average density of one marker per 22.5 kb using genotyping by sequencing technology. The linkage disequilibrium in the panel was high (r(2)>0.6, on average, for 20 kb mean distances between markers). The plants were grown in transparent 50 cm × 20 cm × 2 cm Plexiglas nailboard sandwiches filled with 1.5 mm glass beads through which a nutrient solution was circulated. Root system architecture and biomass traits were measured in 30-day-old plants. The panel showed a moderate to high diversity in the various traits, particularly for deep (below 30 cm depth) root mass and the number of deep roots. Association analyses were conducted using a mixed model involving both population structure and kinship to control for false positives. Nineteen associations were significant at P<1e-05, and 78 were significant at P<1e-04. The greatest numbers of significant associations were detected for deep root mass and the number of deep roots, whereas no significant associations were found for total root biomass or deep root proportion. Because several QTLs for different traits were co-localized, 51 unique loci were detected; several co-localized with meta-QTLs for root traits, but none co-localized with rice genes known to be involved in root growth. Several likely candidate genes were found in close proximity to these loci. Additional work is necessary to assess whether these markers are relevant in other backgrounds and whether the genes identified are robust candidates.

Tripal v1.1: a standards-based toolkit for construction of online genetic and genomic databases

Tripal is an open-source freely available toolkit for construction of online genomic and genetic databases. It aims to facilitate development of community-driven biological websites by integrating the GMOD Chado database schema with Drupal, a popular website creation and content management software. Tripal provides a suite of tools for interaction with a Chado database and display of content therein. The tools are designed to be generic to support the various ways in which data may be stored in Chado. Previous releases of Tripal have supported organisms, genomic libraries, biological stocks, stock collections and genomic features, their alignments and annotations. Also, Tripal and its extension modules provided loaders for commonly used file formats such as FASTA, GFF, OBO, GAF, BLAST XML, KEGG heir files and InterProScan XML. Default generic templates were provided for common views of biological data, which could be customized using an open Application Programming Interface to change the way data are displayed. Here, we report additional tools and functionality that are part of release v1.1 of Tripal. These include (i) a new bulk loader that allows a site curator to import data stored in a custom tab delimited format; (ii) full support of every Chado table for Drupal Views (a powerful tool allowing site developers to construct novel displays and search pages); (iii) new modules including ‘Feature Map’, ‘Genetic’, ‘Publication’, ‘Project’, ‘Contact’ and the ‘Natural Diversity’ modules. Tutorials, mailing lists, download and set-up instructions, extension modules and other documentation can be found at the Tripal website located at http://tripal.info.

Database URL: http://tripal.info/

Proteomic profiling of developing cotton fibers from wild and domesticated Gossypium barbadense

Pima cotton (Gossypium barbadense) is widely cultivated because of its long, strong seed trichomes ('fibers') used for premium textiles. These agronomically advanced fibers were derived following domestication and thousands of years of human-mediated crop improvement. To gain an insight into fiber development and evolution, we conducted comparative proteomic and transcriptomic profiling of developing fiber from an elite cultivar and a wild accession. Analyses using isobaric tag for relative and absolute quantification (iTRAQ) LC-MS/MS technology identified 1317 proteins in fiber. Of these, 205 were differentially expressed across developmental stages, and 190 showed differential expression between wild and cultivated forms, 14.4% of the proteome sampled. Human selection may have shifted the timing of developmental modules, such that some occur earlier in domesticated than in wild cotton. A novel approach was used to detect possible biased expression of homoeologous copies of proteins. Results indicate a significant partitioning of duplicate gene expression at the protein level, but an approximately equal degree of bias for each of the two constituent genomes of allopolyploid cotton. Our results demonstrate the power of complementary transcriptomic and proteomic approaches for the study of the domestication process. They also provide a rich database for mining for functional analyses of cotton improvement or evolution.

Genome-wide copy number variations in Oryza sativa L

Background

Copy number variation (CNV) can lead to intra-specific genome variations. It is not only part of normal genetic variation, but also is the source of phenotypic differences. Rice (Oryza sativa L.) is a model organism with a well-annotated genome, but investigation of CNVs in rice lags behind its mammalian counterparts.

Results

We comprehensively assayed CNVs using high-density array comparative genomic hybridization in a panel of 20 Asian cultivated rice comprising six indica, three aus, two rayada, two aromatic, three tropical japonica, and four temperate japonica varieties. We used a stringent criterion to identify a total of 2886 high-confidence copy number variable regions (CNVRs), which span 10.28 Mb (or 2.69%) of the rice genome, overlapping 1321 genes. These genes were significantly enriched for specific biological functions involved in cell death, protein phosphorylation, and defense response. Transposable elements (TEs) and other repetitive sequences were identified in the majority of CNVRs. Chromosome 11 showed the greatest enrichment for CNVs. Of subspecies-specific CNVRs, 55.75% and 61.96% were observed in only one cultivar of ssp. indica and ssp. japonica, respectively. Some CNVs with high frequency differences among groups resided in genes underlying rice adaptation.

Conclusions

Higher recombination rates and the presence of homologous gene clusters are probably predispositions for generation of the higher number of CNVs on chromosome 11 by non-allelic homologous recombination events. The subspecies-specific variants are enriched for rare alleles, which suggests that CNVs are relatively recent events that have arisen within breeding populations. A number of the CNVs identified in this study are candidates for generation of group-specific phenotypes.

Physiological mechanisms in plant growth models: do we need a supra-cellular systems biology approach?

In the first part of this paper, we review the extent to which various types of plant growth models incorporate ecophysiological mechanisms. Many growth models have a central role for the process of photosynthesis; and often implicitly assume C-gain to be the rate-limiting step for biomass accumulation. We subsequently explore the extent to which this assumption actually holds and under what condition constraints on growth due to a limited sink strength are likely to occur. By using generalized dose-response curves for growth with respect to light and CO₂, models can be tested against a benchmark for their overall performance. In the final part, a call for a systems approach at the supra-cellular level is made. This will enable a better understanding of feedbacks and trade-offs acting on plant growth and its component processes. Mechanistic growth models form an indispensable element of such an approach and will, in the end, provide the link with the (sub-)cellular approaches that are yet developing. Improved insight will be gained if model output for the various physiological processes and morphological variables ('virtual profiling') is compared with measured correlation networks among these processes and variables. Two examples of these correlation networks are presented.

Genome-wide prediction of traits with different genetic architecture through efficient variable selection

In genome-based prediction there is considerable uncertainty about the statistical model and method required to maximize prediction accuracy. For traits influenced by a small number of quantitative trait loci (QTL), predictions are expected to benefit from methods performing variable selection [e.g., BayesB or the least absolute shrinkage and selection operator (LASSO)] compared to methods distributing effects across the genome [ridge regression best linear unbiased prediction (RR-BLUP)]. We investigate the assumptions underlying successful variable selection by combining computer simulations with large-scale experimental data sets from rice (Oryza sativa L.), wheat (Triticum aestivum L.), and Arabidopsis thaliana (L.). We demonstrate that variable selection can be successful when the number of phenotyped individuals is much larger than the number of causal mutations contributing to the trait. We show that the sample size required for efficient variable selection increases dramatically with decreasing trait heritabilities and increasing extent of linkage disequilibrium (LD). We contrast and discuss contradictory results from simulation and experimental studies with respect to superiority of variable selection methods over RR-BLUP. Our results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice, assumed to be influenced by a few major QTL. We extend our conclusions to the analysis of whole-genome sequence data and infer upper bounds for the number of causal mutations which can be identified by LASSO. Our results have major impact on the choice of statistical method needed to make credible inferences about genetic architecture and prediction accuracy of complex traits.

A systems-genetics approach and data mining tool to assist in the discovery of genes underlying complex traits in Oryza sativa

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.

Comprehensive genotyping of the USA national maize inbred seed bank

BACKGROUND

Genotyping by sequencing, a new low-cost, high-throughput sequencing technology was used to genotype 2,815 maize inbred accessions, preserved mostly at the National Plant Germplasm System in the USA. The collection includes inbred lines from breeding programs all over the world.

RESULTS

The method produced 681,257 single-nucleotide polymorphism (SNP) markers distributed across the entire genome, with the ability to detect rare alleles at high confidence levels. More than half of the SNPs in the collection are rare. Although most rare alleles have been incorporated into public temperate breeding programs, only a modest amount of the available diversity is present in the commercial germplasm. Analysis of genetic distances shows population stratification, including a small number of large clusters centered on key lines. Nevertheless, an average fixation index of 0.06 indicates moderate differentiation between the three major maize subpopulations. Linkage disequilibrium (LD) decays very rapidly, but the extent of LD is highly dependent on the particular group of germplasm and region of the genome. The utility of these data for performing genome-wide association studies was tested with two simply inherited traits and one complex trait. We identified trait associations at SNPs very close to known candidate genes for kernel color, sweet corn, and flowering time; however, results suggest that more SNPs are needed to better explore the genetic architecture of complex traits.

CONCLUSIONS

The genotypic information described here allows this publicly available panel to be exploited by researchers facing the challenges of sustainable agriculture through better knowledge of the nature of genetic diversity.

Identification of genetic markers linked to anthracnose resistance in sorghum using association analysis

Anthracnose in sorghum caused by Colletotrichum sublineolum is one of the most destructive diseases affecting sorghum production under warm and humid conditions. Markers and genes linked to resistance to the disease are important for plant breeding. Using 14,739 SNP markers, we have mapped eight loci linked to resistance in sorghum through association analysis of a sorghum mini-core collection consisting of 242 diverse accessions evaluated for anthracnose resistance for 2 years in the field. The mini-core was representative of the International Crops Research Institute for the Semi-Arid Tropics' world-wide sorghum landrace collection. Eight marker loci were associated with anthracnose resistance in both years. Except locus 8, disease resistance-related genes were found in all loci based on their physical distance from linked SNP markers. These include two NB-ARC class of R genes on chromosome 10 that were partially homologous to the rice blast resistance gene Pib, two hypersensitive response-related genes: autophagy-related protein 3 on chromosome 1 and 4 harpin-induced 1 (Hin1) homologs on chromosome 8, a RAV transcription factor that is also part of R gene pathway, an oxysterol-binding protein that functions in the non-specific host resistance, and homologs of menthone:neomenthol reductase (MNR) that catalyzes a menthone reduction to produce the antimicrobial neomenthol. These genes and markers may be developed into molecular tools for genetic improvement of anthracnose resistance in sorghum.

Genomics-based precision breeding approaches to improve drought tolerance in rice

Rice (Oryza sativa L.), the major staple food crop of the world, faces a severe threat from widespread drought. The development of drought-tolerant rice varieties is considered a feasible option to counteract drought stress. The screening of rice germplasm under drought and its characterization at the morphological, genetic, and molecular levels revealed the existence of genetic variation for drought tolerance within the rice gene pool. The improvements made in managed drought screening and selection for grain yield under drought have significantly contributed to progress in drought breeding programs. The availability of rice genome sequence information, genome-wide molecular markers, and low-cost genotyping platforms now makes it possible to routinely apply marker-assisted breeding approaches to improve grain yield under drought. Grain yield QTLs with a large and consistent effect under drought have been indentified and successfully pyramided in popular rice mega-varieties. Various rice functional genomics resources, databases, tools, and recent advances in "-omics" are facilitating the characterization of genes and pathways involved in drought tolerance, providing the basis for candidate gene identification and allele mining. The transgenic approach is successful in generating drought tolerance in rice under controlled conditions, but field-level testing is necessary. Genomics-assisted drought breeding approaches hold great promise, but a well-planned integration with standardized phenotyping is highly essential to exploit their full potential.