Genetic Diversity and Linkage Disequilibrium in Chinese Bread Wheat (Triticum aestivum L.) Revealed by SSR Markers

Two hundred and fifty bread wheat lines, mainly Chinese mini core accessions, were assayed for polymorphism and linkage disequilibrium (LD) based on 512 whole-genome microsatellite loci representing a mean marker density of 5.1 cM. A total of 6,724 alleles ranging from 1 to 49 per locus were identified in all collections. The mean PIC value was 0.650, ranging from 0 to 0.965. Population structure and principal coordinate analysis revealed that landraces and modern varieties were two relatively independent genetic sub-groups. Landraces had a higher allelic diversity than modern varieties with respect to both genomes and chromosomes in terms of total number of alleles and allelic richness. 3,833 (57.0%) and 2,788 (41.5%) rare alleles with frequencies of <5% were found in the landrace and modern variety gene pools, respectively, indicating greater numbers of rare variants, or likely new alleles, in landraces. Analysis of molecular variance (AMOVA) showed that A genome had the largest genetic differentiation and D genome the lowest. In contrast to genetic diversity, modern varieties displayed a wider average LD decay across the whole genome for locus pairs with r2>0.05 (P<0.001) than the landraces. Mean LD decay distance for the landraces at the whole genome level was <5 cM, while a higher LD decay distance of 5–10 cM in modern varieties. LD decay distances were also somewhat different for each of the 21 chromosomes, being higher for most of the chromosomes in modern varieties (<5∼25 cM) compared to landraces (<5∼15 cM), presumably indicating the influences of domestication and breeding. This study facilitates predicting the marker density required to effectively associate genotypes with traits in Chinese wheat genetic resources

Generation of ESTs for Flowering Gene Discovery and SSR Marker Development in Upland Cotton

BACKGROUND: Upland cotton, Gossypium hirsutum L., is one of the world's most important economic crops. In the absence of the entire genomic sequence, a large number of expressed sequence tag (EST) resources of upland cotton have been generated and used in several studies. However, information about the flower development of this species is rare. METHODOLOGY/PRINCIPAL FINDINGS: To clarify the molecular mechanism of flower development in upland cotton, 22,915 high-quality ESTs were generated and assembled into 14,373 unique sequences consisting of 4,563 contigs and 9,810 singletons from a normalized and full-length cDNA library constructed from pooled RNA isolated from shoot apexes, squares, and flowers. Comparative analysis indicated that 5,352 unique sequences had no high-degree matches to the cotton public database. Functional annotation showed that several upland cotton homologs with flowering-related genes were identified in our library. The majority of these genes were specifically expressed in flowering-related tissues. Three GhSEP (G. hirsutum L. SEPALLATA) genes determining floral organ development were cloned, and quantitative real-time PCR (qRT-PCR) revealed that these genes were expressed preferentially in squares or flowers. Furthermore, 670 new putative microsatellites with flanking sequences sufficient for primer design were identified from the 645 unigenes. Twenty-five EST-simple sequence repeats were randomly selected for validation and transferability testing in 17 Gossypium species. Of these, 23 were identified as true-to-type simple sequence repeat loci and were highly transferable among Gossypium species. CONCLUSIONS/SIGNIFICANCE: A high-quality, normalized, full-length cDNA library with a total of 14,373 unique ESTs was generated to provide sequence information for gene discovery and marker development related to upland cotton flower development. These EST resources form a valuable foundation for gene expression profiling analysis, functional analysis of newly discovered genes, genetic linkage, and quantitative trait loci analysis

A high density genetic map of tobacco (Nicotiana tabacum L.) obtained from large scale microsatellite marker development

Tobacco (Nicotiana tabacum L.) is a species in the large family of the Solanaceae and is important as an agronomic crop and as a model system in plant biotechnology. Despite its importance, only limited molecular marker resources are available that can be used for genome analysis, genetic mapping and breeding. We report here on the development and characterization of 5,119 new and functional microsatellite markers and on the generation of a high-resolution genetic map for the tetraploid tobacco genome. The genetic map was generated using an F2 mapping population derived from the intervarietal cross of Hicks Broadleaf × Red Russian and merges the polymorphic markers from this new set with those from a smaller set previously used to produce a lower density map. The genetic map described here contains 2,317 microsatellite markers and 2,363 loci, resulting in an average distance between mapped microsatellite markers which is less than 2 million base pairs or 1.5 cM. With this new and expanded marker resource, a sufficient number of markers are now available for multiple applications ranging from tobacco breeding to comparative genome analysis. The genetic map of tobacco is now comparable in marker density and resolution with the best characterized genomes of the Solanaceae: tomato and potato

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

AB-QTL analysis in winter wheat: II. Genetic analysis of seedling and field resistance against leaf rust in a wheat advanced backcross population

The present study aimed to localize exotic quantitative trait locus (QTL) alleles for the improvement of leaf rust (P.triticina) resistance in an advanced backcross (AB) population, B22, which is derived from a cross between the winter wheat cultivar Batis (Triticumaestivum) and the synthetic wheat accession Syn022L. The latter was developed from hybridization of T.turgidum ssp. dicoccoides and T.tauschii. Altogether, 250 BC2F3 lines of B22 were assessed for seedling resistance against the leaf rust isolate 77WxR under controlled conditions. In addition, field resistance against leaf rust was evaluated by assessing symptom severity under natural infestation across multiple environments. Simultaneously, population B22 was genotyped with a total of 97 SSR markers, distributed over the wheat A, B and D genomes. The phenotype and genotype data were subjected to QTL analysis by applying a 3-factorial mixed model analysis of variance including the marker genotype as a fixed effect and the environments, the lines and the marker by environment interactions as random effects. The QTL analysis revealed six putative QTLs for seedling resistance and seven for field resistance. For seedling resistance, the effects of exotic QTL alleles improved resistance at all detected loci. The maximum decrease of disease symptoms (−46.3%) was associated with marker locus Xbarc149 on chromosome 1D. For field resistance, two loci had stable main effects across environments and five loci exhibited marker by environment interaction effects. The strongest effects were detected at marker locus Xbarc149 on chromosome 1D, at which the exotic allele decreased seedling symptoms by 46.3% and field symptoms by 43.6%, respectively. Some of the detected QTLs co-localized with known resistance genes, while others appear to be as novel resistance loci. Our findings indicate, that the exotic wheat accession Syn022L may be useful for the improvement of leaf rust resistance in cultivated wheat

The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator

Shifting Attention within Memory Representations Involves Early Visual Areas

Prior studies have shown that spatial attention modulates early visual cortex retinotopically, resulting in enhanced processing of external perceptual representations. However, it is not clear whether the same visual areas are modulated when attention is focused on, and shifted within a working memory representation. In the current fMRI study participants were asked to memorize an array containing four stimuli. After a delay, participants were presented with a verbal cue instructing them to actively maintain the location of one of the stimuli in working memory. Additionally, on a number of trials a second verbal cue instructed participants to switch attention to the location of another stimulus within the memorized representation. Results of the study showed that changes in the BOLD pattern closely followed the locus of attention within the working memory representation. A decrease in BOLD-activity (V1–V3) was observed at ROIs coding a memory location when participants switched away from this location, whereas an increase was observed when participants switched towards this location. Continuous increased activity was obtained at the memorized location when participants did not switch. This study shows that shifting attention within memory representations activates the earliest parts of visual cortex (including V1) in a retinotopic fashion. We conclude that even in the absence of visual stimulation, early visual areas support shifting of attention within memorized representations, similar to when attention is shifted in the outside world. The relationship between visual working memory and visual mental imagery is discussed in light of the current findings

Neural network model of the primary visual cortex: From functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene

Land Cover and Rainfall Interact to Shape Waterbird Community Composition

Human land cover can degrade estuaries directly through habitat loss and fragmentation or indirectly through nutrient inputs that reduce water quality. Strong precipitation events are occurring more frequently, causing greater hydrological connectivity between watersheds and estuaries. Nutrient enrichment and dissolved oxygen depletion that occur following these events are known to limit populations of benthic macroinvertebrates and commercially harvested species, but the consequences for top consumers such as birds remain largely unknown. We used non-metric multidimensional scaling (MDS) and structural equation modeling (SEM) to understand how land cover and annual variation in rainfall interact to shape waterbird community composition in Chesapeake Bay, USA. The MDS ordination indicated that urban subestuaries shifted from a mixed generalist-specialist community in 2002, a year of severe drought, to generalist-dominated community in 2003, of year of high rainfall. The SEM revealed that this change was concurrent with a sixfold increase in nitrate-N concentration in subestuaries. In the drought year of 2002, waterbird community composition depended only on the direct effect of urban development in watersheds. In the wet year of 2003, community composition depended both on this direct effect and on indirect effects associated with high nitrate-N inputs to northern parts of the Bay, particularly in urban subestuaries. Our findings suggest that increased runoff during periods of high rainfall can depress water quality enough to alter the composition of estuarine waterbird communities, and that this effect is compounded in subestuaries dominated by urban development. Estuarine restoration programs often chart progress by monitoring stressors and indicators, but rarely assess multivariate relationships among them. Estuarine management planning could be improved by tracking the structure of relationships among land cover, water quality, and waterbirds. Unraveling these complex relationships may help managers identify and mitigate ecological thresholds that occur with increasing human land cover