High levels of nucleotide diversity and fast decline of linkage disequilibrium in rye (Secale cereale L.) genes involved in frost response

<p>Abstract</p> <p>Background</p> <p>Rye (<it>Secale cereale </it>L.) is the most frost tolerant cereal species. As an outcrossing species, rye exhibits high levels of intraspecific diversity, which makes it well-suited for allele mining in genes involved in the frost responsive network. For investigating genetic diversity and the extent of linkage disequilibrium (LD) we analyzed eleven candidate genes and 37 microsatellite markers in 201 lines from five Eastern and Middle European rye populations.</p> <p>Results</p> <p>A total of 147 single nucleotide polymorphisms (SNPs) and nine insertion-deletion polymorphisms were found within 7,639 bp of DNA sequence from eleven candidate genes, resulting in an average SNP frequency of 1 SNP/52 bp. Nucleotide and haplotype diversity of candidate genes were high with average values <it>π </it>= 5.6 × 10^-3and <it>Hd </it>= 0.59, respectively. According to an analysis of molecular variance (AMOVA), most of the genetic variation was found between individuals within populations. Haplotype frequencies varied markedly between the candidate genes. <it>ScCbf14</it>, <it>ScVrn1</it>, and <it>ScDhn1 </it>were dominated by a single haplotype, while the other 8 genes (<it>ScCbf2</it>, <it>ScCbf6</it>, <it>ScCbf9b</it>, <it>ScCbf11</it>, <it>ScCbf12</it>, <it>ScCbf15</it>, <it>ScIce2</it>, and <it>ScDhn3</it>) had a more balanced haplotype frequency distribution. Intra-genic LD decayed rapidly, within approximately 520 bp on average. Genome-wide LD based on microsatellites was low.</p> <p>Conclusions</p> <p>The Middle European population did not differ substantially from the four Eastern European populations in terms of haplotype frequencies or in the level of nucleotide diversity. The low LD in rye compared to self-pollinating species promises a high resolution in genome-wide association mapping. SNPs discovered in the promoters or coding regions, which attribute to non-synonymous substitutions, are suitable candidates for association mapping.</p

From RNA-seq to large-scale genotyping - genomics resources for rye (Secale cereale L.)

<p>Abstract</p> <p>Background</p> <p>The improvement of agricultural crops with regard to yield, resistance and environmental adaptation is a perpetual challenge for both breeding and research. Exploration of the genetic potential and implementation of genome-based breeding strategies for efficient rye (<it>Secale cereale </it>L.) cultivar improvement have been hampered by the lack of genome sequence information. To overcome this limitation we sequenced the transcriptomes of five winter rye inbred lines using Roche/454 GS FLX technology.</p> <p>Results</p> <p>More than 2.5 million reads were assembled into 115,400 contigs representing a comprehensive rye expressed sequence tag (EST) resource. From sequence comparisons 5,234 single nucleotide polymorphisms (SNPs) were identified to develop the Rye5K high-throughput SNP genotyping array. Performance of the Rye5K SNP array was investigated by genotyping 59 rye inbred lines including the five lines used for sequencing, and five barley, three wheat, and two triticale accessions. A balanced distribution of allele frequencies ranging from 0.1 to 0.9 was observed. Residual heterozygosity of the rye inbred lines varied from 4.0 to 20.4% with higher average heterozygosity in the pollen compared to the seed parent pool.</p> <p>Conclusions</p> <p>The established sequence and molecular marker resources will improve and promote genetic and genomic research as well as genome-based breeding in rye.</p

A Large Maize (Zea mays L.) SNP Genotyping Array: Development and Germplasm Genotyping, and Genetic Mapping to Compare with the B73 Reference Genome

SNP genotyping arrays have been useful for many applications that require a large number of molecular markers such as high-density genetic mapping, genome-wide association studies (GWAS), and genomic selection. We report the establishment of a large maize SNP array and its use for diversity analysis and high density linkage mapping. The markers, taken from more than 800,000 SNPs, were selected to be preferentially located in genes and evenly distributed across the genome. The array was tested with a set of maize germplasm including North American and European inbred lines, parent/F1 combinations, and distantly related teosinte material. A total of 49,585 markers, including 33,417 within 17,520 different genes and 16,168 outside genes, were of good quality for genotyping, with an average failure rate of 4% and rates up to 8% in specific germplasm. To demonstrate this array's use in genetic mapping and for the independent validation of the B73 sequence assembly, two intermated maize recombinant inbred line populations – IBM (B73×Mo17) and LHRF (F2×F252) – were genotyped to establish two high density linkage maps with 20,913 and 14,524 markers respectively. 172 mapped markers were absent in the current B73 assembly and their placement can be used for future improvements of the B73 reference sequence. Colinearity of the genetic and physical maps was mostly conserved with some exceptions that suggest errors in the B73 assembly. Five major regions containing non-colinearities were identified on chromosomes 2, 3, 6, 7 and 9, and are supported by both independent genetic maps. Four additional non-colinear regions were found on the LHRF map only; they may be due to a lower density of IBM markers in those regions or to true structural rearrangements between lines. Given the array's high quality, it will be a valuable resource for maize genetics and many aspects of maize breeding

High-latitude adaptation through early floral induction in maize

Resumen del póster presentado en Jacksonville, Florida, entre el 17 y el 20 de marzo de 2016.- Unterseer, Sandra et al.Comparación del genoma del maíz liso y el maíz dentado para identificar genes relacionados con la adaptación a Europa.Funding acknowledgement: Federal Ministry of Education and Research (BMBF, Germany) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (grant 0315528).Peer reviewe

Genome-Wide Association Studies with a Genomic Relationship Matrix: A Case Study with Wheat and Arabidopsis.

International audienceStandard genome-wide association studies (GWAS) scan for relationships between each of p molecular markers and a continuously distributed target trait. Typically, a marker-based matrix of genomic similarities among individuals ( G: ) is constructed, to account more properly for the covariance structure in the linear regression model used. We show that the generalized least-squares estimator of the regression of phenotype on one or on m markers is invariant with respect to whether or not the marker(s) tested is(are) used for building G,: provided variance components are unaffected by exclusion of such marker(s) from G: The result is arrived at by using a matrix expression such that one can find many inverses of genomic relationship, or of phenotypic covariance matrices, stemming from removing markers tested as fixed, but carrying out a single inversion. When eigenvectors of the genomic relationship matrix are used as regressors with fixed regression coefficients, e.g., to account for population stratification, their removal from G: does matter. Removal of eigenvectors from G: can have a noticeable effect on estimates of genomic and residual variances, so caution is needed. Concepts were illustrated using genomic data on 599 wheat inbred lines, with grain yield as target trait, and on close to 200 Arabidopsis thaliana accessions

Bayesian Networks Illustrate Genomic and Residual Trait Connections in Maize (Zea mays L.)

Relationships among traits were investigated on the genomic and residual levels using novel methodology. This included inference on these relationships via Bayesian networks and an assessment of the networks with structural equation models. The methodology employed three steps. First, a Bayesian multiple-trait Gaussian model was fitted to the data to decompose phenotypic values into their genomic and residual components. Second, genomic and residual network structures among traits were learned from estimates of these two components. Network learning was performed using six different algorithmic settings for comparison, of which two were score-based and four were constraint-based approaches. Third, structural equation model analyses ranked the networks in terms of goodness of fit and predictive ability, and compared them with the standard multiple-trait fully recursive network. The methodology was applied to experimental data representing the European heterotic maize pools Dent and Flint (Zea mays L.). Inferences on genomic and residual trait connections were depicted separately as directed acyclic graphs. These graphs provide information beyond mere pairwise genetic or residual associations between traits, illustrating for example conditional independencies and hinting at potential causal links among traits. Network analysis suggested some genetic correlations as potentially spurious. Genomic and residual networks were compared between Dent and Flint

Generating Plants with Improved Water Use Efficiency

To improve sustainability of agriculture, high yielding crop varieties with improved water use efficiency (WUE) are needed. Despite the feasibility of assessing WUE using different measurement techniques, breeding for WUE and high yield is a major challenge. Factors influencing the trait under field conditions are complex, including different scenarios of water availability. Plants with C3 photosynthesis are able to moderately increase WUE by restricting transpiration, resulting in higher intrinsic WUE (iWUE) at the leaf level. However, reduced CO2 uptake negatively influences photosynthesis and possibly growth and yield as well. The negative correlation of growth and WUE could be partly disconnected in model plant species with implications for crops. In this paper, we discuss recent insights obtained for Arabidopsis thaliana (L.) and the potential to translate the findings to C3 and C4 crops. Our data on Zea mays (L.) lines subjected to progressive drought show that there is potential for improvements in WUE of the maize line B73 at the whole plant level (WUEplant). However, changes in iWUE of B73 and Arabidopsis reduced the assimilation rate relatively more in maize. The trade-off observed in the C4 crop possibly limits the effectiveness of approaches aimed at improving iWUE but not necessarily efforts to improve WUEplant

Data from Unterseer et.al 2016 Genome Biology

This data package accompanies the following publication:<br><br>Unterseer et al. 2016 (Genome Biology 2016,<strong> 17</strong>:137)<br>A comprehensive study of the genomic differentiation<br>between temperate Dent and Flint maize.<br><strong>DOI: </strong>10.1186/s13059-016-1009-x<br><br>It contains a copy of the data files used in this paper as well as further accompanying files (see README.txt).<br><br

Image_4_Prediction of Complex Traits: Robust Alternatives to Best Linear Unbiased Prediction.TIFF

<p>A widely used method for prediction of complex traits in animal and plant breeding is “genomic best linear unbiased prediction” (GBLUP). In a quantitative genetics setting, BLUP is a linear regression of phenotypes on a pedigree or on a genomic relationship matrix, depending on the type of input information available. Normality of the distributions of random effects and of model residuals is not required for BLUP but a Gaussian assumption is made implicitly. A potential downside is that Gaussian linear regressions are sensitive to outliers, genetic or environmental in origin. We present simple (relative to a fully Bayesian analysis) to implement robust alternatives to BLUP using a linear model with residual t or Laplace distributions instead of a Gaussian one, and evaluate the methods with milk yield records on Italian Brown Swiss cattle, grain yield data in inbred wheat lines, and using three traits measured on accessions of Arabidopsis thaliana. The methods do not use Markov chain Monte Carlo sampling and model hyper-parameters, viewed here as regularization “knobs,” are tuned via some cross-validation. Uncertainty of predictions are evaluated by employing bootstrapping or by random reconstruction of training and testing sets. It was found (e.g., test-day milk yield in cows, flowering time and FRIGIDA expression in Arabidopsis) that the best predictions were often those obtained with the robust methods. The results obtained are encouraging and stimulate further investigation and generalization.</p