Genomic Analysis Confirms Population Structure and Identifies Inter-Lineage Hybrids in Aegilops tauschii

Aegilops tauschii, the D-genome donor of bread wheat, Triticum aestivum, is a storehouse of genetic diversity, and an important resource for future wheat improvement. Genomic and population analysis of 549 Ae. tauschii and 103 wheat accessions was performed by using 13,135 high quality SNPs. Population structure, principal component, and cluster analysis confirmed the differentiation of Ae. tauschii into two lineages; lineage 1 (L1) and lineage 2 (L2), the latter being the wheat D-genome donor. Lineage L1 contributes only 2.7% of the total introgression from Ae. tauschii for a set of United States winter wheat lines, confirming the great amount of untapped genetic diversity in L1. Lineage L2 accessions had overall greater allelic diversity and wheat accessions had the least allelic diversity. Both lineages also showed intra-lineage differentiation with L1 being driven by longitudinal gradient and L2 differentiated by altitude. There has previously been little reported on natural hybridization between L1 and L2. We found nine putative inter-lineage hybrids in the population structure analysis, each containing numerous lineage-specific private alleles from both lineages. One hybrid was confirmed as a recombinant inbred between the two lineages, likely artificially post collection. Of the remaining eight putative hybrids, a group of seven from Georgia carry 713 SNPs with private alleles, which points to the possibility of a novel L1–L2 hybrid lineage. To facilitate the use of Ae. tauschii in wheat improvement, a MiniCore consisting of 29 L1 and 11 L2 accessions, has been developed based on genotypic, phenotypic and geographical data. MiniCore reduces the collection size by over 10-fold and captures 84% of the total allelic diversity in the whole collection

Computational Design of a PAK1 Binding Protein

We describe a computational protocol, called DDMI, for redesigning scaffold proteins to bind to a specified region on a target protein. The DDMI protocol is implemented within the Rosetta molecular modeling program and uses rigid-body docking, sequence design, and gradient-based minimization of backbone and side chain torsion angles to design low energy interfaces between the scaffold and target protein. Iterative rounds of sequence design and conformational optimization were needed to produce models that have calculated binding energies that are similar to binding energies calculated for native complexes. We also show that additional conformation sampling with molecular dynamics can be iterated with sequence design to further lower the computed energy of the designed complexes. To experimentally test the DDMI protocol we redesigned the human hyperplastic discs protein to bind to the kinase domain of p21-activated kinase 1 (PAK1). Six designs were experimentally characterized. Two of the designs aggregated and were not characterized further. Of the remaining four designs, three bound to the PAK1 with affinities tighter than 350 μM. The tightest binding design, named Spider Roll, bound with an affinity of 100 μM. NMR –based structure prediction of Spider Roll based on backbone and 13Cβ chemical shifts using the program CS-ROSETTA indicated that the architecture of human hyperplastic discs protein is preserved. Mutagenesis studies confirmed that Spider Roll binds the target patch on PAK1. Additionally, Spider Roll binds to full length PAK1 in its activated state, but does not bind PAK1 when it forms an auto-inhibited conformation that blocks the Spider Roll target site. Subsequent NMR characterization of the binding of Spider Roll to PAK1 revealed a comparably small binding `on-rate' constant (<< 105 M−1 s−1). The ability to rationally design the site of novel protein-protein interactions is an important step towards creating new proteins that are useful as therapeutics or molecular probes

Snow accumulation variability over the West Antarctic Ice Sheet since 1900: a comparison of ice core records with ERA-20C reanalysis

This study uses a set of 37 firn core records over the West Antarctic Ice Sheet (WAIS) to test the performance of ERA-20C reanalysis for snow accumulation and quantify temporal variability in snow accumulation since 1900. The firn cores are allocated to four geographical areas demarcated by drainage divides (i.e., Antarctic Peninsula (AP), western WAIS, central WAIS and eastern WAIS) to calculate stacked records of regional snow accumulation. Our results show that the inter-annual variability in ERA-20C precipitation minus evaporation (P-E) agrees well with the corresponding ice core snow accumulation composites in each of the four geographical regions, suggesting its skill for simulating snow accumulation changes before the modern satellite era (pre-1979). Snow accumulation experiences significantly positive trends for the AP and eastern WAIS, a negative trend for the western WAIS, and no significant trend for the central WAIS from 1900 to 2010. The contrasting trends are associated with changes in the large-scale moisture transport driven by a deepening of the low-pressure systems and anomalies of sea ice in the Amundsen Sea Low (ASL) region

Aegilops sharonensis genome-assisted identification of stem rust resistance gene Sr62

The wild relatives and progenitors of wheat have been widely used as sources of disease resistance (R) genes. Molecular identification and characterization of these R genes facilitates their manipulation and tracking in breeding programmes. Here, we develop a reference-quality genome assembly of the wild diploid wheat relative Aegilops sharonensis and use positional mapping, mutagenesis, RNA-Seq and transgenesis to identify the stem rust resistance gene Sr62, which has also been transferred to common wheat. This gene encodes a tandem kinase, homologues of which exist across multiple taxa in the plant kingdom. Stable Sr62 transgenic wheat lines show high levels of resistance against diverse isolates of the stem rust pathogen, highlighting the utility of Sr62 for deployment as part of a polygenic stack to maximize the durability of stem rust resistance

Quantitative trait loci for resistance to Fusarium head blight in the Chinese wheat landrace Huangfangzhu

Citation: Li, T., Bai, G., Wu, S. et al. Euphytica (2012) 185: 93. https://doi.org/10.1007/s10681-012-0631-2The Chinese wheat landrace Huangfangzhu (HFZ) has a high level of resistance to Fusarium head blight (FHB). To identify chromosomal regions that are responsible for FHB resistance in HFZ, F8 recombinant inbred lines (RIL) were developed from a cross between HFZ and Wheaton, a U.S. hard spring wheat. FHB was evaluated by single floret inoculation in both greenhouse and field environments. Two quantitative trait loci (QTL) with major effects were identified. One QTL was located on the short arm of chromosome 3B, and explained 35.4% of the phenotypic variation; the other QTL was assigned to 7AL and explained 18.0% of the phenotypic variation for FHB response. In addition, three minor QTL were detected on chromosomes 1AS, 1B and 5AS by single marker regression. HFZ contributed all favorable alleles. The RIL with HFZ alleles at the QTL on 3BS and 7AL displayed significantly lower percentages of infected spikelets than RIL without these alleles in both greenhouse and field environments. HFZ combined several alleles from germplasm reported previously and is a promising alternative source for improving wheat FHB resistance

Molecular mapping of stem rust resistance genes in wheat

Master of ScienceDepartment of AgronomyGuihua BaiStem rust, caused by Puccinia graminis f. sp. tritici, has successfully prevented rust epidemics by Deployment of resistant cultivars in the past several decades. Unfortunately, race TTKS (termed Ug99) has defeated most stem rust resistance genes existing in commercial cultivars. Sr40, a stem rust resistance gene from Triticum timopheevii ssp. araraticum, was transferred to wheat and provides effective levels of seedling and adult plant resistance against Ug99. To characterize Sr40 in wheat, two mapping populations were developed from the crosses RL6088 / Lakin and RL6088 / 2174. RL6088 is an Ug99-resistant parent with Sr40. Since race TTKS is a quarantined pathogen, a US stem rust isolate RKQQ that is avirulent to Sr40 was used to evaluate the rust resistance in the F[subscript]2 and F[subscript]2:3 populations at the seedling stage. A total of 83 simple sequence repeats (SSR) primers on chromosome 2B were used to screen the parents for polymorphism. Each F[subscript]2 population was analyzed with the markers polymorphic between two parents. Marker Xwmc344 was the most closely linked to Sr40, at 0.7 cM proximal, in the linkage map constructed from the population RL6088 / Lakin, while Xwmc474 and Xgwm374 were also tightly linked. Xwmc474 was mapped 2.5 cM proximal to Sr40 in the RL6088 / 2174 population. Xwmc474 and Xwmc661 were flanking markers for Sr40 in both populations. Markers linked to Sr40 will be useful for marker-assisted integration of Sr40 into elite wheat breeding lines. In addition, a unknown stem rust resistance gene from another source, OK01307, a breeding line from Oklahoma State University shows partial resistance to Ug99, and was characterized using SSRs in this study. Two mapping populations were developed from cross OK01307 / Chinese Spring and OK01307 / LMPG-6. A total of 1300 SSR primers were screened for polymorphism between OK01307 and Chinese spring, and 1000 SSR primers were screened for polymorphism between OK01307 and LMPG-6. Polymorphic primers between parents and between bulks were used to screen the corresponding population. One Sr gene in OK01307 was mapped on chromosome 1BS of the both populations, which was closely linked to Sr24. Whether the gene is Sr24 per se or a new Sr gene that closely linked to Sr24 needs further investigation

Genomic selection in wheat breeding using genotyping-by-sequencing

Citation: Poland, Jesse, Jeffrey Endelman, Julie Dawson, Jessica Rutkoski, Shuangye Wu, Yann Manes, Susanne Dreisigacker, et al. “Genomic Selection in Wheat Breeding Using Genotyping-by-Sequencing.” The Plant Genome 5, no. 3 (2012): 103–13. https://doi.org/10.3835/plantgenome2012.06.0006.Genomic selection (GS) uses genomewide molecular markers to predict breeding values and make selections of individuals or breeding lines prior to phenotyping. Here we show that genotyping-by-sequencing (GBS) can be used for de novo genotyping of breeding panels and to develop accurate GS models, even for the large, complex, and polyploid wheat (Triticum aestivum L.) genome. With GBS we discovered 41,371 single nucleotide polymorphisms (SNPs) in a set of 254 advanced breeding lines from CIMMYT’s semiarid wheat breeding program. Four different methods were evaluated for imputing missing marker scores in this set of unmapped markers, including random forest regression and a newly developed multivariate-normal expectation-maximization algorithm, which gave more accurate imputation than heterozygous or mean imputation at the marker level, although no signifi cant differences were observed in the accuracy of genomic-estimated breeding values (GEBVs) among imputation methods. Genomic-estimated breeding value prediction accuracies with GBS were 0.28 to 0.45 for grain yield, an improvement of 0.1 to 0.2 over an established marker platform for wheat. Genotyping-bysequencing combines marker discovery and genotyping of large populations, making it an excellent marker platform for breeding applications even in the absence of a reference genome sequence or previous polymorphism discovery. In addition, the flexibility and low cost of GBS make this an ideal approach for genomics-assisted breeding

Temperature Trends in the Northwestern Tibetan Plateau Constrained by Ice Core Water Isotopes Over the Past 7,000 Years

International audienc

Application of Genotyping-by-Sequencing on Semiconductor Sequencing Platforms: A Comparison of Genetic and Reference-Based Marker Ordering in Barley

<div><p>The rapid development of next-generation sequencing platforms has enabled the use of sequencing for routine genotyping across a range of genetics studies and breeding applications. Genotyping-by-sequencing (GBS), a low-cost, reduced representation sequencing method, is becoming a common approach for whole-genome marker profiling in many species. With quickly developing sequencing technologies, adapting current GBS methodologies to new platforms will leverage these advancements for future studies. To test new semiconductor sequencing platforms for GBS, we genotyped a barley recombinant inbred line (RIL) population. Based on a previous GBS approach, we designed bar code and adapter sets for the Ion Torrent platforms. Four sets of 24-plex libraries were constructed consisting of 94 RILs and the two parents and sequenced on two Ion platforms. In parallel, a 96-plex library of the same RILs was sequenced on the Illumina HiSeq 2000. We applied two different computational pipelines to analyze sequencing data; the reference-independent TASSEL pipeline and a reference-based pipeline using SAMtools. Sequence contigs positioned on the integrated physical and genetic map were used for read mapping and variant calling. We found high agreement in genotype calls between the different platforms and high concordance between genetic and reference-based marker order. There was, however, paucity in the number of SNP that were jointly discovered by the different pipelines indicating a strong effect of alignment and filtering parameters on SNP discovery. We show the utility of the current barley genome assembly as a framework for developing very low-cost genetic maps, facilitating high resolution genetic mapping and negating the need for developing <i>de novo</i> genetic maps for future studies in barley. Through demonstration of GBS on semiconductor sequencing platforms, we conclude that the GBS approach is amenable to a range of platforms and can easily be modified as new sequencing technologies, analysis tools and genomic resources develop.</p> </div

Genotyping-by-sequencing marker order based on the International Barley Sequencing Consortium reference framework (y-axis) compared to a <i>de novo</i> genetic order (x-axis) from 94 Morex x Barke recombinant inbred lines genotyped on either the Ion Torrent PGM or Proton platform.

<p>Each dot corresponds to one of 1,584 markers from the <i>de </i><i>novo</i> map that was positioned to the physical and genetic framework of barley.</p