Statistical sampling of missing environmental variables improves biophysical genomic prediction in wheat.

KEY MESSAGE: The integration of genomic prediction with crop growth models enabled the estimation of missing environmental variables which improved the prediction accuracy of grain yield. Since the invention of whole-genome prediction (WGP) more than two decades ago, breeding programmes have established extensive reference populations that are cultivated under diverse environmental conditions. The introduction of the CGM-WGP model, which integrates crop growth models (CGM) with WGP, has expanded the applications of WGP to the prediction of unphenotyped traits in untested environments, including future climates. However, CGMs require multiple seasonal environmental records, unlike WGP, which makes CGM-WGP less accurate when applied to historical reference populations that lack crucial environmental inputs. Here, we investigated the ability of CGM-WGP to approximate missing environmental variables to improve prediction accuracy. Two environmental variables in a wheat CGM, initial soil water content (InitlSoilWCont) and initial nitrate profile, were sampled from different normal distributions separately or jointly in each iteration within the CGM-WGP algorithm. Our results showed that sampling InitlSoilWCont alone gave the best results and improved the prediction accuracy of grain number by 0.07, yield by 0.06 and protein content by 0.03. When using the sampled InitlSoilWCont values as an input for the traditional CGM, the average narrow-sense heritability of the genotype-specific parameters (GSPs) improved by 0.05, with GNSlope, PreAnthRes, and VernSen showing the greatest improvements. Moreover, the root mean square of errors for grain number and yield was reduced by about 7% for CGM and 31% for CGM-WGP when using the sampled InitlSoilWCont values. Our results demonstrate the advantage of sampling missing environmental variables in CGM-WGP to improve prediction accuracy and increase the size of the reference population by enabling the utilisation of historical data that are missing environmental records.

Integrating biophysical crop growth models and whole genome prediction for their mutual benefit: a case study in wheat phenology.

Running crop growth models (CGM) coupled with whole genome prediction (WGP) as a CGM-WGP model introduces environmental information to WGP and genomic relatedness information to the genotype-specific parameters modelled through CGMs. Previous studies have primarily used CGM-WGP to infer prediction accuracy without exploring its potential to enhance CGM and WGP. Here, we implemented a heading and maturity date wheat phenology model within a CGM-WGP framework and compared it with CGM and WGP. The CGM-WGP resulted in more heritable genotype-specific parameters with more biologically realistic correlation structures between genotype-specific parameters and phenology traits compared with CGM-modelled genotype-specific parameters that reflected the correlation of measured phenotypes. Another advantage of CGM-WGP is the ability to infer accurate prediction with much smaller and less diverse reference data compared with that required for CGM. A genome-wide association analysis linked the genotype-specific parameters from the CGM-WGP model to nine significant phenology loci including Vrn-A1 and the three PPD1 genes, which were not detected for CGM-modelled genotype-specific parameters. Selection on genotype-specific parameters could be simpler than on observed phenotypes. For example, thermal time traits are theoretically more independent candidates, compared with the highly correlated heading and maturity dates, which could be used to achieve an environment-specific optimal flowering period. CGM-WGP combines the advantages of CGM and WGP to predict more accurate phenotypes for new genotypes under alternative or future environmental conditions.

Using genomic prediction with crop growth models enables the prediction of associated traits in wheat.

Crop growth models (CGM) can predict the performance of a cultivar in untested environments by sampling genotype-specific parameters. As they cannot predict the performance of new cultivars, it has been proposed to integrate CGMs with whole genome prediction (WGP) to combine the benefits of both models. Here, we used a CGM-WGP model to predict the performance of new wheat (Triticum aestivum) genotypes. The CGM was designed to predict phenology, nitrogen, and biomass traits. The CGM-WGP model simulated more heritable GSPs compared with the CGM and gave smaller errors for the observed phenotypes. The WGP model performed better when predicting yield, grain number, and grain protein content, but showed comparable performance to the CGM-WGP model for heading and physiological maturity dates. However, the CGM-WGP model was able to predict unobserved traits (for which there were no phenotypic records in the reference population). The CGM-WGP model also showed superior performance when predicting unrelated individuals that clustered separately from the reference population. Our results demonstrate new advantages for CGM-WGP modelling and suggest future efforts should focus on calibrating CGM-WGP models using high-throughput phenotypic measures that are cheaper and less laborious to collect.

A giant NLR gene confers broad-spectrum resistance to Phytophthora sojae in soybean.

Phytophthora root and stem rot caused by P. sojae is a destructive soybean soil-borne disease found worldwide. Discovery of genes conferring broad-spectrum resistance to the pathogen is a need to prevent the outbreak of the disease. Here, we show that soybean Rps11 is a 27.7-kb nucleotide-binding site-leucine-rich repeat (NBS-LRR or NLR) gene conferring broad-spectrum resistance to the pathogen. Rps11 is located in a genomic region harboring a cluster of large NLR genes of a single origin in soybean, and is derived from rounds of unequal recombination. Such events result in promoter fusion and LRR expansion that may contribute to the broad resistance spectrum. The NLR gene cluster exhibits drastic structural diversification among phylogenetically representative varieties, including gene copy number variation ranging from five to 23 copies, and absence of allelic copies of Rps11 in any of the non-Rps11-donor varieties examined, exemplifying innovative evolution of NLR genes and NLR gene clusters.

Comparative genomics and phylogenetic discordance of cultivated tomato and close wild relatives.

Background. Studies of ancestry are difficult in the tomato because it crosses with many wild relatives and species in the tomato clade that have diverged very recently. As a result, the phylogeny in relation to its closest relatives remains uncertain. By using the coding sequence from Solanum lycopersicum, S. galapagense, S. pimpinellifolium, S. corneliomuelleri, and S. tuberosum and the genomic sequence from S. lycopersicum 'Heinz', an heirloom line, S. lycopersicum 'Yellow Pear', and two of cultivated tomato's closest relatives, S. galapagense and S. pimpinellifolium, we have aimed to resolve the phylogenies of these closely related species as well as identify phylogenetic discordance in the reference cultivated tomato. Results. Divergence date estimates suggest that the divergence of S. lycopersicum, S. galapagense, and S. pimpinellifolium happened less than 0.5 MYA. Phylogenies based on 8,857 coding sequences support grouping of S. lycopersicum and S. galapagense, although two secondary trees are also highly represented. A total of 25 genes in our analysis had sites with evidence of positive selection along the S. lycopersicum lineage. Whole genome phylogenies showed that while incongruence is prevalent in genomic comparisons between these genotypes, likely as a result of introgression and incomplete lineage sorting, a primary phylogenetic history was strongly supported. Conclusions. Based on analysis of these genotypes, S. galapagense appears to be closely related to S. lycopersicum, suggesting they had a common ancestor prior to the arrival of an S. galapagense ancestor to the Galápagos Islands, but after divergence of the sequenced S. pimpinellifolium. Genes showing selection along the S. lycopersicum lineage may be important in domestication or selection occurring post-domestication. Further analysis of intraspecific data in these species will help to establish the evolutionary history of cultivated tomato. The use of an heirloom line is helpful in deducing true phylogenetic information of S. lycopersicum and identifying regions of introgression from wild species.

Fine mapping of a preharvest sprouting QTL interval on chromosome 2B in white wheat.

KEY MESSAGE: Fine mapping by recombinant backcross populations revealed that a preharvest sprouting QTL on 2B contained two QTLs linked in coupling with different effects on the phenotype. Wheat preharvest sprouting (PHS) occurs when grain germinates on the plant before harvest, resulting in reduced grain quality. Previous mapping of quantitative trait locus (QTL) revealed a major PHS QTL, QPhs.cnl-2B.1, located on chromosome 2B significant in 16 environments that explained from 5 to 31 % of the phenotypic variation. The objective of this project was to fine map the QPhs.cnl-2B.1 interval. Fine mapping was carried out in recombinant backcross populations (BC1F4 and BC1F5) that were developed by backcrossing selected doubled haploids to a recurrent parent and self-pollinating the BC1F4 and BC1F5 generations. In each generation, three markers in the QPhs.cnl-2B.1 interval were used to screen for recombinants. Fine mapping revealed that the QPhs.cnl-2B.1 interval contained two PHS QTLs linked in coupling. The distal PHS QTL, located between Wmc453c and Barc55, contributed 8 % of the phenotypic variation and also co-located with a major seed dormancy QTL determined by germination index. The proximal PHS QTL, between Wmc474 and CNL415-rCDPK, contributed 16 % of the variation. Several candidate genes including Mg-chelatase H subunit family protein, GTP-binding protein and calmodulin/Ca(2+)-dependent protein kinase were linked to the PHS QTL. Although many recombinant lines were identified, the lack of polymorphism for markers in the QTL interval prevented the localization of the recombination breakpoints and identification of the gene underlying the phenotype.

Systems genetics of environmental response in the mature wheat embryo.

Quantitative phenotypic traits are influenced by genetic and environmental variables as well as the interaction between the two. Underlying genetic × environment interaction is the influence that the surrounding environment exerts on gene expression. Perturbation of gene expression by environmental factors manifests itself in alterations to gene co-expression networks and ultimately in phenotypic plasticity. Comparative gene co-expression networks have been used to uncover biological mechanisms that differentiate tissues or other biological factors. In this study, we have extended consensus and differential Weighted Gene Co-Expression Network Analysis to compare the influence of different growing environments on gene co-expression in the mature wheat (Triticum aestivum) embryo. This network approach was combined with mapping of individual gene expression QTL to examine the genetic control of environmentally static and variable gene expression. The approach is useful for gene expression experiments containing multiple environments and allowed for the identification of specific gene co-expression modules responsive to environmental factors. This procedure identified conserved coregulation of gene expression between environments related to basic developmental and cellular functions, including protein localization and catabolism, vesicle composition/trafficking, Golgi transport, and polysaccharide metabolism among others. Environmentally unique modules were found to contain genes with predicted functions in responding to abiotic and biotic environmental variables. These findings represent the first report using consensus and differential Weighted Gene Co-expression Network Analysis to characterize the influence of environment on coordinated transcriptional regulation.

Comparative genetic analysis of a wheat seed dormancy QTL with rice and Brachypodium identifies candidate genes for ABA perception and calcium signaling.

Wheat preharvest sprouting (PHS) occurs when seed germinates on the plant before harvest resulting in reduced grain quality. In wheat, PHS susceptibility is correlated with low levels of seed dormancy. A previous mapping of quantitative trait loci (QTL) revealed a major PHS/seed dormancy QTL, QPhs.cnl-2B.1, located on wheat chromosome 2B. A comparative genetic study with the related grass species rice (Oryza sativa L.) and Brachypodium distachyon at the homologous region to the QPhs.cnl-2B.1 interval was used to identify the candidate genes for marker development and subsequent fine mapping. Expressed sequence tags and a comparative mapping were used to design 278 primer pairs, of which 22 produced polymorphic amplicons that mapped to the group 2 chromosomes. Fourteen mapped to chromosome 2B, and ten were located in the QTL interval. A comparative analysis revealed good macrocollinearity between the PHS interval and 3 million base pair (mb) region on rice chromosomes 7 and 3, and a 2.7-mb region on Brachypodium Bd1. The comparative intervals in rice were found to contain three previously identified rice seed dormancy QTL. Further analyses of the interval in rice identified genes that are known to play a role in seed dormancy, including a homologue for the putative Arabidopsis ABA receptor ABAR/GUN5. Additional candidate genes involved in calcium signaling were identified and were placed in a functional protein association network that includes additional proteins critical for ABA signaling and germination. This study provides promising candidate genes for seed dormancy in both wheat and rice as well as excellent molecular markers for further comparative and fine mapping.

Mapping quantitative trait loci for preharvest sprouting resistance in white wheat.

The premature germination of seeds before harvest, known as preharvest sprouting (PHS), is a serious problem in all wheat growing regions of the world. In order to determine genetic control of PHS resistance in white wheat from the relatively uncharacterized North American germplasm, a doubled haploid population consisting of 209 lines from a cross between the PHS resistant variety Cayuga and the PHS susceptible variety Caledonia was used for QTL mapping. A total of 16 environments were used to detect 15 different PHS QTL including a major QTL, QPhs.cnl-2B.1, that was significant in all environments tested and explained from 5 to 31% of the trait variation in a given environment. Three other QTL QPhs.cnl-2D.1, QPhs.cnl-3D.1, and QPhs.cnl-6D.1 were detected in six, four, and ten environments, respectively. The potentially related traits of heading date (HD), plant height (HT), seed dormancy (DOR), and rate of germination (ROG) were also recorded in a limited number of environments. HD was found to be significantly negatively correlated with PHS score in most environments, likely due to a major HD QTL, QHd.cnl-2B.1, found to be tightly linked to the PHS QTL QPhs.cnl-2B.1. Using greenhouse grown material no overlap was found between seed dormancy and the four most consistent PHS QTL, suggesting that greenhouse environments are not representative of field environments. This study provides valuable information for marker-assisted breeding for PHS resistance, future haplotyping studies, and research into seed dormancy.

Comparative DNA sequence analysis of wheat and rice genomes.

The use of DNA sequence-based comparative genomics for evolutionary studies and for transferring information from model species to crop species has revolutionized molecular genetics and crop improvement strategies. This study compared 4485 expressed sequence tags (ESTs) that were physically mapped in wheat chromosome bins, to the public rice genome sequence data from 2251 ordered BAC/PAC clones using BLAST. A rice genome view of homologous wheat genome locations based on comparative sequence analysis revealed numerous chromosomal rearrangements that will significantly complicate the use of rice as a model for cross-species transfer of information in nonconserved regions.