Large-scale deployment of a rice 6 K SNP array for genetics and breeding applications

Background

Fixed arrays of single nucleotide polymorphism (SNP) markers have advantages over reduced representation sequencing in their ease of data analysis, consistently higher call rates, and rapid turnaround times. A 6 K SNP array represents a cost-benefit “sweet spot” for routine genetics and breeding applications in rice. Selection of informative SNPs across species and subpopulations during chip design is essential to obtain useful polymorphism rates for target germplasm groups. This paper summarizes results from large-scale deployment of an Illumina 6 K SNP array for rice.

Results

Design of the Illumina Infinium 6 K SNP chip for rice, referred to as the Cornell_6K_Array_Infinium_Rice (C6AIR), includes 4429 SNPs from re-sequencing data and 1571 SNP markers from previous BeadXpress 384-SNP sets, selected based on polymorphism rate and allele frequency within and between target germplasm groups. Of the 6000 attempted bead types, 5274 passed Illumina’s production quality control. The C6AIR was widely deployed at the International Rice Research Institute (IRRI) for genetic diversity analysis, QTL mapping, and tracking introgressions and was intensively used at Cornell University for QTL analysis and developing libraries of interspecific chromosome segment substitution lines (CSSLs) between O. sativa and diverse accessions of O. rufipogon or O. meridionalis. Collectively, the array was used to genotype over 40,000 rice samples. A set of 4606 SNP markers was used to provide high quality data for O. sativa germplasm, while a slightly expanded set of 4940 SNPs was used for O. sativa X O. rufipogon populations. Biparental polymorphism rates were generally between 1900 and 2500 well-distributed SNP markers for indica x japonica or interspecific populations and between 1300 and 1500 markers for crosses within indica, while polymorphism rates were lower for pairwise crosses within U.S. tropical japonica germplasm. Recently, a second-generation array containing ~7000 SNP markers, referred to as the C7AIR, was designed by removing poor-performing SNPs from the C6AIR and adding markers selected to increase the utility of the array for elite tropical japonica material.

Conclusions

The C6AIR has been successfully used to generate rapid and high-quality genotype data for diverse genetics and breeding applications in rice, and provides the basis for an optimized design in the C7AIR.

     

Association mapping reveals loci associated with multiple traits that affect grain yield and adaptation in soft winter wheat

Genome-wide association studies (GWAS) are useful to facilitate crop improvement via enhanced knowledge of marker-trait associations (MTA). A GWAS for grain yield (GY), yield components, and agronomic traits was conducted using a diverse panel of 239 soft red winter wheat (Triticum aestivum) genotypes evaluated across two growing seasons and eight site-years. Analysis of variance showed significant environment, genotype, and genotype-by-environment effects for GY and yield components. Narrow sense heritability of GY (h ²  = 0.48) was moderate compared to other traits including plant height (h ²  = 0.81) and kernel weight (h ²  = 0.77). There were 112 significant MTA (p < 0.0005) detected for eight measured traits using compressed mixed linear models and 5715 single nucleotide polymorphism markers. MTA for GY and agronomic traits coincided with previously reported QTL for winter and spring wheat. Highly significant MTA for GY showed an overall negative allelic effect for the minor allele, indicating selection against these alleles by breeders. Markers associated with multiple traits observed on chromosomes 1A, 2D, 3B, and 4B with positive minor effects serve as potential targets for marker assisted breeding to select for improvement of GY and related traits. Following marker validation, these multi-trait loci have the potential to be utilized for MAS to improve GY and adaptation of soft red winter wheat.     

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

Context

Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.

Objectives

We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.

Methods

We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.

Results

Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.

Conclusions

This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.

     

Participatory mapping of landscape values in a Pan-European perspective

Context

Human–nature interactions are reflected in the values people assign to landscapes. These values shape our understanding and actions as landscape co-creators, and need to be taken into account to achieve an integrated management of the landscape that involves civil society.

Objectives

The aim of this research was to increase the current knowledge on the most and least common landscape values perceived by local stakeholders, the patterns in the spatial distribution of values, and their connection to different socio-economic backgrounds and landscape characteristics across Europe.

Methods

The research consisted of a cross-site comparison study on how landscape values are perceived in six areas of Europe using Public Participation GIS surveys. Answers were analysed combining contingency tables, spatial autocorrelation and bivariate correlation methods, kernel densities, land cover ratios, and viewshed analyses. Results were discussed in the light of findings derived from other European participatory mapping studies.

Results

We identified shared patterns in the perception of landscape values across Europe. Recreation, aesthetics, and social fulfilment were the most common values. Landscape values showed common spatial patterns mainly related to accessibility and the presence of water, settlements, and cultural heritage. However, respondents in each study site had their own preferences connected to the intrinsic characteristics of the local landscape and culture.

Conclusions

The results encourage land planners and researchers to approach landscape values in relation to socio-cultural and bio-physical land characteristics comprehensibly, acknowledging the complexity in the relationship between people’s perception and the landscape, to foster more effective and inclusive landscape management strategies.

     

Temporal and spatial progress of the diseases caused by the crinivirus tomato chlorosis virus and the begomovirus tomato severe rugose virus in tomatoes in Brazil

Efficient management of whitefly-borne diseases remains a challenge due to the lack of a comprehensive understanding of their epidemiology, particularly of the diseases tomato golden mosaic and tomato yellowing. Here, by monitoring 16 plots in four commercial fields, the temporal and spatial distribution of these two diseases were studied in tomato fields in Brazil. In the experimental plots these diseases were caused by tomato severe rugose virus (ToSRV) and tomato chlorosis virus (ToCV), respectively. The incidence of each virus was similar in the plots within a field but varied greatly among fields. Plants with symptoms for both diseases were randomly distributed in three of four spatial analyses. The curves representing the progress of both diseases were similar and contained small fluctuations, indicating that the spread of both viruses was similar under field conditions. In transmission experiments of ToSRV and ToCV by Bemisia tabaci MEAM1 (former biotype B), these viruses had a similar transmission rate in single or mixed infections. It was then shown that primary and secondary spread of ToCV were not efficiently controlled by insecticide applications. Finally, in a typical monomolecular model of disease progress, simulation of the primary dissemination of ToSRV and ToCV showed that infected plants were predominantly randomly distributed. It is concluded that, although the manner of vector transmission differs between ToSRV (persistent) and ToCV (semipersistent), the main dispersal mechanisms are most probably similar for these two diseases: primary spread is the predominant mechanism, and epidemics of these diseases have been caused by several influxes of viruliferous whiteflies.