Sugarcane has a complex, highly polyploid genome with multi-species ancestry. Additive models for genomic prediction of clonal performance might not capture interactions between genes and alleles from different ploidies and ancestral species. As such genomic prediction in sugarcane presents an interesting case for machine learning methods, which are purportedly able to deal with high levels of complexity in prediction. Here we investigate deep learning networks (DL), including Multilayer networks (MLP) and convolution neural networks (CNN), and Random Forest (RF) for genomic prediction in sugarcane. The data set was 2912 sugarcane clones, scored for 26,086 genome wide SNP markers, with final assessment trial (FAT) data for total cane harvested (TCH), Commercial cane sugar (CCS) and Fibre content. The clones in the latest trial (2017) were used as a validation set. We compared performances of these methods to GBLUP extended to include dominance and epistatic effects. The prediction accuracies from GBLUPs were 0.37 for TCH, 0.37 for CCS and 0.48 for Fibre, while the DL models had accuracies of 0.33 for TCH prediction, 0.38 for CCS prediction and 0.43 for Fibre. Optimised RF achieved a prediction accuracy of 0.35 for TCH, 0.38 for CCS and 0.48 for Fibre. Both DL and RF predictions were more accurate additive GBLUP but generally lower than extended GBLUP. Finally, we identified a partially shared distribution of SNP selections between RF and GBLUP models. We conclude RF may have some utility for genomic prediction for crops with highly complex genomes, particularly if non-additive interactions can be captured with clonal propagation.

Drought alone and with associated abiotic stress such as heat and nutrient deficiency leads to significant agricultural crop loss. Thus, with changing climatic conditions, it is important to develop resilience measures in agricultural systems against drought stress. In this review, we discuss the modifications in plants while responding to drought giving special focus on roots as they are the primary sense organs in this context. Prospects of genomic crop improvement by pointing out the focus areas to engineer root system architecture and genomic regions involved in the related traits are also discussed. We have also listed instruments and software facilitating high throughput phenotyping of root system in field conditions as the phenotyping of root system architecture in the field is a challenge.

Climate influences on below-ground plant traits seldom receive due attention. Climate change is varying the availability of resources, soil physical properties, rainfall events, soil mineral weathering and leaching intensity which collectively determines soil physical and chemical properties. Soil constraints – acidity (pH<6), salinity(pH≤8.5), sodicity and dispersion (pH>8.5) are major causes of wheat yield loss in arid and semi-arid cropping systems. To cope with changing environment, plants employ adaptive strategies such as phenotypic plasticity; a key multifaceted trait, to promote shifts in phenotypes. Adaptive strategies are complex, determined by key functional traits and Genotype × Environment interactions. The understanding of molecular basis of stress tolerance is particularly challenging for plasticity traits. Advances in sequencing and high-throughput genomics technologies has identified functional alleles in gene-rich regions, haplotypes, candidate genes, mechanisms and in silico gene expression profiles at various growth developmental stages. Our review focuses on favourable alleles for enhanced gene expression, QTLs and epigenetic regulation of plant responses to soil constraints including heavy metal stress and nutrient limitations. A strategy is then described for quantitative traits in wheat by investigating significant alleles, functional characterization of variants, followed by gene validation using advanced genomic tools and marker development for molecular breeding and genome editing. Also, the review highlights the progress of gene editing in wheat, multiplex gene editing and novel alleles for smart control of gene expression. Integration of these genomic technologies will be effective to enhance plasticity traits and stabilizing wheat yields on constrained soils in the face of climate change.

Maize (Zea mays L.) is a crop of major economic and food security importance globally. The fall armyworm (FAW), Spodoptera frugiperda, can devastate entire maize crops, especially in countries or markets that do not allow the use of transgenic crops. Host-plant insect resistance is an economical and environmentally benign way to control FAW, and this study sought to identify maize lines, genes, and pathways that contribute to resistance to FAW. Of 289 maize lines phenotyped for FAW damage in artificially infested, replicated field trials over three years, 31 were identified with good levels of resistance that could donate FAW resistance into elite but susceptible hybrid parents. The 289 lines were genotyped by sequencing to provide SNP markers for a genome-wide association study (GWAS), followed by a metabolic pathway analysis using the Pathway Association Study Tool (PAST). GWAS identified 15 SNPs linked to 7 genes, and PAST identified multiple pathways, associated with FAW damage. Top pathways, and thus useful resistance mechanisms for further study, include hormone signaling pathways and the biosynthesis of carotenoids (particularly zeaxanthin), chlorophyll compounds, cuticular wax, known antibiosis agents, and 1,4-dihydroxy-2-naphthoate. Targeted metabolite analysis confirmed that maize genotypes with lower levels of FAW damage tend to have higher levels of chlorophyll a than genotypes with high FAW damage, which also tend to have lower levels of pheophytin, lutein, chlorophyll b and β-carotene. The list of resistant genotypes, and the results from the genetic, pathway, and metabolic study, can all contribute to efficient creation of FAW resistant cultivars.

Sequencing-based genotyping of heterozygous diploids requires sufficient depth to accurately call heterozygous genotypes. In interspecific hybrids, alignment of reads to both parental genomes simultaneously can generate haploid data, potentially eliminating the problem of heterozygosity. Two populations of interspecific hybrid rootstocks of walnut (Juglans) and pistachio (Pistacia) were genotyped using alignment to the maternal genome, paternal genome, and dual alignment to both genomes simultaneously. Downsampling was used to examine concordance of imputed genotype calls as a function of sequencing depth. Dual alignment resulted in datasets essentially free of heterozygous genotypes, simplifying the identification and removal of cross-contaminated samples. Concordance between full and downsampled genotype calls was always highest after dual alignment. Nearly all SNPs in dual alignment datasets were shared with the corresponding single-parent datasets, but 60-90% of single-parent SNPs were private to that dataset. Private SNPs in single-parent datasets had higher rates of heterozygosity, lower levels of concordance, and were enriched in fixed differences between parental genomes (“homeo-SNPs”) compared to shared SNPs in the same dataset. In multi-parental walnut hybrids, the paternal-aligned dataset was ineffective at resolving population structure in the maternal parent. Overall, the dual alignment strategy effectively produced phased, haploid data, increasing data quality and reducing cost.

Date palm (Phoenix dactylifera) fruit are an economically and culturally significant crop in the Middle East and North Africa. There are hundreds of different commercial cultivars producing dates with distinctive shapes, colors, and sizes. Genetic studies of some Date palm traits have been performed, including for date palm sex-determination, sugar content and fresh fruit colour. In this study, we used genome sequences and image data of 199 dry date fruit (Tamar) samples collected from 14 countries to identify genetic loci associated with the color of this fruit stage. Here, we find loci across multiple linkage groups (LG) associated with dry fruit color phenotype. We recover the previously identified VIR genotype associated with fresh fruit yellow or red color and new associations with the lightness and darkness of dry fruit. This study will add resolution to our understanding of the date palm fruit color phenotype especially at the most commercially important tamar stage.

The development of strawberry (Fragaria × ananassa) cultivars resistant to Phytophthora crown rot (PhCR), a devastating disease caused by the soil-borne pathogen Phytophthora cactorum, has been challenging, partly because resistance phenotypes are quantitative and only moderately heritable. To develop deeper insights into the genetics of resistance and build the foundation for applying genomic selection, a genetically diverse training population was screened for resistance to California isolates of the pathogen. Here we show that genetic gains in breeding for resistance to PhCR have been negligible (3% of the cultivars tested were highly resistant and none surpassed early twentieth century cultivars). Narrow-sense heritability for PhCR resistance ranged from 0.35-0.57. Using multivariate GWAS, we identified a large-effect locus (predicted to be RPc2) that appears to be ubiquitous, slowed symptom development, explained 43.6-51.6% of the genetic variance, was necessary but not sufficient for resistance, and was strongly associated with calcium channel and other genes with known plant defense functions. The addition of underutilized gene bank resources to our training population doubled additive genetic variance, increased the accuracy of genomic selection, and enabled the discovery of individuals carrying favorable alleles that are either rare or not present in modern cultivars. The incorporation of an RPc2-associated SNP as a fixed effect increased genomic prediction accuracy from 0.40 to 0.55. Finally, we show that parent selection using genomic-estimated breeding values, genetic variances, and cross-usefulness holds promise for enhancing resistance to PhCR in strawberry.