Advances in plant genotyping: where the future will take us

Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals

Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.

Association analysis revealed loci linked to post-drought recovery and traits related to persistence of smooth bromegrass (Bromus inermis)

Association analysis has been proven as a powerful tool for the genetic dissection of complex traits. This study was conducted to identify association of recovery, persistence, and summer dormancy with sequence related amplified polymorphism (SRAP) markers in 36 smooth bromegrass genotypes under two moisture conditions and find stable associations. In this study, a diverse panel of polycross-derived progenies of smooth bromegrass was phenotyped under normal and water deficit regimes for three consecutive years. Under water deficit, dry matter yield of cut 1 was approximately reduced by 36, 39, and 37% during 2013, 2014, and 2015, respectively, compared with the normal regime. For dry matter yield of cut 2, these reductions were approximately 38, 60, and 56% in the same three consecutive years relative to normal regime. Moreover, water deficit decreased the RY and PER of the genotypes by 35 and 28%, respectively. Thirty primer combinations were screened by polymerase chain reaction (PCR). From these, 541 polymorphic bands were developed and subjected to association analysis using the mixed linear model (MLM). Population structure analysis identified five main subpopulations possessing significant genetic differences. Association analysis identified 69 and 46 marker-trait associations under normal and water deficit regimes, respectively. Some of these markers were associated with more than one trait; which can be attributed to pleiotropic effects or tightly linked genes affecting several traits. In normal and water-deficit regimes, these markers could potentially be incorporated into marker-assisted selection and targeted trait introgression for the improvement of drought tolerance of smooth bromegrass.

Genetic Evaluation of In Vitro Micropropagated and Regenerated Plants of Cannabis sativa L. Using SSR Molecular Markers

Simple sequence repeat (SSR) markers were used to evaluate the genetic stability of the acclimatized micropropagated and regenerated plants of a high cannabidiol (H-CBD) and a high cannabigerol (H-CBG) variety of Cannabis sativa L. Shoot regeneration and proliferation were achieved by culturing calli in Murashige and Skoog basal medium (MS) supplemented with several concentrations of 6-benzyladenine (BA) or thidiazuron (TDZ). Calli derived mostly from stem explants, rather than leaves, cultured on MS supplemented with 2,4-Dichlorophenoxyacetic acid (2,4-D) or combination of kinetin (KIN) with 1-Naphthaleneacetic acid (NAA) or 2,4-D. Rooting of the regenerated plantlets accomplished on half-strength MS medium supplemented with indole-3-butyric acid (IBA). Previous studies performed have developed an efficient in vitro micropropagation protocol for mass production. Both in vitro methodologies can be employed in genetic breeding via molecular techniques. The genetic stability of micropropagated and regenerated plants was accomplished using twelve SSR primer pairs that produced reproducible and clear bands, ranging from 90 to 330 bp in size, and resulted in amplification of one or two alleles, corresponding to homozygous or heterozygous individuals. The SSR amplification products were monomorphic across all the micropropagated and regenerated plants and comparable to mother plants. The monomorphic banding pattern confirmed the genetic homogeneity of the in vitro cultured acclimatized and mother plants as no somaclonal variation was detected in clones for these specific SSRs. Our results evidently suggest that the developed culture protocols for in vitro multiplication is appropriate and applicable for clonal mass propagation of the C. sativa varieties and demonstrate the reliability of this in vitro propagation system.

Marker-trait association analysis for drought tolerance in smooth bromegrass

BACKGROUND

Little information is available on the application of marker-trait association (MTA) analysis for traits related to drought tolerance in smooth bromegrass. The objectives of this study were to identify marker loci associated with important agronomic traits and drought tolerance indices as well as fining stable associations in a diverse panel of polycross derived genotypes of smooth bromegrass. Phenotypic evaluations were performed at two irrigation regimes (normal and deficit irrigation) during 2 years; and association analysis was done with 626 SRAP markers.

RESULTS

The results of population structure analysis identified three main subpopulations possessing significant genetic differences. Under normal irrigation, 68 and 57 marker-trait associations were identified using general linear model (GLM) and mixed linear mode1 (MLM), respectively. While under deficit irrigation, 61 and 54 markers were associated with the genes controlling the studied traits, based on these two models, respectively. Some of the markers were associated with more than one trait. It was revealed that markers Me1/Em5-11, Me1/Em3-15, and Me5/Em4-7 were consistently linked with drought-tolerance indices.

CONCLUSION

Following marker validation, the MTAs reported in this panel could be useful tools to initiate marker-assisted selection (MAS) and targeted trait introgression of smooth bromegrass under normal and deficit irrigation regimes, and possibly fine mapping and cloning of the underlying genes and QTLs.

Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat

BACKGROUND

Genomic selection has the potential to increase genetic gains by using molecular markers as predictors of breeding values of individuals. This study evaluated the accuracy of predictions for grain yield, heading date, plant height, and yield components in soft red winter wheat under different prediction scenarios. Response to selection for grain yield was also compared across different selection strategies- phenotypic, marker-based, genomic, combination of phenotypic and genomic, and random selections.

RESULTS

Genomic selection was implemented through a ridge regression best linear unbiased prediction model in two scenarios- cross-validations and independent predictions. Accuracy for cross-validations was assessed using a diverse panel under different marker number, training population size, relatedness between training and validation populations, and inclusion of fixed effect in the model. The population in the first scenario was then trained and used to predict grain yield of biparental populations for independent validations. Using subsets of significant markers from association mapping increased accuracy by 64-70% for grain yield but resulted in lower accuracy for traits with high heritability such as plant height. Increasing size of training population resulted in an increase in accuracy, with maximum values reached when ~ 60% of the lines were used as a training panel. Predictions using related subpopulations also resulted in higher accuracies. Inclusion of major growth habit genes as fixed effect in the model caused increase in grain yield accuracy under a cross-validation procedure. Independent predictions resulted in accuracy ranging between - 0.14 and 0.43, dependent on the grouping of site-year data for the training and validation populations. Genomic selection was "superior" to marker-based selection in terms of response to selection for yield. Supplementing phenotypic with genomic selection resulted in approximately 10% gain in response compared to using phenotypic selection alone.

CONCLUSIONS

Our results showed the effects of different factors on accuracy for yield and agronomic traits. Among the factors studied, training population size and relatedness between training and validation population had the greatest impact on accuracy. Ultimately, combining phenotypic with genomic selection would be relevant for accelerating genetic gains for yield in winter wheat.

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F₂, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

Genetic diversity and population structure in Physalis peruviana and related taxa based on InDels and SNPs derived from COSII and IRG markers

The genus Physalis is common in the Americas and includes several economically important species, among them Physalis peruviana that produces appetizing edible fruits. We studied the genetic diversity and population structure of P. peruviana and characterized 47 accessions of this species along with 13 accessions of related taxa consisting of 222 individuals from the Colombian Corporation of Agricultural Research (CORPOICA) germplasm collection, using Conserved Orthologous Sequences (COSII) and Immunity Related Genes (IRGs). In addition, 642 Single Nucleotide Polymorphism (SNPs) markers were identified and used for the genetic diversity analysis. A total of 121 alleles were detected in 24 InDels loci ranging from 2 to 9 alleles per locus, with an average of 5.04 alleles per locus. The average number of alleles in the SNP markers was two. The observed heterozygosity for P. peruviana with InDel and SNP markers was higher (0.48 and 0.59) than the expected heterozygosity (0.30 and 0.41). Interestingly, the observed heterozygosity in related taxa (0.4 and 0.12) was lower than the expected heterozygosity (0.59 and 0.25). The coefficient of population differentiation F_ST was 0.143 (InDels) and 0.038 (SNPs), showing a relatively low level of genetic differentiation among P. peruviana and related taxa. Higher levels of genetic variation were instead observed within populations based on the AMOVA analysis. Population structure analysis supported the presence of two main groups and PCA analysis based on SNP markers revealed two distinct clusters in the P. peruviana accessions corresponding to their state of cultivation. In this study, we identified molecular markers useful to detect genetic variation in Physalis germplasm for assisting conservation and crossbreeding strategies.