Introgression of novel genetic diversity to improve soybean yield

Crop-Weed Introgression Plays Critical Roles in Genetic Differentiation and Diversity of Weedy Rice: A Case Study of Human-Influenced Weed Evolution

As an important driving force, introgression plays an essential role in shaping the evolution of plant species. However, knowledge concerning how introgression affects plant evolution in agroecosystems with strong human influences is still limited. To generate such knowledge, we used InDel (insertion/deletion) molecular fingerprints to determine the level of introgression from japonica rice cultivars into the indica type of weedy rice. We also analyzed the impact of crop-to-weed introgression on the genetic differentiation and diversity of weedy rice, using InDel (insertion/deletion) and SSR (simple sequence repeat) molecular fingerprints. Results based on the STRUCTURE analysis indicated an evident admixture of some weedy rice samples with indica and japonica components, suggesting different levels of introgression from japonica rice cultivars to the indica type of weedy rice. The principal coordinate analyses indicated indica-japonica genetic differentiation among weedy rice samples, which was positively correlated with the introgression of japonica-specific alleles from the rice cultivars. In addition, increased crop-to-weed introgression formed a parabola pattern of dynamic genetic diversity in weedy rice. Our findings based on this case study provide evidence that human activities, such as the frequent change in crop varieties, can strongly influence weed evolution by altering genetic differentiation and genetic diversity through crop-weed introgression in agroecosystems.

Comparison of dynamic 3D chromatin architecture uncovers heterosis for leaf size in Brassica napus

INTRODUCTION

Heterosis is the major event driving plant development and promoting crop breeding, but the molecular bases for this phenomenon remain elusive.

OBJECTIVES

We aim to explore the effect of three-dimensional (3D) chromatin architecture on the underlying mechanism of heterosis.

METHODS

Here, we constructed the North Carolina II (NC-II) population to select superior and inferior heterosis sets by comparing mid-parent heterosis (MPH) in Brassica napus. To decipher the impact of 3D chromatin architecture on the underlying mechanism of heterosis, we combined genetics, transcriptomics and 3D genomics approaches.

RESULTS

We suggest that F1 hybrids with superior heterosis tend to contain more transcriptionally active A compartments compared with F1 hybrids with inferior heterosis, and approximately 19-21% compartment significantly altered in the F1 hybrids relative to the parental lines. Further analyses show that chromatin compartments correlate with genetic variance among parents, which may form the basis for differentially active chromatin compartments. Having more A compartments in F1 hybrids confers a more accessible chromatin circumstance, which promotes a higher proportion of highly expressed ELD (expression level dominance) genes in superior heterosis F1 hybrids (46-64%) compared with inferior heterosis F1 hybrids (22-31%). Moreover, genes related to hormones which affect plant growth, are more up-regulated with changes of 3D genome architecture, and we validate that increased hormone content contributes to cell proliferation and expansion by influencing the key genes of cell cycle thereby promoting leaf size.

CONCLUSION

Dynamic 3D chromatin architecture correlates with genetic variance among parents and contributes to heterosis in Brassica napus.

Genome-wide association study and genomic selection for yield and related traits in soybean

Soybean [Glycine max (L.) Merr.] is a crop of great interest worldwide. Exploring molecular approaches to increase yield genetic gain has been one of the main challenges for soybean breeders and geneticists. Agronomic traits such as maturity, plant height, and seed weight have been found to contribute to yield. In this study, a total of 250 soybean accessions were genotyped with 10,259 high-quality SNPs postulated from genotyping by sequencing (GBS) and evaluated for grain yield, maturity, plant height, and seed weight over three years. A genome-wide association study (GWAS) was performed using a Bayesian Information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) model. Genomic selection (GS) was evaluated using a ridge regression best linear unbiased predictor (rrBLUP) model. The results revealed that 20, 31, 37, and 23 SNPs were significantly associated with maturity, plant height, seed weight, and yield, respectively; Many SNPs were mapped to previously described maturity and plant height loci (E2, E4, and Dt1) and a new plant height locus was mapped to chromosome 20. Candidate genes were found in the vicinity of the two SNPs with the highest significant levels associated with yield, maturity, plant height, seed weight, respectively. A 11.5-Mb region of chromosome 10 was associated with both yield and seed weight. Overall, the accuracy of GS was dependent on the trait, year, and population structure, and high accuracy indicates that these agronomic traits can be selected in molecular breeding through GS. The SNP markers identified in this study can be used to improve yield and agronomic traits through the marker-assisted selection and GS in breeding programs.

Genomic prediction using training population design in interspecific soybean populations

Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-021-01203-6.

Understanding the combining ability for physiological traits in soybean

Photosynthetic efficiency has become the target of several breeding programs since the positive correlation between photosynthetic rate and yield in soybean suggests that the improvement of photosynthetic efficiency may be a promising target for new yield gains. However, studies on combining ability of soybean genotypes for physiological traits are still scarce in the literature. The objective of this study was to estimate the combining ability of soybean genotypes based on F2 generation aiming to identify superior parents and segregating populations for physiological traits. Twenty-eight F2 populations resulting from partial diallel crossings between eleven lines were evaluated in two crop seasons for the physiological traits: photosynthesis, stomatal conductance, internal CO2 concentration, and transpiration. General combining ability (GCA) of the parents and specific combining ability (SCA) of the F2 populations were estimated. Our findings reveal the predominance of additive effects in controlling the traits. The genotype TMG 7062 IPRO is the most promising parent for programs aiming at photosynthetic efficiency. We have also identified other promising parents and proposed cross-breeding with higher potential for obtaining superior lines for photosynthetic efficiency.