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The availability of dense molecular markers has made possible the use of genomic selection (GS) for plant breeding. However, the evaluation of models for GS in real plant populations is very limited. This article evaluates the performance of parametric and semiparametric models for GS using wheat (Triticum aestivum L.) and maize (Zea mays) data in which different traits were measured in several environmental conditions. The findings, based on extensive cross-validations, indicate that models including marker information had higher predictive ability than pedigree-based models. In the wheat data set, and relative to a pedigree model, gains in predictive ability due to inclusion of markers ranged from 7.7 to 35.7%. Correlation between observed and predictive values in the maize data set achieved values up to 0.79. Estimates of marker effects were different across environmental conditions, indicating that genotype × environment interaction is an important component of genetic variability. These results indicate that GS in plant breeding can be an effective strategy for selecting among lines whose phenotypes have yet to be observed.

Genomic selection (GS) has become an important aid in plant and animal breeding. Multienvironment (multitrait) models allow borrowing of information across environments (traits), which could enhance prediction accuracy. This study presents multienvironment (multitrait) models for GS and compares the predictive accuracy of these models with: (i) multienvironment analysis without pedigree and marker information, and (ii) multienvironment pedigree or/and marker-based models. A statistical framework for incorporating pedigree and molecular marker information in models for multienvironment data is described and applied to data that originate from wheat (Triticum aestivum L.) multienvironment trials. Two prediction problems relevant to plant breeders are considered: (CV1) predicting the performance of untested genotypes (“newly” developed lines), and (CV2) predicting the performance of genotypes that have been evaluated in some environments but not in others. Results confirmed the superiority of models using both marker and pedigree information over those based on pedigree information only. Models with pedigree and/or markers had better predictive accuracy than simple linear mixed models that do not include either of these two sources of information. We concluded that the evaluation of such trials can benefit greatly from using multienvironment GS models.

Biological nitrification inhibition (BNI) is a plant function where root systems release antibiotic compounds (BNIs) specifically aimed at suppressing nitrifiers to limit soil-nitrate formation in the root zone. Little is known about BNI-activity in maize ( Zea mays L.), the most important food, feed, and energy crop. Two categories of BNIs are released from maize roots; hydrophobic and hydrophilic BNIs, that determine BNI-capacity in root systems. Zeanone is a recently discovered hydrophobic compound with BNI-activity, released from maize roots. The objectives of this study were to understand/quantify the relationship between zeanone activity and hydrophobic BNI-capacity. We assessed genetic variability among 250 CIMMYT maize lines (CMLs) characterized for hydrophobic BNI-capacity and zeanone activity, towards developing genetic markers linked to this trait in maize. CMLs with high BNI-capacity and ability to release zeanone from roots were identified. GWAS was performed using 27,085 SNPs (with unique positions on the B73v.4 reference genome, and false discovery rate = 10), and phenotypic information for BNI-capacity and zeanone production from root systems. Eighteen significant markers were identified; three associated with specific BNI-activity (SBNI), four with BNI-activity per plant (BNIPP), another ten were common between SBNI and BNIPP, and one with zeanone release. Further, 30 annotated genes were associated with the significant SNPs; most of these genes are involved in pathways of “biological process”, and one (AMT5) in ammonium regulation in maize roots. Although the inbred lines in this study were not developed for BNI-traits, the identification of markers associated with BNI-capacity suggests the possibility of using these genomic tools in marker-assisted selection to improve hydrophobic BNI-capacity in maize.

Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype × environment interaction, thereby making possible the identification of markers contributing to both additive and additive × additive interaction effects of traits.

Maize (Zea mays L.) productivity in Sub-Saharan Africa is increasingly constrained by recurrent drought linked to climate change. Improving yield stability under contrasting moisture conditions remains challenging, especially when breeding materials are derived from parental lines within the same heterotic group (e.g., Group A × Group A), where genetic divergence is limited. We hypothesized that doubled haploid (DH) lines derived from biparental populations still harbor sufficient within-population genetic variation to generate exploitable phenotypic diversity and adaptive differentiation. Furthermore, crossing each DH line with a single-cross tester from the opposite heterotic group provides an effective framework to capture this variation through hybrid performance. In the present work therefore, 855 DH testcross hybrids and six commercial checks were evaluated under optimal and managed drought conditions using an alpha-lattice design. Drought stress was imposed two weeks before flowering until harvest. The mean grain yield under optimal conditions were between 3.66 to 10.36 t ha-1, while 0.16 to 6.13 t ha-1 under drought condition. The mean of top 10 hybrids (≈1.2% selection intensity) outperformed the mean of commercial checks by 60% under optimal conditions and 161% under drought stress. Under optimum conditions, 629 and 286 hybrids exhibited higher heterosis over the mean of checks and the best check, respectively. Similarly, 536 and 235 hybrids surpassed the mean of checks and the best check, respectively, in terms of standard heterosis. The mean and best check yields under drought stress were 1.97 and 3.10 while, 5.91 and 7.09 t ha-1 under optimum condition, respectively. The multi-trait clustering grouped the hybrids into four distinct adaptive categories. Cluster 3, defined primarily by grain yield, integrated temperate introgression with elite tropical backgrounds consistently expressed superior yield performance along with desirable secondary traits. These results demonstrate that DH lines derived from within-group parental crosses can generate functional diversity and predictable adaptive clusters. Thus supports a cluster-based selection strategy to improve drought tolerance, yield potential, and adaptation in maize breeding programs.

The human gastrointestinal tract is colonized by trillions of microorganisms that interact with the host to maintain structural and functional homeostasis. Acting as the interface between the site of the highest microbial burden in the human body and the richest immune compartment, a single layer of intestinal epithelial cells specializes in nutrient absorption, stratifies microorganisms to limit colonization of tissues and shapes the responses of the subepithelial immune cells. In this Review, we focus on the expression, regulation and functions of Toll-like receptors (TLRs) in the different intestinal epithelial lineages to analyse how epithelial recognition of bacteria participates in establishing homeostasis in the gut. In particular, we elaborate on the involvement of epithelial TLR signalling in controlling crypt dynamics, enhancing epithelial barrier integrity and promoting immune tolerance towards the gut microbiota. Furthermore, we comment on the regulatory mechanisms that fine-tune TLR-driven immune responses towards pathogens and revisit the role of TLRs in epithelial repair after injury. Finally, we discuss how dysregulation of epithelial TLRs can lead to the generation of dysbiosis, thereby increasing susceptibility to colitis and tumorigenesis.Interactions between intestinal epithelial cells, the gut microbiota and immune cells have a key role in maintaining gut homeostasis. This Review describes how epithelial recognition of bacteria through Toll-like receptors participates in establishing homeostasis, and how dysregulation of these receptors can lead to dysbiosis, increasing susceptibility to colitis and tumorigenesis.

In maize, doubled haploid (DH) line production capacity of large-sized maize breeding programs often exceeds the capacity to phenotypically evaluate the complete set of testcross candidates in multi-location trials. The ability to partially select DH lines based on genotypic data while maintaining or improving genetic gains for key traits using phenotypic selection can result in significant resource savings. The present study aimed to evaluate genomic selection (GS) prediction scenarios for grain yield and agronomic traits of one of the tropical maize breeding pipelines of CIMMYT in eastern Africa, based on multi-year empirical data for designing a GS-based strategy at the early stages of the pipeline. We used field data from 3,068 tropical maize DH lines genotyped using rAmpSeq markers and evaluated as test crosses in well-watered (WW) and water-stress (WS) environments in Kenya from 2017 to 2019. Three prediction schemes were compared: (1) 1 year of performance data to predict a second year; (2) 2 years of pooled data to predict performance in the third year, and (3) using individual or pooled data plus converting a certain proportion of individuals from the testing set (TST) to the training set (TRN) to predict the next year's data. Employing five-fold cross-validation, the mean prediction accuracies for grain yield (GY) varied from 0.19 to 0.29 under WW and 0.22 to 0.31 under WS, when the 1-year datasets were used training set to predict a second year's data as a testing set. The mean prediction accuracies increased to 0.32 under WW and 0.31 under WS when the 2-year datasets were used as a training set to predict the third-year data set. In a forward prediction scenario, good predictive abilities (0.53 to 0.71) were found when the training set consisted of the previous year's breeding data and converting 30% of the next year's data from the testing set to the training set. The prediction accuracy for anthesis date and plant height across WW and WS environments obtained using 1-year data and integrating 10, 30, 50, 70, and 90% of the TST set to TRN set was much higher than those trained in individual years. We demonstrate that by increasing the TRN set to include genotypic and phenotypic data from the previous year and combining only 10–30% of the lines from the year of testing, the predicting accuracy can be increased, which in turn could be used to replace the first stage of field-based screening partially, thus saving significant costs associated with the testcross formation and multi-location testcross evaluation.

Public-private partnerships (PPPs) are globally recognized for their potential to accelerate genetic improvement and delivery of new high-yielding seed varieties in developing countries. However, despite the strong advocacy for PPPs in crop improvement, there is little empirical evidence about their performance, capacities, and contribution to the development of seed industries and the promotion of competitive seed markets. This paper uses the experience of the MasAgro maize consortium, a PPP in Mexico, to examine crop variety innovation and delivery through PPPs, assess PPPs’ capacities to commercialize public germplasm-based varieties, and derive lessons for the design and implementation of future PPPs. Drawing on a combination of multiple data sources, we examined the PPP’s performance in the generation, dissemination, and commercialization of new maize hybrids. Our examination over the period 2011–2019 shows that the consortium was successful in maintaining a substantial flow of agronomically competitive maize hybrids, which compared favourably with the number of new varieties generated by national and international seed companies and the public sector. The partnership also contributed to refreshing and rejuvenating the variety portfolios of the consortium companies, which appear to have succeeded in bringing MasAgro varieties quickly into the market. However, seed sales achieved by MasAgro hybrids over this period remained small and multinational companies consistently maintained their leadership in the maize seed market. Our analysis shows that PPPs have strong capacities for the development of competitive seed varieties, but they face significant challenges in scaling up the uptake and adoption of these innovations in highly concentrated markets. To succeed in their objective of delivering affordable, high-quality seed on a large scale to smallholder farmers in developing countries, PPPs need to urgently incorporate a commercial and market-oriented perspective along all steps of the plant breeding and dissemination process.

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-environment model, and have advantages and limitations. In this study, we propose two multi-environment Bayesian genomic models: the first model considers genetic effects (u) that can be assessed by the Kronecker product of variance–covariance matrices of genetic correlations between environments and genomic kernels through markers under two linear kernel methods, linear (genomic best linear unbiased predictors, GBLUP) and Gaussian (Gaussian kernel, GK). The other model has the same genetic component as the first model (u) plus an extra component, f, that captures random effects between environments that were not captured by the random effects u. We used five CIMMYT data sets (one maize and four wheat) that were previously used in different studies. Results show that models with G × E always have superior prediction ability than single-environment models, and the higher prediction ability of multi-environment models with u and f over the multi-environment model with only u occurred 85% of the time with GBLUP and 45% of the time with GK across the five data sets. The latter result indicated that including the random effect f is still beneficial for increasing prediction ability after adjusting by the random effect u.

Gray leaf spot (GLS) disease is caused by two fungal pathogens, Cercospora zeae-maydis and Cercospora zeina . The current study evaluated 427 elite tropical/subtropical lines for their responses to GLS under artificial inoculation in Kakamega in western Kenya for 4 years. Furthermore, a subset of 140 lines was used for a high-resolution genome-wide association study (GWAS) for GLS resistance. Among the 427 lines evaluated, 14 were identified as resistant on the basis of a <4 (on a scale of 1–9) GLS disease severity score. Among these 14 lines, three lines, namely CML540, CML559, and CML566, are also known for resistance to MSV, tolerance to drought, and resistance to MLN, respectively. The phenotypic evaluation revealed significant ( P < 0.01) genotypic and genotype x environment interaction variances and moderate to high heritability for GLS disease severity, area under disease progress curve (AUDPC), and other agronomic traits. GLS disease severity traits were negatively and significantly correlated ( P < 0.01) with anthesis date, silking date, plant height, and ear height. A subset of 140 lines was genotyped with 33,740 DART-GBS SNP markers. Population structure and principal component analysis grouped the lines into two major clusters with moderate structure in the population. GWAS revealed 13 and 11 SNPs significantly associated with GLS disease severity and AUDPC values. Six among the 13 SNPs detected for GLS resistance are overlapped with earlier studies, which can be used for fine mapping and improvement of GLS resistance through marker-assisted selection. However, SNPs on chromosomes 9 and 10 were unique to the present study. Genomic prediction on GLS traits revealed moderate to high prediction correlations, suggesting its usefulness in the selection of desirable candidates with favorable alleles for GLS resistance. Overall, 14 GLS resistance lines identified in this study can be used as donor lines in both genetic studies and resistance breeding programs.