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Key messageNovel disease resistance gene paralogues are generated by targeted chromosome cleavage of tandem duplicated NBS-LRR gene complexes and subsequent DNA repair in soybean. This study demonstrates accelerated diversification of innate immunity of plants using CRISPR.Nucleotide-binding-site-leucine-rich-repeat (NBS-LRR) gene families are key components of effector-triggered immunity. They are often arranged in tandem duplicated arrays in the genome, a configuration that is conducive to recombinations that will lead to new, chimeric genes. These rearrangements have been recognized as major sources of novel disease resistance phenotypes. Targeted chromosome cleavage by CRISPR/Cas9 can conceivably induce rearrangements and thus emergence of new resistance gene paralogues. Two NBS-LRR families of soy have been selected to demonstrate this concept: a four-copy family in the Rpp1 region (Rpp1L) and a large, complex locus, Rps1 with 22 copies. Copy-number variations suggesting large-scale, CRISPR/Cas9-mediated chromosome rearrangements in the Rpp1L and Rps1 complexes were detected in up to 58.8% of progenies of primary transformants using droplet-digital PCR. Sequencing confirmed development of novel, chimeric paralogs with intact open reading frames. These novel paralogs may confer new disease resistance specificities. This method to diversify innate immunity of plants by genome editing is readily applicable to other disease resistance genes or other repetitive loci.

Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs’ effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi‐environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100‐seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48 g/year (SD), 0.68 g/year (MD), and 0.55 g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding. Core Ideas Historical cowpea yield trial data were exploited to estimate genetic gain realized in the past 12–15 years. Realized genetic gain estimates for grain yield depicted positive progress in three key cowpea breeding pipelines. Predicted gain estimates portrayed a future increase in the rate of genetic gain in cowpea. Accelerated genetic improvement is expected with the recent integration of genomics into the breeding program. Plain Language Summary Cowpea is a crucial food security crop feeding millions of people in sub‐Saharan Africa. Tremendous breeding efforts have been made at the International Institute of Tropical Agriculture (IITA) to develop improved cowpea varieties. Whether such actions translated into yield gain in the past decade has not been evaluated. This study assessed the breeding progress realized over the years by analyzing historical yield data from 2010 to 2022 and examining the trend. Results revealed a yearly grain yield increase of 22.75, 7.91, and 22.82 kg/ha for short, medium, and late maturity groups. Given the recent adoption of robust breeding approaches and tools, a positive continuous increase in cowpea grain yield was predicted. The finding implied that the breeding program is on track for accelerated delivery of high‐yielding varieties to meet the food demands of the ever‐growing population.

In particular, the authors highlight that “the use of additive effects under a linear mixed model framework (GBLUP) showed the best balance between efficiency and accuracy.” [...]Liu et al.investigated prediction methods based on genes known to be relevant for fiber length in cotton.Pégard et al.considered GS for poplar in the context of forest tree breeding and highlight “that genomic evaluation performance could be comparable to the already well-optimized pedigree-based evaluation under certain conditions […] The authors highlight that differences in the organizational structure of plant and animal breeding institutions, as well as differences in the cost-benefit structures of the use of GS in private and public plant breeding may have been the cause for differences in the adoption of GS.Gianolacontributed with his reflections on trends and developments in statistical genetics addressing for instance the “deconstruction of genetic architecture” and highlighting that “quantitative genetics provides just a linear (local) approximation to complexity with little (if any) mechanistic value.” [...]the author emphasized the principal of parsimony in genetic models and that a bias of a statistical method does not need to be a problem but that “practically all machine learning methods (e.g., random forests) provide biased predictions that, on average, will be better than unbiased machines.” The rough outline of the content of our Research Topic emphasizes that GS is now well-established across many plant species. [...]five out of 12 research articles were related to GEI indicating the relevance of this topic in current research.

ABSTRACT Weak stems or stalks in grass crop species increase the likelihood of stalk failure, thereby reducing yield. Three‐point bending tests are often employed in selective breeding studies to characterize stalk strength. However, it is hypothesized that the loading setup used during three‐point bending experiments may significantly alter test results. To investigate this hypothesis, two different loading configurations were employed in conducting three‐point bending experiments of corn (Zea mays L.) stalks. In the first configuration, stalks were loaded and supported at nodes. In the second configuration, stalks were loaded and supported at internodal segments. Significantly higher bending moments were experienced at internodal segments during the node‐loaded configuration than was required to fail the same segment during internode‐loaded tests. This is because the loading anvil significantly deforms the stalk's cross section when it is placed on an internodal segment, thereby inducing premature failure. In addition, internode‐loaded tests were observed to produce unnatural failure patterns, while node‐loaded tests demonstrated natural variability in failure location. While transverse deformation of the stalk cross section cannot be eliminated in three‐point bending tests, its effects can be mitigated by placing the loading anvil at nodal locations, which are much stiffer than internode regions. Maximizing the span length of bending tests likewise reduces transverse deformation of stalk cross sections. These results are relevant to selective breeding studies designed to produce lodging resistant crop hybrids.

Key messageThe integration of known and latent environmental covariates within a single-stage genomic selection approach provides breeders with an informative and practical framework to utilise genotype by environment interaction for prediction into current and future environments.This paper develops a single-stage genomic selection approach which integrates known and latent environmental covariates within a special factor analytic framework. The factor analytic linear mixed model of Smith et al. (2001) is an effective method for analysing multi-environment trial (MET) datasets, but has limited practicality since the underlying factors are latent so the modelled genotype by environment interaction (GEI) is observable, rather than predictable. The advantage of using random regressions on known environmental covariates, such as soil moisture and daily temperature, is that the modelled GEI becomes predictable. The integrated factor analytic linear mixed model (IFA-LMM) developed in this paper includes a model for predictable and observable GEI in terms of a joint set of known and latent environmental covariates. The IFA-LMM is demonstrated on a late-stage cotton breeding MET dataset from Bayer CropScience. The results show that the known covariates predominately capture crossover GEI and explain 34.4% of the overall genetic variance. The most notable covariates are maximum downward solar radiation (10.1%), average cloud cover (4.5%) and maximum temperature (4.0%). The latent covariates predominately capture non-crossover GEI and explain 40.5% of the overall genetic variance. The results also show that the average prediction accuracy of the IFA-LMM is 0.02-0.10 higher than conventional random regression models for current environments and 0.06-0.24 higher for future environments. The IFA-LMM is therefore an effective method for analysing MET datasets which also utilises crossover and non-crossover GEI for genomic prediction into current and future environments. This is becoming increasingly important with the emergence of rapidly changing environments and climate change.

Varying fiber length distributions of cotton, Gossypium hirsutum L., impacts its spinning performance. Advanced Fiber Information System (AFIS) facilitates the analysis of the length distribution of individual fibers in cotton. Quantile regression is a variant of standard regression with which conditional quantile values can be calculated by minimizing weighted sums of absolute deviations across the entire distribution. Quantile regression was used to analyze AFIS fiber length distribution among five genotypes of upland cotton grown at the Texas Agricultural Experiment Station Research Farm near College Station, TX during 2001 and 2002. The shape of the distribution of 'CAMD-E', a short-staple variety, was actually similar to 'Acala 1517-99', a long staple variety with good spinning quality, even though CAMD-E had consistently lower fiber lengths. 'FM 832', and 'TAM 94L-25' had similar mean fiber lengths to Acala 1517-99, but their distribution shape was less skewed. 'TTU 202' had high cross entropy values, but little difference was detected in distribution shape by quantile regression. Year had a significant impact on distribution of fiber lengths, affecting distribution scale and location, which may be due to lower fiber fineness and maturity in 2001. Quantile regression was found to be an effective method for analyzing AFIS fiber length distributions, although further testing with a larger set of genotypes and environments with spinning data is needed.

Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48 g/year (SD), 0.68 g/year (MD), and 0.55 g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding.Genetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48 g/year (SD), 0.68 g/year (MD), and 0.55 g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding.

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi‐environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics‐IITA/Estimating‐Realized‐Genetic‐Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains. Core Ideas Annual genetic gain assessment drives breeding progress and efficiency. Realized genetic gain is preferably estimated using historical data with at least two long‐term checks. Realized genetic gain is estimated using the genotypes best linear unbiased estimators from the linear mixed model, regressed on their years of origin. State‐of‐the‐art breeding techniques, especially genomic selection, improve genetic gains. Plain Language Summary Global hunger is a growing problem. Plant breeding can help produce better crops and higher yields. Monitoring breeding programs' successes by estimating genetic trends is therefore essential. Different methods of genetic trend estimation exist. To figure out how much genotypes have improved over time, we recommend a statistical model that looks at the genetic information of the plants. The model analyzes how the genotypes' performances relate to the years they were developed. This helps estimate the actual improvement in the genotypes over different years. Regular measurement of genetic trends allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. However, genetic progress is improved by adopting genomics and other methods to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing.

Abstract Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi‐environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin. An illustrative example of estimating realized genetic gain was provided by analyzing historical data on fresh cassava (Manihot esculenta Crantz) yield in West Africa (https://github.com/Biometrics‐IITA/Estimating‐Realized‐Genetic‐Gain). This approach can serve as a model applicable to other crops and regions. Modernization of breeding programs is necessary to maximize the rate of genetic gain. This can be achieved by adopting genomics to enable faster breeding, accurate selection, and improved traits through genomic selection and gene editing. Tracking operational costs, establishing robust, digitalized data management and analytics systems, and developing effective varietal selection processes based on customer insights are also crucial for success. Capacity building and collaboration of breeding programs and institutions also play a significant role in accelerating genetic gains.