FieldSimR: an R package for simulating plot data in multi-environment field trials

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR's capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR's value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids' genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.</p

FieldSimR: an R package for simulating plot data in multi-environment field trials

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments

Reciprocal recurrent selection based on genetic complementation: An efficient way to build heterosis in diploids due to directional dominance

Depending on the trait architecture and reproduction system, selection strategies in plant breeding focus on the accumulation of additive, dominance effects, or both. Innovation in the exploitation of dominance‐effect‐based heterosis has been limited since the proposal of general combining ability (GCA)‐based approaches. We propose the use of a new surrogate of genetic complementation between genetic pools to increase accumulation of dominance effects and heterosis. We simulated breeding programs to show how reciprocal recurrent selection (RRS) by genetic complementation would build the dominance‐based heterosis cheaper than GCA‐based approaches and used real phenotypic data from hybrid maize (Zea mays) to demonstrate the underlying concepts. We found RRS by genetic complementation to be an attractive and viable strategy to exploit dominance, build de novo heterotic pools, and enhance the current GCA‐based approaches. If demonstrated in practice, we hypothesized that this approach would lower the cost of hybrid breeding drastically and contribute to food security

Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation

Breeding can change the dominance as well as additive genetic value of populations, thus utilizing heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability. However, the relative performances of RRS and other breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths, but these are sometimes outweighed by its ability to harness heterosis due to dominance. Here, we used stochastic simulation to compare genetic gain per unit cost of RRS, terminal crossing, recurrent selection on breeding value, and recurrent selection on cross performance considering different amounts of population heterosis due to dominance, relative cycle lengths, time horizons, estimation methods, selection intensities, and ploidy levels. In diploids with phenotypic selection at high intensity, whether RRS was the optimal breeding strategy depended on the initial population heterosis. However, in diploids with rapid-cycling genomic selection at high intensity, RRS was the optimal breeding strategy after 50 years over almost all amounts of initial population heterosis under the study assumptions. Diploid RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity and time horizon decreased. The optimal strategy depended on selection intensity, a proxy for inbreeding rate. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically did not outperform one-pool strategies regardless of the initial population heterosis

Towards understanding the traits contributing to performance of pearl millet open-pollinated varieties in phosphorus-limited environments of West Africa

Aims Pearl millet [Pennisetum glaucum (L.) R. Br.] open-pollinated varieties, which are the predominant cultivars, have never been systematically evaluated for adaptation to low-soil phosphorus (P), a major constraint on pearl millet production in West Africa (WA). Methods We evaluated grain yield (GY), flowering time (FLO), harvest index (HI), and residual grain yields (RGY) of 102 open-pollinated varieties from WA under low-P (−P) and high-P (+P) field conditions in six environments of WA. In addition, PE-related traits of the varieties were evaluated at early growth stage in a pot experiment. Results Significant genetic variation was observed for GY, FLO, HI and PE-related traits. P-efficient varieties had higher yield under −P conditions. Varietal performance under −P varied across environments depending on FLO, relative flowering delay under −P (FD) and RGY measured in the field. Low-P-susceptible varieties had higher FLO, lower HI than low-P-tolerant varieties. Response to direct selection under −P field conditions was 20.1 g m−2, whereas indirect selection response under +P was 16.3 g m−2. Conclusions Selection under −P field conditions while taking into account seasonal variations for FLO, FD and PE is expected to be important for improving GY specifically targeting −P environments in WA

DataSheet_2_FieldSimR: an R package for simulating plot data in multi-environment field trials.zip

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.</p

DataSheet_1_FieldSimR: an R package for simulating plot data in multi-environment field trials.docx

This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR’s capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR’s value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids’ genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.</p

Assembly of whole-chromosome pseudomolecules for polyploid plant genomes using outbred mapping populations

Despite advances in sequencing technologies, assembly of complex plant genomes remains elusive due to polyploidy and high repeat content. Here we report PolyGembler for grouping and ordering contigs into pseudomolecules by genetic linkage analysis. Our approach also provides an accurate method with which to detect and fix assembly errors. Using simulated data, we demonstrate that our approach is of high accuracy and outperforms three existing state-of-the-art genetic mapping tools. Particularly, our approach is more robust to the presence of missing genotype data and genotyping errors. We used our method to construct pseudomolecules for allotetraploid lawn grass utilizing PacBio long reads in combination with restriction site-associated DNA sequencing, and for diploid Ipomoea trifida and autotetraploid potato utilizing contigs assembled from Illumina reads in combination with genotype data generated by single-nucleotide polymorphism arrays and genotyping by sequencing, respectively. We resolved 13 assembly errors for a published I. trifida genome assembly and anchored eight unplaced scaffolds in the published potato genome

Q&A: Methods for estimating genetic gain in sub‐Saharan Africa and achieving improved gains

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

Development of diagnostic SNP markers for quality assurance and control in sweetpotato [Ipomoea batatas (L.) Lam.] breeding programs.

Quality assurance and control (QA/QC) is an essential element of a breeding program's optimization efforts towards increased genetic gains. Due to auto-hexaploid genome complexity, a low-cost marker platform for routine QA/QC in sweetpotato breeding programs is still unavailable. We used 662 parents of the International Potato Center (CIP)'s global breeding program spanning Peru, Uganda, Mozambique and Ghana, to develop a low-density highly informative single nucleotide polymorphism (SNP) marker set to be deployed for routine QA/QC. Segregation of the selected 30 SNPs (two SNPs per base chromosome) in a recombined breeding population was evaluated using 282 progeny from some of the parents above. The progeny were replicated from in-vitro, screenhouse and field, and the selected SNP-set was confirmed to identify relatively similar mislabeling error rates as a high density SNP-set of 10,159 markers. Six additional trait-specific markers were added to the selected SNP set from previous quantitative trait loci mapping studies. The 36-SNP set will be deployed for QA/QC in breeding pipelines and in fingerprinting of advanced clones or released varieties to monitor genetic gains in famers' fields. The study also enabled evaluation of CIP's global breeding population structure and the effect of some of the most devastating stresses like sweetpotato virus disease on genetic variation management. These results will inform future deployment of genomic selection in sweetpotato