Genomic Prediction Within and Among Doubled-Haploid Libraries from Maize Landraces

Genomic selection in plant breeding: Key factors shaping two decades of progress

Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.

Multi-Trait Genomic Prediction Improves Accuracy of Selection among Doubled Haploid Lines in Maize

Recent advances in maize doubled haploid (DH) technology have enabled the development of large numbers of DH lines quickly and efficiently. However, testing all possible hybrid crosses among DH lines is a challenge. Phenotyping haploid progenitors created during the DH process could accelerate the selection of DH lines. Based on phenotypic and genotypic data of a DH population and its corresponding haploids, we compared phenotypes and estimated genetic correlations between the two populations, compared genomic prediction accuracy of multi-trait models against conventional univariate models within the DH population, and evaluated whether incorporating phenotypic data from haploid lines into a multi-trait model could better predict performance of DH lines. We found significant phenotypic differences between DH and haploid lines for nearly all traits; however, their genetic correlations between populations were moderate to strong. Furthermore, a multi-trait model taking into account genetic correlations between traits in the single-environment trial or genetic covariances in multi-environment trials can significantly increase genomic prediction accuracy. However, integrating information of haploid lines did not further improve our prediction. Our findings highlight the superiority of multi-trait models in predicting performance of DH lines in maize breeding, but do not support the routine phenotyping and selection on haploid progenitors of DH lines.

High-resolution association mapping with libraries of immortalized lines from ancestral landraces

Association mapping with immortalized lines of landraces offers several advantages including a high mapping resolution, as demonstrated here in maize by identifying the causal variants underlying QTL for oil content and the metabolite allantoin. Landraces are traditional varieties of crops that present a valuable yet largely untapped reservoir of genetic variation to meet future challenges of agriculture. Here, we performed association mapping in a panel comprising 358 immortalized maize lines from six European Flint landraces. Linkage disequilibrium decayed much faster in the landraces than in the elite lines included for comparison, permitting a high mapping resolution. We demonstrate this by fine-mapping a quantitative trait locus (QTL) for oil content down to the phenylalanine insertion F469 in DGAT1-2 as the causal variant. For the metabolite allantoin, related to abiotic stress response, we identified promoter polymorphisms and differential expression of an allantoinase as putative cause of variation. Our results demonstrate the power of this approach to dissect QTL potentially down to the causal variants, toward the utilization of natural or engineered alleles in breeding. Moreover, we provide guidelines for studies using ancestral landraces for crop genetic research and breeding.

Applications of Doubled Haploids in Plant Breeding and Applied Research

Manifold and diverse applications of doubled haploid (DH) plants have emerged in academy and in the plant breeding industry since the first discovery of a haploid mutant in the Jimson Weed (Datura stramonium), followed by the first reports about anther culture in the same species, maternal haploids by wide crosses in tobacco (Nicotiana tabacum L.) and barley (Hordeum vulgare L.), interspecific hybridization, ovary culture (gynogenesis), isolated microspore culture, and more recently the CENH3 approach in thale cress (Arabidopsis thaliana L.) and other species. Research and development efforts were and are still significant in both user groups. Luckily, often academic and industrial partners cooperate in challenging and sometimes voluminous projects worldwide. Not only to develop innovative DH protocols and technologies per se, but also to exploit the advantages of DH plants in a huge variety of research and development experiments. This review concentrates not on the DH technologies per se, but on the application of DHs in plant-related research and development projects.

Genomic prediction and genomic heritability of grain yield and its related traits in a safflower genebank collection

Safflower, a minor oilseed crop, is gaining increased attention for food and industrial uses. Safflower genebank collections are an important genetic resource for crop enhancement and future breeding programs. In this study, we investigated the population structure of a safflower collection sourced from the Australian Grain Genebank and assessed the potential of genomic prediction (GP) to evaluate grain yield and related traits using single and multi-site models. Prediction accuracies (PA) of genomic best linear unbiased prediction (GBLUP) from single site models ranged from 0.21 to 0.86 for all traits examined and were consistent with estimated genomic heritability (h² ), which varied from low to moderate across traits. We generally observed a low level of genome × environment interactions (g × E). Multi-site g × E GBLUP models only improved PA for accessions with at least some phenotypes in the training set. We observed that relaxing quality filtering parameters for genotype-by-sequencing (GBS), such as missing genotype call rate, did not affect PA but upwardly biased h² estimation. Our results indicate that GP is feasible in safflower evaluation and is potentially a cost-effective tool to facilitate fast introgression of desired safflower trait variation from genebank germplasm into breeding lines.

Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools

KEY MESSAGE

High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r =  - 0.27 to 0.20; [Formula: see text]=  - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.

Genomics-Assisted Breeding for Quantitative Disease Resistances in Small-Grain Cereals and Maize

Generating genomics-driven knowledge opens a way to accelerate the resistance breeding process by family or population mapping and genomic selection. Important prerequisites are large populations that are genomically analyzed by medium- to high-density marker arrays and extensive phenotyping across locations and years of the same populations. The latter is important to train a genomic model that is used to predict genomic estimated breeding values of phenotypically untested genotypes. After reviewing the specific features of quantitative resistances and the basic genomic techniques, the possibilities for genomics-assisted breeding are evaluated for six pathosystems with hemi-biotrophic fungi: Small-grain cereals/Fusarium head blight (FHB), wheat/Septoria tritici blotch (STB) and Septoria nodorum blotch (SNB), maize/Gibberella ear rot (GER) and Fusarium ear rot (FER), maize/Northern corn leaf blight (NCLB). Typically, all quantitative disease resistances are caused by hundreds of QTL scattered across the whole genome, but often available in hotspots as exemplified for NCLB resistance in maize. Because all crops are suffering from many diseases, multi-disease resistance (MDR) is an attractive aim that can be selected by specific MDR QTL. Finally, the integration of genomic data in the breeding process for introgression of genetic resources and for the improvement within elite materials is discussed.

Discovery of beneficial haplotypes for complex traits in maize landraces

Genetic variation is of crucial importance for crop improvement. Landraces are valuable sources of diversity, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we map haplotype-trait associations at high resolution in ~1000 doubled-haploid lines derived from three maize landraces to make their native diversity for early development traits accessible for elite germplasm improvement. A comparative genomic analysis of the discovered haplotypes in the landrace-derived lines and a panel of 65 breeding lines, both genotyped with 600k SNPs, points to untapped beneficial variation for target traits in the landraces. The superior phenotypic performance of lines carrying favorable landrace haplotypes as compared to breeding lines with alternative haplotypes confirms these findings. Stability of haplotype effects across populations and environments as well as their limited effects on undesired traits indicate that our strategy has high potential for harnessing beneficial haplotype variation for quantitative traits from genetic resources.

Genetic variations present in landraces are critical for crop genetic improvement. Here, the authors map haplotype-trait associations in ~1000 doubled haploid lines derived from three European maize landraces and identify beneficial haplotypes for quantitative traits that are not present in breeding lines.

Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

Selective Loss of Diversity in Doubled-Haploid Lines from European Maize Landraces

Maize landraces are well adapted to their local environments and present valuable sources of genetic diversity for breeding and conservation. But the maintenance of open-pollinated landraces in ex-situ programs is challenging, as regeneration of seed can often lead to inbreeding depression and the loss of diversity due to genetic drift. Recent reports suggest that the production of doubled-haploid (DH) lines from landraces may serve as a convenient means to preserve genetic diversity in a homozygous form that is immediately useful for modern breeding. The production of doubled-haploid (DH) lines presents an extreme case of inbreeding which results in instantaneous homozygosity genome-wide. Here, we analyzed the effect of DH production on genetic diversity, using genome-wide SNP data from hundreds of individuals of five European landraces and their related DH lines. In contrast to previous findings, we observe a dramatic loss of diversity at both the haplotype level and that of individual SNPs. We identify thousands of SNPs that exhibit allele frequency differences larger than expected under models of neutral genetic drift and document losses of shared haplotypes. We find evidence consistent with selection at functional sites that are potentially involved in the diversity differences between landrace and DH populations. Although we were unable to uncover more details about the mode of selection, we conclude that landrace DH lines may be a valuable tool for the introduction of variation into maize breeding programs but come at the cost of decreased genetic diversity.

Seed Weight as a Covariate in Association and Prediction Studies for Biomass Traits in Maize Seedlings

BACKGROUND

The seedling stage has received little attention in maize breeding to identify genotypes tolerant to water deficit. The aim of this study is to evaluate incorporation of seed weight (expressed as hundred kernel weight, HKW) as a covariate into genomic association and prediction studies for three biomass traits in a panel of elite inbred lines challenged by water withholding at seedling stage.

METHODS

109 genotyped-by-sequencing (GBS) elite maize inbreds were phenotyped for HKW and planted in controlled conditions (16/8 day/night, 25 °C, 50% RH, 200 µMol/m2/s) in trays filled with soil. Plants in control (C) were watered every two days, while watering was stopped for 10 days in water withholding (WW). Fresh weight (FW), dry weight (DW), and dry matter content (DMC) were measured.

RESULTS

Adding HKW as a covariate increased the power of detection of associations in FW and DW by 44% and increased genomic prediction accuracy in C and decreased in WW.

CONCLUSIONS

Seed weight was effectively incorporated into association studies for biomass traits in maize seedlings, whereas the incorporation into genomic predictions, particularly in water-stressed plants, was not worthwhile.

Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs

Collaborative diversity panels and genomic prediction seem relevant to identify and harness genetic resources for polygenic trait-specific enrichment of elite germplasms. In plant breeding, genetic diversity is important to maintain the pace of genetic gain and the ability to respond to new challenges in a context of climatic and social expectation changes. Many genetic resources are accessible to breeders but cannot all be considered for broadening the genetic diversity of elite germplasm. This study presents the use of genomic predictions trained on a collaborative diversity panel, which assembles genetic resources and elite lines, to identify resources to enrich an elite germplasm. A maize collaborative panel (386 lines) was considered to estimate genome-wide marker effects. Relevant predictive abilities (0.40-0.55) were observed on a large population of private elite materials, which supported the interest of such a collaborative panel for diversity management perspectives. Grain-yield estimated marker effects were used to select a donor that best complements an elite recipient at individual loci or haplotype segments, or that is expected to give the best-performing progeny with the elite. Among existing and new criteria that were compared, some gave more weight to the donor-elite complementarity than to the donor value, and appeared more adapted to long-term objective. We extended this approach to the selection of a set of donors complementing an elite population. We defined a crossing plan between identified donors and elite recipients. Our results illustrated how collaborative projects based on diversity panels including both public resources and elite germplasm can contribute to a better characterization of genetic resources in view of their use to enrich elite germplasm.

Genomic prediction with multiple biparental families

For genomic prediction within biparental families using multiple biparental families, combined training sets comprising full-sibs from the same family and half-sib families are recommended to reach high and robust prediction accuracy, whereas inclusion of unrelated families is risky and can have negative effects. In recycling breeding, where elite inbreds are recombined to generate new source material, genomic and phenotypic information from lines of numerous biparental families (BPFs) is commonly available for genomic prediction (GP). For each BPF with a large number of candidates in the prediction set (PS), the training set (TS) can be composed of lines from the same full-sib family or multiple related and unrelated families to increase the TS size. GP was applied to BPFs generated in silico and from two published experiments to evaluate the prediction accuracy ([Formula: see text]) of different TS compositions. We compared [Formula: see text] for individual pairs of BPFs using as TS either full-sib, half-sib, or unrelated BPFs. While full-sibs yielded highly positive [Formula: see text] and half-sibs also mostly positive [Formula: see text] values, unrelated families had often negative [Formula: see text], and including these families in a combined TS reduced [Formula: see text]. By simulations, we demonstrated that optimized TS compositions exist, yielding 5-10% higher [Formula: see text] than the TS including all available BPFs. However, identification of poorly predictive families and finding the optimal TS composition with various quantitative-genetic parameters estimated from available data was not successful. Therefore, we suggest omitting unrelated families and combining in the TS full-sib and few half-sib families produced by specific mating designs, with a medium number (~ 50) of genotypes per family. This helps in balancing high [Formula: see text] in GP with a sufficient effective population size of the entire breeding program for securing high short- and long-term selection progress.

Doubled haploid technology for line development in maize: technical advances and prospects

KEY MESSAGE

Increased efficiencies achieved in different steps of DH line production offer greater benefits to maize breeding programs. Doubled haploid (DH) technology has become an integral part of many commercial maize breeding programs as DH lines offer several economic, logistic and genetic benefits over conventional inbred lines. Further, new advances in DH technology continue to improve the efficiency of DH line development and fuel its increased adoption in breeding programs worldwide. The established method for maize DH production covered in this review involves in vivo induction of maternal haploids by a male haploid inducer genotype, identification of haploids from diploids at the seed or seedling stage, chromosome doubling of haploid (D0) seedlings and finally, selfing of fertile D0 plants. Development of haploid inducers with high haploid induction rates and adaptation to different target environments have facilitated increased adoption of DH technology in the tropics. New marker systems for haploid identification, such as the red root marker and high oil marker, are being increasingly integrated into new haploid inducers and have the potential to make DH technology accessible in germplasm such as some Flint, landrace, or tropical material, where the standard R1-nj marker is inhibited. Automation holds great promise to further reduce the cost and time in haploid identification. Increasing success rates in chromosome doubling protocols and/or reducing environmental and human toxicity of chromosome doubling protocols, including research on genetic improvement in spontaneous chromosome doubling, have the potential to greatly reduce the production costs per DH line.

Testcross performance of doubled haploid lines from European flint maize landraces is promising for broadening the genetic base of elite germplasm

Selected doubled haploid lines averaged similar testcross performance as their original landraces, and the best of them approached the yields of elite inbreds, demonstrating their potential to broaden the narrow genetic diversity of the flint germplasm pool. Maize landraces represent a rich source of genetic diversity that remains largely idle because the high genetic load and performance gap to elite germplasm hamper their use in modern breeding programs. Production of doubled haploid (DH) lines can mitigate problems associated with the use of landraces in pre-breeding. Our objective was to assess in comparison with modern materials the testcross performance (TP) of the best 89 out of 389 DH lines developed from six landraces and evaluated in previous studies for line per se performance (LP). TP with a dent tester was evaluated for the six original landraces, ~ 15 DH lines from each landrace selected for LP, and six elite flint inbreds together with nine commercial hybrids for grain and silage traits. Mean TP of the DH lines rarely differed significantly from TP of their corresponding landrace, which averaged in comparison with the mean TP of the elite flint inbreds ~ 20% lower grain yield and ~ 10% lower dry matter and methane yield. Trait correlations of DH lines closely agreed with the literature; correlation of TP with LP was zero for grain yield, underpinning the need to evaluate TP in addition to LP. For all traits, we observed substantial variation for TP among the DH lines and the best showed similar TP yields as the elite inbreds. Our results demonstrate the high potential of landraces for broadening the narrow genetic base of the flint heterotic pool and the usefulness of the DH technology for exploiting idle genetic resources from gene banks.