European Maize Landraces Research Articles

Consistent prokaryotic community patterns along the radial root axis of two Zea mays L. landraces across two distinct field locations.

The close interconnection of plants with rhizosphere- and root-associated microorganisms is well recognized, and high expectations are raised for considering their symbioses in the breeding of future crop varieties. However, it is unclear how consistently plant-mediated selection, a potential target in crop breeding, influences microbiome members compared to selection imposed by the agricultural environment. Landraces may have traits shaping their microbiome, which were lost during the breeding of modern varieties, but knowledge about this is scarce. We investigated prokaryotic community composition along the radial root axis of two European maize (Zea mays L.) landraces. A sampling gradient included bulk soil, a distal and proximal rhizosphere fraction, and the root compartment. Our study was replicated at two field locations with differing edaphic and climatic conditions. Further, we tested for differences between two plant developmental stages and two precipitation treatments. Community data were generated by metabarcoding of the V4 SSU rRNA region. While communities were generally distinct between field sites, the effects of landrace variety, developmental stage, and precipitation treatment were comparatively weak and not statistically significant. Under all conditions, patterns in community composition corresponded strongly to the distance to the root. Changes in α- and β-diversity, as well as abundance shifts of many taxa along this gradient, were similar for both landraces and field locations. Most affected taxa belonged to a core microbiome present in all investigated samples. Remarkably, we observed consistent enrichment of Actinobacteriota (particularly Streptomyces, Lechevalieria) and Pseudomonadota (particularly Sphingobium) toward the root. Further, we report a depletion of ammonia-oxidizers along this axis at both field sites. We identified clear enrichment and depletion patterns in microbiome composition along the radial root axis of Z. mays. Many of these were consistent across two distinct field locations, plant developmental stages, precipitation treatments, and for both landraces. This suggests a considerable influence of plant-mediated effects on the microbiome. We propose that the affected taxa have key roles in the rhizosphere and root microbiome of Z. mays. Understanding the functions of these taxa appears highly relevant for the development of methods aiming to promote microbiome services for crops.

Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity.

The ECPGR European Evaluation Network (EVA) for Maize involves genebanks, research institutions, and private breeding companies from nine countries focusing on the valorization of maize genetic resources across Europe. This study describes a diverse collection of 626 local landraces and traditional varieties of maize (Zea mays L.) from nine European genebanks, including criteria for selection of the collection and its genetic and phenotypic diversity. High-throughput pool genotyping grouped the landraces into nine genetic groups with a threshold of 0.6 admixture, while 277 accessions were designated admixed and likely to have resulted from previous breeding activities. The grouping correlated well with the geographic origins of the collection, also reflecting the various pathways of introduction of maize to Europe. Phenotypic evaluations of 588 accessions for flowering time and plant architecture in multilocation trials over three years confirmed the great diversity within the collection, although phenotypic clusters only partially correlated with the genetic grouping. The EVA approach promotes conservation of genetic resources and opens an opportunity to increase genetic variability for developing improved varieties and populations for farmers, with better adaptation to specific environments and greater tolerance to various stresses. As such, the EVA maize collection provides valuable sources of diversity for facing climate change due to the varieties' local adaptation.

Genomic prediction within and across maize landrace derived populations using haplotypes.

Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.

Doubled Haploid Lines Derived from a European Maize Flint Landrace Contrast in Recovery from Cold Stress

Suboptimal temperatures at sowing and emergence affect the early development of maize, with potentially irreversible effects later in the growing season. We studied recovery from cold stress of an inbred line (B73) and 13 Doubled Haploid lines derived from a European flint maize landrace. After a cold treatment (20–12 °C, day–night) from sowing to seedling establishment, seedlings were transplanted and grown in the greenhouse until the V8 stage (eight leaves fully developed), when we measured agronomically relevant plant traits and spectral indices of mature leaves. Survival rates of transplanted seedlings after cold treatment ranged from 10% to 100%. After a strong delay in early development due to cold, the surviving plants were able to compensate for this delay at later stages of recovery. They reached the V8 stage after only five more growing degree days than plants grown under the control treatment (25–18 °C, day–night). Plants from the most cold-tolerant genotypes (PE0401 and PE0100) accumulated more root and shoot biomass at the end of the recovery phase compared with the same genotypes exposed to the control treatment. The genotypes with the most plastic leaf morphological traits (PE0161 and PE0072) had little reduction in leaf biomass at the end of the recovery phase in comparison with less responsive genotypes such as PE0171. We conclude that genotypes that survived cold treatment with minimal cold damage of seedling leaves can be candidates for further cold recovery studies and breeding. Nevertheless, such studies must take trait acclimation for other suboptimal conditions into consideration.

Genotyping of DNA pools identifies untapped landraces and genomic regions to develop next-generation varieties.

Landraces, that is, traditional varieties, have a large diversity that is underexploited in modern breeding. A novel DNA pooling strategy was implemented to identify promising landraces and genomic regions to enlarge the genetic diversity of modern varieties. As proof of concept, DNA pools from 156 American and European maize landraces representing 2340 individuals were genotyped with an SNP array to assess their genome-wide diversity. They were compared to elite cultivars produced across the 20th century, represented by 327 inbred lines. Detection of selective footprints between landraces of different geographic origin identified genes involved in environmental adaptation (flowering times, growth) and tolerance to abiotic and biotic stress (drought, cold, salinity). Promising landraces were identified by developing two novel indicators that estimate their contribution to the genome of inbred lines: (i) a modified Roger's distance standardized by gene diversity and (ii) the assignation of lines to landraces using supervised analysis. It showed that most landraces do not have closely related lines and that only 10 landraces, including famous landraces as Reid's Yellow Dent, Lancaster Surecrop and Lacaune, cumulated half of the total contribution to inbred lines. Comparison of ancestral lines directly derived from landraces with lines from more advanced breeding cycles showed a decrease in the number of landraces with a large contribution. New inbred lines derived from landraces with limited contributions enriched more the haplotype diversity of reference inbred lines than those with a high contribution. Our approach opens an avenue for the identification of promising landraces for pre-breeding.

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models' prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.

Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks.

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing genotyping technologies when resources are limited. In this work, we are proposing a new imputation pipeline ("HBimpute") that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and subsequently use the reads of all locally similar lines in the variant calling for a specific line. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced at 0.5X read-depth. The overall imputing error rates are cut in half compared to state-of-the-art software like BEAGLE and STITCH, while the average read-depth is increased to 83X, thus enabling the calling of copy number variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance of sequence data in common breeding applications to that of genomic data generated with a genotyping array. For both genome-wide association studies and genomic prediction, results are on par or even slightly better than results obtained with high-density array data (600k). In particular for genomic prediction, we observe slightly higher data quality for the sequence data compared to the 600k array in the form of higher prediction accuracies. This occurred specifically when reducing the data panel to the set of overlapping markers between sequence and array, indicating that sequencing data can benefit from the same marker ascertainment as used in the array process to increase the quality and usability of genomic data.

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

Key messageHigh 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 ({r}_{g}) correlations between GER severity and three agronomic traits were low (r = − 0.27 to 0.20; {r}_{g}hspace{0.17em}= − 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 (rho ) of weighted GS (wRR-BLUP) were higher than rho 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 rho that could be improved by using fixed marker effects in the GS model.

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.

Improving Imputation Quality in BEAGLE for Crop and Livestock Data.

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

European maize landraces made accessible for plant breeding and genome-based studies

Key messageDoubled-haploid libraries from landraces capture native genetic diversity for a multitude of quantitative traits and make it accessible for breeding and genome-based studies.Maize landraces comprise large allelic diversity. We created doubled-haploid (DH) libraries from three European flint maize landraces and characterized them with respect to their molecular diversity, population structure, trait means, variances, and trait correlations. In total, 899 DH lines were evaluated using high-quality genotypic and multi-environment phenotypic data from up to 11 environments. The DH lines covered 95% of the molecular variation present in 35 landraces of an earlier study and represent the original three landrace populations in an unbiased manner. A comprehensive analysis of the target trait plant development at early growth stages as well as other important agronomic traits revealed large genetic variation for line per se and testcross performance. The majority of the 378 DH lines evaluated as testcrosses outperformed the commercial hybrids for early development. For total biomass yield, we observed a yield gap of 15% between mean testcross yield of the commercial hybrids and mean testcross yield of the DH lines. The DH lines also exhibited genetic variation for undesirable traits like root lodging and tillering, but correlations with target traits early development and yield were low or nonsignificant. The presented diversity atlas is a valuable, publicly available resource for genome-based studies to identify novel trait variation and evaluate the prospects of genomic prediction in landrace-derived material.

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.

Breeding Potential of European Flint Maize Landraces Evaluated by their Testcross Performance

ABSTRACTA plethora of maize (Zea mays L.) landraces is stored in gene banks worldwide. However, information about their value in breeding is scarce and strategies for identifying the most promising landraces in prebreeding are largely lacking. This study was conducted to (i) evaluate the testcross performance of 70 European flint landraces in combination with two elite dent testers and compare these results with the performance of modern hybrids for important agronomic traits, (ii) estimate the genetic variances among landraces and trait correlations for these two testcross series as well as the correlation between them, and (iii) devise a testing scheme for assessing the breeding potential of a large number of landraces for hybrid breeding. Grain yield of the landrace testcrosses was on average about 26% lower than modern hybrids. Genotypic variances among landrace testcrosses were significant for all traits, and genetic correlations were moderate to high for most trait combinations in both testcross series. Thus, it seems promising to tap this huge genetic reserve for enlarging the genetic base of the elite flint germplasm pool in Central Europe. Since the genetic correlation between the two testcross series exceeded 0.74 for all traits, we recommend assessing the breeding potential of landraces for broadening existing heterotic groups by evaluating their testcross performance in combination with one or two elite single‐cross tester(s) from the opposite heterotic pool. Subsequently, doubled haploid lines from a few of the most promising landraces could be produced to exploit the large genetic variation within landraces to the full extent.

Variability in European Maize (Zea mays L.) Landraces under High and Low Nitrogen Inputs

An European maize (Zea mays L.) landrace core collection (EMLCC) was formed with samples from several countries. Evaluation of the EMLCC may contribute to broad the genetic base of maize breeding programs. The objective of this study was to assess the variability of EMLCC under low nitrogen (N) in relation to high N input. Eighty-five landraces of the EMLCC, grouped in four maturity groups, and three check hybrids were evaluated for response to low (0 kg ha−1) and high (150 kg ha−1) N in Spain and Greece. Five plant size traits (plant height, ear height, leaf length, leaf width and leaf area index), two grain traits (1000-kernel weight and grain yield), and two agronomic traits [growing degree units (GDU) and lodging] were studied. Overall means of plant size and grain traits increased when genotypes were grown at 150-N relative to 0-N input. The relative increase for grain traits was smaller in landraces than in hybrids. This suggests that landraces had lower grain yield response to N supply compared to hybrids. Linear regressions of plant size traits on GDU indicated that vegetative development was primarily associated with flowering lateness. The maturity group was the main source of variation for all traits. Landrace variability within maturity groups was significant for all traits across environments, despite significant landrace × environment interactions. Estimates of genetic and genotype × environment variances, and heritabilities at both high and low N inputs were not significantly different from each other. However estimates were generally larger at high N. Genetic and phenotypic correlation coefficients between the two N levels were very high for all traits.