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We present a method to visually score 10 root architectural traits of the root crown of an adult maize plant in the field in a few minutes. Phenotypic profiling of three recombinant inbred line (RIL) populations of maize (Zea mays L.; B73xMo17, Oh43xW64a, Ny821xH99) was conducted in 2008 in a silt loam soil in Pennsylvania and in a sandy soil in Wisconsin, and again in 2009 in Pennsylvania. Numbers, angles and branching pattern of crown and brace roots were assessed visually at flowering. Depending on the soil type in which plants were grown, sample processing took from three (sand) to 8 min (silt-loam). Visual measurement of the root crown required 2 min per sample irrespective of the environment. Visual scoring of root crowns gave a reliable estimation of values for root architectural traits as indicated by high correlations between measured and visually scored trait values for numbers (r ² = 0.46-0.97), angles (r ² = 0.66-0.76), and branching (r ² = 0.54-0.88) of brace and crown roots. Based on the visual evaluation of root crown traits it was possible to discriminate between populations. RILs derived from the cross NY821 x H99 generally had the greatest number of roots, the highest branching density and the most shallow root angles, while inbred lines from the cross between OH43 x W64a generally had the steepest root angles. The ranking of genotypes remained the same across environments, emphasizing the suitability of the method to evaluate genotypes across environments. Scoring of brace roots was better correlated with the actual measurements compared to crown roots. The visual evaluation of root architecture will be a valuable tool in tailoring crop root systems to specific environments.

Edward Buckler, Sarah Hearne and colleagues integrate two approaches to characterize the genetic diversity of a large number of geographically distributed maize landraces. They examine flowering time and adaptation to altitude and find that the majority of the associated SNPs overlap both traits. Landraces (traditional varieties) of domesticated species preserve useful genetic variation, yet they remain untapped due to the genetic linkage between the few useful alleles and hundreds of undesirable alleles 1 . We integrated two approaches to characterize the diversity of 4,471 maize landraces. First, we mapped genomic regions controlling latitudinal and altitudinal adaptation and identified 1,498 genes. Second, we used F-one association mapping (FOAM) to map the genes that control flowering time, across 22 environments, and identified 1,005 genes. In total, we found that 61.4% of the single-nucleotide polymorphisms (SNPs) associated with altitude were also associated with flowering time. More than half of the SNPs associated with altitude were within large structural variants (inversions, centromeres and pericentromeric regions). The combined mapping results indicate that although floral regulatory network genes contribute substantially to field variation, over 90% of the contributing genes probably have indirect effects. Our dual strategy can be used to harness the landrace diversity of plants and animals.

High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20\" N, 109°54\" W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.

To increase genetic gain for tolerance to drought, we aimed to identify environmentally stable QTL in and testcross combination under well-watered (WW) and drought stressed (DS) conditions and evaluate the possible deployment of QTL using marker assisted and/or genomic selection (QTL/GS-MAS). A total of 169 doubled haploid lines derived from the cross between CML495 and LPSC7F64 and 190 testcrosses (tester CML494) were evaluated in a total of 11 treatment-by-population combinations under WW and DS conditions. In response to DS, grain yield (GY) and plant height (PHT) were reduced while time to anthesis and the anthesis silking interval (ASI) increased for both lines and hybrids. Forty-eight QTL were detected for a total of nine traits. The allele derived from CML495 generally increased trait values for anthesis, ASI, PHT, the normalized difference vegetative index (NDVI) and the green leaf area duration (GLAD; a composite trait of NDVI, PHT and senescence) while it reduced trait values for leaf rolling and senescence. The LOD scores for all detected QTL ranged from 2.0 to 7.2 explaining 4.4 to 19.4% of the observed phenotypic variance with R ranging from 0 (GY, DS, lines) to 37.3% (PHT, WW, lines). Prediction accuracy of the model used for genomic selection was generally higher than phenotypic variance explained by the sum of QTL for individual traits indicative of the polygenic control of traits evaluated here. We therefore propose to use QTL-MAS in forward breeding to enrich the allelic frequency for a few desired traits with strong additive QTL in early selection cycles while GS-MAS could be used in more mature breeding programs to additionally capture alleles with smaller additive effects.

Maize genotypes may adapt to dry environments by avoiding desiccation by means of a deeper root system or by maintaining growth and water extraction at low water potentials. The aim of this study was to determine the quantitative genetic control of root growth and root morphology in a population of 236 recombinant inbred lines (RILs) from the cross between CML444 (high-yielding) × SC-Malawi (low-yielding), which segregates for the response to drought stress at flowering. The RILs and the parental lines were grown on blotting paper in growth pouches until the two-leaf stage under non-stressed conditions; the parents were additionally exposed to desiccation stress induced by polyethylene glycol with a molecular weight of 8000 Dalton (PEG-8000). The lengths of axile and lateral roots were measured non-destructively at 2, 5, 7 and 9 days after germination, by scanning with an A4 scanner followed by digital image analysis. CML444 had a lower rate constant of lateral root elongation (k Lat ) than SC-Malawi, but the two genotypes did not differ in their response to desiccation. QTLs affecting root vigor, as depicted by increments in k Lat , the elongation rate of axile roots (ER Ax ) and the number of axile roots (No Ax ) were identified in bins 2.04 and 2.05. QTLs for No Ax and ER Ax collocated with QTLs for yield parameters in bins 1.03–1.04 and 7.03–04. The correspondence of QTLs for axile root traits in bins 1.02–1.03 and 1.08 and QTLs for lateral roots traits in bins 2.04–2.07 in several mapping populations suggests the presence of genes controlling root growth in a wide range of genetic backgrounds.

We aimed to identify quantitative trait loci (QTL) for secondary traits related to grain yield (GY) in two BC1F2:3 backcross populations (LPSpop and DTPpop) under well-watered (4 environments; WW) and drought stressed (6; DS) conditions to facilitate breeding efforts towards drought tolerant maize. GY reached 5.6 and 5.8 t/ha under WW in the LPSpop and the DTPpop, respectively. Under DS, grain yield was reduced by 65% (LPSpop) to 59% (DTPpop) relative to WW. GY was strongly associated with the normalized vegetative index (NDVI; r ranging from 0.61 to 0.96) across environmental conditions and with an early flowering under drought stressed conditions (r ranging from -0.18 to -0.25) indicative of the importance of early vigor and drought escape for GY. Out of the 105 detected QTL, 53 were overdominant indicative of strong heterosis. For 14 out of 18 detected vigor QTL, as well as for eight flowering time QTL the trait increasing allele was derived from CML491. Collocations of early vigor QTL with QTL for stay green (bin 2.02, WW, LPSpop; 2.07, DS, DTPpop), the number of ears per plant (bins 2.02, 2.05, WW, LPSpop; 5.02, DS, LPSpop) and GY (bin 2.07, WW, DTPpop; 5.04, WW, LPSpop), reinforce the importance of the observed correlations. LOD scores for early vigor QTL in these bins ranged from 2.2 to 11.25 explaining 4.6 (additivity: +0.28) to 19.9% (additivity: +0.49) of the observed phenotypic variance. A strong flowering QTL was detected in bin 2.06 across populations and environmental conditions explaining 26-31.3% of the observed phenotypic variation (LOD: 13-17; additivity: 0.1-0.6d). Improving drought tolerance while at the same time maintaining yield potential could be achieved by combining alleles conferring early vigor from the recurrent parent with alleles advancing flowering from the donor. Additionally bin 8.06 (DTPpop) harbored a QTL for GY under WW (additivity: 0.27 t/ha) and DS (additivity: 0.58 t/ha). R2 ranged from 0 (DTPpop, WW) to 26.54% (LPSpop, DS) for NDVI, 18.6 (LPSpop, WW) to 42.45% (LPSpop, DS) for anthesis and from 0 (DTPpop, DS) to 24.83% (LPSpop, WW) for GY. Lines out-yielding the best check by 32.5% (DTPpop, WW) to 60% (DTPpop, DS) for all population-by-irrigation treatment combination (except LPSpop, WW) identified are immediately available for the use by breeders.

[This corrects the article DOI: 10.1371/journal.pone.0149636.].

Carbon allocation between vegetative and reproductive tissues impacts cereal grain production. Despite great agricultural importance, sink–source relationships have not been fully characterized at the early reproductive stages in maize. Here, we quantify the accumulation of non-structural carbohydrates and patterns of gene expression in the top internode of the stem and the female inflorescence of maize at the onset of grain filling (reproductive stage R1). Top internode stem and female inflorescence tissues of the Puma maize inbred line were collected at reproductive stage R1 (without pollination) and non-structural carbohydrates were quantified by spectrophotometry. The female inflorescence accumulated starch at higher levels than the top internode of the stem. Global mRNA transcript levels were then evaluated in both tissues by RNA sequencing. Gene expression analysis identified 491 genes differentially expressed between the female inflorescence and the top stem internode. Gene ontology classification of differentially expressed genes showed enrichment for sucrose synthesis, the light-dependent reactions of photosynthesis, and transmembrane transporters. Our results suggest that sugar transporters play a key role in sugar partitioning in the maize stem and reveal previously uncharacterized differences between the female inflorescence and the top internode of the stem at early reproductive stages.

High throughput phenotyping technologies are lagging behind modern marker technology impairing the use of secondary traits to increase genetic gains in plant breeding. We aimed to assess whether the combined use of hyperspectral data with modern marker technology could be used to improve across location pre-harvest yield predictions using different statistical models. A maize bi-parental doubled haploid (DH) population derived from F1, which consisted of 97 lines was evaluated in testcross combination under heat stress as well as combined heat and drought stress during the 2014 and 2016 summer season in Ciudad Obregon, Sonora, Mexico (27°20\" N, 109°54\" W, 38 m asl). Full hyperspectral data, indicative of crop physiological processes at the canopy level, was repeatedly measured throughout the grain filling period and related to grain yield. Partial least squares regression (PLSR), random forest (RF), ridge regression (RR) and Bayesian ridge regression (BayesB) were used to assess prediction accuracies on grain yield within (two-fold cross-validation) and across environments (leave-one-environment-out-cross-validation) using molecular markers (M), hyperspectral data (H) and the combination of both (HM). Highest prediction accuracy for grain yield averaged across within and across location predictions (rGP) were obtained for BayesB followed by RR, RF and PLSR. The combined use of hyperspectral and molecular marker data as input factor on average had higher predictions for grain yield than hyperspectral data or molecular marker data alone. The highest prediction accuracy for grain yield across environments was measured for BayesB when molecular marker data and hyperspectral data were used as input factors, while the highest within environment prediction was obtained when BayesB was used in combination with hyperspectral data. It is discussed how the combined use of hyperspectral data with molecular marker technology could be used to introduce physiological genomic estimated breeding values (PGEBV) as a pre-harvest decision support tool to select genetically superior lines.

Key messageThe recent release of the maize genome (AGPv4) contains annotation errors of invertase genes and therefore the enzymes are bestly curated manually at the protein level in a comprehensible fashionThe synthesis, transport and degradation of sucrose are determining factors for biomass allocation and yield of crop plants. Invertase (INV) is a key enzyme of carbon metabolism in both source and sink tissues. Current releases of the maize genome correctly annotates only two vacuolar invertases (ivr1 and ivr2) and four cell wall invertases (incw1, incw2 (mn1), incw3, and incw4). Our comprehensive survey identified 21 INV isogenes for which we propose a standard nomenclature grouped phylogenetically by amino acid similarity: three vacuolar (INVVR), eight cell wall (INVCW), and ten alkaline/neutral (INVAN) isogenes which form separate dendogram branches due to distinct molecular features. The acidic enzymes were curated for the presence of the DPN tripeptide which is coded by one of the smallest exons reported in plants. Particular attention was placed on the molecular role of INV in vascular tissues such as the nodes, internodes, leaf sheath, husk leaves and roots. We report the expression profile of most members of the maize INV family in nine tissues in two developmental stages, R1 and R3. INVCW7, INVVR2, INVAN8, INVAN9, INVAN10, and INVAN3 displayed the highest absolute expressions in most tissues. INVVR3, INVCW5, INVCW8, and INVAN1 showed low mRNA levels. Expressions of most INVs were repressed from stage R1 to R3, except for INVCW7 which increased significantly in all tissues after flowering. The mRNA levels of INVCW7 in the vegetative stem correlated with a higher transport rate of assimilates from leaves to the cob which led to starch accumulation and growth of the female reproductive organs.