Simulating the effect of flowering time on maize individual leaf area in contrasting environmental scenarios.

The quality of yield prediction is linked to that of leaf area. We first analysed the consequences of flowering time and environmental conditions on the area of individual leaves in 127 genotypes presenting contrasting flowering times in fields of Europe, Mexico, and Kenya. Flowering time was the strongest determinant of leaf area. Combined with a detailed field experiment, this experiment showed a large effect of flowering time on the final leaf number and on the distribution of leaf growth rate and growth duration along leaf ranks, in terms of both length and width. Equations with a limited number of genetic parameters predicted the beginning, end, and maximum growth rate (length and width) for each leaf rank. The genotype-specific environmental effects were analysed with datasets in phenotyping platforms that assessed the effects (i) of the amount of intercepted light on leaf width, and (ii) of temperature, evaporative demand, and soil water potential on leaf elongation rate. The resulting model was successfully tested for 31 hybrids in 15 European and Mexican fields. It potentially allows prediction of the vertical distribution of leaf area of a large number of genotypes in contrasting field conditions, based on phenomics and on sensor networks.

Modernising breeding for orphan crops: tools, methodologies, and beyond.

MAIN CONCLUSION: In spite of the limited investment in orphan crops, access to new technologies such as bioinformatics and low-cost genotyping opens new doors to modernise their breeding effectively. Innovation in plant breeding is imperative to meet the world's growing demand for staple food and feed crops, and orphan crops can play a significant role in increasing productivity and quality, especially in developing countries. The short breeding history of most orphan crops implies that genetic gain should be achievable through easy-to-implement approaches such as forward breeding for simple traits or introgression of elite alleles at key target trait loci. However, limited financial support and access to sufficient, relevant and reliable phenotypic data continue to pose major challenges in terms of resources and capabilities. Digitalisation of orphan-crop breeding programmes can help not only to improve data quality and management, but also to mitigate data scarcity by allowing data to be accumulated and analysed over time and across teams. Bioinformatics tools and access to technologies such as molecular markers, some of them provided as services via specific platforms, allow breeders to implement modern strategies to improve breeding efficiency. In orphan crops, more marker-trait associations relevant to breeding germplasm are generally needed, but implementing digitalization, marker-based quality control or simple trait screening and introgression will help modernising breeding. Finally, the development of local capacities-of both people and infrastructure-remains a necessity to ensure the sustainable adoption of modern breeding approaches.

Analytical and Decision Support Tools for Genomics-Assisted Breeding.

To successfully implement genomics-assisted breeding (GAB) in crop improvement programs, efficient and effective analytical and decision support tools (ADSTs) are 'must haves' to evaluate and select plants for developing next-generation crops. Here we review the applications and deployment of appropriate ADSTs for GAB, in the context of next-generation sequencing (NGS), an emerging source of massive genomic information. We discuss suitable software tools and pipelines for marker-based approaches (markers/haplotypes), including large-scale genotypic and phenotypic, data management, and molecular breeding approaches. Although phenotyping remains expensive and time consuming, prediction of allelic effects on phenotypes opens new doors to enhance genetic gain across crop cycles, building on reliable phenotyping approaches and good crop information systems, including pedigree information and target haplotypes.

Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize.

Identifying quantitative trait loci (QTL) of sizeable effects that are expressed in diverse genetic backgrounds across contrasting water regimes particularly for secondary traits can significantly complement the conventional drought tolerance breeding efforts. We evaluated three tropical maize biparental populations under water-stressed and well-watered regimes for drought-related morpho-physiological traits, such as anthesis-silking interval (ASI), ears per plant (EPP), stay-green (SG) and plant-to-ear height ratio (PEH). In general, drought stress reduced the genetic variance of grain yield (GY), while that of morpho-physiological traits remained stable or even increased under drought conditions. We detected consistent genomic regions across different genetic backgrounds that could be target regions for marker-assisted introgression for drought tolerance in maize. A total of 203 QTL for ASI, EPP, SG and PEH were identified under both the water regimes. Meta-QTL analysis across the three populations identified six constitutive genomic regions with a minimum of two overlapping traits. Clusters of QTL were observed on chromosomes 1.06, 3.06, 4.09, 5.05, 7.03 and 10.04/06. Interestingly, a ~8-Mb region delimited in 3.06 harboured QTL for most of the morpho-physiological traits considered in the current study. This region contained two important candidate genes viz., zmm16 (MADS-domain transcription factor) and psbs1 (photosystem II unit) that are responsible for reproductive organ development and photosynthate accumulation, respectively. The genomic regions identified in this study partially explained the association of secondary traits with GY. Flanking single nucleotide polymorphism markers reported herein may be useful in marker-assisted introgression of drought tolerance in tropical maize.

Genome-wide association analysis for nine agronomic traits in maize under well-watered and water-stressed conditions.

Drought can cause severe reduction in maize production, and strongly threatens crop yields. To dissect this complex trait and identify superior alleles, 350 tropical and subtropical maize inbred lines were genotyped using a 1536-SNP array developed from drought-related genes and an array of 56,110 random SNPs. The inbred lines were crossed with a common tester, CML312, and the testcrosses were phenotyped for nine traits under well-watered and water-stressed conditions in seven environments. Using genome-wide association mapping with correction for population structure, 42 associated SNPs (P ≤ 2.25 × 10(-6) 0.1/N) were identified, located in 33 genes for 126 trait × environment × treatment combinations. Of these genes, three were co-localized to drought-related QTL regions. Gene GRMZM2G125777 was strongly associated with ear relative position, hundred kernel weight and timing of male and female flowering, and encodes NAC domain-containing protein 2, a transcription factor expressed in different tissues. These results provide some good information for understanding the genetic basis for drought tolerance and further studies on identified candidate genes should illuminate mechanisms of drought tolerance and provide tools for designing drought-tolerant maize cultivars tailored to different environmental scenarios.

The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis.

Genotype-by-environment interaction (GEI) is an important phenomenon in plant breeding. This paper presents a series of models for describing, exploring, understanding, and predicting GEI. All models depart from a two-way table of genotype by environment means. First, a series of descriptive and explorative models/approaches are presented: Finlay-Wilkinson model, AMMI model, GGE biplot. All of these approaches have in common that they merely try to group genotypes and environments and do not use other information than the two-way table of means. Next, factorial regression is introduced as an approach to explicitly introduce genotypic and environmental covariates for describing and explaining GEI. Finally, QTL modeling is presented as a natural extension of factorial regression, where marker information is translated into genetic predictors. Tests for regression coefficients corresponding to these genetic predictors are tests for main effect QTL expression and QTL by environment interaction (QEI). QTL models for which QEI depends on environmental covariables form an interesting model class for predicting GEI for new genotypes and new environments. For realistic modeling of genotypic differences across multiple environments, sophisticated mixed models are necessary to allow for heterogeneity of genetic variances and correlations across environments. The use and interpretation of all models is illustrated by an example data set from the CIMMYT maize breeding program, containing environments differing in drought and nitrogen stress. To help readers to carry out the statistical analyses, GenStat® programs, 15th Edition and Discovery® version, are presented as "Appendix."

QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance.

Despite numerous published reports of quantitative trait loci (QTL) for drought-related traits, practical applications of such QTL in maize improvement are scarce. Identifying QTL of sizeable effects that express more or less uniformly in diverse genetic backgrounds across contrasting water regimes could significantly complement conventional breeding efforts to improve drought tolerance. We evaluated three tropical bi-parental populations under water-stress (WS) and well-watered (WW) regimes in Mexico, Kenya and Zimbabwe to identify genomic regions responsible for grain yield (GY) and anthesis-silking interval (ASI) across multiple environments and diverse genetic backgrounds. Across the three populations, on average, drought stress reduced GY by more than 50 % and increased ASI by 3.2 days. We identified a total of 83 and 62 QTL through individual environment analyses for GY and ASI, respectively. In each population, most QTL consistently showed up in each water regime. Across the three populations, the phenotypic variance explained by various individual QTL ranged from 2.6 to 17.8 % for GY and 1.7 to 17.8 % for ASI under WS environments and from 5 to 19.5 % for GY under WW environments. Meta-QTL (mQTL) analysis across the three populations and multiple environments identified seven genomic regions for GY and one for ASI, of which six mQTL on chr.1, 4, 5 and 10 for GY were constitutively expressed across WS and WW environments. One mQTL on chr.7 for GY and one on chr.3 for ASI were found to be 'adaptive' to WS conditions. High throughput assays were developed for SNPs that delimit the physical intervals of these mQTL. At most of the QTL, almost equal number of favorable alleles was donated by either of the parents within each cross, thereby demonstrating the potential of drought tolerant × drought tolerant crosses to identify QTL under contrasting water regimes.

Genetic association mapping identifies single nucleotide polymorphisms in genes that affect abscisic acid levels in maize floral tissues during drought.

In maize, water stress at flowering causes loss of kernel set and productivity. While changes in the levels of sugars and abscisic acid (ABA) are thought to play a role in this stress response, the mechanistic basis and genes involved are not known. A candidate gene approach was used with association mapping to identify loci involved in accumulation of carbohydrates and ABA metabolites during stress. A panel of single nucleotide polymorphisms (SNPs) in genes from these metabolic pathways and in genes for reproductive development and stress response was used to genotype 350 tropical and subtropical maize inbred lines that were well watered or water stressed at flowering. Pre-pollination ears, silks, and leaves were analysed for sugars, starch, proline, ABA, ABA-glucose ester, and phaseic acid. ABA and sugar levels in silks and ears were negatively correlated with their growth. Association mapping with 1229 SNPs in 540 candidate genes identified an SNP in the maize homologue of the Arabidopsis MADS-box gene, PISTILLATA, which was significantly associated with phaseic acid in ears of well-watered plants, and an SNP in pyruvate dehydrogenase kinase, a key regulator of carbon flux into respiration, that was associated with silk sugar concentration. An SNP in an aldehyde oxidase gene was significantly associated with ABA levels in silks of water-stressed plants. Given the short range over which decay of linkage disequilibrium occurs in maize, the results indicate that allelic variation in these genes affects ABA and carbohydrate metabolism in floral tissues during drought.

Joint linkage-linkage disequilibrium mapping is a powerful approach to detecting quantitative trait loci underlying drought tolerance in maize.

This paper describes two joint linkage-linkage disequilibrium (LD) mapping approaches: parallel mapping (independent linkage and LD analysis) and integrated mapping (datasets analyzed in combination). These approaches were achieved using 2,052 single nucleotide polymorphism (SNP) markers, including 659 SNPs developed from drought-response candidate genes, screened across three recombinant inbred line (RIL) populations and 305 diverse inbred lines, with anthesis-silking interval (ASI), an important trait for maize drought tolerance, as the target trait. Mapping efficiency was improved significantly due to increased population size and allele diversity and balanced allele frequencies. Integrated mapping identified 18 additional quantitative trait loci (QTL) not detected by parallel mapping. The use of haplotypes improved mapping efficiency, with the sum of phenotypic variation explained (PVE) increasing from 5.4% to 23.3% for single SNP-based analysis. Integrated mapping with haplotype further improved the mapping efficiency, and the most significant QTL had a PVE of up to 34.7%. Normal allele frequencies for 113 of 277 (40.8%) SNPs with minor allele frequency (<5%) in 305 lines were recovered in three RIL populations, three of which were significantly associated with ASI. The candidate genes identified by two significant haplotype loci included one for a SET domain protein involved in the control of flowering time and the other encoding aldo/keto reductase associated with detoxification pathways that contribute to cellular damage due to environmental stress. Joint linkage-LD mapping is a powerful approach for detecting QTL underlying complex traits, including drought tolerance.

More genomic resources for less-studied crops.

Many of the crop species considered to be minor on a global scale, yet are important locally for food security in the developing world, have remained less-studied crops. Recent years have witnessed the development of large-scale genomic and genetic resources, including simple sequence repeat, single nucleotide polymorphism and diversity array technology markers, expressed sequence tags or transcript reads, bacterial artificial chromosome libraries, genetic and physical maps, and genetic stocks with rich genetic diversity, such as core reference sets and introgression lines in these crops. These resources have the potential to accelerate gene discovery and initiate molecular breeding in these crops, thereby enhancing crop productivity to ensure food security in developing countries.

Drought stress and tropical maize: QTL-by-environment interactions and stability of QTLs across environments for yield components and secondary traits.

A recombinant inbred line (RIL) population was evaluated in seven field experiments representing four environments: water stress at flowering (WS) and well-watered (WW) conditions in Mexico and Zimbabwe. The QTLs were identified for each trait in each individual experiment (single-experiment analysis) as well as per environment, per water regime across locations and across all experiments (joint analyses). For the six target traits (male flowering, anthesis-to-silking interval, grain yield, kernel number, 100-kernel fresh weight and plant height) 81, 57, 51 and 34 QTLs were identified in the four step-wise analyses, respectively. Despite high values of heritability, the phenotypic variance explained by QTLs was reduced, indicating epistatic interactions. About 80, 60 and 6% of the QTLs did not present significant QTL-by-environment interactions (QTL x E) in the joint analyses per environment, per water regime and across all experiments. The expression of QTLs was quite stable across years at a given location and across locations under the same water regime. However, the stability of QTLs decreased drastically when data were combined across water regimes, reflecting a different genetic basis of the target traits in the drought and well-watered trials. Several clusters of QTLs for different traits were identified by the joint analyses of the WW (chromosomes 1 and 8) and WS (chromosomes 1, 3 and 5) treatments and across water regimes (chromosome 1). Those regions are clear targets for future marker-assisted breeding, and our results confirm that the best approach to breeding for drought tolerance includes selection under water stress.

Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations.

It has long been recognized that epistasis or interactions between non-allelic genes plays an important role in the genetic control and evolution of quantitative traits. However, the detection of epistasis and estimation of epistatic effects are difficult due to the complexity of epistatic patterns, insufficient sample size of mapping populations and lack of efficient statistical methods. Under the assumption of additivity of QTL effects on the phenotype of a trait in interest, the additive effect of a QTL can be completely absorbed by the flanking marker variables, and the epistatic effect between two QTL can be completely absorbed by the four marker-pair multiplication variables between the two pairs of flanking markers. Based on this property, we proposed an inclusive composite interval mapping (ICIM) by simultaneously considering marker variables and marker-pair multiplications in a linear model. Stepwise regression was applied to identify the most significant markers and marker-pair multiplications. Then a two-dimensional scanning (or interval mapping) was conducted to identify QTL with significant digenic epistasis using adjusted phenotypic values based on the best multiple regression model. The adjusted values retain the information of QTL on the two current mapping intervals but exclude the influence of QTL on other intervals and chromosomes. Epistatic QTL can be identified by ICIM, no matter whether the two interacting QTL have any additive effects. Simulated populations and one barley doubled haploids (DH) population were used to demonstrate the efficiency of ICIM in mapping both additive QTL and digenic interactions.

Marker-assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives.

A number of different marker-assisted selection (MAS) approaches do exist for the improvement of polygenic traits. Results of a marker-assisted backcross (MABC) selection experiment aimed at improving grain yield under drought conditions in tropical maize are presented and compared with alternative MAS strategies. The introgression of favourable alleles at five target regions involved in the expression of yield components and flowering traits increased grain yield and reduced the asynchrony between male and female flowering under water-limited conditions. Eighty-five per cent of the recurrent parent's genotype at non-target loci was recovered in only four generations of MABC by screening large segregating populations (2200 individuals) for three of the four generations. Selected MABC-derived BC(2)F(3) families were crossed with two testers and evaluated under different water regimes. Mean grain yield of MABC-derived hybrids was consistently higher than that of control hybrids (crosses from the recurrent parent to the same two testers as the MABC-derived families) under severe water stress conditions. Under those conditions, the best five MABC-derived hybrids yielded, on average, at least 50% more than control hybrids. Under mild water stress, defined as resulting in <50% yield reduction, no difference was observed between MABC-derived hybrids and the control plants, thus confirming that the genetic regulation for drought tolerance is dependent on stress intensity. MABC conversions involving several target regions are likely to result in partial rather than complete line conversion. Simulations were conducted to assess the utility of such partial conversions, i.e. containing favourable donor alleles at non-target regions, for subsequent phenotypic selection. The results clearly showed that selecting several genotypes (10-20) at each MABC cycle was most efficient. In the light of these results, alternative approaches to MABC are discussed, including recurrent selection, illustrated by an example of improving the adaptation of maize to low temperatures. Given the current approaches for MAS and the choices of marker technologies available now and potential for future developments, the use of MAS techniques in further improving grain yield under abiotic stresses in maize appears very promising.

Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods.

The study of QTL x environment interaction (QEI) is important for understanding genotype x environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F2 derived F3 families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs.

Maize HSP101 plays important roles in both induced and basal thermotolerance and primary root growth.

HSP101 belongs to the ClpB protein subfamily whose members promote the renaturation of protein aggregates and are essential for the induction of thermotolerance. We found that maize HSP101 accumulated in mature kernels in the absence of heat stress. At optimal temperatures, HSP101 disappeared within the first 3 days after imbibition, although its levels increased in response to heat shock. In embryonic cells, HSP101 concentrated in the nucleus and in some nucleoli. Hsp101 maps near the umc132 and npi280 markers on chromosome 6. Five maize hsp101-m-::Mu1 alleles were isolated. Mutants were null for HSP101 and defective in both induced and basal thermotolerance. Moreover, during the first 3 days after imbibition, primary roots grew faster in the mutants at optimal temperature. Thus, HSP101 is a nucleus-localized protein that, in addition to its role in thermotolerance, negatively influences the growth rate of the primary root. HSP101 is dispensable for proper embryo and whole plant development in the absence of heat stress.