Jean-Marcel Ribaut

Identification of quantitative trait loci under drought conditions in tropical maize. 2. Yield components and marker-assisted selection strategies

Abstract In most maize-growing areas yield reductions due to drought have been observed. Drought at flowering time is, in some cases, the most damaging. In the experiment reported here, trials with F3 families, derived from a segregating F2 population, were conducted in the field under well-watered conditions (WW) and two other water-stress regimes affecting flowering (intermediate stress, IS, and severe stress, SS). Several yield components were measured on equal numbers of plants per family: grain yield (GY), ear number (ENO), kernel number (KNO), and 100-kernel weight (HKWT). Correlation analysis of these traits showed that they were not independent of each other. Drought resulted in a 60% decrease of GY under SS conditions. By comparing yield under WW and SS conditions, the families that performed best under WW conditions were found to be proportionately more affected by stress, and the yield reductions due to SS conditions were inversely proportional to the performance under drought. Moreover, no positive correlation was observed between a drought-tolerance index (DTI) and yield under WW conditions. The correlation between GY under WW and SS conditions was 0.31. Therefore, in this experiment, selection for yield improvement under WW conditions only, would not be very effective for yield improvement under drought. Quantitative trait loci (QTLs) were identified for GY, ENO and KNO using composite interval mapping (CIM). No major QTLs, expressing more then 13% of the phenotypic variance, were detected for any of these traits, and there were inconsistencies in their genomic positions across water regimes. The use of CIM allowed the evaluation of QTL-by-environment interactions (Q×E) and could thus identify “stable” QTLs CIMMYT, Apartado Postal 6-641, 06600 Mexico D.F., Mexico across drought environments. Two such QTLs for GY, on chromosomes 1 and 10, coincided with two stable QTLs for KNO. Moreover, four genomic regions were identified for the expression of both GY and the anthesis-silking interval (ASI). In three of these, the allelic contributions were for short ASI and GY increase, while for that on chromosome 10 the allelic contribution for short ASI corresponded to a yield reduction. From these results, we hypothesize that to improve yield under drought, marker-assisted selection (MAS) using only the QTLs involved in the expression of yield components appears not to be the best strategy, and neither does MAS using only QTLs involved in the expression of ASI. We would therefore favour a MAS strategy that takes into account a combination of the “best QTLs” for different traits. These QTLs should be stable across target environments, represent the largest percentage possible of the phenotypic variance, and, though not involved directly in the expression of yield, should be involved in the expression of traits significantly correlated with yield, such as ASI.

Marker-assisted selection: new tools and strategies

The development of molecular genetics and associated technology has facilitated a quantum leap in our understanding of the underlying genetics of the traits sought through plant breeding. The usefulness of DNA markers for germplasm characterization, and of marker-assisted selection—the manipulation through DNA markers of genomic regions that are involved in the expression of traits of interest—for single-gene transfer, has been well demonstrated. However, when several genomic regions must be manipulated, marker-assisted selection has turned out to be less useful. The efficient and effective application of marker-assisted selection for polygenic trait improvement certainly needs new technology but, more importantly, it requires the development of innovative strategies that bypass the conceptual bottlenecks imposed by current approaches.

Identification of quantitative trait loci under drought conditions in tropical maize. 1. Flowering parameters and the anthesis-silking interval

Drought is an important climatic phenomenon which, after soil infertility, ranks as the second most severe limitation to maize production in developing countries. When drought stress occurs just before or during the flowering period, a delay in silking is observed, resulting in an increase in the length of the anthesis-silking interval (ASI) and in a decrease in grain yield. Selection for reduced ASI in tropical open-pollinated varieties has been shown to be correlated with improved yields under drought stress. Since efficient selection for drought tolerance requires carefully managed experimental conditions, molecular markers were used to identify the genomic segments responsible for the expression of ASI, with the final aim of developing marker-assisted selection (MAS) strategies. An F2population of 234 individuals was genotyped at 142 loci and F3 families were evaluated in the field under several water regimes for male flowering (MFLW), male sterility (STER), female flowering (FFLW) and ASI. The genetic variance of ASI increased as a function of the stress intensity, and the broad-sense heritabilites of MFLW, FFLW and ASI were high under stress conditions, being 86%, 82% and 78%, respectively. Putative quantitative trait loci (QTLs) involved in the expression of MFLW and/or FFLW under drought were detected on chromosomes 1, 2, 4, 5, 8, 9 and 10, accounting for around 48% of the phenotypic variance for both traits. For ASI, six putative QTLs were identified under drought on chromosomes 1, 2, 5, 6, 8 and 10, and together accounted for approximately 47% of the phenotypic variance. Under water stress conditions, four QTLs were common for the expression of MFLW and FFLW, one for the expression of ASI and MFLW, and four for the expression of ASI and FFLW. The number of common QTLs for two traits was related to the level of linear correlation between these two traits. Segregation for ASI was found to be transgressive with the drought-susceptible parent contributing alleles for reduced ASI (4 days) at two QTL positions. Alleles contributed by the resistant line at the other four QTLs were responsible for a 7-day reduction of ASI. These four QTLs represented around 9% of the linkage map, and were stable over years and stress levels. It is argued that MAS based on ASI QTLs should be a powerful tool for improving drought tolerance of tropical maize inbred lines.

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.

A method for combining molecular markers and phenotypic attributes for classifying plant genotypes

Abstract  Classifying genotypes into clusters based on DNA fingerprinting, and/or agronomic attributes, for studying genetic and phenotypic diversity is a common practice. Researchers are interested in knowing the minimum number of fragments (and markers) needed for finding the underlying structural patterns of diversity in a population of interest, and using this information in conjunction with the phenotypic attributes to obtain more precise clusters of genotypes. The objectives of this study are to present: (1) a retrospective method of analysis for selecting a minimum number of fragments (and markers) from a study needed to produce the same classification of genotypes as that obtained using all the fragments (and markers), and (2) a classification strategy for genotypes that allows the combination of the minimum set of fragments with available phenotypic attributes. Results obtained on seven experimental data sets made up of different plant species, number of individuals per species’ and number of markers, showed that the retrospective analysis did indeed find few relevant fragments (and markers) that best discriminated the genotypes. In two data sets, the classification strategy of combining the information on the relevant minimum fragments with the available morpho-agronomic attributes produced compact and well-differentiated groups of genotypes.

A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)

Despite QTL mapping being a routine procedure in plant breeding, approaches that fully exploit data from multi-trait multi-environment (MTME) trials are limited. Mixed models have been proposed both for multi-trait QTL analysis and multi-environment QTL analysis, but these approaches break down when the number of traits and environments increases. We present models for an efficient QTL analysis of MTME data with mixed models by reducing the dimensionality of the genetic variance–covariance matrix by structuring this matrix using direct products of relatively simple matrices representing variation in the trait and environmental dimension. In the context of MTME data, we address how to model QTL by environment interactions and the genetic basis of heterogeneity of variance and correlations between traits and environments. We illustrate our approach with an example including five traits across eight stress trials in CIMMYT maize. We detected 36 QTLs affecting yield, anthesis-silking interval, male flowering, ear number, and plant height in maize. Our approach does not require specialised software as it can be implemented in any statistical package with mixed model facilities.

Molecular linkage map for an intraspecific recombinant inbred population of durum wheat (Triticum turgidum L. var. durum)

Abstract Durum wheat (Triticum turgidum L. var. durum) is an economically and nutritionally important cereal crop in the Mediterranean region. To further our understanding of durum genome organization we constructed a durum linkage map using restriction fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) known as Gatersleben wheat microsatellites (GWMs), amplified fragment length polymorphisms (AFLPs), and seed storage proteins (SSPs: gliadins and glutenins). A population of 110 F9 recombinant inbred lines (RILs) was derived from an intraspecific cross between two durum cultivars, Jennah Khetifa and Cham 1. The two parents exhibit contrasting traits for resistance to biotic and abiotic stresses and for grain quality. In total, 306 markers have been placed on the linkage map – 138 RFLPs, 26 SSRs, 134 AFLPs, five SSPs, and three known genes (one pyruvate decarboxylase and two lipoxygenases). The map is 3598 cM long, with an average distance between markers of 11.8 cM, and 12.1% of the markers deviated significantly from the expected Mendelian ratio 1:1. The molecular markers were evenly distributed between the A and B genomes. The chromosome with the most markers is 1B (41 markers), followed by 3B and 7B, with 25 markers each. The chromosomes with the fewest markers are 2A (11 markers), 5A (12 markers), and 4B (15 markers). In general, there is a good agreement between the map obtained and the Triticeae linkage consensus maps. This intraspecific map provides a useful tool for marker-assisted selection and map-based breeding for resistance to biotic and abiotic stresses and for improvement of grain quality.

Genetic analysis of cold-tolerance of photosynthesis in maize

The genetic basis of cold-tolerance was investigated by analyzing the quantitative trait loci (QTL) of an F2:3 population derived from a cross between two lines bred for contrasting cold-tolerance using chlorophyll fluorescence as a selection tool. Chlorophyll fluorescence parameters, CO2 exchange rate, leaf greenness, shoot dry matter and shoot nitrogen content were determined in plants grown under controlled conditions at 25/22 °C or 15/13 °C (day/night). The analysis revealed the presence of 18 and 19 QTLs (LOD > 3.5) significantly involved in the variation of nine target traits in plants grown at 25/22 °C and 15/13 °C, respectively. Only four QTLs were clearly identified in both temperatures regimes for the same traits, demonstrating that the genetic control of the performance of the photosynthetic apparatus differed, depending on the temperature regime. A major QTL for the cold-tolerance of photosynthesis was identified on chromosome 6. This QTL alone explained 37.4 of the phenotypic variance in the chronic photoinhibition at low temperature and was significantly involved in the expression of six other traits, including the rate of carbon fixation and shoot dry matter accumulation, indicating that the tolerance to photoinhibition is a key factor in the tolerance of maize to low growth temperature. An additional QTL on chromosomes 2 corresponded to a QTL identified previously in another population, suggesting some common genetic basis of the cold-tolerance of photosynthesis in different maize germplasms.

Drought Tolerance in Maize

Drought is the single most common cause of severe crop production shortage in developing countries, and global warming is predicted to further exacerbate droughts impact. This chapter describes how to best evaluate segregating germplasm under water-limited conditions, what are the secondary traits to be included in the selection and why is it important to identify key regulatory genes involved in selected metabolic pathways and signaling. The chapter also summarizes the current understanding of the genetic basis for drought tolerance. From this review, one can project that sustained progress in breeding for drought tolerance in maize is likely to entail the selection of plants with a reduced leaf area (especially in the upper part of the plant), short thick stems, small tassels, erect leaves, delayed senescence, smaller root biomass, and a deep root system with little lateral root branching. Tolerant genotypes are also expected to have robust spikelet and kernel growth at the cell-division and expansion-growth phases, with good osmotic adjustment to assist in cell retention of water during drought. In this way, root and ear growth is not completely inhibited, and leaf survival is enhanced despite limited water. Extensive genetic dissections of drought tolerance traits have been carried out in maize over the last decade, yielding numerous QTL involved in the determination of morphological traits and regularoty pathways. A better understanding of the physiological mechanisms and the genetic basis of the response of maize to drought should make it increasingly feasible and efficient to stack favorable alleles at key genes, so as to select for genotypes that will develop phenotypes described above. The final section of this chapter focuses on the genetic gains achieved in maize via an interdisciplinary approach, and includes an exploration of new strategies combining marker-assisted breeding and phenotyping selection to accelerate drought tolerance breeding in maize.

Money matters (I): costs of field and laboratory procedures associated with conventional and marker-assisted maize breeding at CIMMYT

This article presents selected results of a study carried out in Mexico at the International Maize and Wheat Improvement Center (CIMMYT) to compare the cost-effectiveness of conventional and marker-assisted maize breeding. Costs associated with use of conventional and marker-assisted selection (MAS) methods were estimated using a spreadsheet-based budgeting approach. This information was used to compare the cost of using conventional screening and MAS to achieve a well-defined breeding objective—identification of plants carrying a mutant recessive form of the opaque2 gene in maize that is associated with Quality Protein Maize (QPM). In addition to generating empirical cost information that will be of use to CIMMYT research managers, the study produced four important insights. First, for any given breeding project, detailed budget analysis will be needed to determine the cost-effectiveness of MAS relative to conventional selection. Second, direct comparisons of unit costs for MAS methods and conventional selection methods provide useful information for research managers, but factors other than cost are likely to play an important role in driving the choice of screening methods. Third, the choice between MAS and conventional selection may be complicated by the fact that the two are not always direct substitutes. Fourth, when used with empirical data from actual breeding programs, spreadsheet-based budgeting tools can be used by research managers to improve the efficiency of existing protocols and to inform decisions about future technology choices.