Genetic and economic analysis of a targeted marker-assisted wheat breeding strategy

The advent of molecular markers as a tool to aid selection has provided plant breeders with the opportunity to rapidly deliver superior genetic solutions to problems in agricultural production systems. However, a major constraint to the implementation of marker-assisted selection (MAS) in pragmatic breeding programs in the past has been the perceived high relative cost of MAS compared to conventional phenotypic selection. In this paper, computer simulation was used to design a genetically effective and economically efficient marker-assisted breeding strategy aimed at a specific outcome. Under investigation was a strategy involving the integration of both restricted backcrossing and doubled haploid (DH) technology. The point at which molecular markers are applied in a selection strategy can be critical to the effectiveness and cost efficiency of that strategy. The application of molecular markers was considered at three phases in the strategy: allele enrichment in the BC1F1 population, gene selection at the haploid stage and the selection for recurrent parent background of DHs prior to field testing. Overall, incorporating MAS at all three stages was the most effective, in terms of delivering a high frequency of desired outcomes and at combining the selected favourable rust resistance, end use quality and grain yield alleles. However, when costs were included in the model the combination of MAS at the BC1F1 and haploid stage was identified as the optimal strategy. A detailed economic analysis showed that incorporation of marker selection at these two stages not only increased genetic gain over the phenotypic alternative but actually reduced the over all cost by 40%.

Power of the joint segregation analysis method for testing mixed major-gene and polygene inheritance models of quantitative traits

Understanding the genetic architecture of quantitative traits can greatly assist the design of strategies for their manipulation in plant-breeding programs. For a number of traits, genetic variation can be the result of segregation of a few major genes and many polygenes (minor genes). The joint segregation analysis (JSA) is a maximum-likelihood approach for fitting segregation models through the simultaneous use of phenotypic information from multiple generations. Our objective in this paper was to use computer simulation to quantify the power of the JSA method for testing the mixed-inheritance model for quantitative traits when it was applied to the six basic generations: both parents (P-1 and P-2), F-1, F-2, and both backcross generations (B-1 and B-2) derived from crossing the F-1 to each parent. A total of 1968 genetic model-experiment scenarios were considered in the simulation study to quantify the power of the method. Factors that interacted to influence the power of the JSA method to correctly detect genetic models were: (1) whether there were one or two major genes in combination with polygenes, (2) the heritability of the major genes and polygenes, (3) the level of dispersion of the major genes and polygenes between the two parents, and (4) the number of individuals examined in each generation (population size). The greatest levels of power were observed for the genetic models defined with simple inheritance; e.g., the power was greater than 90% for the one major gene model, regardless of the population size and major-gene heritability. Lower levels of power were observed for the genetic models with complex inheritance (major genes and polygenes), low heritability, small population sizes and a large dispersion of favourable genes among the two parents; e.g., the power was less than 5% for the two major-gene model with a heritability value of 0.3 and population sizes of 100 individuals. The JSA methodology was then applied to a previously studied sorghum data-set to investigate the genetic control of the putative drought resistance-trait osmotic adjustment in three crosses. The previous study concluded that there were two major genes segregating for osmotic adjustment in the three crosses. Application of the JSA method resulted in a change in the proposed genetic model. The presence of the two major genes was confirmed with the addition of an unspecified number of polygenes.

Yield response of rice (Oryza sativa L.) genotypes to different types of drought under rainfed lowlands - Part 1. Grain yield and yield components

Responses of rice genotypes to drought stress may be different when characteristics of the drought stress environments differ. The performance of 128 genotypes was examined under irrigation and four different types of drought stress, to determine genotypic consistency in yield and factors determining yields under different drought stress conditions. The different drought conditions were mild drought during grain filling, short and severe drought at flowering, prolonged severe drought during the reproductive to grain filling, and prolonged mild drought during vegetative and grain filling. Genotypic grain yield under mild stress conditions was associated with yield under irrigated conditions, indicating the importance of potential yield in environments where the yield reduction was less than 50%. However, yields under irrigated conditions differed over time and locations. Under prolonged or severe drought conditions, flowering time was an important determinant of grain yield. Earlier flowering genotypes escaped the severe stress and had higher grain yields indicating large genotype by environment (G x E) interactions which have implications for plant breeding even for mild stress. It is suggested that variations in flowering time, potential yields and drought patterns need to be considered for development of drought-resistant cultivars using specific physiological traits. (C) 2002 Elsevier Science B.V. All rights reserved.

Future contributions of crop modelling - from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.

The E(NK) model: extending the NK model to incorporate gene-by-environment interactions and epistasis for diploid genomes

The haploid NK model developed by Kauffman can be extended to diploid genomes and to incorporate gene-by-environment interaction effects in combination with epistasis. To provide the flexibility to include a wide range of forms of gene-by-environment interactions, a target population of environment types (TPE) is defined. The TPE consists of a set of E different environment types, each with their own frequency of occurrence. Each environment type conditions a different NK gene network structure or series of gene effects for a given network structure, providing the framework for defining gene-by-environment interactions. Thus, different NK models can be partially or completely nested within the E environment types of a TPE, giving rise to the E(NK) model for a biological system. With this model it is possible to examine how populations of genotypes evolve in context with properties of the environment that influence the contributions of genes to the fitness values of genotypes. We are using the E(NK) model to investigate how both epistasis and gene-by-environment interactions influence the genetic improvement of quantitative traits by plant breeding strategies applied to agricultural systems. © 2002 Wiley Periodicals, Inc.

The GP problem: quantifying gene-to-phenotype relationships

In this paper we refer to the gene-to-phenotype modeling challenge as the GP problem. Integrating information across levels of organization within a genotype-environment system is a major challenge in computational biology. However, resolving the GP problem is a fundamental requirement if we are to understand and predict phenotypes given knowledge of the genome and model dynamic properties of biological systems. Organisms are consequences of this integration, and it is a major property of biological systems that underlies the responses we observe. We discuss the E(NK) model as a framework for investigation of the GP problem and the prediction of system properties at different levels of organization. We apply this quantitative framework to an investigation of the processes involved in genetic improvement of plants for agriculture. In our analysis, N genes determine the genetic variation for a set of traits that are responsible for plant adaptation to E environment-types within a target population of environments. The N genes can interact in epistatic NK gene-networks through the way that they influence plant growth and development processes within a dynamic crop growth model. We use a sorghum crop growth model, available within the APSIM agricultural production systems simulation model, to integrate the gene-environment interactions that occur during growth and development and to predict genotype-to-phenotype relationships for a given E(NK) model. Directional selection is then applied to the population of genotypes, based on their predicted phenotypes, to simulate the dynamic aspects of genetic improvement by a plant-breeding program. The outcomes of the simulated breeding are evaluated across cycles of selection in terms of the changes in allele frequencies for the N genes and the genotypic and phenotypic values of the populations of genotypes.

Quantitative trait loci for agronomic and seed quality traits in an F-2 and F-4 : 6 soybean population

Molecular breeding is becoming more practical as better technology emerges. The use of molecular markers in plant breeding for indirect selection of important traits can favorably impact breeding efficiency. The purpose of this research is to identify quantitative trait loci (QTL) on molecular linkage groups (MLG) which are associated with seed protein concentration, seed oil concentration, seed size, plant height, lodging, and maturity, in a population from a cross between the soybean cultivars 'Essex' and 'Williams.' DNA was extracted from F-2 generation soybean leaves and amplified via polymerase chain reaction (PCR) using simple sequence repeat (SSR) markers. Markers that were polymorphic between the parents were analyzed against phenotypic trait data from the F-2 and F-4:6 generation. For the F-2 population, significant additive QTL were Satt540 (MLG M, maturity, r(2)=0.11; height, r(2)=0.04, seed size, r(2)=0.061, Satt373 (MLG L, seed size, r(2)=0.04; height, r(2)=0.14), Satt50 (MLG A1, maturity r(2)=0.07), Satt14 (MLG D2, oil, r(2)=0.05), and Satt251 (protein r(2)=0.03, oil, r(2)=0.04). Significant dominant QTL for the F-2 population were Satt540 (MLG M, height, r(2)=0.04; seed size, r(2)=0.06) and Satt14 (MLG D2, oil, r(2)=0.05). In the F-4:6 generation significant additive QTL were Satt239 (MLG I, height, r(2)=0.02 at Knoxville, TN and r(2)=0.03 at Springfield, TN), Satt14 (MLG D2, seed size, r(2)=0.14 at Knoxville, TN), Satt373 (MLG L, protein, r(2)=0.04 at Knoxville, TN) and Satt251 (MLG B I, lodging r(2)=0.04 at Springfield, TN). Averaged over both environments in the F-4:6 generation, significant additive QTL were identified as Satt251 (MLG B 1, protein, r(2)=0.03), and Satt239 (MLG 1, height, r(2)=0.03). The results found in this study indicate that selections based solely on these QTL would produce limited gains (based on low r(2) values). Few QTL were detected to be stable across environments. Further research to identify stable QTL over environments is needed to make marker-assisted approaches more widely adopted by soybean breeders.

Markers associated with stalk number and suckering in sugarcane colocate with tillering and rhizomatousness QTLs in sorghum

Two important factors influencing sugar yield, the primary focus of sugarcane plant breeding programs, are stalk number and suckering. Molecular markers linked to both of these traits are sought to assist in the identification of high sugar yield, high stalk number, low-suckering sugarcane clones. In this preliminary mapping study, 108 progeny from a biparental cross involving two elite Australian sugarcane clones were evaluated at two sites for two years for both stalk number and suckering. A total of 258 DNA markers, including both restriction fragment length polymorphisms (RFLPs) and radio-labelled amplified fragments (RAFs), were scored and evaluated using single-factor analysis. Sixteen (7 RFLPs and 9 RAFs) and 14 (6 RFLPs and 8 RAFs) markers were identified that were significantly associated (P < 0.01) with stalk number and suckering, respectively, across both years and sites. The seven and six RFLP markers associated with stalk number and suckering, respectively, were generated by eight different RFLP probes, of which seven had been mapped in sorghum and (or) sugarcane. Of significant interest was the observation that all seven RFLP probes could be shown to be located within or near QTLs associated with tillering and rhizomatousness in sorghum. This observation highlights the usefulness of comparative mapping between sorghum and sugarcane and suggests that the identification of useful markers for stalk number and suckering in sugarcane would be facilitated by focussing on sorghum QTLs associated with related traits.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

Potential to increase yield in lucerne (Medicago sativa subsp sativa) through introgression of Medicago sativa subsp falcata into Australian adapted material

The effect of interspecific heterosis in crosses between Medicago sativa subsp. sativa and M. sativa subsp. falcata was assessed. Three sativa and 3 falcata plants were crossed in a diallel design. Progeny dry matter yield and natural plant height were assessed in a replicated field experiment at Gatton, Queensland. Yield data were analysed using the method of residual maximum likelihood (REML) and Griffing's model 1. There were significant differences between the reciprocal, general combining ability (GCA), and specific combining ability (SCA) effects. As expected, S-1 populations were lower yielding than their respective intraspecific cross and falcata x falcata crosses were significantly lower yielding than sativa x sativa crosses. Some of the interspecific crosses indicated substantial SCA effects, yielding at least as well as the best sativa x sativa crosses. We have demonstrated the potential usefulness of unselected M. sativa subsp. falcata as a heterotic group in the improvement of yield in northern Australian adapted lucerne material, and discuss how it could be incorporated into future breeding to overcome the yield stagnation currently being experienced in Australian programs.

Adaptive responses of wild mungbean (Vigna radiata ssp sublobata) to photo-thermal environment. II. Growth, biomass, and seed yield

The leaf growth, dry matter production, and seed yield of 11 wild mungbean ( Vigna radiata ssp. sublobata) accessions of diverse geographic origin were observed under natural and artificial photoperiod temperature conditions, to determine the extent to which genotypic differences could be attributed to adaptive responses to photo-thermal environment. Environments included serial sowings in the field in SE Queensland, complemented by artificial photoperiod extension and controlled-environment growth rooms. Photo-thermal environment influenced leaf growth, total dry matter production ( TDM), and seed yield directly, through effects of ( mainly cool) temperature on growth, and indirectly, through effects on phenology. In terms of direct effects, leaf production, leaf expansion, and leaf area were all sensitive to temperature, with implied base temperatures higher than usually observed in cultivated mungbean ( V. radiata ssp. radiata). Genotypic sensitivity to temperature varied systematically with accession provenance and appeared to be of adaptive significance. In terms of the indirect effects of photo-thermal environment, genotypic and environmental effects on TDM were positively related to changes in total growth duration, and harvest index was negatively related to the period from sowing to flowering, similar to cultivated mungbean. However, seed yield was positively related to the duration of reproductive growth, reflecting the indeterminate growth habit of the wild accessions. As a consequence, the wild accessions are more responsive to favourable environments than typically observed in cultivated mungbean, which is determinate in habit. It is suggested that the introduction of the indeterminate trait into mungbean from the wild subspecies would increase the responsiveness of mungbean to favourable environments, analogous to that of black gram ( V. mungo). Although the wild subspecies appeared more sensitive to cool temperature than cultivated mungbean, it may provide a source of tolerance to the warmer temperatures experienced during the wet season in the tropics.

Improving drought tolerance in rainfed lowland rice: An example from Thailand

A large portion of the world's poor farm in rainfed systems where the water supply is unpredictable and droughts are common. In Thailand there are approximately 6.2 million ha of rain fed lowland rice, which account for 67% of the country's total rice-growing area. This rice system is often characterised by too much and too little water in the same season. Farmers' estimates of their annual losses to drought are as high as 45% in the upper parts of the toposequence. In contrast to irrigated rice systems, gains from crop improvement of rainfed rice have been modest, in part because there has been little effort to breed and select for drought tolerance for the target rainfed environments. The crop improvement strategy being used in Thailand considers three mechanisms that influence yield in the drought prone targets: yield potential as an important mechanism for mild drought (where yield loss is less than 50%), drought escape (appropriate phenology) and drought tolerance traits of leaf water potential, sterility, flower delay and drought response index for more severe drought conditions. Genotypes are exposed to managed drought environments for selection of drought tolerant genotypes. A marker assisted selection (MAS) scheme has been developed and applied for selection of progenies in the backcrossing program. The plant breeding program uses rapid generation advance techniques that enable early yield testing in the target population of environments (TPE) through inter-station (multi-location yield testing) and on-farm trials. A farmer participatory approach has been used to identify the TPE for the breeding program. Four terrace paddy levels have been identified, upper (drought), middle (drought prone to favorable) and lower (flooded). This paper reports the change in the breeding program for the drought prone tainted lowland rice environments of North and Northeast Thailand by incorporating our knowledge on adaptation and on response of rice to drought. (c) 2005 Elsevier B.V. All rights reserved.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.