Reinventing quantitative genetics for plant breeding: something old,something new,something borrowed,something BLUE

The goals of quantitative genetics differ according to its field of application. In plant breeding, the main focus of quantitative genetics is on identifying candidates with the best genotypic value for a target population of environments. Keeping quantitative genetics current requires keeping old concepts that remain useful, letting go of what has become archaic, and introducing new concepts and methods that support contemporary breeding. The core concept of continuous variation being due to multiple Mendelian loci remains unchanged. Because the entirety of germplasm available in a breeding program is not in Hardy–Weinberg equilibrium, classical concepts that assume random mating, such as the average effect of an allele and additive variance, need to be retired in plant breeding. Doing so is feasible because with molecular markers, mixed-model approaches that require minimal genetic assumptions can be used for best linear unbiased estimation (BLUE) and prediction. Plant breeding would benefit from borrowing approaches found useful in other disciplines. Examples include reliability as a new measure of the influence of genetic versus nongenetic effects, and operations research and simulation approaches for designing breeding programs. The genetic entities in such simulations should not be generic but should be represented by the pedigrees, marker data, and phenotypic data for the actual germplasm in a breeding program. Over the years, quantitative genetics in plant breeding has become increasingly empirical and computational and less grounded in theory. This trend will continue as the amount and types of data available in a breeding program increase.Subject terms: Plant sciences, Genetics     

转基因植物的表型变异、分子检测与遗传分析

本讨论了转化方法、体细胞克隆和选育过程等影响转基因植物表型变异的因素,并对转基因植物不同群体的表型变异组成和效应进行了比较分析,提出了转基因植物分子检测和遗传分析的技术策略。多数情况下,分析转基因植物回交后代(BClF1)比分析T1代能获得更可靠和有价值的结论。     

Historical changes in population structure during rice breeding programs in the northern limits of rice cultivation

Key message

The rice local population was clearly differentiated into six groups over the 100-year history of rice breeding programs in the northern limit of rice cultivation over the world.

Abstract

Genetic improvements in plant breeding programs in local regions have led to the development of new cultivars with specific agronomic traits under environmental conditions and generated the unique genetic structures of local populations. Understanding historical changes in genome structures and phenotypic characteristics within local populations may be useful for identifying profitable genes and/or genetic resources and the creation of new gene combinations in plant breeding programs. In the present study, historical changes were elucidated in genome structures and phenotypic characteristics during 100-year rice breeding programs in Hokkaido, the northern limit of rice cultivation in the world. We selected 63 rice cultivars to represent the historical diversity of this local population from landraces to the current breeding lines. The results of the phylogenetic analysis demonstrated that these cultivars clearly differentiated into six groups over the history of rice breeding programs. Significant differences among these groups were detected in five of the seven traits, indicating that the differentiation of the Hokkaido rice population into these groups was correlated with these phenotypic changes. These results demonstrated that breeding practices in Hokkaido have created new genetic structures for adaptability to specific environmental conditions and breeding objectives. They also provide a new strategy for rice breeding programs in which such unique genes in local populations in the world can explore the genetic potentials of the local populations.     

Practical Applications of Manipulating Plant Architecture by Regulating Gibberellin Metabolism

The international trade in floriculture is estimated to be worth about US$150 billion, with the global demand for ornamentals steadily increasing. Consumer choice is influenced by factors such as plant architecture and flower colour. Conventional breeding has been responsible for the introduction of novel traits into ornamental plants and has played an important role in the development of new cultivars. However, a restricted gene pool and failure of distant crosses have led to the exploitation of somatic cell techniques, particularly genetic transformation, to generate plants with desirable traits. Gibberellins (GAs) are endogenous plant hormones that control key aspects of growth and development. Chemical growth regulators that modify GA biosynthesis are used extensively in horticulture to control plant stature, increasing production costs, manpower, and environmental risks. An alternative strategy involves genetic manipulation of GA metabolism to induce phenotypic changes, particularly alteration of stature. Because ornamentals are not used for human consumption, genetic manipulation approaches with these plants may be more acceptable in the immediate future to the general public, in certain parts of the world, than genetically manipulated food crops.     

Marker-Based Estimation of Heritability in Immortal Populations

Heritability is a central parameter in quantitative genetics, from both an evolutionary and a breeding perspective. For plant traits heritability is traditionally estimated by comparing within- and between-genotype variability. This approach estimates broad-sense heritability and does not account for different genetic relatedness. With the availability of high-density markers there is growing interest in marker-based estimates of narrow-sense heritability, using mixed models in which genetic relatedness is estimated from genetic markers. Such estimates have received much attention in human genetics but are rarely reported for plant traits. A major obstacle is that current methodology and software assume a single phenotypic value per genotype, hence requiring genotypic means. An alternative that we propose here is to use mixed models at the individual plant or plot level. Using statistical arguments, simulations, and real data we investigate the feasibility of both approaches and how these affect genomic prediction with the best linear unbiased predictor and genome-wide association studies. Heritability estimates obtained from genotypic means had very large standard errors and were sometimes biologically unrealistic. Mixed models at the individual plant or plot level produced more realistic estimates, and for simulated traits standard errors were up to 13 times smaller. Genomic prediction was also improved by using these mixed models, with up to a 49% increase in accuracy. For genome-wide association studies on simulated traits, the use of individual plant data gave almost no increase in power. The new methodology is applicable to any complex trait where multiple replicates of individual genotypes can be scored. This includes important agronomic crops, as well as bacteria and fungi.     

High throughput screening with chlorophyll fluorescence imaging and its use in crop improvement

Marker assisted plant breeding is a powerful technique for targeted crop improvement in horticulture and agriculture. It depends upon the correlation of desirable phenotypic characteristics with specific genetic markers. This can be determined by statistical models that relate the variation in the value of genetic markers to variation in phenotypic traits. It therefore depends upon the convergence of three technologies; the creation of genetically characterised (and thus marked) populations, high throughput screening procedures, and statistical procedures. While a large number of high throughput screening technologies are available, real-time screening techniques are usually based on some kind of imaging technologies, such as chlorophyll fluorescence imaging, that offers physiological data that are eminently suitable as a quantitative trait for genetic mapping.     

Association of an insulin-like growth factor 1 gene microsatellite with phenotypic variation and estimated breeding values of growth traits in Canchim cattle

A population of 1398 Canchim (CA) cattle was genotyped to assess the association of an insulin-like growth factor 1 (IGF1) gene microsatellite with phenotypic variation and estimated breeding values of pre-weaning, weaning and post-weaning growth traits. After an initial analysis, the IGF1 genotype only had a significant effect (P < 0.05) on birth weight (BW) and weaning weight adjusted to 240 days (WW240). For these two traits, direct and maternal breeding values were estimated using the restricted maximum likelihood (reml). Two analyses were carried out. In the first (Model I), all fixed effects were fitted. In the second (Model II), the fixed effect of the IGF1 genotype was omitted. The estimated genetic and phenotypic components of variance were similar for every trait in both models. For Model I, estimated direct and maternal heritabilities were 0.26 and 0.16 for BW and 0.23 and 0.14 for WW240 respectively. The genetic and phenotypic correlations between BW and WW240 were 0.38 and 0.38 (Model I) and 0.19 and 0.38 (Model II) respectively. Fifty animals were classified according to their direct and maternal breeding values for both traits. Spearman rank-order correlation between animal rankings in the two models was used to assess the effect of including the IGF1 genotype in the model. Non-significant values from this correlation were indicative of a difference in breeding value rankings between the two approaches. The IGF1 gene was found to be associated with phenotypic variation and breeding values in the early phase of growth.     

Mapping QTL for agronomic traits in breeding populations

Detection of quantitative trait loci (QTL) in breeding populations offers the advantage that these QTL are of direct relevance for the improvement of crops via knowledge-based breeding. As phenotypic data are routinely generated in breeding programs and the costs for genotyping are constantly decreasing, it is tempting to exploit this information to unravel the genetic architecture underlying important agronomic traits in crops. This review characterizes the germplasm from breeding populations available for QTL detection, provides a classification of the different QTL mapping approaches that are available, and highlights important considerations concerning study design and biometrical models suitable for QTL analysis.     

Disentangling plastic and genetic changes in body mass of Siberian jays

Spatial and temporal phenotypic differentiation in mean body size is of commonplace occurrence, but the underlying causes remain often unclear: both genetic differentiation in response to selection (or drift) and environmentally induced plasticity can create similar phenotypic patterns. Studying changes in body mass in Siberian jays (Perisoreus infaustus) over three decades, we discovered that mean body mass declined drastically (ca. 10%) over the first two decades, but increased markedly thereafter back to almost the initial level. Quantitative genetic analyses revealed that although body mass was heritable (h² = 0.46), the pronounced temporal decrease in body mass was mainly a product of phenotypic plasticity. However, a concomitant and statistically significant decrease in predicted breeding values suggests a genetic component to this change. The subsequent increase in mean body mass was indicated to be entirely due to plasticity. Selection on body mass was estimated to be too weak to fully account for the observed genetic decline in body mass, but bias in selection differential estimates due to environmental covariance between body mass and fitness is possible. Hence, the observed body mass changes appear to be driven mainly by phenotypic plasticity. Although we were not able to identify the ecological driver of the observed plastic changes, the results highlight the utility of quantitative genetic approaches in disentangling genetic and phenotypic changes in natural populations.     

东北春大豆种质资源表型分析及综合评价

种质资源是大豆遗传育种和解析复杂数量性状的基础,通过对种质资源的评价,可指导育种实践中优异互补亲本的选择,提高优异基因交流累加和新品种培育的效率。本研究选用来自东北三省一区1923-2010年间选育的340份春大豆种质资源,通过在牡丹江地区对12个表型性状的2年综合鉴定,评价品种群体遗传变异特点和筛选优异种质资源,结果表明:(1)春大豆种质资源表型变异丰富。除生育期年份间差异不显著外,其他性状品种间和年份间均呈显著的差异,且2年变化趋势相同。有效分枝数变异幅度最大,其次是主茎荚数、单株粒重和株高,这些性状选择潜力较大,品质性状的变异幅度较小,选择潜力有限;(2)表型性状特征频率分布均符合正态分布。受育成单位纬度和育种目标的影响,生育期呈现北早南晚,北部育成品种营养体较小、植株矮小、节数相对较少、脂肪含量较高,南部育成品种营养体较大、植株高大、单株有效节数多且主茎单节最多荚数多,部分品种蛋白质含量相对较高;(3)采用主成分分析方法综合评价表明,吉育71的ZF值最高,综合性状表现最好,表型性状与ZF值相关分析结果显示,生育期、株高、主茎节数、地上部生物产量、收获指数、主茎荚数和主茎单节最多荚数等7个表型性状可作为春大豆种质资源综合评价指标。在大豆育种中应重视利用具有丰富遗传多样性的基因资源,在亲本选配时适当选择综合性状优良、育种性状优势互补的种质。     

一种基于高密度遗传标记的亲子鉴定方法及其应用

系谱是人类遗传及动植物育种研究与实践的重要信息来源之一。系谱记录错误是育种生产中普遍存在的一种记录错误,影响基因定位、遗传值及表型值预测等相关研究结果的可靠性。现有的方法软件可以利用遗传标记信息对疑似亲子进行亲子鉴定,但这些软件方法操作复杂,限制标记数量,如Cervus。针对当前高密度SNP标记在人类及动植物研究中广泛应用的现状,文章提出了一种基于全基因组高密度SNP数据的亲子鉴定新方法,命名为EasyPC。对EasyPC及Cervus的运行效率进行了对比,并用中国荷斯坦牛(n=2180)和杜洛克猪(n=191)的全基因组SNP芯片数据对EasyPC进行了验证。结果表明：EasyPC运行效率高于Cervus,牛和猪群体系谱错误率分别为20%和6%,与相关研究报道相符。通过使用全基因组SNP标记对群体孟德尔错误率的经验分布进行分析,该方法不仅可以简单、快速、准确地判别系谱的正确性,而且还可以对错误系谱进行校正。EasyPC为解决全基因组研究中基因型及系谱数据前处理过程中的系谱校正问题提供了一种新的途径。     

Methods of high-throughput plant phenotyping for large-scale breeding and genetic experiments

Phenomics is a field of science at the junction of biology and informatics which solves the problems of rapid, accurate estimation of the plant phenotype; it was rapidly developed because of the need to analyze phenotypic characteristics in large scale genetic and breeding experiments in plants. It is based on using the methods of computer image analysis and integration of biological data. Owing to automation, new approaches make it possible to considerably accelerate the process of estimating the characteristics of a phenotype, to increase its accuracy, and to remove a subjectivism (inherent to humans). The main technologies of high-throughput plant phenotyping in both controlled and field conditions, their advantages and disadvantages, and also the prospects of their use for the efficient solution of problems of plant genetics and breeding are presented in the review.     

Dynamic root growth and architecture responses to limiting nutrient availability: linking physiological models and experimentation

In recent years the study of root phenotypic plasticity in response to sub-optimal environmental factors and the genetic control of these responses have received renewed attention. As a path to increased productivity, in particular for low fertility soils, several applied research projects worldwide target the improvement of crop root traits both in plant breeding and biotechnology contexts. To assist these tasks and address the challenge of optimizing root growth and architecture for enhanced mineral resource use, the development of realistic simulation models is of great importance. We review this research field from a modeling perspective focusing particularly on nutrient acquisition strategies for crop production on low nitrogen and low phosphorous soils. Soil heterogeneity and the dynamics of nutrient availability in the soil pose a challenging environment in which plants have to forage efficiently for nutrients in order to maintain their internal nutrient homeostasis throughout their life cycle. Mathematical models assist in understanding plant growth strategies and associated root phenes that have potential to be tested and introduced in physiological breeding programs. At the same time, we stress that it is necessary to carefully consider model assumptions and development from a whole plant-resource allocation perspective and to introduce or refine modules simulating explicitly root growth and architecture dynamics through ontogeny with reference to key factors that constrain root growth. In this view it is important to understand negative feedbacks such as plant–plant competition. We conclude by briefly touching on available and developing technologies for quantitative root phenotyping from lab to field, from quantification of partial root profiles in the field to 3D reconstruction of whole root systems. Finally, we discuss how these approaches can and should be tightly linked to modeling to explore the root phenome.     

Evolution of plant breeding systems

Breeding systems are important, and often neglected, aspects of the natural biology of organisms, affecting homozygosity and thus many aspects of their biology, including levels and patterns of genetic diversity and genome evolution. Among the different plant mating systems, it is useful to distinguish two types of systems: 'sex systems', hermaphroditic versus male/female and other situations; and the 'mating systems' of hermaphroditic populations, inbreeding, outcrossing or intermediate. Evolutionary changes in breeding systems occur between closely related species, and some changes occur more often than others. Understanding why such changes occur requires combined genetical and ecological approaches. I review the ideas of some of the most important theoretical models, showing how these are based on individual selection using genetic principles to ask whether alleles affecting plants' outcrossing rates or sex morphs will spread in populations. After discussing how the conclusions are affected by some of the many relevant ecological factors, I relate these theoretical ideas to empirical data from some of the many recent breeding system studies in plant populations.     

Factor analytic and reduced animal models for the investigation of additive genotype-by-environment interaction in outcrossing plant species with application to a Pinus radiata breeding programme

Key message

Modelling additive genotype-by-environment interaction is best achieved with the use of factor analytic models. With numerous environments and for outcrossing plant species, computation is facilitated using reduced animal models.

Abstract

The development of efficient plant breeding strategies requires a knowledge of the magnitude and structure of genotype-by-environment interaction. This information can be obtained from appropriate linear mixed model analyses of phenotypic data from multi-environment trials. The use of factor analytic models for genotype-by-environment effects is known to provide a reliable, parsimonious and holistic approach for obtaining estimates of genetic correlations between all pairs of trials. When breeding for outcrossing species the focus is on estimating additive genetic correlations and effects which is achieved by including pedigree information in the analysis. The use of factor analytic models in this setting may be computationally prohibitive when the number of environments is moderate to large. In this paper, we present an approach that uses an approximate reduced animal model to overcome the computational issues associated with factor analytic models for additive genotype-by-environment effects. The approach is illustrated using a Pinus radiata breeding dataset involving 77 trials, located in environments across New Zealand and south eastern Australia, and with pedigree information on 315,581 trees. Using this approach we demonstrate the existence of substantial additive genotype-by-environment interaction for the trait of stem diameter measured at breast height. This finding has potentially significant implications for both breeding and deployment strategies. Although our approach has been developed for forest tree breeding programmes, it is directly applicable for other outcrossing plant species, including sugarcane, maize and numerous horticultural crops.     

The role and basics of computer simulation in support of critical decisions in plant breeding

A number of crucial decisions face the plant breeder before any field activities directed to crop genetic improvement are actually initiated, primarily related to choice of parents and breeding strategy options. Because of the impact, the complexity of these decisions, and the cost of implementing multiple options, computer simulation can be an important resource for the modern breeder. To maximize utility, the simulation tool must be based on effective models of the genome, the breeding process, and other ‘processes’ involved in genetic recombination, identification, and production of new cultivars. Additionally, the statistical methodology employed has ramifications for predicting performance and breeding outcome. The objective of this work is to highlight the role of computer simulation in the planning phases of crop genetic improvement, the basics of model building, statistical considerations, and key issues to be addressed. Examples of publicly available simulation software for plant breeding scenarios are described (features, functionalities, and assumptions) and new directions for improved/expanded approaches and tools are discussed.     

Association genetics in Pinus taeda L. II. Carbon isotope discrimination

Dissection of complex traits that influence fitness is not only a central topic in evolutionary research but can also assist breeding practices for economically important plant species, such as loblolly pine (Pinus taeda L). In this study, 46 single nucleotide polymorphisms (SNPs) from 41 disease and abiotic stress-inducible genes were tested for their genetic association with carbon isotope discrimination (CID), a time-integrated trait measure of stomatal conductance. A family-based approach to detect genotype/phenotype genetic association was developed for the first time in plants by applying the quantitative transmission disequilibrium test on an association population of 961 clones from 61 families (adopted from previous breeding programs) evaluated for phenotypic expression of CID at two sites. Two particularly promising candidates for their genetic effects on CID are: dhn-1, involved in stabilization of cell structures, and lp5-like, a glycine rich protein putatively related to cell wall reinforcement proteins, both of which were shown in previous studies to be water-deficit inducible. Moreover, association in lp5-like involves a nonsynonymous mutation in linkage disequilibrium with two other nonsynonymous polymorphisms that could, by acting together, enhance overall phenotypic effects. This study highlights the complexity of dissecting CID traits and provides insights for designing second-generation association studies based on candidate gene approaches in forest trees.     

Goals and hurdles for a successful implementation of genomic selection in breeding programme for selected annual and perennial crops

Genomic Selection is an important topic in quantitative genetics and breeding. Not only does it allow the full use of current molecular genetic technologies, it stimulates also the development of new methods and models. Genomic selection, if fully implemented in commercial farming, should have a major impact on the productivity of various agricultural systems. But suggested approaches need to be applicable in commercial breeding populations. Many of the published research studies focus on methodologies. We conclude from the reviewed publications, that a stronger focus on strategies for the implementation of genomic selection in advanced breeding lines, introduction of new varieties, hybrids or multi-line crosses is needed. Efforts to find solutions for a better prediction and integration of environmental influences need to continue within applied breeding schemes. Goals of the implementation of genomic selection into crop breeding should be carefully defined and crop breeders in the private sector will play a substantial part in the decision-making process. However, the lack of published results from studies within, or in collaboration with, private companies diminishes the knowledge on the status of genomic selection within applied breeding programmes. Studies on the implementation of genomic selection in plant breeding need to evaluate models and methods with an enhanced emphasis on population-specific requirements and production environments. Adaptation of methods to breeding schemes or changes to breeding programmes for a better integration of genomic selection strategies are needed across species. More openness with a continuous exchange will contribute to successes.