Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

With the recent development of genomic resources and high‐throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C₄ grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype–phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.     

Optimising the identification of causal variants across varying genetic architectures in crops

Association studies use statistical links between genetic markers and the phenotype variation across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome‐wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures. In this study, we employ real‐world genotype datasets from four crop species with distinct minor allele frequency distributions, population structures and linkage disequilibrium patterns. We demonstrate that different GWAS statistical approaches provide favourable trade‐offs between power and accuracy for traits controlled by different types of genetic architectures. FarmCPU provides the most favourable outcomes for moderately complex traits while a Bayesian approach adopted from genomic prediction provides the most favourable outcomes for extremely complex traits. We assert that by estimating the complexity of genetic architectures for target traits and selecting an appropriate statistical approach for the degree of complexity detected, researchers can substantially improve the ability to dissect the genetic factors controlling complex traits such as flowering time, plant height and yield component.     

The influence of genetics on future crop production strategies: from traits to genes, and genes to traits

This paper discusses how a genetical approach to plant physiology can contribute to research underpinning the production of new crop varieties. It highlights the interactions between genetics and plant breeding and how the current advances in genetics and the new science of genomics can contribute to our understanding of the genetical control of key agronomic traits ‐ the process of ‘translating’ traits to identified and mapped genes. Advances in genomics, such as the sequencing of whole genomes and expressed sequence tags, are producing information on genes and gene structures, but without knowing their function. A great deal more biology will be necessary to translate gene structure to function ‐ the process of translating genes to traits. Combining these ‘forward’ and ‘reverse’ genetic approaches will allow us to get comprehensive knowledge of the biology of agronomic traits at the physiological, biochemical and molecular levels, so that the ‘circuitry’ of our crop plants can be elucidated. This will enable plant breeders to manipulate crop phenotype using marker‐assisted breeding or genetic engineering approaches with a precision not previously possible.     

Natural genetic variation in plant photosynthesis

Natural genetic variation in plant photosynthesis is a largely unexplored and as a result an underused genetic resource for crop improvement. Numerous studies show genetic variation in photosynthetic traits in both crop and wild species, and there is an increasingly detailed knowledge base concerning the interaction of photosynthetic phenotypes with their environment. The genetic factors that cause this variation remain largely unknown. Investigations into natural genetic variation in photosynthesis will provide insights into the genetic regulation of this complex trait. Such insights can be used to understand evolutionary processes that affect primary production, allow greater understanding of the genetic regulation of photosynthesis and ultimately increase the productivity of our crops.     

数量性状由表型变异到基因发现的研究进展

分子生物学的快速发展为研究数量性状的遗传基础提供了更为有效的途径。我们可以沿着由表型变异去发现基因之路, 更准确地剖析数量性状的遗传基础; 尤其是对作物的许多重要的数量性状进行的QTL研究越来越受到重视。文章对数量遗传发展, QTL作图群体和方法的发展, QTL定位和QTG(quantitative traits genes)的鉴别方面的现状进行了综述。     

High-throughput Phenotyping and Genomic Selection:The Frontiers of Crop Breeding Converge

Genomic selection (GS) and high-throughput phenotyping have recently been captivating the interest of the crop breeding com-munity from both the public and private sectors world-wide.Both approaches promise to revolutionize the prediction of complex traits,including growth,yield and adaptation to stress.Whereas high-throughput phenotyping may help to improve understanding of crop physiology,most powerful techniques for high-throughput field phenotyping are empirical rather than analytical and compa-rable to genomic selection.Despite the fact that the two method-ological approaches represent the extremes of what is understood as the breeding process (phenotype versus genome),they both consider the targeted traits (e.g.grain yield,growth,phenology,plant adaptation to stress) as a black box instead of dissecting them as a set of secondary traits (i.e.physiological) putatively related to the target trait.Both GS and high-throughput phenotyping have in common their empirical approach enabling breeders to use genome profile or phenotype without understanding the underlying biology.This short review discusses the main aspects of both approaches and focuses on the case of genomic selection of maize flowering traits and near-infrared spectroscopy (NIRS) and plant spectral reflectance as high-throughput field phenotyping methods for complex traits such as crop growth and yield.     

Dissection of the genetic architecture of three seed‐quality traits and consequences for breeding in Brassica napus

Genome‐wide association studies (GWASs) combining high‐throughput genome resequencing and phenotyping can accelerate the dissection of genetic architecture and identification of genes for plant complex traits. In this study, we developed a rapeseed genomic variation map consisting of 4 542 011 SNPs and 628 666 INDELs. GWAS was performed for three seed‐quality traits, including erucic acid content (EAC), glucosinolate content (GSC) and seed oil content (SOC) using 3.82 million polymorphisms in an association panel. Six, 49 and 17 loci were detected to be associated with EAC, GSC and SOC in multiple environments, respectively. The mean total contribution of these loci in each environment was 94.1% for EAC and 87.9% for GSC, notably higher than that for SOC (40.1%). A high correlation was observed between phenotypic variance and number of favourable alleles for associated loci, which will contribute to breeding improvement by pyramiding these loci. Furthermore, candidate genes were detected underlying associated loci, based on functional polymorphisms in gene regions where sequence variation was found to correlate with phenotypic variation. Our approach was validated by detection of well‐characterized FAE1 genes at each of two major loci for EAC on chromosomes A8 and C3, along with MYB28 genes at each of three major loci for GSC on chromosomes A9, C2 and C9. Four novel candidate genes were detected by correlation between GSC and SOC and observed sequence variation, respectively. This study provides insights into the genetic architecture of three seed‐quality traits, which would be useful for genetic improvement of B. napus.     

Insect mating signal and mate preference phenotypes covary among host plant genotypes

Sexual selection acting on small initial differences in mating signals and mate preferences can enhance signal–preference codivergence and reproductive isolation during speciation. However, the origin of initial differences in sexual traits remains unclear. We asked whether biotic environments, a source of variation in sexual traits, may provide a general solution to this problem. Specifically, we asked whether genetic variation in biotic environments provided by host plants can result in signal–preference phenotypic covariance in a host‐specific, plant‐feeding insect. We used a member of the Enchenopa binotata species complex of treehoppers (Hemiptera: Membracidae) to assess patterns of variation in male mating signals and female mate preferences induced by genetic variation in host plants. We employed a novel implementation of a quantitative genetics method, rearing field‐collected treehoppers on a sample of naturally occurring replicated host plant clone lines. We found remarkably high signal–preference covariance among host plant genotypes. Thus, genetic variation in biotic environments influences the sexual phenotypes of organisms living on those environments in a way that promotes assortative mating among environments. This consequence arises from conditions likely to be common in nature (phenotypic plasticity and variation in biotic environments). It therefore offers a general answer to how divergent sexual selection may begin.     

Association genetics of complex traits in conifers

Association studies are becoming the experimental approach of choice to dissect complex traits in many organisms from humans to model plant systems. The candidate gene based-association approach has several important advantages for complex trait dissection in many coniferous forest tree species, including random mating and unstructured populations, adequate levels of nucleotide diversity, rapid decay of linkage disequilibrium, and precise evaluation of phenotype from clonal or progeny testing. Allele discovery using association approaches should lead to more-efficient methods of marker-assisted breeding and a deeper understanding of genetic adaptation in forest trees.     

Genome‐wide association studies on the phyllosphere microbiome: Embracing complexity in host–microbe interactions

Environmental sequencing shows that plants harbor complex communities of microbes that vary across environments. However, many approaches for mapping plant genetic variation to microbe‐related traits were developed in the relatively simple context of binary host–microbe interactions under controlled conditions. Recent advances in sequencing and statistics make genome‐wide association studies (GWAS) an increasingly promising approach for identifying the plant genetic variation associated with microbes in a community context. This review discusses early efforts on GWAS of the plant phyllosphere microbiome and the outlook for future studies based on human microbiome GWAS. A workflow for GWAS of the phyllosphere microbiome is then presented, with particular attention to how perspectives on the mechanisms, evolution and environmental dependence of plant–microbe interactions will influence the choice of traits to be mapped.     

Carbon and nitrogen economy of four Triticum aestivum cultivars differing in relative growth rate and water use efficiency

We investigated physiological and morphological traits underlying variation in relative growth rate (KGR) among wheat cultivars. Subsequently, we determined whether higher RGR is correlated with higher water demand and lower plant water use efficiency (WUE_p). Further, the correlation between water use efficiency and leaf nitrogen concentration was examined. For this purpose we chose lour cultivars contrasting in RGR or WUE_p. Gas exchange of shoots and respiration of roots were measured on intact plants over a 24 h period, and total carbon and nitrogen concentrations of all plant parts were determined. The highest RGR was achieved by the cultivars with the highest leaf area ratio. WUE_p was strongly dependent on photosynthetic water use efficiency and was highest for the cultivars with the highest rate of photosynthesis, which achieved higher rates of photosynthesis per unit leaf nitrogen. We found no evidence for a functional or genetic link between the physiological traits underlying differences in RGR (specific leaf area and leaf area ratio) and those causing variation in water use efficiency (photosynthetic rate and transpiration rate). These results indicate that, in wheat, it may be possible to select simultaneously for traits associated with a high WUE_p and a high RGR.     

Hyperspectral imaging combined with machine learning as a tool to obtain high‐throughput plant salt‐stress phenotyping

The rapid selection of salinity‐tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high‐throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non‐destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis‐related parameters, which require laborious, time‐consuming measurements, were also investigated. Traditional laboratory‐based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state‐of‐the‐art performance of the deep‐learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high‐throughput phenotyping for studying plant salinity stress using a combination of HSI and deep‐learning approaches.     

Effects of larval crowding on quantitative variation for development time and viability in Drosophila melanogaster

Competition between individuals belonging to the same species is a universal feature of natural populations and is the process underpinning organismal adaptation. Despite its importance, still comparatively little is known about the genetic variation responsible for competitive traits. Here, we measured the phenotypic variation and quantitative genetics parameters for two fitness‐related traits—egg‐to‐adult viability and development time—across a panel of Drosophila strains under varying larval densities. Both traits exhibited substantial genetic variation at all larval densities, as well as significant genotype‐by‐environment interactions (GEIs). GEI was attributable to changes in the rank order of reaction norms for both traits, and additionally to differences in the between‐line variance for development time. The coefficient of genetic variation increased under stress conditions for development time, while it was higher at both high and low densities for viability. While development time also correlated negatively with fitness at high larval densities—meaning that fast developers have high fitness—there was no correlation with fitness at low density. This result suggests that GEI may be a common feature of fitness‐related genetic variation and, further, that trait values under noncompetitive conditions could be poor indicators of individual fitness. The latter point could have significant implications for animal and plant breeding programs, as well as for conservation genetics.     

Phenoscope: an automated large‐scale phenotyping platform offering high spatial homogeneity

Increased phenotyping accuracy and throughput are necessary to improve our understanding of quantitative variation and to be able to deconstruct complex traits such as those involved in growth responses to the environment. Still, only a few facilities are known to handle individual plants of small stature for non‐destructive, real‐time phenotype acquisition from plants grown in precisely adjusted and variable experimental conditions. Here, we describe Phenoscope, a high‐throughput phenotyping platform that has the unique feature of continuously rotating 735 individual pots over a table. It automatically adjusts watering and is equipped with a zenithal imaging system to monitor rosette size and expansion rate during the vegetative stage, with automatic image analysis allowing manual correction. When applied to Arabidopsis thaliana, we show that rotating the pots strongly reduced micro‐environmental disparity: heterogeneity in evaporation was cut by a factor of 2.5 and the number of replicates needed to detect a specific mild genotypic effect was reduced by a factor of 3. In addition, by controlling a large proportion of the micro‐environmental variance, other tangible sources of variance become noticeable. Overall, Phenoscope makes it possible to perform large‐scale experiments that would not be possible or reproducible by hand. When applied to a typical quantitative trait loci (QTL) mapping experiment, we show that mapping power is more limited by genetic complexity than phenotyping accuracy. This will help to draw a more general picture as to how genetic diversity shapes phenotypic variation.     

To clone or not to clone plant QTLs: present and future challenges

Recent technical advancements and refinement of analytical methods have enabled the loci (quantitative trait loci, QTLs) responsible for the genetic control of quantitative traits to be dissected molecularly. To date, most plant QTLs have been cloned using a positional cloning approach following identification in experimental crosses. In some cases, an association between sequence variation at a candidate gene and a phenotype has been established by analysing existing genetic accessions. These strategies can be refined using appropriate genetic materials and the latest developments in genomics platforms. We foresee that although QTL analysis and cloning addressing naturally occurring genetic variation should shed light on mechanisms of plant adaptation, a greater emphasis on approaches relying on mutagenesis and candidate gene validation is likely to accelerate the pace of discovering the genes underlying QTLs.     

Preadaptation and post‐introduction evolution facilitate the invasion of Phragmites australis in North America

Compared with non‐invasive species, invasive plant species may benefit from certain advantageous traits, for example, higher photosynthesis capacity and resource/energy‐use efficiency. These traits can be preadapted prior to introduction, but can also be acquired through evolution following introduction to the new range. Disentangling the origins of these advantageous traits is a fundamental and emerging question in invasion ecology. We conducted a multiple comparative experiment under identical environmental condition with the invasive haplotype M lineage of the wetland grass Phragmites australis and compared the ecophysiological traits of this invasive haplotype M in North America with those of the European ancestor and the conspecific North American native haplotype E lineage, P. australis ssp. americanus. The invasive haplotype M differed significantly from the native North American conspecific haplotype E in several ecophysiological and morphological traits, and the European haplotype M had a more efficient photosynthetic apparatus than the native North American P. australis ssp. americanus. Within the haplotype M lineage, the introduced North American P. australis exhibited different biomass allocation patterns and resource/energy‐use strategies compared to its European ancestor group. A discriminant analysis of principal components separated the haplotype M and the haplotype E lineages completely along the first canonical axis, highly related to photosynthetic gas‐exchange parameters, photosynthetic energy‐use efficiency and payback time. The second canonical axis, highly related to photosynthetic nitrogen use efficiency and construction costs, significantly separated the introduced P. australis in North America from its European ancestor. Synthesis. We conclude that the European P. australis lineage was preadapted to be invasive prior to its introduction, and that the invasion in North America is further stimulated by rapid post‐introduction evolution in several advantageous traits. The multicomparison approach used in this study could be an effective approach for distinguishing preadaptation and post‐introduction evolution of invasive species. Further research is needed to link the observed changes in invasive traits to the genetic variation and the interaction with the environment.     

Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high‐throughput image analysis

Phenotyping large numbers of genotypes still represents the rate‐limiting step in many plant genetic experiments and in breeding. To address this issue, novel automated phenotyping technologies have been developed. We investigated for a core set of barley cultivars if high‐throughput image analysis can help to dissect vegetative biomass accumulation in response to two different watering regimes under semi‐controlled greenhouse conditions. We found that experiments, treatments, genotypes and genotype by environment interaction (G × E) can be characterized at any time point by certain digital traits. Biomass accumulation under control and stress conditions was highly heritable. Growth model‐derived maximum vegetative biomass (K_max), inflection point (I) and regrowth rate (k) were identified as promising candidate traits for genome‐wide association studies. Drought stress symptoms can be visualized, dissected and modelled. Especially the highly heritable regrowth rate, which had the biggest influence on biomass accumulation in stress treatment, seems promising for future studies to improve drought tolerance in different crop species. A proof of concept study revealed potential correlations between digital traits obtained from pot experiments under greenhouse conditions and agronomic traits from field experiments. Overall, non‐invasive, imaging‐based phenotyping platforms under greenhouse conditions offer excellent possibilities for trait discovery, trait development and industrial applications.     

从QTL到QTG的路还有多远?

植物大多数重要的经济性状都是数量性状, 人们对许多植物进行了数量性状基因座(QTL)的研究, 并取得了长足的发展。文章详尽地分析了数量性状表型与基因型的复杂关系, 介绍了当前QTL研究领域里的几种精细作图策略。讨论了当前挖掘控制目标性状QTL基因的研究过程中存在的困难和问题, 提出几个有待发展的研究方向, 并展望了该领域的发展前景。因目前的QTL仍然是一个相当大的染色体区段, 往往含有多个候选基因。文章就怎样从QTL粗放位点研究进一步发展到数量性状基因(quantitative trait gene, QTG)水平上的变异, 再从QTG到相应于基因内多态性的数量性状核苷酸(quantitative trait nucleotides, QTN), 提出了一些见解。来迎接后基因组时代数量遗传领域的挑战。